Hepatic fibrosis（HF） is a common pathological link of a variety of chronic hepatic diseases， and its complex pathological mechanism and prolonged clinical course pose a major challenge to modern medicine. Modern conventional therapies for HF cannot reverse the pathological vascular remodeling of the liver， and targeted vascular treatment for HF is a current research hotspot. There is a contradiction between the inhibition of pathological repair and the promotion of physiological regeneration with a single targeted therapy. The dynamic equilibrium concept of "achieving equilibrium of Yin and Yang" of traditional Chinese medicine can provide a new treatment strategy， and multi-target traditional Chinese medicine compounds can achieve two-way regulation of pathological mechanisms. According to the research on the modernization of traditional Chinese medicine， the "collaterals and vascular system" are highly compatible in structure and function， and they can guide the treatment of HF at different stages by identifying their common pathological links in HF. The intrahepatic collaterals are an important component of the hepatic collaterals， and the theory of "hepatic collateral disease" based on this physiology has important guiding significance for the clinical diagnosis and treatment of HF. Hepatic sinusoidal obstruction caused by endothelial dysfunction in the early stage of HF is a pathological manifestation of stagnant nutrient Yin in collateral passages. It can be treated by diffusing Qi to resolve stagnation and promoting circulation to unblock collaterals. Repeated stimulation of angiogenesis by hypoxia and inflammation in the medium stage is the pathological manifestation of lingering stagnation of damp and heat in collateral passages. It can be treated by clearing and draining damp and heat， eliminating turbidity， and unblocking collaterals. Pathological vascular remodeling induced by hemodynamic abnormalities in the later stage is a pathological manifestation of the consumption of collateral passages by pathogenic toxins. At this stage with excessive pathogenic factors and deficient healthy Qi， combined therapy of dredging and nourishing is adopted to eliminate toxins， resolve blood stasis， nourish Yin， and supplement Qi simultaneously. Moreover， the holistic concept of harmony between human and nature in traditional Chinese medicine emphasizes the time， place， and treatment based on individual conditions， so the practical application of the theory should consider the specific regional characteristics. This paper aims to discuss the characteristics of pathogenesis， treatment principles， prescriptions， and medicines in different stages of HF based on the correlation between "collaterals and vascular system" as well as the theory of "hepatic collateral disease". It was proposed that Qi deficiency and collateral obstruction were the core pathogenesis of HF， and that hepatic collateral damage was the core pathological basis for the deterioration and prognosis of HF. The scientific connotation and pathogenesis evolution of collateral damage and mass generation in HF were discussed. Sichuan was taken as an example to investigate the treatment of HF according to local conditions， providing new ideas for the treatment of HF.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective:To investigate the longitudinal mutual predictive relationship between college students' psychological capital and meaning in life across time.Methods:A total of 604 college students were selected for a one-year two-stage longitudinal tracking(T1 and T2),using the Psychological Capital Questionnaire for Adolescent Students(PC-QAS)and Chinese Meaning in Life Questionnaire(C-MLQ)for measurement.Results:The T1 PC-QAS scores were positively correlated with the T1 C-MLQ scores and T2 PC-QAS scores(r=0.61,0.51,Ps＜0.001).The T2 C-MLQ scores were positively correlated with the T2 PC-QAS scores and T1 C-MLQ scores(r=0.58,0.57,Ps＜0.001).The results of cross lagged regression analysis indicated that the T1 PC-QAS scores posi-tively predicted the T2 C-MLQ scores(β=0.15,P＜0.001),and T1 C-MLQ scores positively predicted T2 PC-QAS scores(β=0.19,P＜0.01).Conclusion:The psychological capital and meaning in life of college students have a certain degree of stability,and the two could predict each other.

The hepatic vascular microenvironment(HVM)plays a pivotal role in maintaining liver function homeostasis,including metabolism,detoxification,and coagulation.The maintenance of HVM homeostasis is governed by an intricate interplay of mechanical forces,chemical signals,and neuroelectrophysiological conduction.Recent studies have shown that an imbalance in HVM promotes the progression of chronic liver disease(CLD),which is characterized by sinusoidal capillarization,vascular deformation and remodeling,and the arterialization of blood supply.This review sum-marizes the dynamic regulatory mechanisms that underpin HVM in physiological conditions,and the primary pathological manifestations observed at various stages of CLD progression,aiming to provide a robust framework for the development of therapeutic strategies targeting HVM homeostatic imbanlance in CLD.

BACKGROUND:Understanding the characteristics and influencing factors of the gut microbiota in athletes can help determine the optimal gut microbial composition for relevant sport events.Further investigation in this area could provide important insights for improving athletic performance and recovery as well as developing personalized nutrition prescriptions.OBJECTIVE:To summarize the characteristics of gut microbiota in athletes,and to elucidate the important factors influencing the gut microbiota characteristics of athletes from the perspectives of exercise training and dietary intake.METHODS:A literature search was conducted using the PubMed,ScienceDirect,CNKI,WanFang and VIP databases for publications from 2004 to 2024.The search terms included"microbiota,microbiome,athlete,exercise,training,diet,nutrition,dietary fiber,protein,ketogenic,fat"in English and Chinese.After excluding studies of poor quality and irrelevant content,a total of 65 articles were included for review and analysis.RESULTS AND CONCLUSION:(1)The gut microbiota of elite athletes differs from that of the general population,characterized by increased α-diversity,elevated Firmicutes/Bacteroidetes ratio,increased abundance of beneficial bacteria,and enrichment of functional pathways contributing to athletic performance.(2)The type of sport and training load are closely related to the species structure and functional expression of the gut microbiota in athletes.(3)The bidirectional communication between the host and gut microbiota mediated by metabolites is an important mechanism by which exercise influences the gut microbiota.(4)Phase training typically induces adaptive changes in the gut microbiota,and alterations in the structure or function of the microbiota have lasting effects.(5)The type,quantity,and combination of macronutrients intake can significantly influence the structure and function of the gut microbiota,and interact synergistically or antagonistically with exercise training.(6)In the future,it is important to continue the exploration of the gut microbiota in athletes,clarify causal relationships,and establish new targets for exercise training interventions.

To investigate the correlation between vitamin D nutritional status and insulin resistance in pubertal adolescents.

To investigate body composition and associated factors in adolescents undergoing long-term regular sports training.

Objective To evaluate the clinical efficacy of a novel sleep-aid decoction in treating elderly insomnia patients with different traditional Chinese medicine (TCM) constitutional types, and its effects on neurotransmitter and inflammatory factor levels. Methods A total of 200 patients with four different TCM constitutions-peaceful, Qi-deficient, Yin-deficient, and Yang-deficient-were recruited. Peripheral blood neurotransmitter and inflammatory factor levels were measured for variations among insomnia patients across different constitutions. These patients were treated using the novel sleep-aid decoction, the effects of which were evaluated based on changes in neurotransmitters and inflammatory factors. Results Compared to the peaceful constitution group, insomnia patients with Qi-deficient, Yin-deficient, and Yang-deficient constitutions exhibited significantly elevated baseline levels of neurotransmitters (5-HT, GABA) and inflammatory factors (IL-6, TNF-α, IL-1β, CRP). Following the treatment, the Qi-deficient and Yin-deficient groups showed a marked increase in 5-HT levels, restored balance of Glu, GABA, and melatonin, and significant reductions in IL-6 and TNF-α levels. The overall effective rate was 83.5%, with optimal efficacy observed in the Qi-deficient (97.72%) and Yin-deficient (95.34%) groups. Conclusion The novel sleep-aid decoction is effective in treating insomnia in elderly patients, with the best results observed in the Qi-deficient and Yin-deficient constitution groups.
Humans ; Sleep Initiation and Maintenance Disorders/blood* ; Aged ; Male ; Female ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Middle Aged ; Tumor Necrosis Factor-alpha/blood* ; Sleep Aids, Pharmaceutical/therapeutic use* ; Anti-Inflammatory Agents/therapeutic use* ; Interleukin-6/blood* ; Interleukin-1beta/blood* ; Neurotransmitter Agents/blood* ; Aged, 80 and over ; C-Reactive Protein/metabolism*

ObjectiveTo explore the impacts of material type and loading volume of rigid containers on the hydrogen peroxide low temperature plasma sterilization of thoracoscopic instruments, to identify the best rigid containers and loading volume of thoracoscopic instruments. MethodsThoracoscopic instruments sterilized by STERRAD^® 100NX hydrogen peroxide low temperature plasma in Shanghai Pulmonary Hospital affiliated to Tongji University from August to September 2024 were selected as the research items. According to the material of rigid containers, the instruments were divided into polyethylene case group (A), stainless steel case group (B) and silicone resin case group (C). In terms of the loading volume, the rigid containers were divided into (loading capacity <80%) groups of 8, 10 and 12 instruments. The results of physical monitoring, the first type of chemical indicator card monitoring, and the five types of card luminal chemical process challenge device (PCD) monitoring of the 9 groups of A8, A10, A12, B8, B10, B12, C8, C10 and C12 were compared and evaluated. ResultsCompared to A8, A10 A12, C8, C10 or C12 groups, the thoracoscope instruments in the stainless steel containers in B8, B10 or B12 group had higher hydrogen peroxide concentrations and shorter elapsed time in the pressure check phases 1 and phases 2, with the differences statistically significant (P<0.05), followed by the silicone resin case group and the polyethylene case group. The nine groups of physical parameter monitoring, the first type of chemical indicator monitoring, and the five types of chemical PCD monitoring for lumen sterilization achieved 100% qualification rates, and there were no significant differences in the qualified rates of sterilization among the 9 groups (P>0.05). ConclusionWhen using hydrogen peroxide low temperature plasma to sterilize thoracoscopic instruments, it is recommended to use stainless steel or silicone resin rigid containers with a controlled loading capacity (≤12) to ensure optimal sterilization quality.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.