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: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.

Objective To develop a novel solid-phase extraction(SPE)method based on a functionalized nanofiber membrane for the efficient co-extraction of structurally diverse antibiotics with markedly different physico-chemical properties from source water,and to establish a high-throughput analysis method by coupling this technique with ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).Methods A polydopamine and zirconium(Ⅳ)fumarate metal-organic frameworks(MOF-801)co-modified polyacrylonitrile nanofiber membrane(PDA@PAN/MOF-801 NFMs)was prepared as the SPE adsorbent through hybrid electrospinning and dopamine self-polymerization.Critical SPE and UPLC-MS/MS parameters were optimized,and the method was applied to analyze antibiotic contamination in source water samples from 14 sources of centralized drinking water supply in Suzhou,China,to evaluate the practical application potential of the method.Results The PDA@PAN/MOF-801 NFMs adsorbent demonstrated efficient adsorption of 32 antibiotics from 6 classes through multiple retention mechanisms,including synergistic electrostatic interactions,hydrogen bonding,and π-π interactions.In combination with UPLC-MS/MS,the SPE method we developed enabled high-throughput detection of multiple antibiotics in source water,with limits of detection(LOD)being 0.001-0.05 ng/L and limits of quantitation(LOQ)being 0.005-500 ng/L.Spiked recoveries were 70.14%-111.50%.Intra-day relative standard deviation(RSD)was below 14.12%and the inter-day RSD was below 15.07%.The method demonstrated excellent sensitivity,accuracy,and precision.Conclusion In this study,we successfully developed an efficient analytical method based on a novel nanofiber membrane adsorbent.This approach provides a new technical reference for the high-throughput detection of multiple antibiotics in environmental waters and shows promising potential for practical applications.

Objective:To explore the factors influencing researchers' satisfaction with technology commercialization and propose improvement suggestions for enhancing satisfaction.Methods:A questionnaire survey of researchers at Beijing Tiantan Hospital was conducted using simple random sampling. The collected data were analyzed in sequence through descriptive statistics, factor analysis, and structural equation modeling to identify the factors influencing satisfaction with technology commercialization and to construct a structural equation model linking these factors to satisfaction.Results:The path coefficients of service capacity, institutional environment, supporting capacity, and researchers′ participation willingness to satisfaction were 0.35, 0.25, 0.22, and 0.40, respectively, all exerting positive effects, with researchers' participation willingness and service capacity having greater influence; supporting capacity's effect on researchers' participation willingness was not statistically significant. Mediating effect tests indicated that service capacity and institutional environment indirectly enhanced satisfaction through researchers′ participation willingness with significant effects, whereas supporting capacity had a low significance of indirect effect.Conclusions:To improve the satisfaction with technology commercialization in hospitals, targeted work strategies are proposed to enhance the willingness of researchers to participate. Efforts are made to improve the professionalism of hospital technology commercialization management staff. Systemic work systems are established and an innovative transformation ecosystem is created.

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: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.

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.

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.