Identification of potential chemical biomarkers of hexaconazole using in vitro metabolite profiling in rat and human liver microsomes and in vivo confirmation through urinary excretion study in rats.

Hexaconazole (HEX) is an azole fungicide widely used in agricultural practices across various countries and numerous studies have reported the toxic effects of HEX, such as endocrine disruption, immunotoxicity, neurotoxicity and carcinogenicity. Despite its widespread agricultural use and toxic effects, the metabolism of HEX is not completely understood, and information on urinary elimination of HEX or its metabolites is limited. Therefore, in the present study, we aimed to identify HEX metabolites in rat and human liver microsomes followed by their in vivo confirmation using a urinary excretion study in rats to identify potential candidate for exposure biomarkers for human biomonitoring studies. From the in vitro assay, a total of 12 metabolites were observed, where the single oxidation metabolites (M5 and M6) were the most abundant metabolites in both rat and human liver microsomes. The triple oxidation followed by dehydration metabolite, M8 (which could also be hexaconazole acid or hydroxy keto-hexaconazole), and the double oxidation metabolite (M9) were the major metabolites found in rat urine and were detectable in rat urine longer than the parent. These metabolites increased with decreasing concentrations of HEX in the rat urine samples. Therefore, metabolites M8, M9 and M5 could be pursued further as potential biomarkers for assessing and monitoring human exposure to HEX.

Attitude, perception and barriers of dental professionals towards artificial intelligence.

Aim: To know attitudes, perceptions and barriers towards the use of Artificial Intelligence (AI) in dentistry in India among undergraduate and postgraduate students. Methodology: A questionnaire-based cross-sectional study was conducted among participants pursuing graduation and postgraduation. The questionnaire consisted of 23 close-ended and 2 open-ended questions divided into various sections of attitude, perception and barriers. The data was analysed using Statistical Package for Social Sciences (SPSS) version 24.0. Result: Out of 937 responses, 55.2% responded that they get information about AI from social media platforms. 51.3% of respondents have basic knowledge about the use of AI in dentistry. 59.6% agreed that AI can be used as a "definitive diagnostic tool" in the diagnosis of diseases. 66.5% agreed that AI can be used for radiographic diagnosis of tooth caries. 71.3% stated that AI can be used as a "treatment planning tool" in dentistry. 55.7% stated that AI should be part of undergraduate dental training. Conclusion: This study concluded that both dental students are aware of the concept of AI. Participants were positive when asked if AI can increase the efficiency of diagnosis, prognosis and treatment planning procedures as well as in managing patient data. Both participants believed that the barriers to the introduction of AI in dentistry are a lack of technical resources and a lack of training personnel in college.

Co-occurrence of mycotoxins: A review on bioanalytical methods for simultaneous analysis in human biological samples, mixture toxicity and risk assessment strategies.

Mycotoxins are the toxic chemical substances that are produced by various fungal species and some of these are harmful to humans. Mycotoxins are ubiquitous in nature and humans could be exposed to multiple mycotoxins simultaneously. Unfortunately, exposure to mixed mycotoxins is not very well studied. Various studies have demonstrated the capacity of mycotoxins to show synergistic effect in the presence of other mycotoxins, thus, increasing the risk of toxicity. Hence, it is important to monitor mixed mycotoxins in human biological samples which would serve as a crucial information for risk assessment. Through this review paper, we aim to summarize the mixture toxicity of mycotoxins and the various bio-analytical techniques that are being used for the simultaneous analysis of mixed mycotoxins in human biological samples. Different sample preparation and clean-up techniques employed till date for eliminating the interferences from human biological samples without affecting the analyses of the mycotoxins are also discussed. Further, a brief introduction of risk assessment strategies that have been or could be adopted for multiple mycotoxin risk assessments is also mentioned. To the best of our knowledge, this is the first review that focuses solely on the occurrence of multiple mycotoxins in human biological samples as well as their risk assessment strategies.

Iron nanoparticles decorated hierarchical carbon fiber forest for the magnetic solid-phase extraction of multi-pesticide residues from water samples.

This study describes a versatile, robust and fast sample pre-concentration novel method based on chemical vapour deposition grown iron nanoparticles dispersed hierarchical carbon fiber forest (Fe-ACF/CNF) for the determination of multi-pesticide residue in water samples. This method was developed by the implementation of Fe-ACF/CNF to magnetic solid-phase extraction method (MSPE) for the adsorption of twenty-nine pesticides of various classes using gas chromatography equipped with an electron capture detector. Fe-ACF/CNF was grown via tip growth mechanism and Fe-nanoparticles are moved to the tip of CNF. The presence of Fe-nanoparticles is responsible for the magnetic property of proposed adsorbents. The Fe-ACF/CNF is competent enough to extract twenty-nine pesticides of different physico-chemical characteristics from water samples. All the predominant parameters including the amount of sorbent desorption time, temperature, sonication effect, regeneration, and reusability of Fe-ACF/CNF were thoroughly investigated. Acceptable linearity was obtained in the range of 20-500 µg/L with a correlation coefficient value ≥ 0.990 for all pesticides. The accuracy of the developed method was evaluated and the obtained recovery of the spiked samples was within 70-120% (standard deviation ≤ 15%) and reusability up to the 4th cycle. The limit of detection and quantification values was in the range of 1.44-5.15 and 4.76-17.0 µg/L, respectively. The obtained results are also cross verified with real water samples from the Gomti river (Lucknow, India) and shown the excellent extraction efficiency of Fe-ACF/CNF.