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.

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.

Cardiovascular disease (CVD) remains one of the leading causes of mortality among adults globally, with continuously rising morbidity and mortality rates. Metabolic disorders are closely linked to various cardiovascular diseases and play a critical role in their pathogenesis and progression, involving multifaceted mechanisms such as altered substrate utilization, mitochondrial structural and functional dysfunction, and impaired ATP synthesis and transport. In recent years, the potential role of peroxisome proliferator-activated receptors (PPARs) in cardiovascular diseases has garnered significant attention, particularly peroxisome proliferator-activated receptor alpha (PPARα), which is recognized as a highly promising therapeutic target for CVD. PPARα regulates cardiovascular physiological and pathological processes through fatty acid metabolism. As a ligand-activated receptor within the nuclear hormone receptor family, PPARα is highly expressed in multiple organs, including skeletal muscle, liver, intestine, kidney, and heart, where it governs the metabolism of diverse substrates. Functioning as a key transcription factor in maintaining metabolic homeostasis and catalyzing or regulating biochemical reactions, PPARα exerts its cardioprotective effects through multiple pathways: modulating lipid metabolism, participating in cardiac energy metabolism, enhancing insulin sensitivity, suppressing inflammatory responses, improving vascular endothelial function, and inhibiting smooth muscle cell proliferation and migration. These mechanisms collectively reduce the risk of cardiovascular disease development. Thus, PPARα plays a pivotal role in various pathological processes via mechanisms such as lipid metabolism regulation, anti-inflammatory actions, and anti-apoptotic effects. PPARα is activated by binding to natural or synthetic lipophilic ligands, including endogenous fatty acids and their derivatives (e.g., linoleic acid, oleic acid, and arachidonic acid) as well as synthetic peroxisome proliferators. Upon ligand binding, PPARα activates the nuclear receptor retinoid X receptor (RXR), forming a PPARα-RXR heterodimer. This heterodimer, in conjunction with coactivators, undergoes further activation and subsequently binds to peroxisome proliferator response elements (PPREs), thereby regulating the transcription of target genes critical for lipid and glucose homeostasis. Key genes include fatty acid translocase (FAT/CD36), diacylglycerol acyltransferase (DGAT), carnitine palmitoyltransferase I (CPT1), and glucose transporter (GLUT), which are primarily involved in fatty acid uptake, storage, oxidation, and glucose utilization processes. Advancing research on PPARα as a therapeutic target for cardiovascular diseases has underscored its growing clinical significance. Currently, PPARα activators/agonists, such as fibrates (e.g., fenofibrate and bezafibrate) and thiazolidinediones, have been extensively studied in clinical trials for CVD prevention. Traditional PPARα agonists, including fenofibrate and bezafibrate, are widely used in clinical practice to treat hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) levels. These fibrates enhance fatty acid metabolism in the liver and skeletal muscle by activating PPARα, and their cardioprotective effects have been validated in numerous clinical studies. Recent research highlights that fibrates improve insulin resistance, regulate lipid metabolism, correct energy metabolism imbalances, and inhibit the proliferation and migration of vascular smooth muscle and endothelial cells, thereby ameliorating pathological remodeling of the cardiovascular system and reducing blood pressure. Given the substantial attention to PPARα-targeted interventions in both basic research and clinical applications, activating PPARα may serve as a key therapeutic strategy for managing cardiovascular conditions such as myocardial hypertrophy, atherosclerosis, ischemic cardiomyopathy, myocardial infarction, diabetic cardiomyopathy, and heart failure. This review comprehensively examines the regulatory roles of PPARα in cardiovascular diseases and evaluates its clinical application value, aiming to provide a theoretical foundation for further development and utilization of PPARα-related therapies in CVD treatment.

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*

OBJECTIVE: To investigate the clinical features and prognostic factors of primary tonsil lymphoma (PTL). METHODS: The clinical data of 41 patients diagnosed with PTL and treated in the Affiliated Hospital of Xuzhou Medical University from January 2015 to December 2022 were collected and retrospectively analyzed. Their clinical features and prognostic factors were analyzed. RESULTS: All the 41 patients were newly diagnosed with PTL, and the median age of onset was 58(19-85) years. Among them, 19 patients started with pharyngeal pain, 12 patients presented with dysphagia, 8 patients presented with pharyngeal mass, and 2 patients presented with blurred articulation. The most common pathological type was diffuse large B-cell lymphoma (24 cases, 58.54%). All patients received chemotherapy, and 3 patients were combined with hematopoietic stem cell transplantation. Among 41 patients, 11 (26.83%) achieved complete response, 14 (34.15%) achieved partial response, and the total response rate was 60.98% (25/41). The median follow-up time was 37(6-107) months, the 5-year overall survival (OS) rate was 70.81% and 5-year progression-free survival (PFS) rate was 66.20%. Univariate analysis showed that B symptoms, Ki-67, β₂-MG and IPI score had significant effects on PFS and OS of patients (all P < 0.05). Multivariate analysis showed that IPI score was an independent risk factor for PFS and OS of patients (P < 0.05). CONCLUSION The clinical manifestations of PTL lack specificity, and the prognosis is relatively good. Most patients can achieve long-term survival after treatment. IPI score is related to the prognosis.
Tonsillar Neoplasms/pathology* ; Lymphoma/pathology* ; Humans ; Prognosis ; Retrospective Studies ; Drug Therapy ; Progression-Free Survival ; Male ; Female ; Young Adult ; Adult ; Middle Aged ; Aged ; Aged, 80 and over ; Lymphoma, B-Cell/pathology* ; Survival Rate

Objective To observe the curative effect of comprehensive traditional Chinese medicine(TCM)therapy for the treatment of refractory sudden hearing loss(i.e.,suffering sudden hearing loss more than 2 weeks),and to analyze the factors that may affect the prognosis.Methods A retrospective analysis was carried out in 405 hospitalized patients with refractory sudden hearing loss who were treated in the Department of Otorhinolaryngology,the First Affiliated Hospital of Guangzhou University of Chinese Medicine from 2005 to 2022.The patients were all treated by comprehensive TCM therapy including oral administration of Chinese medicine,acupuncture,acupoint seed-pressing application after individualized syndrome differentiation.The overall clinical efficacy was evaluated,and the difference of efficacy in the patients with various courses of disease,degrees of deafness,types of hearing curve,concomitant symptoms and TCM syndrome types,having or not having previous treatment history was analyzed.Results For the 405 patients with refractory sudden hearing loss,the cure rate was 5.7％and the total effective rate was 28.1％.Among the 405 patients,the best efficacy was achieved in the patients with mild hearing loss,low-frequency decline type of hearing curve,and having no previous treatment history,and the differences were statistically significant(P＜0.05 or P＜0.01).There was no significant difference in the efficacy of patients with different courses of disease,with or without concomitant symptoms,or with various syndrome types(P＞0.05).Conclusion The comprehensive TCM therapy has a certain effect on refractory sudden hearing loss.Patients with poor efficacy after conventional western medicine can still benefit from the comprehensive TCM therapy.

Objective:The expression of serum free light chain(FLC)is abnormal in various diseases,but its role in lung cancer remains unclear.This study aims to investigate the expression and diagnostic value of serum FLC in lung cancer. Methods:A total of 80 lung cancer patients treated at Xiangdong Hospital,Hunan Normal University from January to December 2021 were selected as the lung cancer group.Another 80 healthy individuals undergoing routine physical examinations during the same period were chosen as the control group.General information and serum κFLC and λFLC levels were collected for all subjects.Clinical indicators such as serum carcinoembryonic antigen(CEA),cytokeratin fragment antigen 21-1(CYFRA21-1)levels,tumor diameter,histological type,TNM stage,and lymph node metastasis status were recorded for lung cancer patients.The expression levels of serum FLC[κFLC,λFLC,and FLC(κ+λ)]were compared between the lung cancer group and the control group.Lung cancer patients were grouped based on gender,age,smoking history,tumor diameter,TNM stage,histological type,and lymph node metastasis to compare differences in serum κFLC and λFLC levels.Receiver operating characteristic(ROC)curves were used to evaluate the diagnostic value of serum FLC alone and in combination with other indicators in lung cancer. Results:The expression levels of serum FLC(κ+λ)and κFLC were significantly higher in the lung cancer group than those in the control group(both P＜0.001),while there was no significant difference in serum λFLC levels between the 2 groups(P＞0.05).There were no significant differences in serum κFLC levels among lung cancer patients with different tumor diameters,histological types,or TNM stages(all P＞0.05);however,serum κFLC levels were higher in lung cancer patients with lymph node metastasis than in those without,with statistical significance(P=0.033).There were no significant differences in serum λFLC levels based on tumor diameter or histological type(both P＞0.05),but serumλFLC levels were higher in stage Ⅲ+Ⅳ and lymph node metastatic lung cancer patients compared to stage Ⅰ+Ⅱ and non-metastatic patients,with statistical significance(P=0.033 and P=0.019,respectively).The area under the curve(AUC)for κFLC and CEA in diagnosing lung cancer showed no significant difference(P=0.333).The combination ofκFLC+CYFRA21-1 had the highest diagnostic efficacy(AUC=0.875)and sensitivity(71.3％).The AUC for the combined diagnosis of κFLC+λFLC+CEA+CYFRA21-1 was 0.915(95％CI 0.860 to 0.953,P＜0.001). Conclusion:Serum FLC is highly expressed in lung cancer and is associated with its invasion and metastasis.Serum FLC,particularly κFLC,has diagnostic value for lung cancer,and the combined detection of FLC,CEA,and CYFRA21-1 offers the best diagnostic efficacy.