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IntroductionThe wellbeing of retired teachers is often easily overlooked. This study aims to explore the mental health status and influencing factors of retired teachers.MethodFrom October to December 2022, a convenient sampling survey was conducted on retired teachers using the Symptom Checklist 90 (SCL-90), mainly using the χ2-test and logistic regression analysis.ResultsA sampling survey was conducted on 353 retired teachers, with an overall positive detection rate of 16.1%. The five factors with the highest positive detection rate were found to be obsessive-compulsive disorder (30.3%), interpersonal sensitivity (21.5%), paranoia (20.1%), anxiety (19.3%), and others (19.3%). The detection rates for the five factors, namely psychosis, depression, hostility, terror, and somatization, are all below 19%. The data on sex (χ2 = 4.626, P = 0.043), professional title (χ2 = 17.670, P = 0.003), income (χ2 = 9.960, P = 0.041), life satisfaction (χ2 = 27.348, P = 0.000), family relationships (χ2 = 51.451, P = 0.000), and physical health status (χ2 = 50.361, P = 0.000) show that the difference in mental health among retired teachers is statistically significant. The multivariate binary logistic regression analysis revealed that family relationships, life satisfaction, and physical health were important factors leading to mental health problems among retired teachers.DiscussionRetired teachers should cultivate a wide range of interests and hobbies, engage in regular physical exercise, develop healthy living habits, foster a positive family atmosphere, establish harmonious family relationships, promote community cultural construction, strengthen psychological intervention, and prevent psychological diseases.

ObjectiveTo investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer.MethodsBased on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC).ResultsIndependent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855.ConclusionEstablishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.

The small hive beetle (SHB), a social parasite of beehives, is native to sub-Saharan Africa and has spread to America, Europe, and Australia. Recently, these beetles invaded China, causing widespread colony collapses in the honeybee, Apis cerana. In this study, single nucleotide polymorphisms (SNPs) were identified in the beetle genome from its native range (Africa), a region that was invaded by SHBs nearly 30 years ago (America), and more recent invasions (Asia). The beetles in the United States formed the earliest branch and show signs of two decades of gene flow and local adaptation to differentiate this population from the native ones. The beetles in China were deep branched and showed the highest fixation index when compared to the US populations. The number of SNPs in overexpressed genes was significantly higher than the transcriptome. Gene-expression profiles presented here distinguish the characters between adult and larvae SHBs.