Overweight and obesity have emerged as a significant public health concern globally.While factors such as genetics,diet,and physical activity are insufficient to fully account for the rise in overweight and obesity,recent studies have indicated a link between environmental pollutants and the development of obesity.Microplastics,a novel type of environmental pollutant,are pervasive in various environmental media and daily life,entering organisms through multiple pathways including the digestive tract,respiratory tract,skin,among others.Evidence from studies has revealed the presence of microplastics in human tissues,organs,and biological samples,suggesting potential health risks to humans.This review outlines the pathways and distribution of microplastics within the human body while summarizing current research progress in relation to obesity.This article aims to raise awareness within society regarding the detrimental effects of microplastics and provide a theoretical foundation for medical professionals addressing public health issues.

Objective To investigate the application efficiency and potential of CT radiomics in differentiating malignant and benign sub-centimeter solid pulmonary nodules. Methods A retrospective study was performed on the sub-centimeter ( ≤ 10 mm) solid pulmonary nodules detected by enhanced CT in our hospital from March 2020 to January 2023. Malignancy was confirmed by surgical pathology, and benignity was confirmed by surgical pathology or follow-up. Lesions were manually segmented and radiomic features were extracted. The feature dimension was reduced via feature correlation analysis and least absolute shrinkage and selection operator (LASSO). The 5-fold cross validation was used to validate the model. Support vector machine, logistic regression, linear classification support vector machine, gradient boosting, and random forest models were established for CT radiomics. Receiver operating characteristic curves were drawn. Delong test was used to compare the diagnostic performance of the five classifiers. The optimal model was selected and compared to radiologists with medium and high seniority. Results A total of 303 nodules, 136 of which were malignant, were examined. Radiomics models were established after feature extraction and selection. On test set, the areas under the receiver operating characteristic curves of support vector machine, logistic regression, linear classification support vector machine, random forest, and gradient boosting models were 0.922 (95%CI: 0.893, 0.950), 0.910 (95%CI: 0.878, 0.942), 0.905 (95%CI: 0.872, 0.938), 0.899 (95%CI: 0.865, 0.933), and 0.896 (95%CI: 0.862, 0.930), respectively. Delong test indicated no significant differences in the performance of the five radiomics models, and the support vector machine model showed the highest accuracy and F1 score. The support vector machine model showed significantly higher diagnostic accuracy as compared to radiologists (83.8% vs. 55.4%, P < 0.001). Conclusion The radiomics models achieved high diagnostic efficiency and may help to reduce the uncertainty in diagnosis of malignant and benign sub-centimeter solid nodules by radiologists.