The beneficial effects of fluoride on human oral health are well studied. There are numerous studies demonstrating that a small amount of fluoride delivered to the oral cavity decreases the prevalence of dental decay and results in stronger teeth and bones. However, ingestion of fluoride more than the recommended limit leads to toxicity and adverse effects. In order to update our understanding of fluoride and its potential toxicity, we have described the mechanisms of fluoride metabolism, toxic effects, and management of fluoride toxicity. The main aim of this review is to highlight the potential adverse effects of fluoride overdose and poorly understood toxicity. In addition, the related clinical significance of fluoride overdose and toxicity has been discussed.

Avicularin is a bioactive flavonoid that mainly found in blueberries, American cranberries, apples, and tea. Avicularin isolated from various plants including, Polygonum aviculare, Rhododendron aureum, and Taxillus kaempferi. Various preclinical studies already demonstrated antioxidant, anti-inflammatory, anti-depressant activity and neuroprotective action of avicularin. Furthermore, avicularin is a promising phytoconstituent with wide range of therapeutic potential in a variety of diseases. Hence, a toxicity study is urgently needed so that the appropriate dose can be used in animal studies to demonstrate its potential effect. The main aim of this research article is to find out maximum tolerated dose (MTD) and to observe if there’s any sign of toxicity in avicularin administered animals. Acute toxicity study was conducted as per OECD guideline (TG 423), a single dose of 300, 2000, 5000 mg/kg was given and the study was carried out for 14 days. In acute toxicity study, animals did not show any mortality or changes in behavioural pattern. Hence, it was concluded that, the maximum tolerated dose of avicularin is more than 5000 mg/kg. In a repeated-dose toxicity study, we have followed OECD guideline (TG 407) and rats were treated with 25, 50, and 100 mg/kg/day for 28 days. In Repeated toxicity study, animal showed no differences in hematologic and biochemical parameters. Histopathologic examination of all organs revealed normal histology. Hence, we can conclude from the findings that repeated avicularin exposure at 25, 50, and 100 mg/kg was determined to be safe in a 28-day toxicity study.

BACKGROUND AND OBJECTIVES: The focus of this study is on the importance of reliable and precise forecasting of acute oral toxicity to bolster chemical safety and advance sustainable development goals, particularly sustainable development goals-3 (good health and well-being), sustainable development goals-6 (clean water and sanitation), and sustainable development goals-12 (responsible consumption and production). Traditional toxicity assessments are often time-consuming and costly, necessitating the exploration of more efficient approaches. The focus of this study is to establish the most efficient method for constructing reliable and precise models for toxicity prediction.METHODS: The random forests were evaluated, a robust ensemble method, for predicting acute oral toxicity using a comprehensive dataset from National Toxicology Program/Interagency Center for the Evaluation of Alternative Toxicological Methods and Environmental Protection Agency/National Center for Competency Testing, which presented significant class imbalance, 8 percent very toxic 92 percent not very toxic. To address this imbalance, strategies such as cost-sensitive learning and data resampling techniques, including both under sampling and oversampling, were utilized. A diverse set of two-dimensional molecular descriptors generated via rational discovery kit were used as input features, and model preprocessing involved normalization, validation, and feature selection. Hyper-parameter tuning was conducted using Bayesian optimization and cross-validation, while the performance of random forests was evaluated in comparison to gradient boosting, extreme gradient boosting, artificial neural networks, and the generalized linear model.FINDINGS: The random forests models, particularly those utilizing under sampling and cost-sensitive learning, demonstrated superior performance, achieving sensitivity of 0.81, Specificity of 0.85, accuracy of 0.85, and an area under the receiver operating characteristic curve of 0.89 on an independent test set. An examination of feature importance has shown that the primary molecular descriptors are those related to the Van der waals surface area and molecular quantum numbers. A surrogate decision tree developed from random forests predictions reached an area under the curve of 0.929.CONCLUSION: Random forest models effectively predicted acute oral toxicity, particularly when addressing class imbalance through cost-sensitive learning and resampling. leveraging explainable artificial intelligence techniques, including permutation feature importance, surrogate decision tree analysis and local interpretable model-agnostic explanations, this study identified key molecular descriptors driving toxicity. This advancement improves model interpretability and represents a significant step toward enhancing chemical safety while supporting sustainable development goals.