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.

BACKGROUND: At present, biopsy is essential for the diagnosis of prostate cancer (PCa) before radical prostatectomy (RP). However, with the development of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) and multiparametric magnetic resonance imaging (mpMRI), it might be feasible to avoid biopsy before RP. Herein, we aimed to explore the feasibility of avoiding biopsy before RP in patients highly suspected of having PCa after assessment of PSMA PET/CT and mpMRI. METHODS: Between December 2017 and April 2022, 56 patients with maximum standardized uptake value (SUVmax) of ≥4 and Prostate Imaging Reporting and Data System (PI-RADS) ≥4 lesions who received RP without preoperative biopsy were enrolled from two tertiary hospitals. The consistency between clinical and pathological diagnoses was evaluated. Preoperative characteristics were compared among patients with different pathological types, T stages, International Society of Urological Pathology (ISUP) grades, and European Association of Urology (EAU) risk groups. RESULTS: Fifty-five (98%) patients were confirmed with PCa by pathology, including 49 (89%) with clinically significant prostate cancer (csPCa, defined as ISUP grade ≥2 malignancy). One patient was diagnosed with high-grade prostatic intraepithelial neoplasia (HGPIN). CsPCa patients, compared with clinically insignificant prostate cancer (cisPCa) and HGPIN patients, were associated with a higher level of prostate-specific antigen (22.9 ng/mL vs . 10.0 ng/mL, P = 0.032), a lower median prostate volume (32.2 mL vs . 65.0 mL, P = 0.001), and a higher median SUVmax (13.3 vs . 5.6, P <0.001). CONCLUSIONS It might be feasible to avoid biopsy before RP for patients with a high probability of PCa based on PSMA PET/CT and mpMRI. However, the diagnostic efficacy of csPCa with PI-RADS ≥4 and SUVmax of ≥4 is inadequate for performing a procedure such as RP. Further prospective multicenter studies with larger sample sizes are necessary to confirm our perspectives and establish predictive models with PSMA PET/CT and mpMRI.
Humans ; Male ; Prostatectomy/methods* ; Prostatic Neoplasms/diagnosis* ; Middle Aged ; Aged ; Positron Emission Tomography Computed Tomography/methods* ; Biopsy ; Multiparametric Magnetic Resonance Imaging ; Prostate-Specific Antigen/metabolism*

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 study was conducted to investigate the mechanism of Xinyang Tablets( XYP) in modulating the fat mass and obesity-associated protein(FTO)/N6-methyladenosine(m6A) signaling pathway to ameliorate ventricular remodeling in heart failure(HF). A mouse model of HF was established by transverse aortic constriction(TAC). Mice were randomized into sham, model, XYP(low, medium, and high doses), and positive control( perindopril) groups(n= 10). From day 3 post-surgery, mice were administrated with corresponding drugs by gavage for 6 consecutive weeks. Following the treatment, echocardiography was employed to evaluate the cardiac function, and RT-qPCR was employed to determine the relative m RNA levels of key markers, including atrial natriuretic peptide( ANP), B-type natriuretic peptide( BNP), β-myosin heavy chain(β-MHC), collagen type I alpha chain(Col1α), collagen type Ⅲ alpha chain(Col3α), alpha smooth muscle actin(α-SMA), and FTO. The cardiac tissue was stained with Masson's trichrome and wheat germ agglutinin(WGA) to reveal the pathological changes. Immunohistochemistry was employed to detect the expression levels of Col1α, Col3α, α-SMA, and FTO in the myocardial tissue. The m6A modification level in the myocardial tissue was measured by the m6A assay kit. An H9c2 cell model of cardiomyocyte injury was induced by angiotensin Ⅱ(AngⅡ), and small interfering RNA(siRNA) was employed to knock down FTO expression. RT-qPCR was conducted to assess the relative m RNA levels of FTO and other genes associated with cardiac remodeling. The m6A modification level was measured by the m6A assay kit, and Western blot was employed to determine the phosphorylated phosphatidylinositol 3-kinase(p-PI3K)/phosphatidylinositol 3-kinase(PI3K) and phosphorylated serine/threonine kinase(p-Akt)/serine/threonine kinase(Akt) ratios in cardiomyocytes. The results of animal experiments showed that the XYP treatment significantly improved the cardiac function, reduced fibrosis, up-regulated the m RNA and protein levels of FTO, and lowered the m6A modification level compared with the model group. The results of cell experiments showed that the XYP-containing serum markedly up-regulated the m RNA level of FTO while decreasing the m6A modification level and the p-PI3K/PI3K and p-Akt/Akt ratios in cardiomyocytes. Furthermore, FTO knockdown reversed the protective effects of XYP-containing serum on Ang Ⅱ-induced cardiomyocyte hypertrophy. In conclusion, XYP may ameliorate ventricular remodeling by regulating the FTO/m6A axis, thereby inhibiting the activation of the PI3K/Akt signaling pathway.
Animals ; Ventricular Remodeling/drug effects* ; Heart Failure/physiopathology* ; Signal Transduction/drug effects* ; Mice ; Male ; Alpha-Ketoglutarate-Dependent Dioxygenase FTO/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Mice, Inbred C57BL ; Humans ; Adenosine/analogs & derivatives* ; Myocytes, Cardiac/metabolism* ; Disease Models, Animal

Osteoporosis(OP) is a senile bone disease characterized by an imbalance between bone remodeling and bone formation. Targeting pathogenesis of kidney deficiency, spleen deficiency, and blood stasis, Bushen Jianpi Huoxue Decoction has a significant effect on the treatment of OP by tonifying kidney, invigorating spleen, and activating blood circulation. MicroRNA(miRNA) and the anti-apoptotic protein B-cell lymphoma-2-like protein 1(BCL2L1) are closely related to bone cell metabolism. Therefore, in this study, the binding of miR-140-5p to BCL2L1 was detected by dual luciferase assay and polymerase chain reaction(PCR). After silencing or overexpressing miR-140-5p, the apoptosis, autophagy, and osteogenic function of human fetal osteoblast cell line 1.19(HFOB1.19) were observed by flow cytometry and Western blot. Bushen Jianpi Huoxue Decoction-containing serum was prepared by intragastric administration of Bushen Jianpi Huoxue Decoction in rats. Different concentrations of Bushen Jianpi Huoxue Decoction-containing serum were used to treat HFOB1.19 with or without miR-140-5p mimic. The expression of osteogenic proteins in each group was observed, and the role of miR-140-5p/BCL2L1 in apoptosis and autophagy of HFOB1.19 was studied, along with the effect of Bushen Jianpi Huoxue Decoction on these processes. As indicated by the dual luciferase assay, miR-140-5p bound to BCL2L1. Flow cytometry and Western blot showed that miR-140-5p promoted apoptosis and inhibited autophagy in HFOB1.19. After intervention with high, medium, and low doses of Bushen Jianpi Huoxue Decoction-medicated serum, compared with the miR-140-5p NC group, the expression of osteocalcin(OCN), osteopontin(OPN), Runt-related transcription factor 2(RUNX2), and transforming growth factor beta 1(TGF-β1) decreased in the miR-140-5p mimic group, while the expression of bone morphogenetic protein 2(BMP2) showed no significant difference under high-dose intervention. Therefore, miR-140-5p/BCL2L1 can promote apoptosis and inhibit autophagy in HFOB1.19. Bushen Jianpi Huoxue Decoction can affect the osteogenic effect of miR-140-5p through BMP2.
MicroRNAs/metabolism* ; Autophagy/drug effects* ; Apoptosis/drug effects* ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Cell Line ; bcl-X Protein/metabolism* ; Osteoblasts/metabolism* ; Rats ; Osteoporosis/physiopathology* ; Male ; Rats, Sprague-Dawley ; Osteogenesis/drug effects*

This study investigates the effect and underlying mechanism of Guizhi Tongluo Tablets(GZTL) in treating atherosclerosis(AS) in a mouse model. Apolipoprotein E-knockout(ApoE~(-/-)) mice were randomly assigned to the following groups: model, high-, medium-, and low-dose GZTL, and atorvastatin(ATV), and age-matched C57BL/6J mice were selected as the control group. ApoE~(-/-) mice in other groups except the control group were fed with a high-fat diet for the modeling of AS and administrated with corresponding drugs via gavage for 8 weeks. General conditions, signs of blood stasis, and body mass of mice were monitored. Aortic plaques and their stability were assessed by hematoxylin-eosin, Masson, and oil red O staining. Serum levels of total cholesterol(TC), triglycerides(TG), and low-density lipoprotein cholesterol(LDL-C) were measured by biochemical assays, and those of interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and interleukin-6(IL-6) were determined via enzyme-linked immunosorbent assay. Apoptosis was assessed by terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL). Single-cell RNA sequencing(scRNA-seq) was employed to analyze the differential expression of CD72hi macrophages(CD72hi-Mφ) in the aortas of AS patients and mice. The immunofluorescence assay was employed to visualize CD72hi-Mφ expression in mouse aortic plaques, and real-time fluorescence quantitative PCR was utilized to determine the mRNA levels of IL-1β, TNF-α, and IL-6 in the aorta. The results demonstrated that compared with the control group, the model group exhibited significant increases in body mass, aortic plaque area proportion, necrotic core area proportion, and lipid deposition, a notable decrease in collagen fiber content, and an increase in apoptosis. Additionally, the model group showcased elevated serum levels of TC, TG, LDL-C, IL-1β, TNF-α, and IL-6, alongside marked upregulations in the mRNA levels of IL-1β, TNF-α, and IL-6 in the aorta. In comparison with the model group, the GZTL groups and the ATV group showed a reduction in body mass, and the medium-and high-dose GZTL groups and the ATV group demonstrated reductions in aortic plaque area proportion, necrotic core area proportion, and lipid deposition, an increase in collagen fiber content, and a decrease in apoptosis. Furthermore, the treatment goups showcased lowered serum levels of TC, TG, LDL-C, IL-1β, TNF-α, and IL-6. The data of scRNA-seq revealed significantly elevated CD72hi-Mφ signaling in carotid plaques of AS patients compared with that in the normal arterial tissue. Animal experiments confirmed that CD72hi-Mφ expression, along with several pro-inflammatory cytokines, was significantly upregulated in the aortas of AS mice, which were downregulated by GZTL treatment. In conclusion, GZTL may alleviate AS by inhibiting CD72hi-Mφ activity.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Atherosclerosis/immunology* ; Mice ; Mice, Inbred C57BL ; Macrophages/immunology* ; Male ; Humans ; Apolipoproteins E/genetics* ; Tablets ; Tumor Necrosis Factor-alpha/genetics* ; Apoptosis/drug effects* ; Interleukin-1beta/genetics* ; Interleukin-6/genetics* ; Disease Models, Animal ; Mice, Knockout