Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

The patient, assigned female at birth and aged 1 year and 7 months, presented with clinical manifestations of 46,XY disorders of sex development. The external genitalia exhibited a severely undermasculinized phenotype. Laboratory tests and gonadal biopsy indicated poor Leydig cell function and good Sertoli cell function. Genetic testing revealed compound heterozygous mutations of c.867-2A>C and c.547G>A (p.G183R) in the LHCGR gene. The patient was ultimately diagnosed with type II Leydig cell hypoplasia. Type II Leydig cell hypoplasia presents a broad spectrum of clinical phenotypes, characterized by a lack of parallel function between Leydig cells and Sertoli cells, and significant individual variability in spermatogenesis and gender assignment. This condition should be considered when there is poor Leydig cell function but good development of Wolffian duct derivatives.
Female ; Humans ; Infant ; Disorder of Sex Development, 46,XY/genetics* ; Leydig Cells/pathology* ; Mutation ; Receptors, LH/genetics* ; Testis/abnormalities*

The patient was a boy aged 1 year and 9 months who presented with 46,XY disorder of sex development (DSD), with severe undermasculinization of the external genitalia. Laboratory tests and ultrasound examinations showed normal functions of Leydig cells and Sertoli cells in the testes. Genetic testing revealed a novel pathogenic heterozygous variant, c.1186dupA (p.T396Nfs*17), in the PPP1R12A gene. Thirteen cases of PPP1R12A gene variants have been reported previously. These variants may cause isolated involvement of the genitourinary or neurological systems, or affect other systems/organs including the digestive tract, eyes, heart, etc. Patients with DSD typically present with a 46,XY karyotype and variable degrees of undermasculinization involving the external genitalia, gonads, and reproductive tract. This article reports a child with 46,XY DSD accompanied by growth retardation caused by a heterozygous variant in the PPP1R12A gene, which expands the clinical disease spectrum associated with PPP1R12A gene variants.
Humans ; Male ; Infant ; Disorder of Sex Development, 46,XY/etiology* ; Protein Phosphatase 1/genetics*

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

The combination of Aidi Injection(ADI) and doxorubicin(DOX) is a common strategy in the treatment of cancer, which can achieve synergistic anti-tumor effects while attenuating the cardiotoxicity caused by DOX. This study aims to investigate the mechanism of ADI in attenuating DOX-induced cardiotoxicity by multi-omics. DOX was used to induce cardiotoxicity in mice, and the cardioprotective effects of ADI were evaluated based on biochemical indicators and pathological changes. Based on the results, transcriptomics, proteomics, and metabolomics were employed to analyze the changes of endogenous substances in different physiological states. Furthermore, data from multiple omics were integrated to screen key regulatory pathways by which ADI attenuated DOX-induced cardiotoxicity, and important target proteins were selected for measurement by ELISA kits and immunohistochemical analysis. The results showed that ADI significantly reduced the levels of cardiac troponin T(cTnT) and N-terminal pro-B-type natriuretic peptide(NT-proBNP) and effectively ameliorated myocardial fibrosis and intracellular vacuolization, indicating that ADI showed therapeutic effect on DOX-induced cardiotoxicity. The transcriptomics analysis screened out a total of 400 differentially expressed genes(DEGs), which were mainly enriched in inflammatory response, oxidative stress, and myocardial fibrosis. After proteomics analysis, 70 differentially expressed proteins were selected, which were mainly enriched in the inflammatory response, cardiac function, and energy metabolism. A total of 51 differentially expressed metabolites were screened by the metabolomics analysis, and they were mainly enriched in multiple signaling pathways, including the inflammatory response, lipid metabolism, and energy metabolism. The integrated data of multiple omics showed that linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism pathways played an important role in DOX-induced cardiotoxicity, and ADI may exert therapeutic effects by modulating these pathways. Target validation experiments suggested that ADI significantly regulated abnormal protein levels of cyclooxygenase-1(COX-1), cyclooxygenase-2(COX-2), prostaglandin H2(PGH2), and prostaglandin D2(PGD2) in the model group. In conclusion, ADI may attenuate DOX-induced cardiotoxicity by regulating linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism, thus alleviating inflammation of the body.
Doxorubicin/toxicity* ; Animals ; Mice ; Cardiotoxicity/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Proteomics ; Metabolomics ; Injections ; Humans ; Multiomics

Objective To explore the potential mechanism of proguanil on the proliferation and apoptosis of bladder cancer cells.Methods 253J cells were randomly divided into control group(normal treatment),proguanil group(42.06 μmol·L-1 proguanil),pcDNA group(transfected with pcDNA+42.06 μmol·L-1 proguanil),FADS2 group[transfected fatty acid desaturase gene 2(FADS2)+42.06 μmol·L-1 proguanil],si-NC(transfection si-NC),si-FADS2(transfection si-FADS2),Ferrostatin-1 group(transfected with si-FADS2+10 μmol·L-1 ferrostatin-1).Real-time fluorescence quantitative polymerase chain reaction(RT-qPCR)assay was used to detect mRNA expression of related genes;Western blot assay was used to detect the expression of each protein;apoptosis was detected by TdT mediated dUDP nick end labeling(Tunel)assay;5-ethynyl-2'-deoxyuridine(EdU)assay to detect cell proliferation;the Transwell assay measures the ability of cells to migrate;Fe2+levels were determined by kit method;DCFH-DA probe was used to detect ROS levels.Results The mRNA levels of FADS2 in control group,proguanil group,pcDNA group and FADS2 group were 1.00±0.11,0.47±0.09,0.49±0.06 and 2.09±0.21,respectively;cell proliferation rate were(100.00±3.50)％,(54.31±4.90)％,(56.46±5.17)％and(78.76±6.50)％,respectively;the apoptosis rate were(3.92±0.53)％,(28.79±3.30)％,(27.20±2.90)％and(7.34±0.68)％,respectively;the migration number were 132.70±9.81,70.10±5.05,68.70±537 and 101.80±11.25,respectively;Fe2+level were(100.00±8.14)％,(201.33±17.84)％,(192.38±21.34)％and(116.70±10.90)％,respectively;GPX4 protein relative expression level were 0.77±0.05,0.31±0.05,0.34±0.05 and 0.68±0.06,respectively.The above indexes in proguanil group were compared with those in control group,the above indexes in FADS2 group were compared with those in pcDNA group,all the differences were statistically significant(all P＜0.05).The ROS levels of si-NC,si-FADS2 and Ferrostatin-1 groups were 9.72±1.18,40.94±5.63 and 13.77±1.40,respectively.Compared the si-FADS2 group with the si-NC group,Ferrostatin-1 group compared with si-FADS2 group,ROS level were significantly different(all P＜0.05).Conclusion Proguanil can induce the apoptosis of bladder cancer cells by inhibiting FADS2 expression mediated by oxidation-reduction driven ferroptosis pathway.

Objective: To investigate the distribution of blood pressure and analyze the associated factors of blood pressure of the elderly with type 2 diabetes in Jiangsu Province. Methods: The elderly over 60 years old participants with type 2 diabetes in the communities of Huai'an City and Changshu City, Jiangsu Province were selected in this study. They were divided into two groups: taking antihypertensive drugs and not taking antihypertensive drugs. The demographic characteristics, such as age and sex, and relevant factors were collected by questionnaire. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by physical examination. The percentile of SBP and DBP in each age group of men and women were described. The kernel density estimation curve was used to show the blood pressure distribution. The trend of blood pressure with age was fitted by locally weighted regression. The logistic regression model was used to analyze relevant factors of blood pressure. Results: A total of 12 949 participants were included in this study, including 7 775 patients in the antihypertensive drug group and 5 174 patients in the group without antihypertensive drugs. The SBP of participants was concentrated at 140-160 mmHg, and their DBP was concentrated at 75-85 mmHg. There were significant differences in the distribution of blood pressure among the subgroups of body mass index (BMI) and rural areas whether taking antihypertensive drugs and not. For participants aged under 80 years old, the SBP showed an increasing trend with age and the DBP showed a decreasing trend with age. Age, BMI ≥24 kg/m², fasting blood glucose ≥7.0 mmol/L, living in rural areas and no smoking were influencing factors of the elevated SBP; BMI ≥24 kg/m², male, living in rural areas, no smoking, drinking alcohol and not receiving drug hypoglycemic treatment were influencing factors of the elevated DBP. Conclusion: The SBP of older diabetic adults in Jiangsu Province is at a high level, and the distribution of blood pressure is significantly different between men and women in taking antihypertensive drugs group. The SBP presents a rising trend and the DBP is decreasing at the age of 60-80 years. The blood pressure level of this population are mainly affected by age, BMI, urban and rural areas, smoking.
Adult ; Aged ; Humans ; Male ; Female ; Middle Aged ; Aged, 80 and over ; Blood Pressure/physiology* ; Diabetes Mellitus, Type 2/epidemiology* ; Antihypertensive Agents/therapeutic use* ; Smoking ; Body Mass Index ; Hypertension/epidemiology*