Kui was first recorded in The Rites of Zhou and is the earliest domesticated wild vegetable in China. In the Qi Min Yao Shu, Kui was called “the master of all vegetables” and has a long history of application in China. As a medicine, Kuizi was first recorded in Shen Nong’s Herbal Classic, which has a history of more than 2 000 years of medicinal use and a long history of clinical application. By researching the ancient and modern herbal literature, the first herbs texts of Kui were examined, various recorded texts, confused products and the history of the original medicinal use were clarified. It was concluded that the ancient herbal texts recorded the base plant of Kui as Malva verticillata L. belonging to family Malvaceae, which provided scientific basis for the development and utilization of Kui.

ObjectiveAs a common lifestyle habit, alcohol consumption has a controversial association with the onset of Parkinson’s disease (PD). To demonstrate the correlation between alcohol consumption and PD and to identify associated genes, we integrated findings from clinical surveys, genomics, transcriptomics, and animal experiments. MethodsWe investigated the alcohol consumption rates (including both before and after disease onset) among 244 PD patients in China and 177 PD patients from the U.S. NHANES database. Mendelian randomization (MR) analysis was performed using genome-wide association study (GWAS) data for three alcohol-related traits and seven PD-related datasets from the MRC IEU OpenGWAS database. Transcriptomic data from the substantia nigra of PD patients were obtained from three GEO datasets (GSE7621, GSE20141, and GSE49036) to analyze RIT2 gene transcription. Finally, three groups of animal experiments (water/20% ethanol/20% liquor, with 4 C57BL/6J mice per group) were conducted to examine changes in brain RIT2 gene expression and transcriptomic profiles following alcohol consumption. ResultsThe alcohol consumption rates among PD patients in China and the U.S. (9%-18.87%) were significantly lower than the general population rates of 15%-45% in their respective regions (P<0.001), suggesting a possible negative association between alcohol consumption and PD. Subsequently, in 21 bidirectional MR analyses using 3 alcohol-related GWAS datasets and 7 PD-related GWAS datasets, the forward MR analyses (alcohol intake as exposure, PD as outcome) yielded 12 negative associations (OR_IVW<1) and 9 positive associations (OR_IVW>1). Among these, only two negative associations reached statistical significance: alcohol intake frequency (OR_IVW=0.75, 95% CI: 0.60-0.93, P=0.010) and alcohol consumption (OR_IVW=0.20, 95% CI: 0.05-0.83, P=0.026). The forward MR analysis (alcohol intake→PD) identified 235 SNPs, annotated to 316 genes, while the reverse MR analyses (PD→alcohol intake) identified 37 SNPs, annotated to 53 genes. Notably, only the RIT2 gene appeared in both the forward and reverse MR analyses (alcohol intake→PD: rs28597806, rs8083110; PD→alcohol intake: rs4588066). RIT2 is selectively expressed in the human brain (FPKM: 5.259±2.103), with low or no expression in peripheral tissues (FPKM: <1). Analysis of three human substantia nigra transcriptomic datasets revealed a decreasing trend in RIT2 gene expression in PD patients (GSE20141 array signal: 3.49±1.23 vs. 2.33±0.87, P=0.044). Animal experiments demonstrated that administration of 20% ethanol or 20% liquor (approximately 8% ethanol) stimulated a >2-fold upregulation of RIT2 gene expression in the mouse brain. Furthermore, transcriptomic sequencing revealed that the two alcohol-treated groups exhibited 96 (20% ethanol vs. water control) and 4 (20% liquor vs. water control) differentially expressed genes, respectively, indicating that low-dose alcohol consumption can achieve RIT2 upregulation while minimizing impact on other brain genes. In addition to its anti-infective effects, low-dose alcohol consumption primarily influences signaling pathways related to neurodegenerative diseases such as PD and Prion diseases. ConclusionAlcohol consumption is generally considered as a harmful lifestyle habit. However, some studies have also shown a lower risk of mortality among individuals who consume low doses of alcohol (100 g/week of ethanol) or drink occasionally. Currently, one of the research focuses on alcohol consumption is whether the human body can benefit from low-dose alcohol intake. This study provides new evidence supporting a negative association between alcohol consumption and PD, and for the first time, through MR analysis, identifies the RIT2 gene as a potential mediator of the effect of alcohol consumption on PD. RIT2 is selectively expressed in the human brain. Building upon existing evidence indicating downregulated RIT2 gene expression in PD pathogenesis, our experiments confirm that low-dose alcohol consumption can upregulate RIT2 expression in the brain. In brief, alcohol consumption may suppress the pathogenesis of PD by upregulating RIT2 expression in the substantia nigra. China is facing a serious problem of population aging. This study offers important insights for long-term PD prevention and treatment strategies, with the aim of benefiting more potential PD patients through lifestyle modifications, thereby improving the quality of life of the aging population and reducing the economic burden on healthcare.

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.

Nodal marginal zone B-cell lymphoma(NMZL),the least common subtype of marginal zone lymphoma,represents a low-grade malignancy arising from the marginal zone of lymph node follicles,composed of small B-cells with an inert non-Hodgkin lymphoma nature.It accounts for 1.5% to 1.8% of all non-Hodgkin lymphomas and 10% of all marginal zone lymphomas.The low incidence and lack of typical clinical and pathological features pose a challenge to the diagnosis and clinical management of NMZL.In this article,we reported the diagnosis and treatment of a case of NMZL located in the parapharyngeal space of the left neck and reviewed the relevant literature from both domestic and international sources.We summarized the clinical manifestations,histopathological features,immunohistochemical characteristics,imaging features,diagnosis and treatment modalities,and prognosis of NMZL.
Humans ; Lymphoma, B-Cell, Marginal Zone/pathology* ; Lymph Nodes/pathology* ; Neck/pathology* ; Male

Functional gastrointestinal disease(FGID)and inflammatory bowel disease(IBD)are two common gastrointestinal diseases in clinical practice.FGID refers to non-structural changes in gastrointestinal function,with irritable bowel syndrome(IBS)and functional dyspepsia(FD)being the common types.On the other hand,IBD is a group of chronic inflammatory bowel diseases with well-defined pathological features,mainly including Crohn's disease(CD)and ulcerative colitis(UC).Over the past decades,the incidence rates of both FG1D and IBD have increased with years,accompanied by high recurrence rates,and the clinical outcomes remain unsatisfactory,which seriously affect the physical and mental health of patients.Further research has revealed that neuropsychiatric abnormalities,including mental and psychological disorders,play important roles in the occurrence and development of FGID and IBD.The comorbidity of gastrointestinal and psychiatric diseases shares a common pathophysiological basis,that is,the abnormalities in the bidirectional communication between the gut and the central nervous system,and the theory of brain-gut axis has emerged as a research hotspot in this field.Better therapeutic outcomes can be achieved by screening and identifying key brain regions closely related to FGID and IBD through quantifiable assessments,and implementing combined pharmacotherapy on both the digestive system and the nervous system based on the theory of brain-gut axis.Current neuroimaging studies have provided the preliminary results for understanding the changes in the nervous system in FGID and IBD patients,but there is still a lack of systematic evaluation of the application of neuroimaging studies based on the brain-gut axis in gastrointestinal diseases.This paper reviewed the changes in brain structure and function associated with FGID and IBD,and the neuroimaging-based analysis of the brain-gut axis theory in the occurrence and development of these diseases,in order to provide theoretical basis for future personalized precision medicine of FGID and IBD based on the brain-gut axis.

Objective To investigate the clinical effect of free electromyography(fEMG)monitoring as-sisted unilateral biportal endoscopy(UBE)in the treatment of lumbar disc herniation.Methods Forty pa-tients with lumbar disc herniation were included.The UBE lumbar nucleus pulposus resection under general anesthesia was performed.The operation whole process was completed under the fEMG monitor.The recorded potential was displayed on the monitor screen and the myoelectrical activity was played through speaker.The treatment effect was evaluated by the Visual Analogue Scale(VAS)score and Oswestry Disability Index(ODI).Results All 40 patients completed the surgery successfully,and the postoperative symptoms were im-proved significantly.The VAS scores before operation and in postoperative 3 months were 7(6,8)points and 0(0,1)points respectively,and the difference between the two time points was statistically significant(P＜0.05).The ODI before operation and in postoperative 3 months were(58.30±11.58)and(13.28±5.20)re-spectively,and the difference between the two time points also was statistically significant(P＜0.05).The VAS score and ODI in postoperative 1 year were 1(0,1)points and(13.13±0.40)respectively,showing sta-tistically significant difference compared with before surgery(P＜0.05),but there was no statistically signifi-cant difference compared with postoperative 3 months(P＞0.05).There were 8 cases developing the abnor-mal EMG changes such as spike,burst or tonic fEMG during surgery,with an incidence rate of 20％.After surgery in 8 cases,7 cases had burning radiculalgia and paresthesia of lower extremity,and 1 case had no obvi-ous neuralgia,with a false positive rate of 12.5％.The patients without abnormal fEMG reaction during sur-gery had no significant postoperative neuralgia,and the false negative rate was zero.Conclusion fEMG moni-toring assisted UBE under general anesthesia is a safe and easy method for the treatment of lumbar disc herni-ation.