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: To elucidate how spinal manipulative therapy (SMT) exerts its analgesic effects through regulating brain function in lumbar disc herniation (LDH) patients by utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: From September 2021 to September 2023, we enrolled LDH patients (LDH group, n=31) and age- and sex-matched healthy controls (HCs, n=28). LDH group underwent rs-fMRI at 2 distinct time points (TPs): prior to the initiation of SMT (TP1) and subsequent to the completion of the SMT sessions (TP2). SMT was administered once every other day for 30 min per session, totally 14 treatment sessions over a span of 4 weeks. HCs did not receive SMT treatment and underwent only one fMRI scan. Additionally, participants in LDH group completed clinical questionnaires on pain using the Visual Analog Scale (VAS) and the Japanese Orthopedic Association (JOA) score, whereas HCs did not undergo clinical scale assessments. The effects on the brain were jointly characterized using the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). Correlation analyses were conducted between specific brain regions and clinical scales. RESULTS: Following SMT treatment, pain symptoms in LDH patients were notably alleviated and accompanied by evident activation of effects in the brain. In comparison to TP1, TP2 exhibited the most significant increase in ALFF values for Temporal_Sup_R and the most notable decrease in ALFF values for Paracentral_Lobule_L (voxelwise P<0.005; clusters >30; FDR correction). Additionally, the most substantial enhancement in ReHo values was observed for the Cuneus_R, while the most prominent reduction was noted for the Olfactory_R (voxelwise P<0.005; clusters >30; FDR correction). Moreover, a comparative analysis revealed that, in contrast to HCs, LDH patients at TP1 exhibited the most significant increase in ALFF values for Temporal_Pole_Sup_L and the most notable decrease in ALFF values for Frontal_Mid_L (voxelwise P<0.005; clusters >30; FDR correction). Furthermore, the most significant enhancement in ReHo values was observed for Postcentral_L, while the most prominent reduction was identified for ParaHippocampal_L (voxelwise P<0.005; clusters >30; FDR correction). Notably, correlation analysis with clinical scales revealed a robust positive correlation between the Cuneus_R score and the rate of change in the VAS score (r=0.9333, P<0.0001). CONCLUSIONS Long-term chronic lower back pain in patients with LDH manifests significant activation of the "AUN-DMN-S1-SAN" neural circuitry. The visual network, represented by the Cuneus_R, is highly likely to be a key brain network in which the analgesic efficacy of SMT becomes effective in treating LDH patients. (Trial registration No. NCT06277739).
Humans ; Magnetic Resonance Imaging ; Intervertebral Disc Displacement/diagnostic imaging* ; Male ; Female ; Brain/diagnostic imaging* ; Adult ; Manipulation, Spinal/methods* ; Middle Aged ; Lumbar Vertebrae/physiopathology* ; Pain Management ; Rest ; Case-Control Studies

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors

OBJECTIVE: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models

Intestinal flora imbalance is closely related to the pathogenesis of rheumatoid arthritis (RA). The existing studies have explored the monomer components such as tripterygium glycosides, total glycosides of Chaenomeles speciosa, and triterpenoid saponins of Clematis, Chinese materia medica such as Tripterygium wilfordii, Caulis Sinomenii, Radix Paeoniae Alba, Fructus Gardeniae, Fructus Chebulae, Radix Ginseng, Radix et Rhizoma Rhei, Rhizoma Atractylodis Macrocephalae, Pterostilbene, and Ginger, as well as the mechanisms of Danggui Sini Decoction, Danggui Niantong Decoction, Duhuo Jisheng Decoction, Yunpi Jiedu Tongluo Qushi Decoction, Qingre Huoxue Decoction, Compound Fengshining, Qingre Yangyin Chushi Decoction, Aconitum Decoction, Zhijing Powder, Jinwu Jiangu Capsule, and Fermented Chinese Medicine Qushi Chubi Decoction in intervening RA by regulating intestinal flora, suggesting that Chinese materia medica can restore intestinal homeostasis, reduce joint inflammation and play a role in the prevention and treatment of RA by regulating immune response, improving intestinal mucosal barrier and regulating intestinal metabolites.

Objective To explore the clinical and genetic characteristics of children with holocarboxylase synthetase(HLCS)deficiency.Methods A retrospective analysis was conducted on the clinical data of 3 children with HLCS deficiency who were admitted to Children's Hospital Affiliated to Zhengzhou University from December 2014 to January 2024.Relevant literature indexed in CNKI,Wanfang Data,PubMed and other databases was reviewed to summarize the clinical characteristics and HLCS gene mutations of children with HLCS deficiency.Results All 3 children were male,with onset age of 4-6 months.The main clinical manifestations included shortness of breath,vomiting,diarrhea,and poor mental state,and partial cases were complicated by growth retardation and neurological symptoms.Laboratory tests showed metabolic acidosis in all cases,blood amino acid and acylcarnitine profiles as well as urinary organic acid analysis suggested multiple carboxylase deficiency.Genetic testing revealed compound heterozygous mutation in the HLCS gene of all 3 children,among which the c.1892delT(p.L631X)mutation was previously unreported.According to the guidelines of the American College of Medical Genetics and Genomics(ACMG),the c.1892delT(p.L631X)mutation was rated as pathogenic mutation(PVS1+PM2_supporting+PM3).Biotin supplementation was effective in all cases.Literature review included 27 English literatures and 29 Chinese literatures,reporting a total of 133 children with HLCS deficiency caused by HLCS gene mutation.Common clinical manifestations included metabolic acidosis,skin lesions,vomiting,feeding difficulties,dyspnea,diarrhea,and neurological symptoms,etc.Conclusions Blood amino acid and acylcarnitine profiles,urine organic acid analysis,and gene testing are helpful for the diagnosis of HLCS deficiency.Timely biotin supplementation leads to a good prognosis.The mutation of HLCS gene is considered as the genetic etiology of HLCS deficiency in 3 children,among which the c.1892delT(p.L631X)mutation is a newly discovered mutation.