2023
DOI: 10.1016/j.taap.2023.116529
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The comparative analysis of gastrointestinal toxicity of azithromycin and 3′-decladinosyl azithromycin on zebrafish larvae

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Cited by 5 publications
(3 citation statements)
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“…Despite its clinical effectiveness, azithromycin is not without its drawbacks, as vomiting and diarrhea have been identified as primary adverse effects associated with its administration [5]. In the context of adverse reactions, this report presents a distinctive case -an inaugural instance of a purpuric-type drug eruption associated with azithromycin administration.…”
Section: Introductionmentioning
confidence: 91%
“…Despite its clinical effectiveness, azithromycin is not without its drawbacks, as vomiting and diarrhea have been identified as primary adverse effects associated with its administration [5]. In the context of adverse reactions, this report presents a distinctive case -an inaugural instance of a purpuric-type drug eruption associated with azithromycin administration.…”
Section: Introductionmentioning
confidence: 91%
“…With the development of in silico tools, machine learningbased models have been applied for prediction of PFAS toxicity, 32,33 and structure−toxicity relationships have been employed to predict toxicity in silico. 34 These tools and their combination enabled the prediction of the interaction energies or activities between unknown PFASs and certain receptors like androgen receptor (AR) 35 based on limited experimental data, which is valuable for prioritizing potential health risk of PFASs.…”
Section: ■ Introductionmentioning
confidence: 99%
“…Although 150 PFASs efficacy for multiple receptors were tested in this experiment, evaluation of PFASs for their toxicity is still a challenge since thousands of PFASs without toxicity information were identified in real sample over the past years. With the development of in silico tools, machine learning-based models have been applied for prediction of PFAS toxicity, , and structure–toxicity relationships have been employed to predict toxicity in silico . These tools and their combination enabled the prediction of the interaction energies or activities between unknown PFASs and certain receptors like androgen receptor (AR) based on limited experimental data, which is valuable for prioritizing potential health risk of PFASs.…”
Section: Introductionmentioning
confidence: 99%