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.

Background Deoxynivalenol (DON), a priority contaminant for food safety risk monitoring, is produced by Fusarium spp. infesting crops, and its common derivatives are 3-acetyl-DON (3A-DON) and 15-acetyl-DON (15A-DON), which have been shown to possess gastrointestinal toxicity, immunotoxicity, reproductive toxicity, and cytotoxicity. Due to the stable physicochemical properties of the DON family of toxins (DONs), they cannot be effectively removed during food processing, thus following the food chain, entering the human body, and posing health risks. Objective To understand the contamination status of DONs in commercial foods (cereals and bakery products) in Shanghai in 2022–2023, and to assess the exposure risk of DONs in pregnant women by combining their dietary consumption data. Methods Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to determine the contamination level of DONs in 1 100 food samples (cereals and baked goods) collected in 2022 and 944 samples collected in 2023 from Shanghai. The dietary monitoring data of pregnant women in Shanghai from 2016 to 2017 were adopted. The monitoring employed the food frequency questionnaire distributed among pregnant women through a combination of online telephone enquiry and offline on-site face-to-face survey to estimate their food consumption levels. An exposure assessment model was established to calculate the exposure level to DONs, and the probability distribution of the DONs exposure level in the pregnant women group in Shanghai was obtained by applying @Risk 7.5 software and simulating the calculation according to the Monte Carlo principle. With reference to the tolerable daily intake (TDI) of DONs [1.00 µg·(kg·d)⁻¹] proposed by the Joint FAO/WHO Expert Committee on Food Additives, the risk of exposure to DONs from commercial cereals and bakery products in pregnant women in Shanghai was assessed. Results DONs were detected in cereal and bakery samples collected in 2022 and 2023 with different levels of contamination. The level of DONs in cereal foods in 2023 (mean: 36.33 µg·kg⁻¹) decreased compared to 2022 (mean: 23.64 µg·kg⁻¹). However, the positive rate (71.67%) and level (mean: 51.22 µg·kg⁻¹) of DONs in bakery products increased significantly compared with 2022 (positive rate: 10.00%, mean: 24.39 µg·kg⁻¹). The mean consumption of cereals in 783 pregnant women was 222.48 g·d⁻¹ and the mean consumption of bakery products was 36.07 g·d⁻¹, and there was no statistically significant difference in the intake of all types of cereals and bakery products across the early, middle, and late stages of pregnancy. The modelled intakes of DONs via commercial cereals and bakery products for pregnant women in Shanghai were calculated to be 0.20 and 0.57 µg·(kg·d)⁻¹ in 2022 for the mean level and the 95th percentile level, respectively, and 0.16 µg·(kg·d)⁻¹ and 0.35 µg·(kg·d)⁻¹ in 2023, respectively. The results of the health risk assessment showed that pregnant women in Shanghai had 2.6% and 1.4% probability of exposure to DONs from cereal consumption in 2022 and 2023, respectively. Conclusion The risk of exposure of pregnant women in Shanghai to DONs via commercial cereals and bakery products is relatively low (1.4%-2.6%). However, considering the physical sensitivity of pregnant women, they should avoid consuming moldy grains and appropriately reduce intake of bakery products.

OBJECTIVE: To investigate the inhibitory effect of Tanreqing Injection (TRQ) on the activation of nucleotide-binding oligomerization domain-like receptor pyrin domain containing 3 (NLRP3) inflammasome in macrophages infected with influenza A virus and the underlying mechanism based on mitophagy pathway. METHODS: The inflammatory model of murine macrophage J774A.1 induced by influenza A virus [strain A/Puerto Rico/8/1934 (H1N1), PR8] was constructed and treated by TRQ, while the mitochondria-targeted antioxidant Mito-TEMPO and autophagy specific inhibitor 3-methyladenine (3-MA) were used as controls to intensively study the anti-inflammatory mechanism of TRQ based on mitophagy-mitochondrial reactive oxygen species (mtROS)-NLRP3 inflammasome pathway. The levels of NLRP3, Caspase-1 p20, microtubule-associated protein 1 light chain 3 II (LC3II) and P62 proteins were measured by Western blot. The release of interleukin-1β (IL-1β) was tested by enzyme linked immunosorbent assay, the mtROS level was detected by flow cytometry, and the immunofluorescence and co-localization of LC3 and mitochondria were observed under confocal laser scanning microscopy. RESULTS: Similar to the effect of Mito-TEMPO and contrary to the results of 3-MA treatment, TRQ could significantly reduce the expressions of NLRP3, Caspase-1 p20, and autophagy adaptor P62, promote the expression of autophagy marker LC3II, enhance the mitochondrial fluorescence intensity, and inhibit the release of mtROS and IL-1β (all P<0.01). Moreover, LC3 was co-localized with mitochondria, confirming the type of mitophagy. CONCLUSION TRQ could reduce the level of mtROS by promoting mitophagy in macrophages infected with influenza A virus, thus inhibiting the activation of NLRP3 inflammasome and the release of IL-1β, and attenuating the inflammatory response.
Mitophagy/drug effects* ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Animals ; Macrophages/virology* ; Inflammasomes/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Mitochondria/metabolism* ; Reactive Oxygen Species/metabolism* ; Influenza A virus/physiology* ; Interleukin-1beta/metabolism* ; Cell Line ; Injections

OBJECTIVE: To assess the efficacy of Qingda Granule (QDG) in ameliorating hypertension-induced cardiac damage and investigate the underlying mechanisms involved. METHODS: Twenty spontaneously hypertensive rats (SHRs) were used to develope a hypertension-induced cardiac damage model. Another 10 Wistar Kyoto (WKY) rats were used as normotension group. Rats were administrated intragastrically QDG [0.9 g/(kg•d)] or an equivalent volume of pure water for 8 weeks. Blood pressure, histopathological changes, cardiac function, levels of oxidative stress and inflammatory response markers were measured. Furthermore, to gain insights into the potential mechanisms underlying the protective effects of QDG against hypertension-induced cardiac injury, a network pharmacology study was conducted. Predicted results were validated by Western blot, radioimmunoassay immunohistochemistry and quantitative polymerase chain reaction, respectively. RESULTS: The administration of QDG resulted in a significant decrease in blood pressure levels in SHRs (P<0.01). Histological examinations, including hematoxylin-eosin staining and Masson trichrome staining revealed that QDG effectively attenuated hypertension-induced cardiac damage. Furthermore, echocardiography demonstrated that QDG improved hypertension-associated cardiac dysfunction. Enzyme-linked immunosorbent assay and colorimetric method indicated that QDG significantly reduced oxidative stress and inflammatory response levels in both myocardial tissue and serum (P<0.01). CONCLUSIONS Both network pharmacology and experimental investigations confirmed that QDG exerted its beneficial effects in decreasing hypertension-induced cardiac damage by regulating the angiotensin converting enzyme (ACE)/angiotensin II (Ang II)/Ang II receptor type 1 axis and ACE/Ang II/Ang II receptor type 2 axis.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/pathology* ; Renin-Angiotensin System/drug effects* ; Rats, Inbred SHR ; Oxidative Stress/drug effects* ; Male ; Rats, Inbred WKY ; Blood Pressure/drug effects* ; Myocardium/pathology* ; Rats ; Inflammation/pathology*

OBJECTIVE: To investigate the effect of traditional Chinese Five-body balance exercise on patients with preserved ratio impaired spirometry (PRISm). METHODS: Fifteen patients with PRISm and 15 patients diagnosed with chronic obstructive pulmonary disease (COPD) were recruited from the Outpatient Department of Guang'anmen Hospital and Beijing Niujie Health Service Center from April to December, 2023. Participants in both groups attended supervised Five-body balance exercise training twice a week for 12 weeks. Patients with COPD continued their regular medication regimen during the intervention period. The endpoints were mean changes in the 6-min walk test (6MWT), St. George's Respiratory Questionnaire (SGRQ) score, cardiopulmonary exercise testing (CPET), pulmonary function, and scores of COPD assessment test (CAT), modified British Medical Research Council, Self-Rating Anxiety Scale, and Self-Rating Depression Scale from baseline to 12 weeks. Adverse events were monitored throughout the study. RESULTS: The PRISm group showed a significant improvement from baseline to week 12 in 6MWT, SGRQ symptom score, and forced vital capacity (FVC) compared to the COPD group (P<0.05). No significant between-group changes were observed in other outcome measurements (P>0.05). In addition, compared with baseline, both groups exhibited improvements in 6MWT, SGRQ score, and CPET at week 12 (P<0.05). The PRISm group also showed a significant increase in forced expiratory volume in 1 s and FVC, as well as a significant decrease in CAT score at week 12 (P<0.05). No adverse events were reported. CONCLUSION Patients with PRISm may benefit from Five-body balance exercise training, which can improve the exercise capacity, health-related quality of life, and lung function. (Registration No. ChiCTR2200059290).
Humans ; Spirometry ; Male ; Female ; Pulmonary Disease, Chronic Obstructive/therapy* ; Lung/physiopathology* ; Middle Aged ; Exercise Tolerance/physiology* ; Exercise Therapy ; Aged ; Medicine, Chinese Traditional ; Respiratory Function Tests ; East Asian People

Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.