Sleep is an instinctive behavior alternating awakening state, sleep entails many active processes occurring at the cellular, circuit and organismal levels. The function of sleep is to restore cellular energy, enhance immunity, promote growth and development, consolidate learning and memory to ensure normal life activities. However, with the increasing of social pressure involved in work and life, the incidence of sleep disorders (SD) is increasing year by year. In the short term, sleep disorders lead to impaired memory and attention; in the longer term, it produces neurological dysfunction or even death. There are many ways to directly or indirectly contribute to sleep disorder and keep the hormones, including pharmacological alternative treatments, light therapy and stimulus control therapy. Exercise is also an effective and healthy therapeutic strategy for improving sleep. The intensities, time periods, and different types of exercise have different health benefits for sleep, which can be found through indicators such as sleep quality, sleep efficiency and total sleep time. So it is more and more important to analyze the mechanism and find effective regulation targets during sleep disorder through exercise. Dopamine (DA) is an important neurotransmitter in the nervous system, which not only participates in action initiation, movement regulation and emotion regulation, but also plays a key role in the steady-state remodeling of sleep-awakening state transition. Appreciable evidence shows that sleep disorder on humans and rodents evokes anomalies in the dopaminergic signaling, which are also implicated in the development of psychiatric illnesses such as schizophrenia or substance abuse. Experiments have shown that DA in different neural pathways plays different regulatory roles in sleep behavior, we found that increasing evidence from rodent studies revealed a role for ventral tegmental area DA neurons in regulating sleep-wake patterns. DA signal transduction and neurotransmitter release patterns have complex interactions with behavioral regulation. In addition, experiments have shown that exercise causes changes in DA homeostasis in the brain, which may regulate sleep through different mechanisms, including cAMP response element binding protein signal transduction, changes in the circadian rhythm of biological clock genes, and interactions with endogenous substances such as adenosine, which affect neuronal structure and play a neuroprotective role. This review aims to introduce the regulatory effects of exercise on sleep disorder, especially the regulatory mechanism of DA in this process. The analysis of intracerebral DA signals also requires support from neurophysiological and chemical techniques. Our laboratory has established and developed an in vivo brain neurochemical analysis platform, which provides support for future research on the regulation of sleep-wake cycles by movement. We hope it can provide theoretical reference for the formulation of exercise prescription for clinical sleep disorder and give some advice to the combined intervention of drugs and exercise.

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.

N6-methyladenosine(m6A)is recognized as the most prevalent mRNA modification in mammals, intricately involved in a multitude of processes pertaining to mRNA metabolism, encompassing RNA transcription, translation, and degradation. It plays a pivotal role in various physiological functions. Under the coordinated actions of methyltransferases, demethylases, and m6A-binding proteins, m6A modifications undergo reversible changes to fulfill their diverse molecular functions.Methyltransferase-like 3(METTL3), as the core catalytic subunit of methyltransferases and the most extensively studied methyltransferase, holds a central position in m6A modification. In recent years, it has been found that METTL3-mediated m6A modification is involved in the occurrence and development of various ocular diseases, such as ocular surface diseases, glaucoma, cataract, retinal diseases, and ocular tumors, by affecting the expression of inflammatory factors and thus regulating the inflammatory response, and by regulating various pathways that affect the proliferation of cells and oxidative stress. In this paper, we comprehensively review the mechanisms under the role of METTL3 in ocular diseases, offering novel insights and perspectives for the prevention and management of these conditions.

Objective: To explore the distribution of traditional Chinese medicine (TCM) syndromes in children with hepatolenticular degeneration (Wilson disease, WD) based on factor analysis and cluster analysis. Methods: From November 2018 to November 2023, general information (gender, age of admission, age of onset, course of disease, clinical staging, Western medicine clinical symptoms, and family history) and TCM four-examination informations (symptoms and signs) were retrospectively collected from 757 cases of children with WD at the First Affiliated Hospital of Anhui University of Chinese Medicine, and factor analysis and cluster analysis were used to investigate TCM syndromes in children with WD. Results: A total of 757 children with WD were included, of which 483 were male and 274 were female; the median age at admission was 12.58 years, the median age at onset was 8.33 years, and the median course of disease was 24.37 months; clinical typing result indicated 506 cases of hepatic type, 133 cases of brain type, 99 cases of mixed-type, and 19 cases of other type; 36.46% of the children had no clinical symptoms (elevated aminotransferases or abnormalities in copper biochemistry); a total of 177 cases had a definite family history, and 10 cases had a suspected family history. Forty-three TCM four-examination information were obtained, with the top 10 in descending order being feeling listless and weak, brown urine, slow action, inappetence, dim complexion, slurred speech, angular salivation, body weight loss, hand and foot tremors, and abdominal fullness. In children with WD, the syndrome element of disease location was primarily characterized by the liver, involving the spleen and kidney, and the syndrome elements of disease nature were characterized by dampness, heat, and yin deficiency. Based on factor analysis and cluster analysis, five TCM syndromes were derived, which were, in order, syndrome of dampness-heat accumulation (265 cases, 35.01%), syndrome of yin deficiency of the liver and kidney (202 cases, 26.68%), syndrome of liver hyperactivity with spleen deficiency (185 cases, 24.44%), syndrome of qi and blood deficiency (79 cases, 10.44%), and syndrome of yang deficiency of the spleen and kidney (26 cases, 3.43%). Conclusion The TCM syndromes of children with WD were primarily syndromes of dampness-heat accumulation, yin deficiency of the liver and kidney, and liver hyperactivity with spleen deficiency. The liver was the main disease location, and the disease nature was characterized by deficiency in origin and excess in superficiality, excess and deficiency mixed. These findings suggest that treating children with WD should be based on the liver while also considering the spleen and kidney.

Objective: To investigate the sleep quality in patients with burning mouth syndrome (BMS) and its influencing factors, providing a basis for developing sleep intervention measures to reduce the impact of BMS symptoms. Methods: This study was reviewed and approved by the Medical Ethics Committee, and informed consent was obtained from patients. A total of 150 patients with BMS and 150 healthy volunteers were enrolled as subjects in this study. The Pittsburgh sleep quality index (PSQI) was used to assess the sleep quality of patients with BMS. Visual analog scale (VAS) was used to assess the degree of oral mucosal pain, generalized anxiety disorder 7-item scale (GAD-7) was used to assess the frequency of anxiety symptoms, and the patient health questionnaire depression questionnaire (PHQ-9) was used to assess the frequency of depression symptoms. Univariate analysis was performed to identify potential influencing factors affecting sleep quality in patients with BMS, and multiple linear regression analysis was employed to determine independent risk factors. Results: The PSQI score for patients with BMS was 7.61 ± 4.29, which was significantly higher than that of healthy controls (P = 0.016). In the PSQI subscale analysis, patients with BMS exhibited increased sleep latency, decreased sleep duration, and lower sleep efficiency compared to healthy controls (P<0.05). Patients with BMS and comorbid sleep difficulties had significantly higher scores on GAD-7 and PHQ-9 compared to the patients with BMS without sleep difficulties (P<0.001), but there was no significant difference in pain VAS scores between the two (P = 0.068). Multiple linear regression analysis revealed that longer disease duration (>6 months), the presence of systemic concomitant symptoms (such as headache and mental stress), and higher depression scores were identified as independent risk factors affecting sleep quality in patients with BMS. Conclusion For patients with BMS, long course of illness, presence of headaches, high mental stress, and depressive symptoms may be independent factors affecting their sleep quality.

[摘 要] 目的：探究钾钙激活通道亚家族N成员4（KCNN4）在胰腺癌组织中的表达及其对胰腺癌进展的影响，解析KCNN4在胰腺癌临床诊断及预后判断中的作用。方法：利用GEPIA2数据分析平台，结合TCGA和GTEx数据库的数据分析KCNN4在胰腺癌组织中的表达水平及其与患者预后的关系。收集24例海军军医大学长海医院手术切除的胰腺癌患者的癌及癌旁组织标本，通过qPCR、WB法和免疫组化染色技术验证KCNN4在胰腺癌组织中的表达水平。利用shRNA敲低人胰腺癌细胞中BXPC3和PANC-1中KCNN4的表达，通过CCK-8和克隆形成实验检测细胞增殖与生长情况。利用小鼠胰腺癌KPC细胞构建胰腺癌原位成瘤模型，观察敲低KCNN4对胰腺原位成瘤的影响，统计小鼠生存期（OS）。结果：整合TCGA和GTEx数据库数据分析结果发现，KCNN4在胰腺癌组织中高表达（P < 0.05），且与患者OS和DFS缩短相关（均P < 0.05）。胰腺癌组织中KCNN4 mRNA和蛋白表达量均显著高于癌旁组织（均P < 0.01）。KCNN4敲低后，胰腺癌细胞生长速率显著减慢、克隆形成数量显著减少（均P < 0.01）。小鼠胰腺原位荷瘤实验结果表明，KCNN4敲低可抑制肿瘤细胞在胰腺原位的生长并延长小鼠OS。结论：KCNN4在胰腺癌组织中高表达，其能促进胰腺癌细胞增殖和胰腺癌进展，与患者预后密切相关，有望作为胰腺癌临床诊断及预后评估的靶点。

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.