Microplastics (MPs) and nanoplastics (NPs) are extensively distributed in the environment. However, a comprehensive review and in‐depth discussion on the effects of MPs and NPs to reproductive capacity and transgenerational toxicity on mammals, especially on humans, is lacked. It is suggested that microplastics and nanoplastics could accumulate in mammalian reproductive organs and exert toxic effects on the reproductive system for both sexes. For males, the damage of microplastics consists of abnormal testicular and sperm structure, decreased sperm vitality, and endocrine disruption, which were caused by oxidative stress, inflammation, apoptosis of testicular cells, autophagy, abnormal cytoskeleton, and abnormal hypothalamic‐pituitary‐testicular axis. For females, the damage of microplastics includes abnormal ovary and uterus structure and endocrine disruption, which were caused by oxidative stress, inflammation, granulosa cell apoptosis, hypothalamic‐pituitary‐ovary axis abnormalities, and tissue fibrosis. For transgenerational toxicity, premature mortality existed in the rodent offspring after maternal exposure to microplastics. Among the surviving offspring, metabolic disorders, reproductive dysfunction, immune, neurodevelopmental, and cognitive disorders were detected, and these events directly correlated with transgenerational translocation of MPs and NPs. Studies on human‐derived cells or organoids demonstrated that transgenerational toxicity studies for both sexes are yet in the phase of exploring suitable experimental models, and more detailed research on the threat of MPs and NPs to human fertility is still urgently needed. Further studies will help assess the MPs and NPs threat to public fertility and reproductive health risks.

Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations.Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ).Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70–0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65–0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001).Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.