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.

The hippocampus, a major component of the limbic system, is the most important region related to emotion regulation and memory processing. Cognitive impairment and depressive symptoms observed in Alzheimer's disease (AD) patients may be attributed to hippocampal damage caused by amyloid β-protein (Aβ). Our previous studies have demonstrated that a steroid sulfatase inhibitor DU-14 can enhance hippocampal synaptic plasticity and spatial memory abilities in a chronic AD murine model by counteracting the toxic effects of Aβ. However, limited experimental evidence exists regarding the efficacy of steroid sulfatase inhibitor on depressive symptoms in AD animal models. In this study, we investigated the effects of DU-14 on depressive symptoms and theta-band neuronal oscillations in rats with intrahippocampal injection of Aβ_1-42 using various behavioral tests such as sucrose preference test, tail suspension test, forced swimming test, and in vivo hippocampal local field potential (LFP) recording. The results demonstrated that, in comparison to the control group: (1) rats in the Aβ group exhibited a decrease in sucrose preference, indicating a loss of interest in pleasurable activities; (2) rats in the Aβ group displayed aggravated depressive-like behavior characterized by prolonged immobility time during tail suspension and forced swimming tests; (3) Aβ disrupted the induction of theta rhythm via tail pinch stimulation, and resulted in a significant reduction in peak power of theta rhythm. In contrast to the Aβ group, pretreatment with DU-14 resulted in: (1) a significant improvement in Aβ-induced anhedonia, as evidenced by increased sucrose preference; (2) significant alleviation of Aβ-induced despair and depressive-like behaviors, reflected by reduced immobility time during tail suspension and forced swimming tests; (3) successful mitigation of Aβ-mediated inhibition on bilateral hippocampal theta rhythm. These findings indicate that steroid sulfatase inhibitor DU-14 can counteract neurotoxicity induced by Aβ, and prevent Aβ-induced depressive-like behavior and suppression of theta rhythm.
Animals ; Amyloid beta-Peptides/toxicity* ; Rats ; Depression/physiopathology* ; Theta Rhythm/drug effects* ; Hippocampus/physiopathology* ; Male ; Rats, Sprague-Dawley ; Alzheimer Disease/physiopathology* ; Steryl-Sulfatase/antagonists & inhibitors* ; Peptide Fragments ; Behavior, Animal/drug effects*

This study aims to systematically review and evaluate the quality of clinical research on the treatment of adenomyosis(AM) with traditional Chinese medicine(TCM) in recent ten years, using evidence graphs. Computer searches were conducted on eight Chinese and English databases, commonly used guideline databases, and guideline-related websites, covering the period from January 1, 2014, to October 1, 2024. Two researchers independently screened, extracted information, and evaluated the quality of the evidence. The distribution and quality of the clinical research evidence were presented using both text and charts. A total of 565 articles were included in the study, comprising 523 intervention studies, 23 observational studies, 18 systematic reviews/Meta-analysis, and 1 guideline. The overall publication volume has shown a downward trend in past two years. The sample sizes of the intervention and observational studies primarily focused on 60 to 120 cases. The intervention schemes mainly involved multi-therapy combinations, including 33 classic prescriptions and 25 Chinese patent medicines. Among these, 48 studies related to 17 classic prescriptions and 45 studies related to 10 types of Chinese patent medicines involved TCM syndrome types. Randomized controlled trial(RCT) tended to focus on overall clinical efficacy and the degree of dysmenorrhea as key outcome measures. Methodological quality issues were found in 97 RCTs related to TCM decoctions and 131 RCTs related to Chinese patent medicines, primarily involving unclear explanations of some information. The AMSTAR scores for the 18 systematic reviews/Meta-analysis ranged from 1 to 8 points, with 16 studies suggesting "evidence of potential therapeutic efficacy". The recommended level for the one included guideline was B-level. TCM shows significant advantages in treating AM. Future clinical research should further standardize study designs, reference relevant reporting guidelines, improve the quality of clinical research, generate higher-level evidence-based results, and promote the high-quality development of clinical research on TCM for treating AM.
Humans ; Adenomyosis/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Female ; Medicine, Chinese Traditional ; Randomized Controlled Trials as Topic

In this study, lipopolysaccharide(LPS), ovalbumin(OVA), and compound 48/80(C48/80) were administered to establish non-infectious pneumonia models under simulated clinical conditions, and the correlation between their pathological characteristics and traditional Chinese medicine(TCM) syndromes was compared, providing the basis for the selection of appropriate animal models for TCM efficacy evaluation. An acute pneumonia model was established by nasal instillation of LPS combined with intraperitoneal injection for intensive stimulation. Three doses of OVA mixed with aluminum hydroxide adjuvant were injected intraperitoneally on days one, three, and five and OVA was administered via endotracheal drip for excitation on days 14-18 to establish an OVA-induced allergic pneumonia model. A single intravenous injection of three doses of C48/80 was adopted to establish a C48/80-induced pneumonia model. By detecting the changes in peripheral blood leukocyte classification, lung tissue and plasma cytokines, immunoglobulins(Ig), histamine levels, and arachidonic acid metabolites, the multi-dimensional analysis was carried out based on pathological evaluation. The results showed that the three models could cause pulmonary edema, increased wet weight in the lung, and obvious exudative inflammation in lung tissue pathology, especially for LPS. A number of pyrogenic cytokines, inclading interleukin(IL)-6, interferon(IFN)-γ, IL-1β, and IL-4 were significantly elevated in the LPS pneumonia model. Significantly increased levels of prostacyclin analogs such as prostaglandin E2(PGE2) and PGD2, which cause increased vascular permeability, and neutrophils in peripheral blood were significantly elevated. The model could partly reflect the clinical characteristics of phlegm heat accumulating in the lung or dampness toxin obstructing the lung. The OVA model showed that the sensitization mediators IgE and leukotriene E4(LTE4) were increased, and the anti-inflammatory prostacyclin 6-keto-PGF2α was decreased. Immune cells(lymphocytes and monocytes) were decreased, and inflammatory cells(neutrophils and basophils) were increased, reflecting the characteristics of "deficiency", "phlegm", or "dampness". Lymphocytes, monocytes, and basophils were significantly increased in the C48/80 model. The phenotype of the model was that the content of histamine, a large number of prostacyclins(6-keto-PGE1, PGF2α, 15-keto-PGF2α, 6-keto-PGF1α, 13,14-D-15-keto-PGE2, PGD2, PGE2, and PGH2), LTE4, and 5-hydroxyeicosatetraenoic acid(5S-HETE) was significantly increased, and these indicators were associated with vascular expansion and increased vascular permeability. The pyrogenic inflammatory cytokines were not increased. The C48/80 model reflected the characteristics of cold and damp accumulation. In the study, three non-infectious pneumonia models were constructed. The LPS model exhibited neutrophil infiltration and elevated inflammatory factors, which was suitable for the efficacy study of TCM for clearing heat, detoxifying, removing dampness, and eliminating phlegm. The OVA model, which took allergic inflammation as an index, was suitable for the efficacy study of Yiqi Gubiao formulas. The C48/80 model exhibited increased vasoactive substances(histamine, PGs, and LTE4), which was suitable for the efficacy study and evaluation of TCM for warming the lung, dispersing cold, drying dampness, and resolving phlegm. The study provides a theoretical basis for model selection for the efficacy evaluation of TCM in the treatment of pneumonia.
Animals ; Disease Models, Animal ; Mice ; Pneumonia/genetics* ; Medicine, Chinese Traditional ; Male ; Humans ; Cytokines/immunology* ; Female ; Lipopolysaccharides/adverse effects* ; Lung/drug effects* ; Drugs, Chinese Herbal ; Ovalbumin ; Mice, Inbred BALB C

OBJECTIVES: To study the clinical characteristics of pediatric Behçet syndrome (BS). METHODS: A retrospective review was conducted on the medical records of children hospitalized in the Department of Pediatrics at the Second Hospital of Hebei Medical University between December 2014 and December 2024 who met diagnostic criteria for BS. RESULTS: Among 45 children with BS, 26 (58%) were male. Oral aphthous ulcers were the most common manifestation (43/45, 96%), followed by genital ulcers (23/45, 51%) and gastrointestinal involvement (18/45, 40%). Genital ulcers were more frequent in girls, whereas ocular involvement was more common in boys (P<0.05). The pathergy test was positive in 10 (22%), and HLA-B51 was positive in 13 (29%). Fecal calprotectin (FC) was elevated in 16 (36%); gastrointestinal involvement was more frequent in children with elevated FC than in those with normal FC (P<0.05). According to the respective criteria, 17 (38%) patients met the International Study Group criteria (1990), 33 (73%) met the International Criteria for Behçet Disease (2014), and 13 (29%) met the Pediatric Behçet Disease criteria (2015). CONCLUSIONS Pediatric BS shows marked clinical heterogeneity. HLA-B51 is associated with disease susceptibility.
Humans ; Behcet Syndrome/genetics* ; Male ; Female ; Child ; Retrospective Studies ; Adolescent ; Child, Preschool ; Leukocyte L1 Antigen Complex/analysis* ; HLA-B51 Antigen

OBJECTIVE: The aim of this study is to investigate the independent risk factors of benign prostatic hyperplasia (BPH) complicated with bladder stones, and construct a nomogram prediction model for clinical progression of bladder stones in patients with BPH. METHODS: The clinical data of 368 BPH patients who underwent transurethral resection of the prostate in the Second Affiliated Hospital of Zhengzhou University from January 2018 to January 2021 were retrospectively analyzed. Patients with BPH were divided into group 1 (with bladder stones, n=94) and group 2 (without bladder stones, n=274). Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of bladder stones in patients with BPH. A nomogram model was developed, and the areas under the ROC curve and calibration curve were calculated to assess the accuracy of clinical application. RESULTS: Logistic analysis showed that age (HR:1.075,95%CI:1.032 to 1.120), hypertension (HR:2.801,95%CI:1.520 to 5.161), blood uric acid (HR:1.006,95%CI:1.002 to 1.010), intravesical prostatic protrusion (HR:1.189,95%CI1.119 to 1.264）, prostatic urethral angel（HR:1.127,95%CI:1.078to 1.178）were independent risk factors for bladder stones in patients with BPH. The discrimination of the nomogram model based on independent risk factors to predict the occurrence of bladder stones in patients with BPH was 0.874. CONCLUSION The nomogram model can predict the risk of bladder stones in BPH patients with good differentiation and calibration, which is a good guide for clinical work on BPH patients with high risk of bladder stones.
Humans ; Male ; Prostatic Hyperplasia/complications* ; Nomograms ; Urinary Bladder Calculi/etiology* ; Retrospective Studies ; Risk Factors ; Aged ; Logistic Models ; Middle Aged ; ROC Curve ; Transurethral Resection of Prostate