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 To investigate the effect and mechanism of Jingangteng capsules in the treatment of non-alcoholic fatty liver disease （NAFLD）. METHODS Thirty-two SD rats were randomly divided into normal group and modeling group. The modeling group was fed a high-fat diet to establish a NAFLD model. The successfully modeled rats were then randomly divided into model group， atorvastatin group[positive control， 2 mg/（kg·d）]， and Jingangteng capsules low- and high-dose groups [0.63 and 2.52 mg/（kg·d）]， with 6 rats in each group. The pathological changes of the liver were observed by hematoxylin-eosin staining and oil red O staining. Enzyme-linked immunosorbent assay was performed to determine the serum levels of triglycerides （TG）， total cholesterol （TC）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， alanine transaminase （ALT）， aspartate transaminase （AST）， tumour necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， IL-6， IL-18. 16S rDNA amplicon sequencing and metabolomics techniques were applied to explore the effects of Jingangteng capsules on gut microbiota and metabolisms in NAFLD rats. Based on the E-mail：591146765@qq.com metabolomics results， Western blot analysis was performed to detect proteins related to the nuclear factor kappa-B （NF-κB）/NOD-like receptor family protein 3 （NLRP3） signaling pathway in the livers of NAFLD rats. RESULTS The experimental results showed that Jingangteng capsules could significantly reduce the serum levels of TG， TC， LDL-C， AST， ALT， TNF-α， IL-1β， IL-6， IL-18， while increased the level of HDL-C， and alleviated the hepatic cellular steatosis and inflammatory infiltration in NAFLD rats. They could regulate the gut microbiota disorders in NAFLD rats， significantly increased the relative abundance of Romboutsia and Oscillospira， and significantly decreased the relative abundance of Blautia （P＜0.05）. They also regulated metabolic disorders primarily by affecting secondary bile acid biosynthesis， fatty acid degradation， O-antigen nucleotide sugar biosynthesis， etc. Results of Western blot assay showed that they significantly reduced the phosphorylation levels of NF-κB p65 and NF-κB inhibitor α， and the protein expression levels of NLRP3， caspase-1 and ASC （P＜0.05 or P＜0.01）. CONCLUSIONS Jingangteng capsules could improve inflammation， lipid accumulation and liver injury in NAFLD rats， regulate the disorders of gut microbiota and metabolisms， and inhibit NF-κB/NLRP3 signaling pathway. Their therapeutic effects against NAFLD are mediated through the inhibition of the NF-κB/NLRP3 signaling pathway.

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 To analyze the characteristics of pediatric medicines with priority review and approval for marketing in China， providing a reference for promoting enterprise R&D and production， as well as improving the supply guarantee mechanism for pediatric medicines. METHODS Based on publicly available data sources such as List of Approved Information for Pediatric Medications Subject to Priority Review and Approval， Pharnexcloud biomedical database， and National Medical Insurance Drug Directory， this study conducted a comprehensive analysis of the main characteristics of pediatric medicines with priority review and approval for marketing. RESULTS As of June 30， 2024， a total of 68 pediatric medicines had been approved through the priority review and approval process， covering 12 therapeutic areas， with oral dosage forms accounting for 64.71%. The median time from application to inclusion in priority review was 35.50 days， with an average of 41.69 days. The median time from inclusion in priority review to market approval was 1.24 years， with an average of 1.42 years. This included 12 domestic new medicines， 21 domestic generic medicines， 35 imported medicines， as well as 29 pediatric-specific medicines and 21 orphan medicines. Additionally， 31 of these medicines had been included in the medical insurance catalog， representing a proportion of 45.59%. CONCLUSIONS Currently， a trend of differentiated competition is emerging between domestic and imported pediatric medicines. The therapeutic areas for pediatric medicines are continuously expanding， and the dosage forms are becoming more tailored to children’s needs. However， there are still issues such as slow progress in new medicine development， insufficient stability in the medicine review and approval process， and a need to increase the proportion of medicines included in medical insurance.

This article reports the clinical features and gene mutation types of a large family with Nascimento form of syndromic X-linked intellectual developmental disorder (MRXSN), involving 9 individuals across 3 generations, and a literature review was conducted. In this family, 9 individuals had similar manifestations including mental retardation and unusual facies, and 4 of them had passed away. Genetic testing showed that the proband had the deletion of exons 2-3 of the UBE2A gene, which was inherited from the mother. Fluorescent quantitative polymerase chain reaction showed that the proband and his uncle had the deletion of exons 2-3 of the UBE2A gene; the proband's mother, grandmother, and great-aunt had a heterozygous deletion of exons 2-3 of the UBE2A gene; the proband's father, sister, and aunt had a normal copy number of exons 2-3 of the UBE2A gene. The 34 patients reported in the literature had diverse clinical phenotypes, and UBE2A gene mutations (22/34, 65%) and large fragment deletions (12/34, 35%) were the main mutation types. Moderate to severe mental retardation (34/34, 100%), speech and language impairment (33/34, 97%), and unusual facies (32/34, 94%) were the main clinical manifestations of MRXSN patients. The disease has obvious phenotypic heterogeneity, and early diagnosis facilitates optimal prenatal and postnatal management to improve reproductive outcomes.
Humans ; Male ; Ubiquitin-Conjugating Enzymes/genetics* ; Female ; X-Linked Intellectual Disability/genetics* ; Gene Deletion ; Child ; Pedigree ; Child, Preschool ; Adult

OBJECTIVES: To investigate the efficacy and safety of empagliflozin in patients with glycogen storage disease (GSD)-associated inflammatory bowel disease (IBD). METHODS: A cross-sectional study was conducted, enrolling 25 patients with GSD-associated IBD who received empagliflozin treatment. General data, details of empagliflozin use, and adverse events were collected. Clinical symptoms and biochemical parameters before and after empagliflozin therapy were compared. RESULTS: Twenty-five patients with GSD-associated IBD were included, with a median age at diagnosis of 0.7 years, and a mean age at initiation of empagliflozin therapy of (11 ± 6) years. The initial dose of empagliflozin was (0.30 ± 0.13) mg/(kg·d), with a maintenance dose of (0.40 ± 0.21) mg/(kg·d), and a treatment duration of (34 ± 6) months. Seventy-eight percent (18/23) of patients' parents reported that empagliflozin therapy reduced the frequency of infections and oral ulcers, and increased neutrophil counts. Clinically, the number of patients with anorexia decreased from 12 to 5 after treatment, and 30% showed improved appetite (P<0.05). The numbers of patients with diarrhea, mucus/bloody stools, perianal disease, and oral ulcers decreased from 19, 9, 11, and 21 before treatment to 7, 1, 0, and 10 after treatment, respectively (P<0.05). Laboratory findings showed that absolute neutrophil counts increased, while platelet counts, lactate, and uric acid levels decreased significantly after empagliflozin treatment (P<0.05). Adverse reactions occurred in 7 patients (28%) during empagliflozin treatment. Two cases occurred in the treatment initiation phase, presenting as hypotension or profuse sweating with dehydration, along with urinary tract infections (UTIs); empagliflozin was discontinued in both cases. During the maintenance phase, 3 cases of UTIs and 2 cases of hypoglycemia (one with profuse sweating) were reported. CONCLUSIONS Empagliflozin therapy can increase neutrophil counts, reduce the incidence of infections and oral ulcers, alleviate diarrhea and abdominal pain, improve appetite, and ameliorate platelet count, lactate, and uric acid levels in patients with GSD-associated IBD, demonstrating significant clinical benefit. UTIs, hypoglycemia, hypotension, profuse sweating, and dehydration may be potential adverse reactions associated with empagliflozin therapy.
Humans ; Benzhydryl Compounds/adverse effects* ; Male ; Female ; Glucosides/adverse effects* ; Inflammatory Bowel Diseases/etiology* ; Child ; Child, Preschool ; Cross-Sectional Studies ; Adolescent ; Glycogen Storage Disease/drug therapy* ; Infant

OBJECTIVES: To evaluate the causal association between circulating levels of zinc, magnesium, and other minerals and autism spectrum disorder (ASD). METHODS: A two-sample Mendelian randomization (MR) analysis was performed using summary statistics from large-scale genome-wide association studies of European populations, including 18 382 ASD cases and 27 969 controls. Genetic data for iron, calcium, and magnesium were obtained from the UK Biobank, and data for zinc and selenium were sourced from an Australian-British cohort. A total of 351 genetic instrumental variables were selected. Causal inference was performed using inverse-variance weighting as the primary analysis method. Sensitivity analyses were performed by Cochran's Q test and MR-PRESSO global test to assess the robustness of the findings. RESULTS: No statistically significant causal effect was observed for circulating zinc, magnesium, calcium, selenium, or iron levels on ASD risk (all P>0.05). The odds ratios and 95% confidence intervals from the inverse-variance weighting analysis were 0.934 (0.869-1.003) for zinc, 1.315 (0.971-1.850) for magnesium, 1.055 (0.960-1.159) for calcium, 1.015 (0.953-1.080) for selenium, and 0.946 (0.687-1.303) for iron. Sensitivity analysis revealed significant heterogeneity in the causal association between circulating calcium and ASD (P=0.006), while the effect estimate remained stable after MR-PRESSO correction (P=0.487). The causal effect estimates for the remaining minerals demonstrated good robustness. CONCLUSIONS This study did not find significant evidence supporting a causal association between circulating zinc, magnesium, calcium, selenium, or iron levels and ASD risk, providing important clues for the etiology of ASD and precision nutritional interventions.
Humans ; Mendelian Randomization Analysis ; Autism Spectrum Disorder/genetics* ; Magnesium/blood* ; Zinc/blood* ; Minerals/blood* ; Genome-Wide Association Study ; Selenium/blood*

Implementation Science is an interdisciplinary field dedicated to systematically studying how to effectively translate evidence-based research findings into practical application and implementation. In the health-related context, it focuses on enhancing the efficiency and quality of healthcare services, thereby facilitating the transition from scientific evidence to real-world practice. This article elaborates on Theories, Models, and Frameworks (TMF) within health-related Implementation Science, clarifying their basic concepts and classifications, and discussing their roles in guiding implementation processes. Furthermore, it reviews and prospects current research from three aspects: the constituent elements of TMF, their practical applications, and future directions. Five representative frameworks are emphasized, including the Consolidated Framework for Implementation Research (CFIR), the Practical Robust Implementation and Sustainability Model (PRISM), the Exploration, Preparation, Implementation, Sustainment (EPIS)framework, the Behavior Change Wheel (BCW), and the Normalization Process Theory (NPT). Additionally, resources such as the Dissemination & Implementation Models Webtool and the T-CaST tool are introduced to assist researchers in selecting appropriate TMFs based on project-specific needs.

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.