PDF
FLIP
Cómo citar
Vargas-Mena, E. (2025). Toxicidad del Fipronil. Revisión sistemática de la literatura. Biosalud, 19(1), 50–87. https://doi.org/10.17151/biosa.2020.19.1.3
Más formatos de cita
Autores/as
Eleázar Vargas-Mena
Instituto Nacional de Medicina Legal y Ciencias Forenses.
Resumen
Introducción: El Fipronil es un pesticida de amplio espectro que pertenece a la familia de los fenilpirazoles. Posee efectos gabaérgicos y glutamatérgicos. Se ha aplicado de manera extensiva, principalmente en cultivos de chontaduro Bactris gasipaes, como control al picudo Rhynchophorus palmarum. Objetivo: La presente revisión tiene como objetivo analizar la información bibliográfica centrada en las investigaciones realizadas acerca de la toxicidad del Fipronil, con especial énfasis en las herramientas de análisis toxicológico, los puntos finales y las rutas de toxicidad en humanos y animales. Materiales y métodos: La búsqueda de publicaciones con las palabras clave “Fipronil” y “toxicity”, se realizó en las bases de datos Thomson Reuters Web of Science (ISI Web of Knowledge) y Scopus en el periodo comprendido entre los años 1993 y 2022. Las 1492 referencias se descargaron para su análisis utilizando la teoría de grafos para determinar los artículos y autores relevantes, las palabras clave, la evolución de la temática y las distintas relaciones entre ellos. Se realizó, utilizando un script de RStudio desarrollado en el Core of science. Resultados y discusión: Esta revisión permitió identificar tendencias en investigación acerca de los efectos toxicológicos relacionados con la exposición a Fipronil en la reducción de los niveles hormonales asociados al desarrollo sexual, alteraciones en el sistema nervioso, malformaciones congénitas y alteraciones al del comportamiento, combinando estudios patológicos con aproximaciones metabolómicas, las metodologías analíticas para la identificación y propuestas de desarrollo de metodologías in silico para el análisis toxicológico.
Citas
1. Acero, C. y Machuca, D. (2021). The substitution program on trial: progress and setbacks of the peace agreement in the policy against illicit crops in Colombia. International Journal of Drug Policy, 89, 103158.
2. Arain, M. S., Hu, X. X. y Li, G. Q. (2014). Assessment of toxicity and potential risk of butenefipronil using Drosophila melanogaster, in comparison to nine conventional insecticides. Bulletin of Environmental Contamination and Toxicology, 92(2), 190-195.
3. Aria, M. y Cuccurullo, C. (2017). Bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
4. Asghari, A., Fahimi, E., Bazregar, M., Rajabi, M. y Boutorabi, L. (2017). Rapid determination of some psychotropic drugs in complex matrices by tandem dispersive liquid–liquid microextraction followed by high performance liquid chromatography. Journal of Chromatography B, 1052, 51-59.
5. Atapattu, S. N. y Rosenfeld, J. M. (2013). Solid phase analytical derivatization as a sample preparation method. Journal of Chromatography A, 1296, 204-213. http://dx.doi.org/10.1016/j.chroma.2013.03.020
6. Benigni, R. (2019). Towards quantitative read across: prediction of Ames mutagenicity in a large database. Regulatory Toxicology and Pharmacology, 108.
7. Benigni, R. y Bossa, C. (2008). Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. Mutation Research, 659(3), 248-261.
8. Biswas, S., Mondal, R., Mukherjee, A., Sarkar, M. y Kole, R. K. (2019). Simultaneous determination and risk assessment of fipronil and its metabolites in sugarcane, using GC-ECD and confirmation by GC-MS/MS. Food Chemistry, 272, 559-567. https://doi.org/10.1016/j.foodchem.2018.08.087
9. Bjørling-Poulsen, M., Andersen, H. R. y Grandjean, P. (2008). Potential developmental neurotoxicity of pesticides used in Europe. Environmental Health, 7, 50.
10. Blascke Carrão, D., Dos Reis Gomes, I. C., Barbosa Junior, F. y Moraes de Oliveira, A. R. (2019). Evaluation of the enantioselective in vitro metabolism of the chiral pesticide fipronil employing a human model: risk assessment through in vitro-in vivo correlation and prediction of toxicokinetic parameters. Food and Chemical Toxicology, 123, 225-232.
11. Bovi, T. S., Zaluski, R. y Orsi, R. O. (2018). Toxicity and motor changes in africanized honey bees (Apis mellifera L.) exposed to fipronil and imidacloprid. Anais da Academia Brasileira de Ciencias, 90(1), 239-245.
12. Borba, J. V., Alves, V. M., Braga, R. C., Korn, D. R., Overdahl, K., Silva, A. C., Hall, S. U., Overdahl, E., Kleinstreuer, N., Strickland, J., Allen, D., Andrade, C. H., Muratov, E. N. y Tropsha, A. (2022). STopTox: an in silico alternative to animal testing for acute systemic and topical toxicity. Environmental Health Perspectives, 130(2).
13. Bownik, A. y Szabelak, A. (2021). Short-term effects of pesticide fipronil on behavioral and physiological endpoints of Daphnia magna. Environmental Science Pollution Research International, 28(25), 33254-33264.
14. Calster, B., Steyerberg, E. W, Wynants, L. y Van Smedenen, M. (2023). There is no such thing as a validated prediction model. BMC Med, 21(70).
15. Carnesecchi, E., Toma, C., Roncaglioni, A., Kramer, N., Benfenati, E. y Dorne, J. L. (2020). Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): data curation using open source databases, performance testing and validation. Science of the Total Environment, 735, 139243. https://doi.org/10.1016/j.scitotenv.2020.139243
16. Castilla-Fernández, D., Moreno-Gonzalez, D., Murillo Cruz, M., García-Reyes, J. F. y Molina-Díaz, A. (2021). Appraisal of different clean-up strategies for the determination of fipronil and its metabolites in eggs by UHPLC-MS/MS. Microchemical Journal, 166(1).
17. Cotterill, J., Price, N., Rorije, E. y Peijnenburg, A. (2020). Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools. Food and Chemical Toxicology, 142, 111494.
18. Cox, C. y Surgan, M. (2006). Unidentified inert ingredients in pesticides: implications for human and environmental health. Environmental Health Perspectives, 114(12),1803-1806.
19. Chaguri, J. L., Godinho, A. F., Horta, D. F., Gonçalves-Rizzi, V. H., Possomato-Vieira, J. S., Nascimento, R. A. y Dias-Junior, C. A. (2016). Exposure to fipronil elevates systolic blood pressure and disturbs related biomarkers in plasma of rats. Environmental Toxicology and Pharmacology, 42, 63-68.
20. Charalampous, A. C., Liapis, K. S. y Bempelou, E. D. (2019). Fipronil in eggs. Is LC-MS/MS the only option? A comparison study of LC-MS/MS and GC-ECD for the analysis of fipronil. Journal of Chromatography B, 1129, 121785. https://doi.org/10.1016/j.jchromb.2019.121785
21. Chen, T., Hu, J., Chen, Y., Liu, Y., Li, Y. y Xu, H. (2022). Tracking the environmental fate of fipronil and three of its metabolites in garlic based on sampling reta-corrected in vivo solid phase microextraction combined with gas chromatography-mass spectrometry. Analytica Chimica Acta, 1190, 339263.
22. Da Silva Pinto, T. J., Rocha, G. S., Moreira, R. A., Menezes Da Silva, L. C., Cardoso Yoshii, M. P., Goulart, B. V., Montagner, C. C., Daam, M. A. y Gaeta Espindola, E. L. (2022). Chronic environmentally relevant levels of pesticides disrupt energy reserves, feeding rates, and life-cycle responses in the amphipod Hyalella meinerti. Aquatic Toxicology, 245, 106117.
23. De Oliveira, P. R., Bechara, G. H., Denardi, S. E., Oliveira, R. J. y Camargo Mathias, M. I. (2012). Genotoxic and mutagenic effects of fipronil on mice. Experimental and Toxicology Pathology, 64(6), 569-573. http://dx.doi.org/10.1016/j.etp.2010.11.015
24. Delso, N. S., Amaral-Rogers, V., Belzunces, L. P., Bonmatin, J. M., Chagnon, M., Downs, C., Furlan, L., Gibbons, D. W., Giorio, C., Girolami, V., Goulson, D., Kreutzweizer, D. P., Krupke, C. H., Liess, M., Long, E., McField, M., Mineau, P., Mitchell, E. A. D., Morrissey, C. A…Wiemers, M. (2015). Systemic insecticides (neonicotinoids and fipronil): trends, uses, mode of action and metabolites. Environmental Science and Pollution Research International, 22(1), 5-34.
25. Domingues Nazario, C. E., Fumes, B., Da Silva, M. R. y Lanças, F. M. (2016). New materials for sample preparation techniques in bioanalysis. Journal of Chromatography B, 1043(945). 26. Duque, P., Meza, O. E., Giraldo, D. y Barreto, K. (2021). Economía social y economía solidaria: un análisis bibliométrico y revisión de literatura. REVESCO, (138), 187-212.
27. Eadie, A., Vásquez, I. C., Liang, X., Wang, X., Souders, C. L., Chehouri, J. E., Hoskote, R., Feswick, A., Cowie, A. M., Loughery, J. R. y Martyniuk, C. J. (2020). Transcriptome network data in larval zebrafish (Danio rerio) following exposure to the phenylpyrazole fipronil. Data in Brief, 33, 106413. https://doi.org/10.1016/j.dib.2020.106413
28. Embry, M. R., Bachman, A. N., Bell, D. R., Boobis, A. R., Cohen, S. M., Dellarco, M., Dewhurst, C. I., Doerrer, N. G., Hines, R. N., Moretto, A., Pastoor, T. P., Phillips, R. D., Rowlands, J. C., Tanir, J. Y., Wolf., D. C. y Doe, J. E. (2014). Risk assessment in the 21st century: roadmap and matrix. Critical Reviews in Toxicology, 44(Suppl.3), 6-16.
29. Food and Agriculture Organization of the United Nations. (1989). Guidelines for legislation on the control of pesticides [Archivo PDF]
30. Fulton, M. H. y Key, P. B. (2001). Acetylcholinesterase inhibition in estuarine fish and invertebrates as an indicator of organophosphorus insecticide exposure and effects. Environmental Toxicology Chemistry, 20(1), 37-45.
31. Gadaleta, D., Vuković, K., Toma, C., Lavado, G. J., Karmaus, A. L. y Mansouri, K. (2019). SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. J Cheminform, 11(1), 1-16.
32. García-Vara, M., Postigo, C., Palma, P. y López de Alda, M. (2023). Development of QuEChERS-based multiresidue analytical methods to determine pesticides in corn, grapes and alfalfa. Food Chemistry, 405, 134870.
33. Gomes, H. O., Cardoso, R., Nonato, C. F., Andrade da Silva, V. P., Nobre, C., Da Costa, J. G., Pereira Teixeira, R. N. y Do Nascimento, R. F. (2023). Optimization and validation of a method using GC–MS and QuEChERS for pesticide determination in banana pulp. Food Analytical Methods, 16, 125-131. https://doi.org/10.1007/s12161-022-02402-3
34. González de Dios, J., Alonso-Arroyo, A. y Aleixandre-Benavent, R. (2019). Half a century of Anales de Pediatría. Evolution of its main bibliometric indicators in the Web of Science and Scopus international databases. Anales de Pediatria, 90(3), 194.e1-194.e11.
35. Grande Martínez, Á., Arrebola, F. J., Martínez-Vidal, J. L., Hernández-Torres, M. E. y Garrido-Frenich, A. (2015). Optimization and validation of a multiresidue pesticide method in rice and wheat flour by modified QuECHERS and GC-MS/MS. Food Analytical Methods, 9(2).
36. Gross, J. H. (2017). Mass Spectrometry. A textbook. Springer.
37. Guelfi, M., Tavares, M. A., Mingatto, F. E. y Maioli, M. A. (2015). Citotoxicity of fipronil on hepatocytes isolated from rat and effects of its biotransformation. Brazilian Archives of Biology and Technology, 58(6), 843-853.
38. Han, C., Hu, B., Li, Z., Liu, C., Wang, N., Fu, C. y Shen, Y. (2021). Determination of fipronil and four metabolites in foodstuffs of animal origin using a modified QuEChERS method and GC–NCI–MS/MS. Food Analytical Methods, 14(2), 237-249.
39. Herin, F., Boutet-Robinet, E., Levant, A., Dulaurent, S., Manika, M., Galatry-Bouju, F., Caron, P. y Soulat, J. M. (2011). Thyroid function tests in persons with occupational exposure to fipronil. Thyroid, 21(7), 701-706.
40. Herrmann, K., Holzwarth, A., Rime, S., Fischer, B. C. y Kneuer, C. (2020). (Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation? Pest Management Science, 76(10), 3316-3325.
41. Hidrovo, A. J. (2004). Plaguicidas usados en la fumigación de cultivos ilícitos y salud humana: ¿Una cuestión de ciencia o política? Revista Salud Pública, 6(2), 199-211.
42. Hurtado-Marín, V. A., Agudelo-Giraldo, J. D., Robledo, S. y Restrepo-Parra, E. (2021). Analysis of dynamic networks based on the Ising model for the case of study of co-authorship of scientific articles. Scientific Reports, 11(5721). https://doi.org/10.1038/s41598-021-85041-8
43. Hyötyläinen, T. y Wiedmer, S. (Eds.). (2013). Chromatographic Methods in Metabolomics. Royal Society of Chemistry.
44. Ivanciuc, T., Ivanciuc, O. y Klein, D. (2011). Network-QSAR with reaction poset quantitative superstructure-activity relationships (QSSAR) for PCB chromatographic properties. Current Bioinformatics, 6(1), 25-34.
45. Jiménez Noblejas, C. y Perianes Rodríguez, A. (2014). Recuperación y visualización de información en Web of Science y Scopus: una aproximación práctica. Investigación Bibliotecológica: Archivonomía, Bibliotecología e Información, 28(64), 15-31.
46. Katchanov, Y. L., Markova, Y. V. y Shmatko, N. A. (2019). Comparing the topological rank of journals in Web of Science and Mendeley. Heliyon, 5(7), e02089.
47. Kennedy, M. C., Hart, A. D., Kruisselbrink, J. W., Van Lenthe, M., De Boer, W. J., Van der Voet, H., Rorije, E., Sprong, C. y Van Klaveren, J. (2020). A retain and refine approach to cumulative risk assessment. Food and Chemical Toxicology, 138.
48. Komsta, L., Vander Heyden, Y. y Sherma J. (Eds.). (2018). Chemometrics in chromatography. CRC press.
49. Kovacic, P. y Jacintho, J. D. (2001). Mechanisms of carcinogenesis: focus on oxidative stress and electron transfer. Current Medicinal Chemistry, 8(7).
50. Krogh, K. A., Halling-Sørensen, B., Mogensen, B. B. y Vejrup, K. V. (2003). Environmental properties and effects of nonionic surfactant adjuvants in pesticides: a review. Chemosphere, 50(7), 871-901.
51. Lankadurai, B. P., Nagato, E. G. y Simpson, M. J. (2013). Environmental metabolomics: an emerging approach to study organism responses to environmental stressors. Environmental Reviews, 21(3), 180-205.
52. Lao, W. (2021). Fiproles as a proxy for ecological risk assessment of mixture of fipronil and its degradates in effluent-dominated surface waters. Water Research, 188, 116510. https://doi.org/10.1016/j.watres.2020.116510
53. Lautz, L. S., Stoopen, G., Ginting, A. J., Hoogenboom, R. y Punt, A. (2022). Fipronil and fipronil sulfone in chicken: from in vitro experiments to in vivo PBK model predictions. Food and Chemical Toxicology, 165, 113086.
54. Lévêque, L., Tahiri, N., Goldsmith, M. R. y Verner, M. A. (2022). Quantitative Structure-Activity relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta. Computational Toxicology, 21, 100211.
55. Li, P., Duan, Y., Ge, H., Zhang, Y. y Wu, X. (2018). Multiresidue analysis of 113 pesticides in different maturity levels of mangoes using an optimized QuEChERS method with GC-MS/MS and UHPLC-MS/MS. Food Analytical Methods, 11(26).
56. Li, Q., Zhang, J., Lin, T., Fan, C., Li, Y. y Zhang, Z. (2023). Migration behavior and dietary exposure risk assessment of pesticides residues in honeysuckle (Lonicera japonica Thunb.) based on modified QuEChERS method coupled with tandem mass spectrometry. Food Research International, 166, 112562.
57. Li, X., Li, H., Ma, W., Guo, Z., Li, X., Song, S., Tang, H., Li, X. y Zhang, Q. (2019). Development of precise GC-EI-MS method to determine the residual fipronil and its metabolites in chicken egg. Food Chemistry, 281, 85-90. https://doi.org/10.1016/j.foodchem.2018.12.041
58. Li, X., Ma, W., Li, H., Zhang, Q. y Ma, Z. (2020). Determination of residual fipronil and its metabolites in food samples: a review. Trends in Food Science and Technology, 97, 185-195. https://doi.org/10.1016/j.tifs.2020.01.018
59. Lima de Barros, A., Lima Rosa, J., Cavariani, M. M., Borges, C. S., Villelae e Silva, P., Bae, J. H., Anselmo-Franci, J. A. y Arena, A. C. (2016). In utero and lactational exposure to fipronil in female rats: pregnancy outcomes and sexual development. Journal of Toxicology and Environmental Health, 79(6), 266-273.
60. Lourenço, C. T., Carvalho, S. M., Malaspina, O. y Nocelli, R. (2012). Determination of fipronil LD50 for the brazilian bee Melipona scutellaris. Julius-Kühn-Archiv, 437, 174-178.
61. Maddadi, S., Qomi, M. y Rajabi, M. (2017). Extraction, preconcentration, and determination of methylphenidate in urine sample using solvent bar microextraction in combination with HPLC–UV: optimization by experimental design. Journal of Liquid Chromatography and Related Technologies, 40(15).
62. Mandal, S., Poi, R., Ansary, I., Hazra, D. K., Bhattacharyya, S. y Karmakarm R. (2020). Validation of a modified QuEChERS method to determine multiclass multipesticide residues in apple, banana and guava using GC–MS and LC–MS/MS and its application in real sample analysis. SN Applied Sciences, 2(188), 1-14. https://doi.org/10.1007/s42452-020-1990-2
63. Martin, M. T., Judson, R. S., Reif, D. M., Kavlock, R. J. y Dix, D. J. (2009). Profiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef database. Environmental Health Perspectives, 117(3), 392-399.
64. Martin, M. T., Mendez, E., Corum, D. G., Judson, R. S., Kavlock, R. J., Rotroff, D. M. y Dix, D. J. (2009). Profiling the reproductive toxicity of chemicals from multigeneration studies in the toxicity reference database. Toxicological Science, 110(1), 181-190.
65. Marzo, M., Lavado, G. J., Como, F., Toropova, A. P., Toropov, A. A. y Baderna, D. (2020). QSAR models for biocides: the example of the prediction of Daphnia magna acute toxicity. SAR and QSAR in Environmental Research, 31(3), 227-243.
66. Mcmahen, R. L., Strynar, M. J., Dagnino, S., Herr, D. W., Moser, V. C., Garantziotis, S., Andersen, E. M., Freeborn, D. L., McMillan, L. y Lindstrom, A. B. (2015). Identification of fipronil metabolites by timeof-flight mass spectrometry for application in a human exposure study. Environment International, 78, 16-23.
67. Milam, C. D., Farris, J. L. y Wilhide, J. D. (2000). Evaluating mosquito control pesticides for effect on target and nontarget organisms. Archives on Environmental Contamination and Toxicology, 39(3), 324-328.
68. Mohamed, F., Senarathna, L., Percy, A., Abeyewardene, M., Cheng, R., Azher, S., (2004). Acute Human Self-Poisoning with the N-Phenylpyrazole Insecticide Fipronil – A GABA A -Gated Chloride Channel Blocker. J Toxicol Clin Toxicol, 42(7), 955-963.
69. Moreira-Filho, J. T., Braga, R. C., Lemos, J. M., Medeiros Alves, V., Borba, J. V., Costa, W. S., Kleinstreuer, N., Muratov, E., Horta Andrade, C. y Neves, B. M. (2021). BeeToxAI: an artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees. Artificial Intelligence Life Sciences, 1, 100013.
70. Moretto, A., Bachman, A., Boobis, A., Solomon, K. R., Pastoor, T. P., Wilks, M. F. y Embry, M. R. (2017). A framework for cumulative risk assessment in the 21st century. Critical Reviews in Toxicology, 47(2), 85-97.
71. Mukherjee, A., Mondal, R., Biswas, S., Saha, S., Ghosh, S. y Kole, R. K. (2021). Dissipation behaviour and risk assessment of fipronil and its metabolites in paddy ecosystem using GC-ECD and confirmation by GC-MS/MS. Heliyon, 7(5), e06889. https://doi.org/10.1016/j.heliyon.2021.e06889
72. Oakes, D. J. y Pollak, J. K. (2000). The in vitro evaluation of the toxicities of three related herbicide formulations containing ester derivatives of 2,4,5-T and 2,4-D using sub-mitochondrial particles.Toxicology, 151(1-3), 1-9.
73. Ou, Y., Yan, Z., Shi, G., Yu, Z., Cai, Y. y Ma, R. (2022). Enantioselective toxicity, degradation and transformation of the chiral insecticide fipronil in two algae culture. Ecotoxicology Environmental Safety, 235, 113424. https://doi.org/10.1016/j.ecoenv.2022.113424
74. Paris, L., Roussel, M., Pereira, B., Delbac, F. y Diogon, M. (2017). Disruption of oxidative balance in the gut of the western honeybee Apis mellifera exposed to the intracellular parasite Nosema ceranae and to the insecticide fipronil. Microbial Biotechnology, 10(6), 1702-1717.
75. Patil, C., Calvayrac, C., Zhou, Y., Romdhane, S., Salvia, M. V., Cooper, J. F., Dayan, F. E. y Bertrand, C. (2016). Environmental metabolic footprinting: a novel application to study the impact of a natural and a synthetic β-triketone herbicide in soil. Science of the Total Environment, 566-567, 552-558. http ://dx.doi.org/10.1016/j.scitotenv.2016.05.071
76. Pedraza Carrillo, M., De Souza Bovi, T., Fava Negrão, A. y De Oliveira Orsi, R. (2013). Influência dos agroquímicos fipronil e imidaclopride no aprendizado de abelhas apis mellifera l. Acta Scientiarum. Animal Sciences, 35(4), 431-434.
77. Pereira, J. L., Antunes, S. C., Castro, B. B., Marques, C. R., Gonçalves, A. M. M., Gonçalves, F. y Pereira, R. (2009). Toxicity evaluation of three pesticides on non-target aquatic and soil organisms:commercial formulation versus active ingredient. Ecotoxicology, 18(4), 455-463.
78. Pfaff, J., Reinwald, H., Ayobahan, S. U., Alvincz, J., Göckener, B., Shomroni, O., Salinas, G., Düring, R. A., Schäfers, C. y Eilebrecht, S. (2021). Toxicogenomic differentiation of functional responses to fipronil and imidacloprid in Daphnia magna. Aquatic Toxicology, 238.
79. Portruneli, N., Bonansea, R. I., Valdés, M. E., Menezes Da Silva, L. C., Viana, N. P., Goulart, B. V., Da Costa Souza, I., Gaeta Espíndola, E. L., Montagner, C. C., Wunderlin, D. A. y Fernandes, M. N. (2021). Whole-body bioconcentration and biochemical and morphological responses of gills of the neotropical fish Prochilodus lineatus exposed to 2,4-dichlorophenoxyacetic acid or fipronil individually or in a mixture. Aquatic Toxicology, 240.
80. Putri, S. P. y Fukusaki, E. (2015). Mass Spectrometry-Based Metabolomics. A practical guide. Boca Raton: CRC Press.
81. Qu, H., Ma, R. X., Liu, D. H., Jing, X., Wang, F., Zhou, Z. Q. y Wang, P. (2016). The toxicity, bioaccumulation, elimination, conversion of the enantiomers of fipronil in Anodonta woodiana. Journal of Hazardous Materials, 312, 169-174. http://dx.doi.org/10.1016/j.jhazmat.2016.03.063
82. Raitano, G., Goi, D., Pieri, V., Passoni, A., Mattiussi, M., Lutman, A., Romeo, I., Manganaro, A., Marzo, M., Porta, N., Baderna, D., Colombo, A., Aneggi, E., Natolino, F., Lodi, M., Bagnati, R. y Benfenati, E. (2018). (Eco)toxicological maps: a new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy). Environment International, 119, 275-286.
83. Richardson, J. R., Fitsanakis, V., Westerink, R. H. S. y Kanthasamy, A. G. (2019). Neurotoxicity of pesticides. Acta Neuropathologica, 138, 343-362. https://doi.org/10.1007/s00401-019-02033-9
84. Roberts, D. W., Aptula, A., Schultz, T. W., Shen, J., Api, A. M., Bhatia, S. y Kromidas, L. (2015). A practical guidance for Cramer class determination. Regulatory Toxicology and Pharmacology, 73(3), 971-984.
85. Ruiz, P., Sack, A., Wampole, M., Bobst, S. y Vracko, M. (2017). Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. Chemosphere, 178, 99-109.
86. Salcedo Monsalve, A. y Melo Trujillo, O. L. (2005). Evaluación del uso de plaguicidas en la actividad agrícola del departamento de Putumayo. Revista Ciencias de la Salud, 3(2),168-185.
87. Salvia, M. V., Ben Jrad, A., Raviglione, D., Zhou, Y. y Bertrand, C. (2018). Environmental Metabolic Footprinting (EMF) vs. half-life: a new and integrative proxy for the discrimination between control and pesticides exposed sediments in order to further characterise pesticides’ environmental impact. Environmental Science and Pollution Research International, 25(30), 29841-29847.
88. Schmitz, A., Dempewolf, S., Tan, S., Bicker, G. y Stern, M. (2021). Developmental neurotoxicity of fipronil and rotenone on a human neuronal in vitro test system. Neurotoxicity Research, 39, 1189-1202. https://doi.org/10.1007/s12640-021-00364-8
89. Shamsayei, M., Yamini, Y., Asiabi, H., Rezazadeh, M. y Seidi, S. (2017). Electromembrane surrounded solid-phase microextraction using a stainless-steel wire coated with a nanocomposite composed of polypyrrole and manganese dioxide. Microchimica Acta, 184(8).
90. Silva, A. C., Borba, J. V., Alves, V. M., Hall, S. U. S., Furnham, N. y Kleinstreuer, N. (2021). Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals. Artif Intell Life Sci, 1, 100028.
91. Silva, C. A., Silva-Zacarin, E. C. M., Domingues, C. E., Abdalla, F. C., Malaspina, O. y Nocelli, R. (2015). Fipronil effect on the frequency of anomalous brood in honeybee reared in vitro. Julius-Kühn-Archiv, (450).
92. Silva, L., Moreira, R. A., Pinto, T. J., Vanderlei, M. R., Athayde, D. B., Lopes, L., Ogura, A. P., Yoshii, M. P., Freitas, J. S., Montagner, C. C., Goulart, B. V., Schiesari, L., Daam, M. A. y Espíndola, E. L. (2021). Lethal and sublethal toxicity of pesticides and vinasse used in sugarcane cultivation to Ceriodaphnia silvestrii (Crustacea: Cladocera). Aquatic Toxicology, 241, 106017. https://doi.org/10.1016/j.aquatox.2021.106017
93. Sinclair, C. J. y Boxall, A. B. A. (2003). Assessing the ecotoxicity of pesticide transformation products. Environmental Science and Technology, 37(20), 4617-4625.
94. Sofalvi, S., Lavins, E. S., Kaspar, C. K., Michel, H. M., Mitchell-Mata, C. L., Huestis, M. A. y Apollonio, L. G. (2020). Development and validation of an LC–MS-MS method for the detection of 40 benzodiazepines and three Z-drugs in blood and urine by solid-phase extraction. Journal of Analytical Toxicology, 44(7), 708-717.
95. Song, N. E., Lee, J. Y., Mansur, A. R., Jang, H. W., Lim, M. C., Lee, Y., Yoo, M. y Nam, T. G. (2019). Determination of 60 pesticides in hen eggs using the QuEChERS procedure followed by LC-MS/MS and GC-MS/MS. Food Chemistry, 298, 125050. https://doi.org/10.1016/j.foodchem.2019.125050
96. Song, X., Wang, X., Liao, G., Pan, Y., Qian, Y. y Qiu, J. (2021). Toxic effects of fipronil and its metabolites on PC12 cell metabolism. Ecotoxicology and Environmental Safety, 224, 112677. https://doi.org/10.1016/j.ecoenv.2021.112677
97. Soosaraei, M., Khasseh, A. A., Fakhar, M. y Hezarjaribi, H. Z. (2018). A decade bibliometric analysis of global research on leishmaniasis in Web of Science database. Annals of Medicine and Surgery, 26, 30-37.
98. Sousa, D. V., Pereira, F. V., Nascentes, C. C., Moreira, J. S., Boratto, V. H. y Orlando, R. M. (2020). Cellulose cone tip as a sorbent material for multiphase electrical field-assisted extraction of cocaine from saliva and determination by LC-MS/MS. Talanta, 208.
99. Speck-Planche, A., Kleandrova, V. V., Luan, F. y Cordeiro, M. N. (2012). Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach. Ecotoxicology and Environmental Safety, 80, 308-313.
100.Stefanelli, P. y Barbini, D. (2022). Advanced and Recent Approaches for Laboratory Methods of Pesticide Residues and Their Metabolites by Mass Spectrometry Techniques. Pesticide Toxicology. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1928-5_1.
101.Stenberg, M. y Andersson, P. L. (2008). Selection of non-dioxin-like PCBs for in vitro testing on the basis of environmental abundance and molecular structure. Chemosphere, 71(10), 1909-1915.
102.Takata, M., Lin, B. L., Xue, M., Zushi, Y., Terada, A. y Hosomi, M. (2020). Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory. Chemosphere, 238.
103.Truver, M. T., Jakobsson, G., Chermà, M. D., Swortwood, M. J., Gréen, H. y Kronstrand, R. (2021). A sensitive LC-MS/MS method for the quantitation of oxycodone, noroxycodone, 6α-oxycodol, 6β-oxycodol, oxymorphone, and noroxymorphone in human blood. Journal of Chromatography B, 1171, 122625. https://doi.org/10.1016/j.jchromb.2021.122625
104.Varela-Martínez, D. A., González-Curbelo, M. Á., González-Sálamo, J. y Hernández-Borges, J. (2019). High-throughput analysis of pesticides in minor tropical fruits from Colombia. Food Chemistry, 280, 221-230.
105. Viana, N. P., Menezes Da Silva, L. C., Portruneli, N., Soares, M. P., Cardoso, I. L., Bonansea, R. I., Veloso Goulart, B., Montagner, C. C., Gaeta Espíndola, E. L., Wunderlin, D. A. y Fernandes, M. N. (2022). Bioconcentration and toxicological impacts of fipronil and 2,4-D commercial formulations
(single and in mixture) in the tropical fish, Danio rerio. Environmental Science and Pollution Research International, 29(8), 11685-11698.
106. Wu, H., Gao, C., Guo, Y., Zhang, Y., Zhang, J. y Ma, E. (2014). Acute toxicity and sublethal effects of fipronil on detoxification enzymes in juvenile zebrafish (Danio rerio). Pesticide Biochemistry and Physiology, 115, 9-14. http://dx.doi.org/10.1016/j.pestbp.2014.07.010
107. Yi, F., Yang, P. y Sheng, H. (2016). Tracing the scientific outputs in the field of Ebola research based on publications in the Web of Science. BMC Research Notes, 9(221).
108. Yousefinejad, S. y Hemmateenejad, B. (2015). Chemometrics tools in QSAR/QSPS studies: a historical perspective. Chemometrics and Intellingent Laboratory Systems, 149.
109. Zainudin, B. H. y Salleh, S. (2017). Method development, optimization and validation of matrix hydration effect on pesticide residues in cocoa beans using modified QuEChERS method and gas chromatography tandem mass spectrometry. Food Analytical Methods, 10(2), 3874-3885.
110. Zhang, C., Cheng, F., Sun, L., Zhuang, S., Li, W., Liu, G., Lee, P. W. y Tang, Y. (2015). In silico prediction of chemical toxicity on avian species using chemical category approaches. Chemosphere, 122, 280-287.
111. Zhao, Y., Bai, X. L., Liu, Y. M. y Liao, X. (2021). Determination of fipronil and its metabolites in egg samples by UHPLC coupled with Q-Exactive high resolution mass spectrometry after magnetic solidphase extraction. Microchemical Journal, 169, 106540. https://doi.org/10.1016/j.microc.2021.106540
112. Acero, C., & Machuca, D. (2021). The substitution program on trial: progress and setbacks of the peace agreement in the policy against illicit crops in Colombia. International Journal of Drug Policy, 89. https://doi.org/10.1016/j.drugpo.2021.103158
Contamination and Toxicology, 92(2), 190–195. https://doi.org/10.1007/s00128-013-1155-8
114.Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
115. Asghari, A., Fahimi, E., Bazregar, M., Rajabi, M., & Boutorabi, L. (2017). Rapid determination of some psychotropic drugs in complex matrices by tandem dispersive liquid–liquid microextraction followed by high performance liquid chromatography. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1052, 51–59. https://doi.org/10.1016/j.jchromb.2017.03.012
116. Atapattu, S. N., & Rosenfeld, J. M. (2013). Solid phase analytical derivatization as a sample preparation method. Journal of Chromatography A, 1296, 204–213. https://doi.org/10.1016/j.chroma.2013.03.020
117. Benigni, R. (2019). Towards quantitative read across: Prediction of Ames mutagenicity in a large database. Regulatory Toxicology and Pharmacology, 108(June). https://doi.org/10.1016/j.yrtph.2019.104434
118.Benigni, R., & Bossa, C. (2008). Structure alerts for carcinogenicity, and the Salmonella assay system: A novel insight through the chemical relational databases technology. Mutation Research - Reviews in Mutation Research, 659(3), 248–261. https://doi.org/10.1016/j.mrrev.2008.05.003
119.Biswas, S., Mondal, R., Mukherjee, A., Sarkar, M., & Kole, R. K. (2019). Simultaneous determination and risk assessment of fipronil and its metabolites in sugarcane, using GC-ECD and confirmation by GCMS/MS. Food Chemistry, 272(July 2018), 559–567. https://doi.org/10.1016/j.foodchem.2018.08.087
120.Bjørling-Poulsen, M., Andersen, H. R., & Grandjean, P. (2008). Potential developmental neurotoxicity of pesticides used in Europe. Environmental Health: A Global Access Science Source, 7. https://doi.org/10.1186/1476-069X-7-50
121. Blascke, D., Gomes, I., Fernando, B., & Anderson, M. (2019). Evaluation of the enantioselective in vitro metabolism of the chiral pesticide fipronil employing a human model: risk assessment through in vitro-in vivo correlation and prediction of toxicokinetic parameters. Food and Chemical Toxicology, 123, 225–232.
122.Borba, J. V. B., Alves, V. M., Braga, R. C., Korn, D. R., Overdahl, K., Silva, A. C., Hall, S. U. S., Overdahl, E., Kleinstreuer, N., Strickland, J., Allen, D., Andrade, C. H., Muratov, E. N., & Tropsha, A. (2022). STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity. Environmental Health Perspectives, 130(2), 1–12. https://doi.org/10.1289/EHP9341
123. Bovi, T. S., Zaluski, R., & Orsi, R. de O. (2018). Toxicity and motor changes in africanized honey bees (Apis mellifera l.) exposed to fipronil and imidacloprid. Anais Da Academia Brasileira de Ciencias, 90(1), 239–245. https://doi.org/10.1590/0001-3765201820150191
124.Bownik, A., & Szabelak, A. (2021). Short-term effects of pesticide fipronil on behavioral and physiological endpoints of Daphnia magna. Environmental Science and Pollution Research, 28(25), 33254–33264. https://doi.org/10.1007/s11356-021-13091-6
125. Calster, B. Van, Steyerberg, E., Wynants, L., & Van Smedenen, M. (2023). There is no such thing as a validates prediction model. BMC Medicine, 21(70). https://doi.org/10.1016/j.jacr.2015.03.015
126. Carnesecchi, E., Toma, C., Roncaglioni, A., Kramer, N., Benfenati, E., & Dorne, J. L. C. M. (2020). Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation. Science of the Total Environment, 735, 139243. https://doi.org/10.1016/j.scitotenv.2020.139243
127. Carrillo, M. P., Bovi, T. de S., Negrão, A. F., & Orsi, R. de O. (2013). Influência dos agroquímicos fipronil e imidaclopride no aprendizado de abelhas apis mellifera l. Acta Scientiarum - Animal Sciences, 35(4), 431–434. https://doi.org/10.4025/actascianimsci.v35i4.18683
128.Castilla-Fernández, D., Moreno-González, D., Murillo-Cruz, M. C. M. C., García-Reyes, J. F., & Molina-Díaz, A. (2021). Appraisal of different clean-up strategies for the determination of fipronil and its metabolites in eggs by UHPLC-MS/MS. Microchemical Journal, 166(March). https://doi.org/10.1016/j.microc.2021.106275
129. Chaguri, J. L., Godinho, A. F., Horta, D. F., Gonçalves-Rizzi, V. H., Possomato-Vieira, J. S., Nascimento, R. A., & Dias-Junior, C. A. (2016). Exposure to fipronil elevates systolic blood pressure and disturbs related biomarkers in plasma of rats. Environmental Toxicology and Pharmacology, 42, 63–68. https://doi.org/10.1016/j.etap.2015.12.020
130. Charalampous, A. C., Liapis, K. S., & Bempelou, E. D. (2019). Fipronil in eggs. Is LC-MS/MS the only option? A comparison study of LC-MS/MS and GC-ECD for the analysis of fipronil. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1129(October 2018), 121785. https://doi.org/10.1016/j.jchromb.2019.121785
131. Chen, T., Hu, J., Chen, Y., Liu, Y., Li, Y., & Xu, H. (2022a). Tracking the environment fate of fipronil and three of its metabolites in garlic based on sampling reta-corrected in vivo solid phase microextraction combined with gas chromatography-mass spectrometry. Analytica Chimica Acta, 1190(339263).
132. Chen, T., Hu, J., Chen, Y., Liu, Y., Li, Y., & Xu, H. (2022b). Tracking the environmental fate of fipronil and three of its metabolites in garlic based on sampling rate-corrected in vivo solid phase microextraction combined with gas chromatography-mass spectrometry. Analytica Chimica Acta, 1190, 339263. https://doi.org/10.1016/j.aca.2021.339263
133. Clara T. Lourenço, Stephan M. Carvalho, Osmar Malaspina, R. C. F. N. (2012). Determination of fipronil LD50 for the brazilian bee Melipona scutellaris. Julius-Kühn-Archiv, 437, 174–178. https://doi.org/10.5073/jka.2012.437.046
134. Cotterill, J., Price, N., Rorije, E., & Peijnenburg, A. (2020). Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools. Food and Chemical Toxicology, 142(June). https://doi.org/10.1016/j.fct.2020.111494
135. Cox, C., & Surgan, M. (2006). Unidentified inert ingredients in pesticides: Implications for human and environmental health. Environmental Health Perspectives, 114(12), 1803–1806. https://doi.org/10.1289/ehp.9374
136. De Barros, A. L., Rosa, J. L., Cavariani, M. M., Borges, C. S., Villelae Silva, P., Bae, J. H., Anselmo-Franci, J. A., & Cristina Arena, A. (2016). In utero and lactational exposure to fipronil in female rats: Pregnancy outcomes and sexual development. Journal of Toxicology and Environmental Health - Part A: Current Issues, 79(6), 266–273. https://doi.org/10.1080/15287394.2016.1149132
137. De Oliveira, P. R., Bechara, G. H., Denardi, S. E., Oliveira, R. J., & Mathias, M. I. C. (2012). Genotoxic and mutagenic effects of fipronil on mice. Experimental and Toxicologic Pathology, 64(6), 569–573. https://doi.org/10.1016/j.etp.2010.11.015
138. Duque, P., Meza, O. E., Giraldo, D., & Barreto, K. (2021). Economía Social y Economía Solidaria: un análisis bibliométrico y revisión de literatura. REVESCO. Revista de Estudios Cooperativos, 138(138), e75566. https://doi.org/10.5209/reve.75566
139. Eadie, A., Vásquez, I. C., Liang, X., Wang, X., Souders, C. L., Chehouri, J. El, Hoskote, R., Feswick, A., Cowie, A. M., Loughery, J. R., & Martyniuk, C. J. (2020). Transcriptome network data in larval zebrafish (Danio rerio) following exposure to the phenylpyrazole fipronil. Data in Brief, 33, 106413. https://doi.org/10.1016/j.dib.2020.106413
140. Embry, M. R., Bachman, A. N., Bell, D. R., Boobis, A. R., Cohen, S. M., Dellarco, M., Dewhurst, I.C., Doerrer, N. G., Hines, R. N., Moretto, A., Pastoor, T. P., Phillips, R. D., Rowlands, J. C., Tanir, J. Y., Wolf, D. C., & Doe, J. E. (2014). Risk assessment in the 21st century: Roadmap and matrix. Critical Reviews in Toxicology, 44(SUPPL.3), 6–16. https://doi.org/10.3109/10408444.2014.931924
141. FAO. (1989). Guidelines for legislation on the control of pesticides.
142. Fulton, M. H., & Key, P. B. (2001). Acetylcholinesterase inhibition in estuarine fish and invertebrates as an indicator of organophosphorus insecticide exposure and effects. Environmental Toxicology and Chemistry, 20(1), 37–45. https://doi.org/10.1002/etc.5620200104
143. Gadaleta, D., Vuković, K., Toma, C., Lavado, G. J., Karmaus, A. L., Mansouri, K., Kleinstreuer, N.C., Benfenati, E., & Roncaglioni, A. (2019). SAR and QSAR modeling of a large collection of LD50 ratacute oral toxicity data. Journal of Cheminformatics, 11(1), 1–16. https://doi.org/10.1186/s13321-019-0383-2
144. García-Vara, M., Postigo, C., Palma, P., & López de Alda, M. (2023). Development of QuEChERSbased multiresidue analytical methods to determine pesticides in corn, grapes and alfalfa. Food Chemistry, 405(May 2022). https://doi.org/10.1016/j.foodchem.2022.134870
145. Gomes, H. O., Cardoso, R. S., Nonato, C. F. A., Silva, V. P. A., Nobre, C. A., Costa, J. G. M., Teixeira, R. N. P., & Nascimento, R. F. (2022). Optimization and Validation of a Method Using GC–MS and QuEChERS for Pesticide Determination in Banana Pulp. Food Analytical Methods, 15(9), 1–8. https://doi.org/10.1007/s12161-022-02402-3
146. González de Dios, J., Alonso-Arroyo, A., & Aleixandre-Benavent, R. (2019). Half a century of ANALES DE PEDIATRÍA. Evolution of its main bibliometric indicators in the Web of Science and Scopus international databases. In Anales de Pediatria (Vol. 90, Issue 3, pp. 194.e1-194.e11). https://doi.org/10.1016/j.anpedi.2018.12.012
147. Grande-Martínez, Á., Arrebola-Liébanas, F. J., Martínez-Vidal, J. L., Hernández-Torres, M. E., & Garrido-Frenich, A. (2015). Optimization and validation of a multiresidue pesticide method in rice and wheat flour by modified QuECHERS and GS-MS/MS. Food Analytical Methods, 9(2), 548–563. https://doi.org/10.1007/s12161-015-0214-7
148. Gross, J. H. (2017). Mass Spectrometry. A textbook (Third). Springer Berlin Heidelberg.
149. Guelfi, M. M., Tavares, M. A., Mingatto, F. E., & Maioli, M. A. (2015). Citotoxicity of fipronil on hepatocytes isolated from rat and effects of its biotransformation. Brazilian Archives of Biology and Technology, 58(6), 843–853. https://doi.org/10.1590/S1516-89132015060298
150. Han, C., Hu, B., Li, Z., Liu, C., Wang, N., Fu, C., & Shen, Y. (2021). Determination of Fipronil and Four Metabolites in Foodstuffs of Animal Origin Using a Modified QuEChERS Method and GC–NCI–MS/MS. Food Analytical Methods, 14(2), 237–249. https://doi.org/10.1007/s12161-020-01872-7
151. Herin, F., Boutet-Robinet, E., Levant, A., Dulaurent, S., Manika, M., Galatry-Bouju, F., Caron, P., & Soulat, J. M. (2011). Thyroid function tests in persons with occupational exposure to fipronil.Thyroid, 21(7), 701–706. https://doi.org/10.1089/thy.2010.0449
152. Herrmann, K., Holzwarth, A., Rime, S., Fischer, B. C., & Kneuer, C. (2020). (Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation? Pest Management Science, 76(10), 3316–3325. https://doi.org/10.1002/ps.5828
153. Hurtado-Marín, V. A., Agudelo-Giraldo, J. D., Robledo, S., & Restrepo-Parra, E. (2021). Analysis of dynamic networks based on the Ising model for the case of study of co-authorship of scientific articles. Scientific Reports, 11(1), 1–10. https://doi.org/10.1038/s41598-021-85041-8
154. Hyötuläinen, T., & Wiedmer, S. (2013). Chromatographic Methods in Metabolomics (18th ed.).RSCPublishing.
155. Idrovo, A. J. (2004). Plaguicidas usados en la Fumigación de Cultivos Ilícitos y Salud Humana: ¿una Cuestión de Ciencia o Política? Rev. Salud Pública, 6(2), 199–211.
156. Ivanciuc, T., Ivanciuc, O., & J. Klein, D. (2012). Network-QSAR with Reaction Poset Quantitative Superstructure-Activity Relationships (QSSAR) for PCB Chromatographic Properties. Current Bioinformatics, 6(1), 25–34. https://doi.org/10.2174/157489311795222310
157. Jiménez Noblejas, C., & Perianes Rodríguez, A. (2014). Recuperación y visualización de información en Web of Science y Scopus: una aproximación práctica. In Investigación Bibliotecológica: Archivonomía, Bibliotecología e Información (Vol. 28, Issue 64, pp. 15–31). https://doi.org/10.1016/s0187-358x(14)70907-4
158. Katchanov, Y. L., Markova, Y. V., & Shmatko, N. A. (2019). Comparing the topological rank of journals in Web of Science and Mendeley. In Heliyon (Vol. 5, Issue 7). https://doi.org/10.1016/j.heliyon.2019.e02089
159. Kennedy, M. C., Hart, A. D. M., Kruisselbrink, J. W., van Lenthe, M., de Boer, W. J., van der Voet, H., Rorije, E., Sprong, C., & van Klaveren, J. (2020). A retain and refine approach to cumulative risk assessment. Food and Chemical Toxicology, 138(October 2019). https://doi.org/10.1016/j.fct.2020.111223
160. Komsta, L., Heyden, Y. Vander, & Sherma, J. (2018). Chemometrics in chromatography. CRC press.
161. Kovacic, P., & Osuna Jr., J. (2005). Mechanisms of carcinogenesis: focus on Oxidative Stress and Electron Transfer. Current Pharmaceutical Design, 6(3), 277–309. https://doi.org/10.2174/1381612003401046
162. Krogh, K. A., Halling-Sørensen, B., Mogensen, B. B., & Vejrup, K. V. (2003). Environmental properties and effects of nonionic surfactant adjuvants in pesticides: A review. Chemosphere, 50(7), 871–901. https://doi.org/10.1016/S0045-6535(02)00648-3
163. Lankadurai, B. P., Nagato, E. G., & Simpson, M. J. (2013). Environmental metabolomics: An emerging approach to study organism responses to environmental stressors. Environmental Reviews, 21(3), 180–205. https://doi.org/10.1139/er-2013-0011
164. Lao, W. (2021). Fiproles as a proxy for ecological risk assessment of mixture of fipronil and its degradates in effluent-dominated surface waters. Water Research, 188, 116510. https://doi.org/10.1016/j.watres.2020.116510
165. Lautz, L. S., Stoopen, G., Ginting, A. J., Hoogenboom, R., & Punt, A. (2022). Fipronil and fipronil sulfone in chicken: from in vitro experiments to in vivo PBK model predictions. Food and Chemical Toxicology, 165, 113086.
166. Lévêque, L., Tahiri, N., Goldsmith, M.-R., & Verner, M.-A. (2022). Quantitative Structure-Activity relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta. Computational Toxicology, 21(100211).
167. Li, P., Duan, Y., Ge, H., Zhang, Y., & Wu, X. (2018). Multiresidue Analysis of 113 Pesticides in Different Maturity Levels of Mangoes Using an Optimized QuEChERS Method with GC-MS/MS and UHPLC-MS/MS. Food Analytical Methods, 11(10), 2742–2757. https://doi.org/10.1007/s12161-018-1263-5
168. Li, Q., Zhang, J., Lin, T., Fan, C., Li, Y., Zhang, Z., & Li, J. (2023). Migration behavior and dietary exposure risk assessment of pesticides residues in honeysuckle (Lonicera japonica Thunb.) based on modified QuEChERS method coupled with tandem mass spectrometry. Food Research International, 166(February). https://doi.org/10.1016/j.foodres.2023.112572
169. Li, X., Li, H., Ma, W., Guo, Z., Li, X., Song, S., Tang, H., Li, X., & Zhang, Q. (2019). Development of precise GC-EI-MS method to determine the residual fipronil and its metabolites in chicken egg.Food Chemistry, 281(18), 85–90. https://doi.org/10.1016/j.foodchem.2018.12.041
170. Li, X., Ma, W., Li, H., Zhang, Q., & Ma, Z. (2020). Determination of residual fipronil and its metabolites in food samples: A review. Trends in Food Science and Technology, 97(January), 185–195.https://doi.org/10.1016/j.tifs.2020.01.018
171. Maddadi, S., Qomi, M., & Rajabi, M. (2017). Extraction, preconcentration, and determination of methylphenidate in urine sample using solvent bar microextraction in combination with HPLC–UV:Optimization by experimental design. Journal of Liquid Chromatography and Related Technologies, 40(15), 806–812. https://doi.org/10.1080/10826076.2017.1369994
172. Mandal, S., Poi, R., Ansary, I., Hazra, D. K., Bhattacharyya, S., & Karmakar, R. (2020). Validation of a modified QuEChERS method to determine multiclass multipesticide residues in apple, banana and guava using GC–MS and LC–MS/MS and its application in real sample analysis. SN Applied Sciences, 2(2), 1–14. https://doi.org/10.1007/s42452-020-1990-2
173. Martin, M. T., Judson, R. S., Reif, D. M., Kavlock, R. J., & Dix, D. J. (2009). Profiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef database. Environmental Health Perspectives, 117(3), 392–399. https://doi.org/10.1289/ehp.0800074
174. Martin, M. T., Mendez, E., Corum, D. G., Judson, R. S., Kavlock, R. J., Rotroff, D. M., & Dix, D. J. (2009). Profiling the reproductive toxicity of chemicals from multigeneration studies in the toxicity reference database. Toxicological Sciences, 110(1), 181–190. https://doi.org/10.1093/toxsci/kfp080
175. Marzo, M., Lavado, G. J., Como, F., Toropova, A. P., Toropov, A. A., Baderna, D., Cappelli, C., Lombardo, A., Toma, C., Blázquez, M., & Benfenati, E. (2020). QSAR models for biocides: The example of the prediction of Daphnia magna acute toxicity. SAR and QSAR in Environmental Research, 31(3), 227–243. https://doi.org/10.1080/1062936X.2019.1709221
176. Mcmahen, R. L., Strynar, M. J., Dagnino, S., Herr, D. W., Virginia, C., Garantziotis, S., Andersen, E. M., Freeborn, D. L., Mcmillan, L., Lindstrom, A. B., Exposure, N., States, U., Environmental, S., Agency, P., Exposure, N., States, U., Environmental, S., Agency, P., Health, N., … States, U. (2017). Identification of fipronil metabolites by time-of-flight mass spectrometry for application in a human exposure study. Environ Int., 78, 16–23. https://doi.org/10.1016/j.envint.2015.01.016.Identification
177. Milam, C. D., Farris, J. L., & Wilhide, J. D. (2000). Evaluating mosquito control pesticides for effect on target and nontarget organisms. Archives of Environmental Contamination and Toxicology, 39(3), 324–328. https://doi.org/10.1007/s002440010111
178. Mohamed, F., Senarathna, L., Percy, A., Abeyewardene, M., Cheng, R., Azher, S., Hittarage, A., & Dissanayake, W. (2004). Acute Human Self-Poisoning with the N-Phenylpyrazole Insecticide Fipronil – A GABA A -Gated Chloride Channel Blocker. J Toxicol Clin Toxicol, 42(7), 955–963.
179. Moreira-Filho, J. T., Braga, R. C., Lemos, J. M., Alves, V. M., Borba, J. V. V. B., Costa, W. S., Kleinstreuer, N., Muratov, E. N., Andrade, C. H., & Neves, B. J. (2021). BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees. Artificial Intelligence
in the Life Sciences, 1(November), 100013. https://doi.org/10.1016/j.ailsci.2021.100013
180. Moretto, A., Bachman, A., Boobis, A., Solomon, K. R., Pastoor, T. P., Wilks, M. F., & Embry, M. R. (2017). A framework for cumulative risk assessment in the 21st century. Critical Reviews in Toxicology, 47(2), 85–97. https://doi.org/10.1080/10408444.2016.1211618
181. Mukherjee, A., Mondal, R., Biswas, S., Saha, S., Ghosh, S., & Kole, R. K. (2021). Dissipation behaviour and risk assessment of fipronil and its metabolites in paddy ecosystem using GC-ECD and confirmation by GC-MS/MS. Heliyon, 7(5), e06889. https://doi.org/10.1016/j.heliyon.2021.e06889
182. Nazario, C. E. D., Fumes, B. H., da Silva, M. R., & Lanças, F. M. (2017). New materials for sample preparation techniques in bioanalysis. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1043, 81–95. https://doi.org/10.1016/j.jchromb.2016.10.041
183. Oakes, D. J., & Pollak, J. K. (2000). The in vitro evaluation of the toxicities of three related herbicide formulations containing ester derivatives of 2,4,5-T and 2,4-D using sub-mitochondrial particles. Toxicology, 151(1–3), 1–9. https://doi.org/10.1016/S0300-483X(00)00244-4
184. Ou, Y., Yan, Z., Shi, G., Yu, Z., Cai, Y., & Ma, R. (2022). Enantioselective toxicity, degradation and transformation of the chiral insecticide fipronil in two algae culture. Ecotoxicology and Environmental Safety, 235(December 2021), 113424. https://doi.org/10.1016/j.ecoenv.2022.113424
185. Paris, L., Roussel, M., Pereira, B., Delbac, F., & Diogon, M. (2017). Disruption of oxidative balance in the gut of the western honeybee Apis mellifera exposed to the intracellular parasite Nosema ceranae and to the insecticide fipronil. Microbial Biotechnology, 10(6), 1702–1717. https://doi.org/10.1111/1751-7915.12772
186. Patil, C., Calvayrac, C., Zhou, Y., Romdhane, S., Salvia, M. V., Cooper, J. F., Dayan, F. E., & Bertrand, C. (2016). Environmental Metabolic Footprinting: A novel application to study the impact of a natural and a synthetic β-triketone herbicide in soil. Science of the Total Environment, 566–567, 552–558. https://doi.org/10.1016/j.scitotenv.2016.05.071
187. Pereira, J. L., Antunes, S. C., Castro, B. B., Marques, C. R., Gonçalves, A. M. M., Gonçalves, F., & Pereira, R. (2009). Toxicity evaluation of three pesticides on non-target aquatic and soil organisms: Commercial formulation versus active ingredient. Ecotoxicology, 18(4), 455–463. https://doi.org/10.1007/s10646-009-0300-y
188. Pfaff, J., Reinwald, H., Ayobahan, S. U., Alvincz, J., Göckener, B., Shomroni, O., Salinas, G., Düring, R. A., Schäfers, C., & Eilebrecht, S. (2021). Toxicogenomic differentiation of functional responses to fipronil and imidacloprid in Daphnia magna. Aquatic Toxicology, 238(February). https://doi.org/10.1016/j.aquatox.2021.105927
189. Pinto, T. J. da S., Rocha, G. S., Moreira, R. A., da Silva, L. C. M., Yoshii, M. P. C., Goulart, B. V., Montagner, C. C., Daam, M. A., & Espindola, E. L. G. (2022). Chronic environmentally relevant levels of pesticides disrupt energy reserves, feeding rates, and life-cycle responses in the amphipod Hyalella meinerti. Aquatic Toxicology, 245(September 2021). https://doi.org/10.1016/j.aquatox.2022.106117
190. Portruneli, N., Bonansea, R. I., Valdés, M. E., da Silva, L. C. M., Viana, N. P., Goulart, B. V., Souza, I. da C., Espíndola, E. L. G., Montagner, C. C., Wunderlin, D. A., & Fernandes, M. N. (2021). Wholebody bioconcentration and biochemical and morphological responses of gills of the neotropical fish Prochilodus lineatus exposed to 2,4-dichlorophenoxyacetic acid or fipronil individually or in a mixture. Aquatic Toxicology, 240(September). https://doi.org/10.1016/j.aquatox.2021.105987
191. Putri, S. P., & Fukusaki, E. (2015). Mass Spectrometry-Based Metabolomics. A practical guide.CRC Press.
192. Qu, H., Ma, R. xue, Liu, D. hui, Jing, X., Wang, F., Zhou, Z. qiang, & Wang, P. (2016). The toxicity, bioaccumulation, elimination, conversion of the enantiomers of fipronil in Anodonta woodiana. Journal of Hazardous Materials, 312, 169–174. https://doi.org/10.1016/j.jhazmat.2016.03.063
193. Raitano, G., Goi, D., Pieri, V., Passoni, A., Mattiussi, M., Lutman, A., Romeo, I., Manganaro, A., Marzo, M., Porta, N., Baderna, D., Colombo, A., Aneggi, E., Natolino, F., Lodi, M., Bagnati, R., & Benfenati, E. (2018). (Eco)toxicological maps: A new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy). Environment International, 119(June), 275–286. https://doi.org/10.1016/j.envint.2018.06.035
194. Richardson, J. R., Fitsanakis, V., Westerink, R. H. S., & Kanthasamy, A. G. (2019). Neurotoxicity of pesticides. Acta Neuropathologica, 138(3), 343–362. https://doi.org/10.1007/s00401-019-02033-9
195. Roberts, D. W., Aptula, A., Schultz, T. W., Shen, J., Api, A. M., Bhatia, S., & Kromidas, L. (2015). A practical guidance for Cramer class determination. Regulatory Toxicology and Pharmacology, 73(3), 971–984. https://doi.org/10.1016/j.yrtph.2015.09.017
196. Ruiz, P., Sack, A., Wampole, M., Bobst, S., & Vracko, M. (2017). Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. Chemosphere, 178, 99–109. https://doi.org/10.1016/j.chemosphere.2017.03.026
197. Salcedo, A., & Melo, O. (2005). Evaluación del uso de plaguicidas en la actividad agrícola del departamento de Putumayo. Rev. Cienc. Salud. , 3(2), 168–185.
198. Salvia, M. V., Ben Jrad, A., Raviglione, D., Zhou, Y., & Bertrand, C. (2018). Environmental Metabolic Footprinting (EMF) vs. half-life: a new and integrative proxy for the discrimination between control and pesticides exposed sediments in order to further characterise pesticides’ environmental impact. Environmental Science and Pollution Research, 25(30), 29841–29847. https://doi.org/10.1007/s11356-017-9600-6
199. Schmitz, A., Dempewolf, S., Tan, S., Bicker, G., & Stern, M. (2021). Developmental Neurotoxicity of Fipronil and Rotenone on a Human Neuronal In Vitro Test System. Neurotoxicity Research, 39(4), 1189–1202. https://doi.org/10.1007/s12640-021-00364-8
200. Shamsayei, M., Yamini, Y., Asiabi, H., Rezazadeh, M., & Seidi, S. (2017). Electromembrane surrounded solid-phase microextraction using a stainless-steel wire coated with a nanocomposite composed of polypyrrole and manganese dioxide. Microchimica Acta, 184(8), 2697–2705. https://doi.org/10.1007/s00604-017-2244-x
201. Silva, A. C., Borba, J. V. V. B., Alves, V. M., Hall, S. U. S., Furnham, N., Kleinstreuer, N., Muratov, E., Tropsha, A., & Andrade, C. H. (2021). Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals. Artificial Intelligence in the Life Sciences, 1, 100028. https://doi.org/10.1016/j.ailsci.2021.100028
202. Silva, Carina Aparecida de Souza, Silva-Zacarin, Elaine Cristina Mathias, Domingues, Caio Eduardo da Costa, Abdalla, Fabio, Malaspina, Osmar, N. R. C. F., & Nocelli. (2015). Fipronil effect on the frequency of anomalous brood in honeybee reared in vitro. Julius-Kühn-Archiv, 0(450), 140.
203. Silva, L. C. M., Moreira, R. A., Pinto, T. J. S., Vanderlei, M. R., Athayde, D. B., Lopes, L. F. P., Ogura, A. P., Yoshii, M. P. C., Freitas, J. S., Montagner, C. C., Goulart, B. V., Schiesari, L., Daam, M. A., & Espíndola, E. L. G. (2021). Lethal and sublethal toxicity of pesticides and vinasse used in sugarcane cultivation to Ceriodaphnia silvestrii (Crustacea: Cladocera). Aquatic Toxicology, 241(November), 106017. https://doi.org/10.1016/j.aquatox.2021.106017
204. Simon-Delso, N., Amaral-Rogers, V., Belzunces, L. P., Bonmatin, J. M., Chagnon, M., Downs, C., Furlan, L., Gibbons, D. W., Giorio, C., Girolami, V., Goulson, D., Kreutzweiser, D. P., Krupke, C. H., Liess, M., Long, E., Mcfield, M., Mineau, P., Mitchell, E. A., Morrissey, C. A., … Wiemers, M. (2015). Systemic insecticides (Neonicotinoids and fipronil): Trends, uses, mode of action and metabolites. Environmental Science and Pollution Research, 22(1), 5–34. https://doi.org/10.1007/s11356-014-3470-y
205. Sinclair, C. J., & Boxall, A. B. A. (2003). Assessing the ecotoxicity of pesticide transformation products. Environmental Science and Technology, 37(20), 4617–4625. https://doi.org/10.1021/es030038m
206. Sofalvi, S., Lavins, E. S., Kaspar, C. K., Michel, H. M., Mitchell-Mata, C. L., Huestis, M. A., & Apollonio, L. G. (2020). Development and validation of an LC–MS-MS method for the detection of 40 benzodiazepines and three Z-drugs in blood and urine by solid-phase extraction. Journal of Analytical Toxicology, 44(7), 708–717. https://doi.org/10.1093/jat/bkaa072
207. Song, N. E., Lee, J. Y., Mansur, A. R., Jang, H. W., Lim, M. C., Lee, Y., Yoo, M., & Nam, T. G. (2019). Determination of 60 pesticides in hen eggs using the QuEChERS procedure followed by LC-MS/MS and GC-MS/MS. Food Chemistry, 298(June), 125050. https://doi.org/10.1016/j.foodchem.2019.125050
208. Song, X., Wang, X., Liao, G., Pan, Y., Qian, Y., & Qiu, J. (2021a). Toxic effects of fipronil and its metabolites on PC12 cell metabolism. Ecotoxicology and Environmental Safety, 224, 112677. https://doi.org/10.1016/j.ecoenv.2021.112677
209. Song, X., Wang, X., Liao, G., Pan, Y., Qian, Y., & Qiu, J. (2021b). Toxic effects of fipronil and its metabolites on PC12 cell metabolism. Ecotoxycology and Enviromental Safety, 224(112677).
210. Soosaraei, M., Khasseh, A. A., Fakhar, M., & Hezarjaribi, H. Z. (2018). A decade bibliometric analysis of global research on leishmaniasis in Web of Science database. In Annals of Medicine and Surgery (Vol. 26, pp. 30–37). https://doi.org/10.1016/j.amsu.2017.12.014
211. Sousa, D. V. M., Pereira, F. V., Nascentes, C. C., Moreira, J. S., Boratto, V. H. M., & Orlando, R. M. (2020). Cellulose cone tip as a sorbent material for multiphase electrical field-assisted extraction of cocaine from saliva and determination by LC-MS/MS. Talanta, 208(September 2019). https://doi.org/10.1016/j.talanta.2019.120353
212. Speck-Planche, A., Kleandrova, V. V., Luan, F., & Cordeiro, M. N. D. S. (2012). Predicting multiple ecotoxicological profiles in agrochemical fungicides: A multi-species chemoinformatic approach. Ecotoxicology and Environmental Safety, 80, 308–313. https://doi.org/10.1016/j.ecoenv.2012.03.018
213. Stefanelli, P., & Barbini, D. (2022). Pesticide Toxicology. Methods in Pharmacology and Toxicology (E. Gallardo & M. Barroso, Eds.). Springer Protocols. https://doi.org/10.1007/978-1-0716-1928-5
214. Stenberg, M., & Andersson, P. L. (2008). Selection of non-dioxin-like PCBs for in vitro testing on the basis of environmental abundance and molecular structure. Chemosphere, 71(10), 1909–1915.https://doi.org/10.1016/j.chemosphere.2008.01.007
215. Takata, M., Lin, B. Le, Xue, M., Zushi, Y., Terada, A., & Hosomi, M. (2020). Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory. Chemosphere, 238, 1–9.https://doi.org/10.1016/j.chemosphere.2019.124604
216. Truver, M. T., Jakobsson, G., Chermà, M. D., Swortwood, M. J., Gréen, H., & Kronstrand, R. (2021). A sensitive LC-MS/MS method for the quantitation of oxycodone, noroxycodone, 6α-oxycodol, 6β-oxycodol, oxymorphone, and noroxymorphone in human blood. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1171, 122625. https://doi.org/10.1016/j.jchromb.2021.122625
217. Varela-Martínez, D. A., González-Curbelo, M. Á., González-Sálamo, J., & Hernández-Borges, J.(2019). High-throughput analysis of pesticides in minor tropical fruits from Colombia. Food Chemistry, 280(November 2018), 221–230. https://doi.org/10.1016/j.foodchem.2018.12.045
218. Viana, N. P., da Silva, L. C. M., Portruneli, N., Soares, M. P., Cardoso, I. L., Bonansea, R. I., Goulart, B. V., Montagner, C. C., Espíndola, E. L. G., Wunderlin, D. A., & Fernandes, M. N. (2022). Bioconcentration and toxicological impacts of fipronil and 2,4-D commercial formulations (single and in mixture) in the tropical fish, Danio rerio. Environmental Science and Pollution Research, 29(8), 11685–11698. https://doi.org/10.1007/s11356-021-16352-6
219. Wu, H., Gao, C., Guo, Y., Zhang, Y., Zhang, J., & Ma, E. (2014). Acute toxicity and sublethal effects of fipronil on detoxification enzymes in juvenile zebrafish (Danio rerio). Pesticide Biochemistry and Physiology, 115, 9–14. https://doi.org/10.1016/j.pestbp.2014.07.010
220. Yi, F., Yang, P., & Sheng, H. (2016). Tracing the scientific outputs in the field of Ebola research based on publications in the Web of Science. In BMC Research Notes (Vol. 9, Issue 1). https://doi.org/10.1186/s13104-016-2026-2
221. Yousefinejad, S., & Hemmateenejad, B. (2015). Chemometrics tools in QSAR/QSPS studies: A historical perspective. Chemometrics and Intellingent Laboratory Systems, 149, 117–204.
222. Zainudin, B. H., & Salleh, S. (2017). Method Development, Optimization and Validation of Matrix Hydration Effect on Pesticide Residues in Cocoa Beans Using Modified QuEChERS Method and Gas Chromatography Tandem Mass Spectrometry. Food Analytical Methods, 10(12), 3874–3885. https://doi.org/10.1007/s12161-017-0954-7
223. Zhang, C., Cheng, F., Sun, L., Zhuang, S., Li, W., Liu, G., Lee, P. W., & Tang, Y. (2015). In silico prediction of chemical toxicity on avian species using chemical category approaches. Chemosphere, 122, 280–287. https://doi.org/10.1016/j.chemosphere.2014.12.001
224. Zhao, Y., Bai, X. L., Liu, Y. M., & Liao, X. (2021). Determination of fipronil and its metabolites in egg samples by UHPLC coupled with Q-Exactive high resolution mass spectrometry after magnetic solid-phase extraction. Microchemical Journal, 169(May), 106540. https://doi.org/10.1016/j.microc.2021.106540
2. Arain, M. S., Hu, X. X. y Li, G. Q. (2014). Assessment of toxicity and potential risk of butenefipronil using Drosophila melanogaster, in comparison to nine conventional insecticides. Bulletin of Environmental Contamination and Toxicology, 92(2), 190-195.
3. Aria, M. y Cuccurullo, C. (2017). Bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
4. Asghari, A., Fahimi, E., Bazregar, M., Rajabi, M. y Boutorabi, L. (2017). Rapid determination of some psychotropic drugs in complex matrices by tandem dispersive liquid–liquid microextraction followed by high performance liquid chromatography. Journal of Chromatography B, 1052, 51-59.
5. Atapattu, S. N. y Rosenfeld, J. M. (2013). Solid phase analytical derivatization as a sample preparation method. Journal of Chromatography A, 1296, 204-213. http://dx.doi.org/10.1016/j.chroma.2013.03.020
6. Benigni, R. (2019). Towards quantitative read across: prediction of Ames mutagenicity in a large database. Regulatory Toxicology and Pharmacology, 108.
7. Benigni, R. y Bossa, C. (2008). Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. Mutation Research, 659(3), 248-261.
8. Biswas, S., Mondal, R., Mukherjee, A., Sarkar, M. y Kole, R. K. (2019). Simultaneous determination and risk assessment of fipronil and its metabolites in sugarcane, using GC-ECD and confirmation by GC-MS/MS. Food Chemistry, 272, 559-567. https://doi.org/10.1016/j.foodchem.2018.08.087
9. Bjørling-Poulsen, M., Andersen, H. R. y Grandjean, P. (2008). Potential developmental neurotoxicity of pesticides used in Europe. Environmental Health, 7, 50.
10. Blascke Carrão, D., Dos Reis Gomes, I. C., Barbosa Junior, F. y Moraes de Oliveira, A. R. (2019). Evaluation of the enantioselective in vitro metabolism of the chiral pesticide fipronil employing a human model: risk assessment through in vitro-in vivo correlation and prediction of toxicokinetic parameters. Food and Chemical Toxicology, 123, 225-232.
11. Bovi, T. S., Zaluski, R. y Orsi, R. O. (2018). Toxicity and motor changes in africanized honey bees (Apis mellifera L.) exposed to fipronil and imidacloprid. Anais da Academia Brasileira de Ciencias, 90(1), 239-245.
12. Borba, J. V., Alves, V. M., Braga, R. C., Korn, D. R., Overdahl, K., Silva, A. C., Hall, S. U., Overdahl, E., Kleinstreuer, N., Strickland, J., Allen, D., Andrade, C. H., Muratov, E. N. y Tropsha, A. (2022). STopTox: an in silico alternative to animal testing for acute systemic and topical toxicity. Environmental Health Perspectives, 130(2).
13. Bownik, A. y Szabelak, A. (2021). Short-term effects of pesticide fipronil on behavioral and physiological endpoints of Daphnia magna. Environmental Science Pollution Research International, 28(25), 33254-33264.
14. Calster, B., Steyerberg, E. W, Wynants, L. y Van Smedenen, M. (2023). There is no such thing as a validated prediction model. BMC Med, 21(70).
15. Carnesecchi, E., Toma, C., Roncaglioni, A., Kramer, N., Benfenati, E. y Dorne, J. L. (2020). Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): data curation using open source databases, performance testing and validation. Science of the Total Environment, 735, 139243. https://doi.org/10.1016/j.scitotenv.2020.139243
16. Castilla-Fernández, D., Moreno-Gonzalez, D., Murillo Cruz, M., García-Reyes, J. F. y Molina-Díaz, A. (2021). Appraisal of different clean-up strategies for the determination of fipronil and its metabolites in eggs by UHPLC-MS/MS. Microchemical Journal, 166(1).
17. Cotterill, J., Price, N., Rorije, E. y Peijnenburg, A. (2020). Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools. Food and Chemical Toxicology, 142, 111494.
18. Cox, C. y Surgan, M. (2006). Unidentified inert ingredients in pesticides: implications for human and environmental health. Environmental Health Perspectives, 114(12),1803-1806.
19. Chaguri, J. L., Godinho, A. F., Horta, D. F., Gonçalves-Rizzi, V. H., Possomato-Vieira, J. S., Nascimento, R. A. y Dias-Junior, C. A. (2016). Exposure to fipronil elevates systolic blood pressure and disturbs related biomarkers in plasma of rats. Environmental Toxicology and Pharmacology, 42, 63-68.
20. Charalampous, A. C., Liapis, K. S. y Bempelou, E. D. (2019). Fipronil in eggs. Is LC-MS/MS the only option? A comparison study of LC-MS/MS and GC-ECD for the analysis of fipronil. Journal of Chromatography B, 1129, 121785. https://doi.org/10.1016/j.jchromb.2019.121785
21. Chen, T., Hu, J., Chen, Y., Liu, Y., Li, Y. y Xu, H. (2022). Tracking the environmental fate of fipronil and three of its metabolites in garlic based on sampling reta-corrected in vivo solid phase microextraction combined with gas chromatography-mass spectrometry. Analytica Chimica Acta, 1190, 339263.
22. Da Silva Pinto, T. J., Rocha, G. S., Moreira, R. A., Menezes Da Silva, L. C., Cardoso Yoshii, M. P., Goulart, B. V., Montagner, C. C., Daam, M. A. y Gaeta Espindola, E. L. (2022). Chronic environmentally relevant levels of pesticides disrupt energy reserves, feeding rates, and life-cycle responses in the amphipod Hyalella meinerti. Aquatic Toxicology, 245, 106117.
23. De Oliveira, P. R., Bechara, G. H., Denardi, S. E., Oliveira, R. J. y Camargo Mathias, M. I. (2012). Genotoxic and mutagenic effects of fipronil on mice. Experimental and Toxicology Pathology, 64(6), 569-573. http://dx.doi.org/10.1016/j.etp.2010.11.015
24. Delso, N. S., Amaral-Rogers, V., Belzunces, L. P., Bonmatin, J. M., Chagnon, M., Downs, C., Furlan, L., Gibbons, D. W., Giorio, C., Girolami, V., Goulson, D., Kreutzweizer, D. P., Krupke, C. H., Liess, M., Long, E., McField, M., Mineau, P., Mitchell, E. A. D., Morrissey, C. A…Wiemers, M. (2015). Systemic insecticides (neonicotinoids and fipronil): trends, uses, mode of action and metabolites. Environmental Science and Pollution Research International, 22(1), 5-34.
25. Domingues Nazario, C. E., Fumes, B., Da Silva, M. R. y Lanças, F. M. (2016). New materials for sample preparation techniques in bioanalysis. Journal of Chromatography B, 1043(945). 26. Duque, P., Meza, O. E., Giraldo, D. y Barreto, K. (2021). Economía social y economía solidaria: un análisis bibliométrico y revisión de literatura. REVESCO, (138), 187-212.
27. Eadie, A., Vásquez, I. C., Liang, X., Wang, X., Souders, C. L., Chehouri, J. E., Hoskote, R., Feswick, A., Cowie, A. M., Loughery, J. R. y Martyniuk, C. J. (2020). Transcriptome network data in larval zebrafish (Danio rerio) following exposure to the phenylpyrazole fipronil. Data in Brief, 33, 106413. https://doi.org/10.1016/j.dib.2020.106413
28. Embry, M. R., Bachman, A. N., Bell, D. R., Boobis, A. R., Cohen, S. M., Dellarco, M., Dewhurst, C. I., Doerrer, N. G., Hines, R. N., Moretto, A., Pastoor, T. P., Phillips, R. D., Rowlands, J. C., Tanir, J. Y., Wolf., D. C. y Doe, J. E. (2014). Risk assessment in the 21st century: roadmap and matrix. Critical Reviews in Toxicology, 44(Suppl.3), 6-16.
29. Food and Agriculture Organization of the United Nations. (1989). Guidelines for legislation on the control of pesticides [Archivo PDF]
30. Fulton, M. H. y Key, P. B. (2001). Acetylcholinesterase inhibition in estuarine fish and invertebrates as an indicator of organophosphorus insecticide exposure and effects. Environmental Toxicology Chemistry, 20(1), 37-45.
31. Gadaleta, D., Vuković, K., Toma, C., Lavado, G. J., Karmaus, A. L. y Mansouri, K. (2019). SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. J Cheminform, 11(1), 1-16.
32. García-Vara, M., Postigo, C., Palma, P. y López de Alda, M. (2023). Development of QuEChERS-based multiresidue analytical methods to determine pesticides in corn, grapes and alfalfa. Food Chemistry, 405, 134870.
33. Gomes, H. O., Cardoso, R., Nonato, C. F., Andrade da Silva, V. P., Nobre, C., Da Costa, J. G., Pereira Teixeira, R. N. y Do Nascimento, R. F. (2023). Optimization and validation of a method using GC–MS and QuEChERS for pesticide determination in banana pulp. Food Analytical Methods, 16, 125-131. https://doi.org/10.1007/s12161-022-02402-3
34. González de Dios, J., Alonso-Arroyo, A. y Aleixandre-Benavent, R. (2019). Half a century of Anales de Pediatría. Evolution of its main bibliometric indicators in the Web of Science and Scopus international databases. Anales de Pediatria, 90(3), 194.e1-194.e11.
35. Grande Martínez, Á., Arrebola, F. J., Martínez-Vidal, J. L., Hernández-Torres, M. E. y Garrido-Frenich, A. (2015). Optimization and validation of a multiresidue pesticide method in rice and wheat flour by modified QuECHERS and GC-MS/MS. Food Analytical Methods, 9(2).
36. Gross, J. H. (2017). Mass Spectrometry. A textbook. Springer.
37. Guelfi, M., Tavares, M. A., Mingatto, F. E. y Maioli, M. A. (2015). Citotoxicity of fipronil on hepatocytes isolated from rat and effects of its biotransformation. Brazilian Archives of Biology and Technology, 58(6), 843-853.
38. Han, C., Hu, B., Li, Z., Liu, C., Wang, N., Fu, C. y Shen, Y. (2021). Determination of fipronil and four metabolites in foodstuffs of animal origin using a modified QuEChERS method and GC–NCI–MS/MS. Food Analytical Methods, 14(2), 237-249.
39. Herin, F., Boutet-Robinet, E., Levant, A., Dulaurent, S., Manika, M., Galatry-Bouju, F., Caron, P. y Soulat, J. M. (2011). Thyroid function tests in persons with occupational exposure to fipronil. Thyroid, 21(7), 701-706.
40. Herrmann, K., Holzwarth, A., Rime, S., Fischer, B. C. y Kneuer, C. (2020). (Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation? Pest Management Science, 76(10), 3316-3325.
41. Hidrovo, A. J. (2004). Plaguicidas usados en la fumigación de cultivos ilícitos y salud humana: ¿Una cuestión de ciencia o política? Revista Salud Pública, 6(2), 199-211.
42. Hurtado-Marín, V. A., Agudelo-Giraldo, J. D., Robledo, S. y Restrepo-Parra, E. (2021). Analysis of dynamic networks based on the Ising model for the case of study of co-authorship of scientific articles. Scientific Reports, 11(5721). https://doi.org/10.1038/s41598-021-85041-8
43. Hyötyläinen, T. y Wiedmer, S. (Eds.). (2013). Chromatographic Methods in Metabolomics. Royal Society of Chemistry.
44. Ivanciuc, T., Ivanciuc, O. y Klein, D. (2011). Network-QSAR with reaction poset quantitative superstructure-activity relationships (QSSAR) for PCB chromatographic properties. Current Bioinformatics, 6(1), 25-34.
45. Jiménez Noblejas, C. y Perianes Rodríguez, A. (2014). Recuperación y visualización de información en Web of Science y Scopus: una aproximación práctica. Investigación Bibliotecológica: Archivonomía, Bibliotecología e Información, 28(64), 15-31.
46. Katchanov, Y. L., Markova, Y. V. y Shmatko, N. A. (2019). Comparing the topological rank of journals in Web of Science and Mendeley. Heliyon, 5(7), e02089.
47. Kennedy, M. C., Hart, A. D., Kruisselbrink, J. W., Van Lenthe, M., De Boer, W. J., Van der Voet, H., Rorije, E., Sprong, C. y Van Klaveren, J. (2020). A retain and refine approach to cumulative risk assessment. Food and Chemical Toxicology, 138.
48. Komsta, L., Vander Heyden, Y. y Sherma J. (Eds.). (2018). Chemometrics in chromatography. CRC press.
49. Kovacic, P. y Jacintho, J. D. (2001). Mechanisms of carcinogenesis: focus on oxidative stress and electron transfer. Current Medicinal Chemistry, 8(7).
50. Krogh, K. A., Halling-Sørensen, B., Mogensen, B. B. y Vejrup, K. V. (2003). Environmental properties and effects of nonionic surfactant adjuvants in pesticides: a review. Chemosphere, 50(7), 871-901.
51. Lankadurai, B. P., Nagato, E. G. y Simpson, M. J. (2013). Environmental metabolomics: an emerging approach to study organism responses to environmental stressors. Environmental Reviews, 21(3), 180-205.
52. Lao, W. (2021). Fiproles as a proxy for ecological risk assessment of mixture of fipronil and its degradates in effluent-dominated surface waters. Water Research, 188, 116510. https://doi.org/10.1016/j.watres.2020.116510
53. Lautz, L. S., Stoopen, G., Ginting, A. J., Hoogenboom, R. y Punt, A. (2022). Fipronil and fipronil sulfone in chicken: from in vitro experiments to in vivo PBK model predictions. Food and Chemical Toxicology, 165, 113086.
54. Lévêque, L., Tahiri, N., Goldsmith, M. R. y Verner, M. A. (2022). Quantitative Structure-Activity relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta. Computational Toxicology, 21, 100211.
55. Li, P., Duan, Y., Ge, H., Zhang, Y. y Wu, X. (2018). Multiresidue analysis of 113 pesticides in different maturity levels of mangoes using an optimized QuEChERS method with GC-MS/MS and UHPLC-MS/MS. Food Analytical Methods, 11(26).
56. Li, Q., Zhang, J., Lin, T., Fan, C., Li, Y. y Zhang, Z. (2023). Migration behavior and dietary exposure risk assessment of pesticides residues in honeysuckle (Lonicera japonica Thunb.) based on modified QuEChERS method coupled with tandem mass spectrometry. Food Research International, 166, 112562.
57. Li, X., Li, H., Ma, W., Guo, Z., Li, X., Song, S., Tang, H., Li, X. y Zhang, Q. (2019). Development of precise GC-EI-MS method to determine the residual fipronil and its metabolites in chicken egg. Food Chemistry, 281, 85-90. https://doi.org/10.1016/j.foodchem.2018.12.041
58. Li, X., Ma, W., Li, H., Zhang, Q. y Ma, Z. (2020). Determination of residual fipronil and its metabolites in food samples: a review. Trends in Food Science and Technology, 97, 185-195. https://doi.org/10.1016/j.tifs.2020.01.018
59. Lima de Barros, A., Lima Rosa, J., Cavariani, M. M., Borges, C. S., Villelae e Silva, P., Bae, J. H., Anselmo-Franci, J. A. y Arena, A. C. (2016). In utero and lactational exposure to fipronil in female rats: pregnancy outcomes and sexual development. Journal of Toxicology and Environmental Health, 79(6), 266-273.
60. Lourenço, C. T., Carvalho, S. M., Malaspina, O. y Nocelli, R. (2012). Determination of fipronil LD50 for the brazilian bee Melipona scutellaris. Julius-Kühn-Archiv, 437, 174-178.
61. Maddadi, S., Qomi, M. y Rajabi, M. (2017). Extraction, preconcentration, and determination of methylphenidate in urine sample using solvent bar microextraction in combination with HPLC–UV: optimization by experimental design. Journal of Liquid Chromatography and Related Technologies, 40(15).
62. Mandal, S., Poi, R., Ansary, I., Hazra, D. K., Bhattacharyya, S. y Karmakarm R. (2020). Validation of a modified QuEChERS method to determine multiclass multipesticide residues in apple, banana and guava using GC–MS and LC–MS/MS and its application in real sample analysis. SN Applied Sciences, 2(188), 1-14. https://doi.org/10.1007/s42452-020-1990-2
63. Martin, M. T., Judson, R. S., Reif, D. M., Kavlock, R. J. y Dix, D. J. (2009). Profiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef database. Environmental Health Perspectives, 117(3), 392-399.
64. Martin, M. T., Mendez, E., Corum, D. G., Judson, R. S., Kavlock, R. J., Rotroff, D. M. y Dix, D. J. (2009). Profiling the reproductive toxicity of chemicals from multigeneration studies in the toxicity reference database. Toxicological Science, 110(1), 181-190.
65. Marzo, M., Lavado, G. J., Como, F., Toropova, A. P., Toropov, A. A. y Baderna, D. (2020). QSAR models for biocides: the example of the prediction of Daphnia magna acute toxicity. SAR and QSAR in Environmental Research, 31(3), 227-243.
66. Mcmahen, R. L., Strynar, M. J., Dagnino, S., Herr, D. W., Moser, V. C., Garantziotis, S., Andersen, E. M., Freeborn, D. L., McMillan, L. y Lindstrom, A. B. (2015). Identification of fipronil metabolites by timeof-flight mass spectrometry for application in a human exposure study. Environment International, 78, 16-23.
67. Milam, C. D., Farris, J. L. y Wilhide, J. D. (2000). Evaluating mosquito control pesticides for effect on target and nontarget organisms. Archives on Environmental Contamination and Toxicology, 39(3), 324-328.
68. Mohamed, F., Senarathna, L., Percy, A., Abeyewardene, M., Cheng, R., Azher, S., (2004). Acute Human Self-Poisoning with the N-Phenylpyrazole Insecticide Fipronil – A GABA A -Gated Chloride Channel Blocker. J Toxicol Clin Toxicol, 42(7), 955-963.
69. Moreira-Filho, J. T., Braga, R. C., Lemos, J. M., Medeiros Alves, V., Borba, J. V., Costa, W. S., Kleinstreuer, N., Muratov, E., Horta Andrade, C. y Neves, B. M. (2021). BeeToxAI: an artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees. Artificial Intelligence Life Sciences, 1, 100013.
70. Moretto, A., Bachman, A., Boobis, A., Solomon, K. R., Pastoor, T. P., Wilks, M. F. y Embry, M. R. (2017). A framework for cumulative risk assessment in the 21st century. Critical Reviews in Toxicology, 47(2), 85-97.
71. Mukherjee, A., Mondal, R., Biswas, S., Saha, S., Ghosh, S. y Kole, R. K. (2021). Dissipation behaviour and risk assessment of fipronil and its metabolites in paddy ecosystem using GC-ECD and confirmation by GC-MS/MS. Heliyon, 7(5), e06889. https://doi.org/10.1016/j.heliyon.2021.e06889
72. Oakes, D. J. y Pollak, J. K. (2000). The in vitro evaluation of the toxicities of three related herbicide formulations containing ester derivatives of 2,4,5-T and 2,4-D using sub-mitochondrial particles.Toxicology, 151(1-3), 1-9.
73. Ou, Y., Yan, Z., Shi, G., Yu, Z., Cai, Y. y Ma, R. (2022). Enantioselective toxicity, degradation and transformation of the chiral insecticide fipronil in two algae culture. Ecotoxicology Environmental Safety, 235, 113424. https://doi.org/10.1016/j.ecoenv.2022.113424
74. Paris, L., Roussel, M., Pereira, B., Delbac, F. y Diogon, M. (2017). Disruption of oxidative balance in the gut of the western honeybee Apis mellifera exposed to the intracellular parasite Nosema ceranae and to the insecticide fipronil. Microbial Biotechnology, 10(6), 1702-1717.
75. Patil, C., Calvayrac, C., Zhou, Y., Romdhane, S., Salvia, M. V., Cooper, J. F., Dayan, F. E. y Bertrand, C. (2016). Environmental metabolic footprinting: a novel application to study the impact of a natural and a synthetic β-triketone herbicide in soil. Science of the Total Environment, 566-567, 552-558. http ://dx.doi.org/10.1016/j.scitotenv.2016.05.071
76. Pedraza Carrillo, M., De Souza Bovi, T., Fava Negrão, A. y De Oliveira Orsi, R. (2013). Influência dos agroquímicos fipronil e imidaclopride no aprendizado de abelhas apis mellifera l. Acta Scientiarum. Animal Sciences, 35(4), 431-434.
77. Pereira, J. L., Antunes, S. C., Castro, B. B., Marques, C. R., Gonçalves, A. M. M., Gonçalves, F. y Pereira, R. (2009). Toxicity evaluation of three pesticides on non-target aquatic and soil organisms:commercial formulation versus active ingredient. Ecotoxicology, 18(4), 455-463.
78. Pfaff, J., Reinwald, H., Ayobahan, S. U., Alvincz, J., Göckener, B., Shomroni, O., Salinas, G., Düring, R. A., Schäfers, C. y Eilebrecht, S. (2021). Toxicogenomic differentiation of functional responses to fipronil and imidacloprid in Daphnia magna. Aquatic Toxicology, 238.
79. Portruneli, N., Bonansea, R. I., Valdés, M. E., Menezes Da Silva, L. C., Viana, N. P., Goulart, B. V., Da Costa Souza, I., Gaeta Espíndola, E. L., Montagner, C. C., Wunderlin, D. A. y Fernandes, M. N. (2021). Whole-body bioconcentration and biochemical and morphological responses of gills of the neotropical fish Prochilodus lineatus exposed to 2,4-dichlorophenoxyacetic acid or fipronil individually or in a mixture. Aquatic Toxicology, 240.
80. Putri, S. P. y Fukusaki, E. (2015). Mass Spectrometry-Based Metabolomics. A practical guide. Boca Raton: CRC Press.
81. Qu, H., Ma, R. X., Liu, D. H., Jing, X., Wang, F., Zhou, Z. Q. y Wang, P. (2016). The toxicity, bioaccumulation, elimination, conversion of the enantiomers of fipronil in Anodonta woodiana. Journal of Hazardous Materials, 312, 169-174. http://dx.doi.org/10.1016/j.jhazmat.2016.03.063
82. Raitano, G., Goi, D., Pieri, V., Passoni, A., Mattiussi, M., Lutman, A., Romeo, I., Manganaro, A., Marzo, M., Porta, N., Baderna, D., Colombo, A., Aneggi, E., Natolino, F., Lodi, M., Bagnati, R. y Benfenati, E. (2018). (Eco)toxicological maps: a new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy). Environment International, 119, 275-286.
83. Richardson, J. R., Fitsanakis, V., Westerink, R. H. S. y Kanthasamy, A. G. (2019). Neurotoxicity of pesticides. Acta Neuropathologica, 138, 343-362. https://doi.org/10.1007/s00401-019-02033-9
84. Roberts, D. W., Aptula, A., Schultz, T. W., Shen, J., Api, A. M., Bhatia, S. y Kromidas, L. (2015). A practical guidance for Cramer class determination. Regulatory Toxicology and Pharmacology, 73(3), 971-984.
85. Ruiz, P., Sack, A., Wampole, M., Bobst, S. y Vracko, M. (2017). Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. Chemosphere, 178, 99-109.
86. Salcedo Monsalve, A. y Melo Trujillo, O. L. (2005). Evaluación del uso de plaguicidas en la actividad agrícola del departamento de Putumayo. Revista Ciencias de la Salud, 3(2),168-185.
87. Salvia, M. V., Ben Jrad, A., Raviglione, D., Zhou, Y. y Bertrand, C. (2018). Environmental Metabolic Footprinting (EMF) vs. half-life: a new and integrative proxy for the discrimination between control and pesticides exposed sediments in order to further characterise pesticides’ environmental impact. Environmental Science and Pollution Research International, 25(30), 29841-29847.
88. Schmitz, A., Dempewolf, S., Tan, S., Bicker, G. y Stern, M. (2021). Developmental neurotoxicity of fipronil and rotenone on a human neuronal in vitro test system. Neurotoxicity Research, 39, 1189-1202. https://doi.org/10.1007/s12640-021-00364-8
89. Shamsayei, M., Yamini, Y., Asiabi, H., Rezazadeh, M. y Seidi, S. (2017). Electromembrane surrounded solid-phase microextraction using a stainless-steel wire coated with a nanocomposite composed of polypyrrole and manganese dioxide. Microchimica Acta, 184(8).
90. Silva, A. C., Borba, J. V., Alves, V. M., Hall, S. U. S., Furnham, N. y Kleinstreuer, N. (2021). Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals. Artif Intell Life Sci, 1, 100028.
91. Silva, C. A., Silva-Zacarin, E. C. M., Domingues, C. E., Abdalla, F. C., Malaspina, O. y Nocelli, R. (2015). Fipronil effect on the frequency of anomalous brood in honeybee reared in vitro. Julius-Kühn-Archiv, (450).
92. Silva, L., Moreira, R. A., Pinto, T. J., Vanderlei, M. R., Athayde, D. B., Lopes, L., Ogura, A. P., Yoshii, M. P., Freitas, J. S., Montagner, C. C., Goulart, B. V., Schiesari, L., Daam, M. A. y Espíndola, E. L. (2021). Lethal and sublethal toxicity of pesticides and vinasse used in sugarcane cultivation to Ceriodaphnia silvestrii (Crustacea: Cladocera). Aquatic Toxicology, 241, 106017. https://doi.org/10.1016/j.aquatox.2021.106017
93. Sinclair, C. J. y Boxall, A. B. A. (2003). Assessing the ecotoxicity of pesticide transformation products. Environmental Science and Technology, 37(20), 4617-4625.
94. Sofalvi, S., Lavins, E. S., Kaspar, C. K., Michel, H. M., Mitchell-Mata, C. L., Huestis, M. A. y Apollonio, L. G. (2020). Development and validation of an LC–MS-MS method for the detection of 40 benzodiazepines and three Z-drugs in blood and urine by solid-phase extraction. Journal of Analytical Toxicology, 44(7), 708-717.
95. Song, N. E., Lee, J. Y., Mansur, A. R., Jang, H. W., Lim, M. C., Lee, Y., Yoo, M. y Nam, T. G. (2019). Determination of 60 pesticides in hen eggs using the QuEChERS procedure followed by LC-MS/MS and GC-MS/MS. Food Chemistry, 298, 125050. https://doi.org/10.1016/j.foodchem.2019.125050
96. Song, X., Wang, X., Liao, G., Pan, Y., Qian, Y. y Qiu, J. (2021). Toxic effects of fipronil and its metabolites on PC12 cell metabolism. Ecotoxicology and Environmental Safety, 224, 112677. https://doi.org/10.1016/j.ecoenv.2021.112677
97. Soosaraei, M., Khasseh, A. A., Fakhar, M. y Hezarjaribi, H. Z. (2018). A decade bibliometric analysis of global research on leishmaniasis in Web of Science database. Annals of Medicine and Surgery, 26, 30-37.
98. Sousa, D. V., Pereira, F. V., Nascentes, C. C., Moreira, J. S., Boratto, V. H. y Orlando, R. M. (2020). Cellulose cone tip as a sorbent material for multiphase electrical field-assisted extraction of cocaine from saliva and determination by LC-MS/MS. Talanta, 208.
99. Speck-Planche, A., Kleandrova, V. V., Luan, F. y Cordeiro, M. N. (2012). Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach. Ecotoxicology and Environmental Safety, 80, 308-313.
100.Stefanelli, P. y Barbini, D. (2022). Advanced and Recent Approaches for Laboratory Methods of Pesticide Residues and Their Metabolites by Mass Spectrometry Techniques. Pesticide Toxicology. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1928-5_1.
101.Stenberg, M. y Andersson, P. L. (2008). Selection of non-dioxin-like PCBs for in vitro testing on the basis of environmental abundance and molecular structure. Chemosphere, 71(10), 1909-1915.
102.Takata, M., Lin, B. L., Xue, M., Zushi, Y., Terada, A. y Hosomi, M. (2020). Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory. Chemosphere, 238.
103.Truver, M. T., Jakobsson, G., Chermà, M. D., Swortwood, M. J., Gréen, H. y Kronstrand, R. (2021). A sensitive LC-MS/MS method for the quantitation of oxycodone, noroxycodone, 6α-oxycodol, 6β-oxycodol, oxymorphone, and noroxymorphone in human blood. Journal of Chromatography B, 1171, 122625. https://doi.org/10.1016/j.jchromb.2021.122625
104.Varela-Martínez, D. A., González-Curbelo, M. Á., González-Sálamo, J. y Hernández-Borges, J. (2019). High-throughput analysis of pesticides in minor tropical fruits from Colombia. Food Chemistry, 280, 221-230.
105. Viana, N. P., Menezes Da Silva, L. C., Portruneli, N., Soares, M. P., Cardoso, I. L., Bonansea, R. I., Veloso Goulart, B., Montagner, C. C., Gaeta Espíndola, E. L., Wunderlin, D. A. y Fernandes, M. N. (2022). Bioconcentration and toxicological impacts of fipronil and 2,4-D commercial formulations
(single and in mixture) in the tropical fish, Danio rerio. Environmental Science and Pollution Research International, 29(8), 11685-11698.
106. Wu, H., Gao, C., Guo, Y., Zhang, Y., Zhang, J. y Ma, E. (2014). Acute toxicity and sublethal effects of fipronil on detoxification enzymes in juvenile zebrafish (Danio rerio). Pesticide Biochemistry and Physiology, 115, 9-14. http://dx.doi.org/10.1016/j.pestbp.2014.07.010
107. Yi, F., Yang, P. y Sheng, H. (2016). Tracing the scientific outputs in the field of Ebola research based on publications in the Web of Science. BMC Research Notes, 9(221).
108. Yousefinejad, S. y Hemmateenejad, B. (2015). Chemometrics tools in QSAR/QSPS studies: a historical perspective. Chemometrics and Intellingent Laboratory Systems, 149.
109. Zainudin, B. H. y Salleh, S. (2017). Method development, optimization and validation of matrix hydration effect on pesticide residues in cocoa beans using modified QuEChERS method and gas chromatography tandem mass spectrometry. Food Analytical Methods, 10(2), 3874-3885.
110. Zhang, C., Cheng, F., Sun, L., Zhuang, S., Li, W., Liu, G., Lee, P. W. y Tang, Y. (2015). In silico prediction of chemical toxicity on avian species using chemical category approaches. Chemosphere, 122, 280-287.
111. Zhao, Y., Bai, X. L., Liu, Y. M. y Liao, X. (2021). Determination of fipronil and its metabolites in egg samples by UHPLC coupled with Q-Exactive high resolution mass spectrometry after magnetic solidphase extraction. Microchemical Journal, 169, 106540. https://doi.org/10.1016/j.microc.2021.106540
112. Acero, C., & Machuca, D. (2021). The substitution program on trial: progress and setbacks of the peace agreement in the policy against illicit crops in Colombia. International Journal of Drug Policy, 89. https://doi.org/10.1016/j.drugpo.2021.103158
Contamination and Toxicology, 92(2), 190–195. https://doi.org/10.1007/s00128-013-1155-8
114.Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
115. Asghari, A., Fahimi, E., Bazregar, M., Rajabi, M., & Boutorabi, L. (2017). Rapid determination of some psychotropic drugs in complex matrices by tandem dispersive liquid–liquid microextraction followed by high performance liquid chromatography. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1052, 51–59. https://doi.org/10.1016/j.jchromb.2017.03.012
116. Atapattu, S. N., & Rosenfeld, J. M. (2013). Solid phase analytical derivatization as a sample preparation method. Journal of Chromatography A, 1296, 204–213. https://doi.org/10.1016/j.chroma.2013.03.020
117. Benigni, R. (2019). Towards quantitative read across: Prediction of Ames mutagenicity in a large database. Regulatory Toxicology and Pharmacology, 108(June). https://doi.org/10.1016/j.yrtph.2019.104434
118.Benigni, R., & Bossa, C. (2008). Structure alerts for carcinogenicity, and the Salmonella assay system: A novel insight through the chemical relational databases technology. Mutation Research - Reviews in Mutation Research, 659(3), 248–261. https://doi.org/10.1016/j.mrrev.2008.05.003
119.Biswas, S., Mondal, R., Mukherjee, A., Sarkar, M., & Kole, R. K. (2019). Simultaneous determination and risk assessment of fipronil and its metabolites in sugarcane, using GC-ECD and confirmation by GCMS/MS. Food Chemistry, 272(July 2018), 559–567. https://doi.org/10.1016/j.foodchem.2018.08.087
120.Bjørling-Poulsen, M., Andersen, H. R., & Grandjean, P. (2008). Potential developmental neurotoxicity of pesticides used in Europe. Environmental Health: A Global Access Science Source, 7. https://doi.org/10.1186/1476-069X-7-50
121. Blascke, D., Gomes, I., Fernando, B., & Anderson, M. (2019). Evaluation of the enantioselective in vitro metabolism of the chiral pesticide fipronil employing a human model: risk assessment through in vitro-in vivo correlation and prediction of toxicokinetic parameters. Food and Chemical Toxicology, 123, 225–232.
122.Borba, J. V. B., Alves, V. M., Braga, R. C., Korn, D. R., Overdahl, K., Silva, A. C., Hall, S. U. S., Overdahl, E., Kleinstreuer, N., Strickland, J., Allen, D., Andrade, C. H., Muratov, E. N., & Tropsha, A. (2022). STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity. Environmental Health Perspectives, 130(2), 1–12. https://doi.org/10.1289/EHP9341
123. Bovi, T. S., Zaluski, R., & Orsi, R. de O. (2018). Toxicity and motor changes in africanized honey bees (Apis mellifera l.) exposed to fipronil and imidacloprid. Anais Da Academia Brasileira de Ciencias, 90(1), 239–245. https://doi.org/10.1590/0001-3765201820150191
124.Bownik, A., & Szabelak, A. (2021). Short-term effects of pesticide fipronil on behavioral and physiological endpoints of Daphnia magna. Environmental Science and Pollution Research, 28(25), 33254–33264. https://doi.org/10.1007/s11356-021-13091-6
125. Calster, B. Van, Steyerberg, E., Wynants, L., & Van Smedenen, M. (2023). There is no such thing as a validates prediction model. BMC Medicine, 21(70). https://doi.org/10.1016/j.jacr.2015.03.015
126. Carnesecchi, E., Toma, C., Roncaglioni, A., Kramer, N., Benfenati, E., & Dorne, J. L. C. M. (2020). Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation. Science of the Total Environment, 735, 139243. https://doi.org/10.1016/j.scitotenv.2020.139243
127. Carrillo, M. P., Bovi, T. de S., Negrão, A. F., & Orsi, R. de O. (2013). Influência dos agroquímicos fipronil e imidaclopride no aprendizado de abelhas apis mellifera l. Acta Scientiarum - Animal Sciences, 35(4), 431–434. https://doi.org/10.4025/actascianimsci.v35i4.18683
128.Castilla-Fernández, D., Moreno-González, D., Murillo-Cruz, M. C. M. C., García-Reyes, J. F., & Molina-Díaz, A. (2021). Appraisal of different clean-up strategies for the determination of fipronil and its metabolites in eggs by UHPLC-MS/MS. Microchemical Journal, 166(March). https://doi.org/10.1016/j.microc.2021.106275
129. Chaguri, J. L., Godinho, A. F., Horta, D. F., Gonçalves-Rizzi, V. H., Possomato-Vieira, J. S., Nascimento, R. A., & Dias-Junior, C. A. (2016). Exposure to fipronil elevates systolic blood pressure and disturbs related biomarkers in plasma of rats. Environmental Toxicology and Pharmacology, 42, 63–68. https://doi.org/10.1016/j.etap.2015.12.020
130. Charalampous, A. C., Liapis, K. S., & Bempelou, E. D. (2019). Fipronil in eggs. Is LC-MS/MS the only option? A comparison study of LC-MS/MS and GC-ECD for the analysis of fipronil. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1129(October 2018), 121785. https://doi.org/10.1016/j.jchromb.2019.121785
131. Chen, T., Hu, J., Chen, Y., Liu, Y., Li, Y., & Xu, H. (2022a). Tracking the environment fate of fipronil and three of its metabolites in garlic based on sampling reta-corrected in vivo solid phase microextraction combined with gas chromatography-mass spectrometry. Analytica Chimica Acta, 1190(339263).
132. Chen, T., Hu, J., Chen, Y., Liu, Y., Li, Y., & Xu, H. (2022b). Tracking the environmental fate of fipronil and three of its metabolites in garlic based on sampling rate-corrected in vivo solid phase microextraction combined with gas chromatography-mass spectrometry. Analytica Chimica Acta, 1190, 339263. https://doi.org/10.1016/j.aca.2021.339263
133. Clara T. Lourenço, Stephan M. Carvalho, Osmar Malaspina, R. C. F. N. (2012). Determination of fipronil LD50 for the brazilian bee Melipona scutellaris. Julius-Kühn-Archiv, 437, 174–178. https://doi.org/10.5073/jka.2012.437.046
134. Cotterill, J., Price, N., Rorije, E., & Peijnenburg, A. (2020). Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools. Food and Chemical Toxicology, 142(June). https://doi.org/10.1016/j.fct.2020.111494
135. Cox, C., & Surgan, M. (2006). Unidentified inert ingredients in pesticides: Implications for human and environmental health. Environmental Health Perspectives, 114(12), 1803–1806. https://doi.org/10.1289/ehp.9374
136. De Barros, A. L., Rosa, J. L., Cavariani, M. M., Borges, C. S., Villelae Silva, P., Bae, J. H., Anselmo-Franci, J. A., & Cristina Arena, A. (2016). In utero and lactational exposure to fipronil in female rats: Pregnancy outcomes and sexual development. Journal of Toxicology and Environmental Health - Part A: Current Issues, 79(6), 266–273. https://doi.org/10.1080/15287394.2016.1149132
137. De Oliveira, P. R., Bechara, G. H., Denardi, S. E., Oliveira, R. J., & Mathias, M. I. C. (2012). Genotoxic and mutagenic effects of fipronil on mice. Experimental and Toxicologic Pathology, 64(6), 569–573. https://doi.org/10.1016/j.etp.2010.11.015
138. Duque, P., Meza, O. E., Giraldo, D., & Barreto, K. (2021). Economía Social y Economía Solidaria: un análisis bibliométrico y revisión de literatura. REVESCO. Revista de Estudios Cooperativos, 138(138), e75566. https://doi.org/10.5209/reve.75566
139. Eadie, A., Vásquez, I. C., Liang, X., Wang, X., Souders, C. L., Chehouri, J. El, Hoskote, R., Feswick, A., Cowie, A. M., Loughery, J. R., & Martyniuk, C. J. (2020). Transcriptome network data in larval zebrafish (Danio rerio) following exposure to the phenylpyrazole fipronil. Data in Brief, 33, 106413. https://doi.org/10.1016/j.dib.2020.106413
140. Embry, M. R., Bachman, A. N., Bell, D. R., Boobis, A. R., Cohen, S. M., Dellarco, M., Dewhurst, I.C., Doerrer, N. G., Hines, R. N., Moretto, A., Pastoor, T. P., Phillips, R. D., Rowlands, J. C., Tanir, J. Y., Wolf, D. C., & Doe, J. E. (2014). Risk assessment in the 21st century: Roadmap and matrix. Critical Reviews in Toxicology, 44(SUPPL.3), 6–16. https://doi.org/10.3109/10408444.2014.931924
141. FAO. (1989). Guidelines for legislation on the control of pesticides.
142. Fulton, M. H., & Key, P. B. (2001). Acetylcholinesterase inhibition in estuarine fish and invertebrates as an indicator of organophosphorus insecticide exposure and effects. Environmental Toxicology and Chemistry, 20(1), 37–45. https://doi.org/10.1002/etc.5620200104
143. Gadaleta, D., Vuković, K., Toma, C., Lavado, G. J., Karmaus, A. L., Mansouri, K., Kleinstreuer, N.C., Benfenati, E., & Roncaglioni, A. (2019). SAR and QSAR modeling of a large collection of LD50 ratacute oral toxicity data. Journal of Cheminformatics, 11(1), 1–16. https://doi.org/10.1186/s13321-019-0383-2
144. García-Vara, M., Postigo, C., Palma, P., & López de Alda, M. (2023). Development of QuEChERSbased multiresidue analytical methods to determine pesticides in corn, grapes and alfalfa. Food Chemistry, 405(May 2022). https://doi.org/10.1016/j.foodchem.2022.134870
145. Gomes, H. O., Cardoso, R. S., Nonato, C. F. A., Silva, V. P. A., Nobre, C. A., Costa, J. G. M., Teixeira, R. N. P., & Nascimento, R. F. (2022). Optimization and Validation of a Method Using GC–MS and QuEChERS for Pesticide Determination in Banana Pulp. Food Analytical Methods, 15(9), 1–8. https://doi.org/10.1007/s12161-022-02402-3
146. González de Dios, J., Alonso-Arroyo, A., & Aleixandre-Benavent, R. (2019). Half a century of ANALES DE PEDIATRÍA. Evolution of its main bibliometric indicators in the Web of Science and Scopus international databases. In Anales de Pediatria (Vol. 90, Issue 3, pp. 194.e1-194.e11). https://doi.org/10.1016/j.anpedi.2018.12.012
147. Grande-Martínez, Á., Arrebola-Liébanas, F. J., Martínez-Vidal, J. L., Hernández-Torres, M. E., & Garrido-Frenich, A. (2015). Optimization and validation of a multiresidue pesticide method in rice and wheat flour by modified QuECHERS and GS-MS/MS. Food Analytical Methods, 9(2), 548–563. https://doi.org/10.1007/s12161-015-0214-7
148. Gross, J. H. (2017). Mass Spectrometry. A textbook (Third). Springer Berlin Heidelberg.
149. Guelfi, M. M., Tavares, M. A., Mingatto, F. E., & Maioli, M. A. (2015). Citotoxicity of fipronil on hepatocytes isolated from rat and effects of its biotransformation. Brazilian Archives of Biology and Technology, 58(6), 843–853. https://doi.org/10.1590/S1516-89132015060298
150. Han, C., Hu, B., Li, Z., Liu, C., Wang, N., Fu, C., & Shen, Y. (2021). Determination of Fipronil and Four Metabolites in Foodstuffs of Animal Origin Using a Modified QuEChERS Method and GC–NCI–MS/MS. Food Analytical Methods, 14(2), 237–249. https://doi.org/10.1007/s12161-020-01872-7
151. Herin, F., Boutet-Robinet, E., Levant, A., Dulaurent, S., Manika, M., Galatry-Bouju, F., Caron, P., & Soulat, J. M. (2011). Thyroid function tests in persons with occupational exposure to fipronil.Thyroid, 21(7), 701–706. https://doi.org/10.1089/thy.2010.0449
152. Herrmann, K., Holzwarth, A., Rime, S., Fischer, B. C., & Kneuer, C. (2020). (Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation? Pest Management Science, 76(10), 3316–3325. https://doi.org/10.1002/ps.5828
153. Hurtado-Marín, V. A., Agudelo-Giraldo, J. D., Robledo, S., & Restrepo-Parra, E. (2021). Analysis of dynamic networks based on the Ising model for the case of study of co-authorship of scientific articles. Scientific Reports, 11(1), 1–10. https://doi.org/10.1038/s41598-021-85041-8
154. Hyötuläinen, T., & Wiedmer, S. (2013). Chromatographic Methods in Metabolomics (18th ed.).RSCPublishing.
155. Idrovo, A. J. (2004). Plaguicidas usados en la Fumigación de Cultivos Ilícitos y Salud Humana: ¿una Cuestión de Ciencia o Política? Rev. Salud Pública, 6(2), 199–211.
156. Ivanciuc, T., Ivanciuc, O., & J. Klein, D. (2012). Network-QSAR with Reaction Poset Quantitative Superstructure-Activity Relationships (QSSAR) for PCB Chromatographic Properties. Current Bioinformatics, 6(1), 25–34. https://doi.org/10.2174/157489311795222310
157. Jiménez Noblejas, C., & Perianes Rodríguez, A. (2014). Recuperación y visualización de información en Web of Science y Scopus: una aproximación práctica. In Investigación Bibliotecológica: Archivonomía, Bibliotecología e Información (Vol. 28, Issue 64, pp. 15–31). https://doi.org/10.1016/s0187-358x(14)70907-4
158. Katchanov, Y. L., Markova, Y. V., & Shmatko, N. A. (2019). Comparing the topological rank of journals in Web of Science and Mendeley. In Heliyon (Vol. 5, Issue 7). https://doi.org/10.1016/j.heliyon.2019.e02089
159. Kennedy, M. C., Hart, A. D. M., Kruisselbrink, J. W., van Lenthe, M., de Boer, W. J., van der Voet, H., Rorije, E., Sprong, C., & van Klaveren, J. (2020). A retain and refine approach to cumulative risk assessment. Food and Chemical Toxicology, 138(October 2019). https://doi.org/10.1016/j.fct.2020.111223
160. Komsta, L., Heyden, Y. Vander, & Sherma, J. (2018). Chemometrics in chromatography. CRC press.
161. Kovacic, P., & Osuna Jr., J. (2005). Mechanisms of carcinogenesis: focus on Oxidative Stress and Electron Transfer. Current Pharmaceutical Design, 6(3), 277–309. https://doi.org/10.2174/1381612003401046
162. Krogh, K. A., Halling-Sørensen, B., Mogensen, B. B., & Vejrup, K. V. (2003). Environmental properties and effects of nonionic surfactant adjuvants in pesticides: A review. Chemosphere, 50(7), 871–901. https://doi.org/10.1016/S0045-6535(02)00648-3
163. Lankadurai, B. P., Nagato, E. G., & Simpson, M. J. (2013). Environmental metabolomics: An emerging approach to study organism responses to environmental stressors. Environmental Reviews, 21(3), 180–205. https://doi.org/10.1139/er-2013-0011
164. Lao, W. (2021). Fiproles as a proxy for ecological risk assessment of mixture of fipronil and its degradates in effluent-dominated surface waters. Water Research, 188, 116510. https://doi.org/10.1016/j.watres.2020.116510
165. Lautz, L. S., Stoopen, G., Ginting, A. J., Hoogenboom, R., & Punt, A. (2022). Fipronil and fipronil sulfone in chicken: from in vitro experiments to in vivo PBK model predictions. Food and Chemical Toxicology, 165, 113086.
166. Lévêque, L., Tahiri, N., Goldsmith, M.-R., & Verner, M.-A. (2022). Quantitative Structure-Activity relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta. Computational Toxicology, 21(100211).
167. Li, P., Duan, Y., Ge, H., Zhang, Y., & Wu, X. (2018). Multiresidue Analysis of 113 Pesticides in Different Maturity Levels of Mangoes Using an Optimized QuEChERS Method with GC-MS/MS and UHPLC-MS/MS. Food Analytical Methods, 11(10), 2742–2757. https://doi.org/10.1007/s12161-018-1263-5
168. Li, Q., Zhang, J., Lin, T., Fan, C., Li, Y., Zhang, Z., & Li, J. (2023). Migration behavior and dietary exposure risk assessment of pesticides residues in honeysuckle (Lonicera japonica Thunb.) based on modified QuEChERS method coupled with tandem mass spectrometry. Food Research International, 166(February). https://doi.org/10.1016/j.foodres.2023.112572
169. Li, X., Li, H., Ma, W., Guo, Z., Li, X., Song, S., Tang, H., Li, X., & Zhang, Q. (2019). Development of precise GC-EI-MS method to determine the residual fipronil and its metabolites in chicken egg.Food Chemistry, 281(18), 85–90. https://doi.org/10.1016/j.foodchem.2018.12.041
170. Li, X., Ma, W., Li, H., Zhang, Q., & Ma, Z. (2020). Determination of residual fipronil and its metabolites in food samples: A review. Trends in Food Science and Technology, 97(January), 185–195.https://doi.org/10.1016/j.tifs.2020.01.018
171. Maddadi, S., Qomi, M., & Rajabi, M. (2017). Extraction, preconcentration, and determination of methylphenidate in urine sample using solvent bar microextraction in combination with HPLC–UV:Optimization by experimental design. Journal of Liquid Chromatography and Related Technologies, 40(15), 806–812. https://doi.org/10.1080/10826076.2017.1369994
172. Mandal, S., Poi, R., Ansary, I., Hazra, D. K., Bhattacharyya, S., & Karmakar, R. (2020). Validation of a modified QuEChERS method to determine multiclass multipesticide residues in apple, banana and guava using GC–MS and LC–MS/MS and its application in real sample analysis. SN Applied Sciences, 2(2), 1–14. https://doi.org/10.1007/s42452-020-1990-2
173. Martin, M. T., Judson, R. S., Reif, D. M., Kavlock, R. J., & Dix, D. J. (2009). Profiling chemicals based on chronic toxicity results from the U.S. EPA ToxRef database. Environmental Health Perspectives, 117(3), 392–399. https://doi.org/10.1289/ehp.0800074
174. Martin, M. T., Mendez, E., Corum, D. G., Judson, R. S., Kavlock, R. J., Rotroff, D. M., & Dix, D. J. (2009). Profiling the reproductive toxicity of chemicals from multigeneration studies in the toxicity reference database. Toxicological Sciences, 110(1), 181–190. https://doi.org/10.1093/toxsci/kfp080
175. Marzo, M., Lavado, G. J., Como, F., Toropova, A. P., Toropov, A. A., Baderna, D., Cappelli, C., Lombardo, A., Toma, C., Blázquez, M., & Benfenati, E. (2020). QSAR models for biocides: The example of the prediction of Daphnia magna acute toxicity. SAR and QSAR in Environmental Research, 31(3), 227–243. https://doi.org/10.1080/1062936X.2019.1709221
176. Mcmahen, R. L., Strynar, M. J., Dagnino, S., Herr, D. W., Virginia, C., Garantziotis, S., Andersen, E. M., Freeborn, D. L., Mcmillan, L., Lindstrom, A. B., Exposure, N., States, U., Environmental, S., Agency, P., Exposure, N., States, U., Environmental, S., Agency, P., Health, N., … States, U. (2017). Identification of fipronil metabolites by time-of-flight mass spectrometry for application in a human exposure study. Environ Int., 78, 16–23. https://doi.org/10.1016/j.envint.2015.01.016.Identification
177. Milam, C. D., Farris, J. L., & Wilhide, J. D. (2000). Evaluating mosquito control pesticides for effect on target and nontarget organisms. Archives of Environmental Contamination and Toxicology, 39(3), 324–328. https://doi.org/10.1007/s002440010111
178. Mohamed, F., Senarathna, L., Percy, A., Abeyewardene, M., Cheng, R., Azher, S., Hittarage, A., & Dissanayake, W. (2004). Acute Human Self-Poisoning with the N-Phenylpyrazole Insecticide Fipronil – A GABA A -Gated Chloride Channel Blocker. J Toxicol Clin Toxicol, 42(7), 955–963.
179. Moreira-Filho, J. T., Braga, R. C., Lemos, J. M., Alves, V. M., Borba, J. V. V. B., Costa, W. S., Kleinstreuer, N., Muratov, E. N., Andrade, C. H., & Neves, B. J. (2021). BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees. Artificial Intelligence
in the Life Sciences, 1(November), 100013. https://doi.org/10.1016/j.ailsci.2021.100013
180. Moretto, A., Bachman, A., Boobis, A., Solomon, K. R., Pastoor, T. P., Wilks, M. F., & Embry, M. R. (2017). A framework for cumulative risk assessment in the 21st century. Critical Reviews in Toxicology, 47(2), 85–97. https://doi.org/10.1080/10408444.2016.1211618
181. Mukherjee, A., Mondal, R., Biswas, S., Saha, S., Ghosh, S., & Kole, R. K. (2021). Dissipation behaviour and risk assessment of fipronil and its metabolites in paddy ecosystem using GC-ECD and confirmation by GC-MS/MS. Heliyon, 7(5), e06889. https://doi.org/10.1016/j.heliyon.2021.e06889
182. Nazario, C. E. D., Fumes, B. H., da Silva, M. R., & Lanças, F. M. (2017). New materials for sample preparation techniques in bioanalysis. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1043, 81–95. https://doi.org/10.1016/j.jchromb.2016.10.041
183. Oakes, D. J., & Pollak, J. K. (2000). The in vitro evaluation of the toxicities of three related herbicide formulations containing ester derivatives of 2,4,5-T and 2,4-D using sub-mitochondrial particles. Toxicology, 151(1–3), 1–9. https://doi.org/10.1016/S0300-483X(00)00244-4
184. Ou, Y., Yan, Z., Shi, G., Yu, Z., Cai, Y., & Ma, R. (2022). Enantioselective toxicity, degradation and transformation of the chiral insecticide fipronil in two algae culture. Ecotoxicology and Environmental Safety, 235(December 2021), 113424. https://doi.org/10.1016/j.ecoenv.2022.113424
185. Paris, L., Roussel, M., Pereira, B., Delbac, F., & Diogon, M. (2017). Disruption of oxidative balance in the gut of the western honeybee Apis mellifera exposed to the intracellular parasite Nosema ceranae and to the insecticide fipronil. Microbial Biotechnology, 10(6), 1702–1717. https://doi.org/10.1111/1751-7915.12772
186. Patil, C., Calvayrac, C., Zhou, Y., Romdhane, S., Salvia, M. V., Cooper, J. F., Dayan, F. E., & Bertrand, C. (2016). Environmental Metabolic Footprinting: A novel application to study the impact of a natural and a synthetic β-triketone herbicide in soil. Science of the Total Environment, 566–567, 552–558. https://doi.org/10.1016/j.scitotenv.2016.05.071
187. Pereira, J. L., Antunes, S. C., Castro, B. B., Marques, C. R., Gonçalves, A. M. M., Gonçalves, F., & Pereira, R. (2009). Toxicity evaluation of three pesticides on non-target aquatic and soil organisms: Commercial formulation versus active ingredient. Ecotoxicology, 18(4), 455–463. https://doi.org/10.1007/s10646-009-0300-y
188. Pfaff, J., Reinwald, H., Ayobahan, S. U., Alvincz, J., Göckener, B., Shomroni, O., Salinas, G., Düring, R. A., Schäfers, C., & Eilebrecht, S. (2021). Toxicogenomic differentiation of functional responses to fipronil and imidacloprid in Daphnia magna. Aquatic Toxicology, 238(February). https://doi.org/10.1016/j.aquatox.2021.105927
189. Pinto, T. J. da S., Rocha, G. S., Moreira, R. A., da Silva, L. C. M., Yoshii, M. P. C., Goulart, B. V., Montagner, C. C., Daam, M. A., & Espindola, E. L. G. (2022). Chronic environmentally relevant levels of pesticides disrupt energy reserves, feeding rates, and life-cycle responses in the amphipod Hyalella meinerti. Aquatic Toxicology, 245(September 2021). https://doi.org/10.1016/j.aquatox.2022.106117
190. Portruneli, N., Bonansea, R. I., Valdés, M. E., da Silva, L. C. M., Viana, N. P., Goulart, B. V., Souza, I. da C., Espíndola, E. L. G., Montagner, C. C., Wunderlin, D. A., & Fernandes, M. N. (2021). Wholebody bioconcentration and biochemical and morphological responses of gills of the neotropical fish Prochilodus lineatus exposed to 2,4-dichlorophenoxyacetic acid or fipronil individually or in a mixture. Aquatic Toxicology, 240(September). https://doi.org/10.1016/j.aquatox.2021.105987
191. Putri, S. P., & Fukusaki, E. (2015). Mass Spectrometry-Based Metabolomics. A practical guide.CRC Press.
192. Qu, H., Ma, R. xue, Liu, D. hui, Jing, X., Wang, F., Zhou, Z. qiang, & Wang, P. (2016). The toxicity, bioaccumulation, elimination, conversion of the enantiomers of fipronil in Anodonta woodiana. Journal of Hazardous Materials, 312, 169–174. https://doi.org/10.1016/j.jhazmat.2016.03.063
193. Raitano, G., Goi, D., Pieri, V., Passoni, A., Mattiussi, M., Lutman, A., Romeo, I., Manganaro, A., Marzo, M., Porta, N., Baderna, D., Colombo, A., Aneggi, E., Natolino, F., Lodi, M., Bagnati, R., & Benfenati, E. (2018). (Eco)toxicological maps: A new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy). Environment International, 119(June), 275–286. https://doi.org/10.1016/j.envint.2018.06.035
194. Richardson, J. R., Fitsanakis, V., Westerink, R. H. S., & Kanthasamy, A. G. (2019). Neurotoxicity of pesticides. Acta Neuropathologica, 138(3), 343–362. https://doi.org/10.1007/s00401-019-02033-9
195. Roberts, D. W., Aptula, A., Schultz, T. W., Shen, J., Api, A. M., Bhatia, S., & Kromidas, L. (2015). A practical guidance for Cramer class determination. Regulatory Toxicology and Pharmacology, 73(3), 971–984. https://doi.org/10.1016/j.yrtph.2015.09.017
196. Ruiz, P., Sack, A., Wampole, M., Bobst, S., & Vracko, M. (2017). Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. Chemosphere, 178, 99–109. https://doi.org/10.1016/j.chemosphere.2017.03.026
197. Salcedo, A., & Melo, O. (2005). Evaluación del uso de plaguicidas en la actividad agrícola del departamento de Putumayo. Rev. Cienc. Salud. , 3(2), 168–185.
198. Salvia, M. V., Ben Jrad, A., Raviglione, D., Zhou, Y., & Bertrand, C. (2018). Environmental Metabolic Footprinting (EMF) vs. half-life: a new and integrative proxy for the discrimination between control and pesticides exposed sediments in order to further characterise pesticides’ environmental impact. Environmental Science and Pollution Research, 25(30), 29841–29847. https://doi.org/10.1007/s11356-017-9600-6
199. Schmitz, A., Dempewolf, S., Tan, S., Bicker, G., & Stern, M. (2021). Developmental Neurotoxicity of Fipronil and Rotenone on a Human Neuronal In Vitro Test System. Neurotoxicity Research, 39(4), 1189–1202. https://doi.org/10.1007/s12640-021-00364-8
200. Shamsayei, M., Yamini, Y., Asiabi, H., Rezazadeh, M., & Seidi, S. (2017). Electromembrane surrounded solid-phase microextraction using a stainless-steel wire coated with a nanocomposite composed of polypyrrole and manganese dioxide. Microchimica Acta, 184(8), 2697–2705. https://doi.org/10.1007/s00604-017-2244-x
201. Silva, A. C., Borba, J. V. V. B., Alves, V. M., Hall, S. U. S., Furnham, N., Kleinstreuer, N., Muratov, E., Tropsha, A., & Andrade, C. H. (2021). Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals. Artificial Intelligence in the Life Sciences, 1, 100028. https://doi.org/10.1016/j.ailsci.2021.100028
202. Silva, Carina Aparecida de Souza, Silva-Zacarin, Elaine Cristina Mathias, Domingues, Caio Eduardo da Costa, Abdalla, Fabio, Malaspina, Osmar, N. R. C. F., & Nocelli. (2015). Fipronil effect on the frequency of anomalous brood in honeybee reared in vitro. Julius-Kühn-Archiv, 0(450), 140.
203. Silva, L. C. M., Moreira, R. A., Pinto, T. J. S., Vanderlei, M. R., Athayde, D. B., Lopes, L. F. P., Ogura, A. P., Yoshii, M. P. C., Freitas, J. S., Montagner, C. C., Goulart, B. V., Schiesari, L., Daam, M. A., & Espíndola, E. L. G. (2021). Lethal and sublethal toxicity of pesticides and vinasse used in sugarcane cultivation to Ceriodaphnia silvestrii (Crustacea: Cladocera). Aquatic Toxicology, 241(November), 106017. https://doi.org/10.1016/j.aquatox.2021.106017
204. Simon-Delso, N., Amaral-Rogers, V., Belzunces, L. P., Bonmatin, J. M., Chagnon, M., Downs, C., Furlan, L., Gibbons, D. W., Giorio, C., Girolami, V., Goulson, D., Kreutzweiser, D. P., Krupke, C. H., Liess, M., Long, E., Mcfield, M., Mineau, P., Mitchell, E. A., Morrissey, C. A., … Wiemers, M. (2015). Systemic insecticides (Neonicotinoids and fipronil): Trends, uses, mode of action and metabolites. Environmental Science and Pollution Research, 22(1), 5–34. https://doi.org/10.1007/s11356-014-3470-y
205. Sinclair, C. J., & Boxall, A. B. A. (2003). Assessing the ecotoxicity of pesticide transformation products. Environmental Science and Technology, 37(20), 4617–4625. https://doi.org/10.1021/es030038m
206. Sofalvi, S., Lavins, E. S., Kaspar, C. K., Michel, H. M., Mitchell-Mata, C. L., Huestis, M. A., & Apollonio, L. G. (2020). Development and validation of an LC–MS-MS method for the detection of 40 benzodiazepines and three Z-drugs in blood and urine by solid-phase extraction. Journal of Analytical Toxicology, 44(7), 708–717. https://doi.org/10.1093/jat/bkaa072
207. Song, N. E., Lee, J. Y., Mansur, A. R., Jang, H. W., Lim, M. C., Lee, Y., Yoo, M., & Nam, T. G. (2019). Determination of 60 pesticides in hen eggs using the QuEChERS procedure followed by LC-MS/MS and GC-MS/MS. Food Chemistry, 298(June), 125050. https://doi.org/10.1016/j.foodchem.2019.125050
208. Song, X., Wang, X., Liao, G., Pan, Y., Qian, Y., & Qiu, J. (2021a). Toxic effects of fipronil and its metabolites on PC12 cell metabolism. Ecotoxicology and Environmental Safety, 224, 112677. https://doi.org/10.1016/j.ecoenv.2021.112677
209. Song, X., Wang, X., Liao, G., Pan, Y., Qian, Y., & Qiu, J. (2021b). Toxic effects of fipronil and its metabolites on PC12 cell metabolism. Ecotoxycology and Enviromental Safety, 224(112677).
210. Soosaraei, M., Khasseh, A. A., Fakhar, M., & Hezarjaribi, H. Z. (2018). A decade bibliometric analysis of global research on leishmaniasis in Web of Science database. In Annals of Medicine and Surgery (Vol. 26, pp. 30–37). https://doi.org/10.1016/j.amsu.2017.12.014
211. Sousa, D. V. M., Pereira, F. V., Nascentes, C. C., Moreira, J. S., Boratto, V. H. M., & Orlando, R. M. (2020). Cellulose cone tip as a sorbent material for multiphase electrical field-assisted extraction of cocaine from saliva and determination by LC-MS/MS. Talanta, 208(September 2019). https://doi.org/10.1016/j.talanta.2019.120353
212. Speck-Planche, A., Kleandrova, V. V., Luan, F., & Cordeiro, M. N. D. S. (2012). Predicting multiple ecotoxicological profiles in agrochemical fungicides: A multi-species chemoinformatic approach. Ecotoxicology and Environmental Safety, 80, 308–313. https://doi.org/10.1016/j.ecoenv.2012.03.018
213. Stefanelli, P., & Barbini, D. (2022). Pesticide Toxicology. Methods in Pharmacology and Toxicology (E. Gallardo & M. Barroso, Eds.). Springer Protocols. https://doi.org/10.1007/978-1-0716-1928-5
214. Stenberg, M., & Andersson, P. L. (2008). Selection of non-dioxin-like PCBs for in vitro testing on the basis of environmental abundance and molecular structure. Chemosphere, 71(10), 1909–1915.https://doi.org/10.1016/j.chemosphere.2008.01.007
215. Takata, M., Lin, B. Le, Xue, M., Zushi, Y., Terada, A., & Hosomi, M. (2020). Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory. Chemosphere, 238, 1–9.https://doi.org/10.1016/j.chemosphere.2019.124604
216. Truver, M. T., Jakobsson, G., Chermà, M. D., Swortwood, M. J., Gréen, H., & Kronstrand, R. (2021). A sensitive LC-MS/MS method for the quantitation of oxycodone, noroxycodone, 6α-oxycodol, 6β-oxycodol, oxymorphone, and noroxymorphone in human blood. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 1171, 122625. https://doi.org/10.1016/j.jchromb.2021.122625
217. Varela-Martínez, D. A., González-Curbelo, M. Á., González-Sálamo, J., & Hernández-Borges, J.(2019). High-throughput analysis of pesticides in minor tropical fruits from Colombia. Food Chemistry, 280(November 2018), 221–230. https://doi.org/10.1016/j.foodchem.2018.12.045
218. Viana, N. P., da Silva, L. C. M., Portruneli, N., Soares, M. P., Cardoso, I. L., Bonansea, R. I., Goulart, B. V., Montagner, C. C., Espíndola, E. L. G., Wunderlin, D. A., & Fernandes, M. N. (2022). Bioconcentration and toxicological impacts of fipronil and 2,4-D commercial formulations (single and in mixture) in the tropical fish, Danio rerio. Environmental Science and Pollution Research, 29(8), 11685–11698. https://doi.org/10.1007/s11356-021-16352-6
219. Wu, H., Gao, C., Guo, Y., Zhang, Y., Zhang, J., & Ma, E. (2014). Acute toxicity and sublethal effects of fipronil on detoxification enzymes in juvenile zebrafish (Danio rerio). Pesticide Biochemistry and Physiology, 115, 9–14. https://doi.org/10.1016/j.pestbp.2014.07.010
220. Yi, F., Yang, P., & Sheng, H. (2016). Tracing the scientific outputs in the field of Ebola research based on publications in the Web of Science. In BMC Research Notes (Vol. 9, Issue 1). https://doi.org/10.1186/s13104-016-2026-2
221. Yousefinejad, S., & Hemmateenejad, B. (2015). Chemometrics tools in QSAR/QSPS studies: A historical perspective. Chemometrics and Intellingent Laboratory Systems, 149, 117–204.
222. Zainudin, B. H., & Salleh, S. (2017). Method Development, Optimization and Validation of Matrix Hydration Effect on Pesticide Residues in Cocoa Beans Using Modified QuEChERS Method and Gas Chromatography Tandem Mass Spectrometry. Food Analytical Methods, 10(12), 3874–3885. https://doi.org/10.1007/s12161-017-0954-7
223. Zhang, C., Cheng, F., Sun, L., Zhuang, S., Li, W., Liu, G., Lee, P. W., & Tang, Y. (2015). In silico prediction of chemical toxicity on avian species using chemical category approaches. Chemosphere, 122, 280–287. https://doi.org/10.1016/j.chemosphere.2014.12.001
224. Zhao, Y., Bai, X. L., Liu, Y. M., & Liao, X. (2021). Determination of fipronil and its metabolites in egg samples by UHPLC coupled with Q-Exactive high resolution mass spectrometry after magnetic solid-phase extraction. Microchemical Journal, 169(May), 106540. https://doi.org/10.1016/j.microc.2021.106540
Descargas
Los datos de descargas todavía no están disponibles.
