BACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning. METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of the two,three core genes were identified,which were germinal center associated signaling and motility like,integrin beta-8,and carboxypeptidase A3.The areas under the ROC curve of the three core genes were 0.744,0.739,and 0.734,respectively,indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome.The results of immune infiltration analysis showed that memory B cells,CD56 bright NK cells,and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group(P<0.05),and were significantly positively correlated with the above three biomarkers(P<0.05).The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways,mainly related to olfactory transduction pathway,neuroactive ligand-receptor interaction,and infection pathway.The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes,abnormal immune regulation,and multiple pathways imbalance.However,the interactions between these genes and immune cells,as well as their relationships with various pathways need to be further investigated.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

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 association between habitual reading/writing postures and the co-occurrence of common health conditions (overweight/obesity, visual impairment, hypertension, and scoliosis) and comorbidities among children and adolescents, in order to provide data support for the joint prevention of common diseases and comorbidities among children and adolescents. Methods: From September 2021 to June 2022, a multi-stage cluster random sampling method was used to select a total of 4 577 children and adolescents from 16 primary and secondary schools in Ningxia: Jinfeng District of Yinchuan City, Shapotou District of Zhongwei City, Yanchi County of Wuzhong City, and Pingluo County of Shizuishan City. A weighted complex sampling design was used to investigate the association of habitual reading and writing postures with common comorbidities in children and adolescents. Results: The prevalence rates of common diseases among children and adolescents in Ningxia were as follows: overweight/obesity was 22.87%, visual impairment was 62.52%, scoliosis was 2.30%, and hypertension was 1.30%. The prevalence of multimorbidity (co-occurrence of ≥2 conditions) among Ningxia children and adolescents was 15.95%. Multivariate unconditional Logistic regression analysis showed that frequent/always collapsing waist and sitting forward with head lowered increased the risk of common comorbidities in children and adolescents ( OR =1.90, P <0.05). Compared with the corresponding reference group, male children and adolescents aged 9 to 12 years and boys had relatively lower risks of overweight/obesity ( OR =0.71, 0.70); the risk of poor vision among children and adolescents aged 9 to 12 years, male, and urban was relatively low ( OR =0.59, 0.60, 0.73)( P < 0.05 ). Children and adolescents who often/always sat leaning to the left or right were at higher risk of poor vision ( OR =1.78); urban children and adolescents had a higher risk of developing scoliosis ( OR =3.71); children and adolescents aged 9 to 12 had a relatively low risk of developing hypertension ( OR =0.09), and children and adolescents who often/always bent their backs and sat forward on their knees had a higher risk of hypertension ( OR =5.03)( P <0.05). Conclusions Ningxia has a high incidence of common diseases and multiple diseases among children and adolescents, frequent or always collapsing waist and sitting forward with head lowered is associated with common comorbidities in children and adolescents in Ningxia. Proper postural measures for reading and writing should be carried out as soon as possible to encourage children and adolescents to develop good reading and writing habits for effectively preventing and controlling the occurrence of common diseases.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

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: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

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.