Diabetic kidney disease（DKD） is a chronic kidney structural and functional disorder caused by diabetes. With the global prevalence of diabetes continuing to rise， DKD has gradually become a major cause of chronic kidney disease and end-stage renal disease（ESRD）， posing a serious threat to patients' quality of life and long-term health outcomes. Studies have shown that apoptosis plays a pivotal role in the development and progression of DKD， with its mechanisms involving abnormal activation of multiple signaling pathways such as Toll-like receptor 4（TLR4）/nuclear transcription factor-κB（NF-κB）/B-cell lymphoma-2（Bcl-2）/cysteinyl aspartate-specific proteinase（Caspase）-3， protein kinase R-like endoplasmic reticulum kinase（PERK）/eukaryotic initiation factor 2α（eIF2α）/activating transcript factor 4（ATF4）/CCAAT enhancer-binding protein homologous protein（CHOP）， phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt）/glycogen synthase kinase-3β（GSK-3β）， Janus kinase 2（JAK2）/signal transducer and activator of transcription 3（STAT3）， adenosine monophosphate-activated protein kinase（AMPK）/mammalian target of rapamycin（mTOR） and silent information regulator 1（SIRT1）/tumor suppressor protein 53（p53）， thereby accelerating renal pathological damage in DKD. Extensive evidence-based medical studies have confirmed that traditional Chinese medicine（TCM）， leveraging its unique therapeutic advantages of multi-target， multi-component and multi-pathway approaches， has demonstrated remarkable efficacy and favorable safety profiles in treating DKD. Recent studies have demonstrated that active components of TCM can specifically target and modulate key effectors in apoptotic signaling pathways. Meanwhile， traditional compound formulations exert synergistic effects through multiple approaches such as replenishing deficiency and activating blood circulation， detoxifying and dredging collaterals， tonifying kidney essence， and removing stasis and purging turbidity， thereby comprehensively regulating critical pathological processes including endoplasmic reticulum stress and mitochondrial apoptosis pathways. This combined therapeutic approach of molecular targeting and holistic regulation provides novel strategies for delaying the progression of DKD. Based on this， this paper provides an in-depth analysis of key apoptotic signaling pathways and their regulatory mechanisms， while systematically summarizing recent research advances regarding the therapeutic effects of TCM active components， compound formulations， and proprietary Chinese medicines on DKD through modulation of these pathways， with particular emphasis on their underlying molecular mechanisms. These findings not only elucidate the modern scientific connotation and theoretical basis of TCM in treating DKD but also establish a solid theoretical and practical foundation for promoting the wider clinical application and further research of TCM in the field of DKD treatment.

Objective To investigate the exposure-response relationships and lag effects between air pollutants (PM_2.5, PM₁₀, O₃, and NO₂) and mortality in Jiading District, Shanghai, and to provide a scientific basis for the formulation of environmental health policies. Methods Using an individual-level time-stratified case-crossover design, conditional logistic regression models in conjunction with a distributed lag nonlinear model (DLNM) were employed to analyze the exposure-response relationship and temporal lag patterns of ambient air pollution on resident mortality in Jiading District (1994–2024). Results A total of 59 048 death cases were collected, including 18,701 deaths from cardiovascular diseases and 11 731 deaths from respiratory diseases. PM_2.5 and NO₂ had a significant impact on all-cause mortality, cardiovascular disease mortality, and respiratory disease mortality, with the most significant effects observed within a lag of 0–3 days. PM₁₀ also had some impact on these three types of mortality, but its effect was generally weaker than that of PM_2.5 and NO₂. The exposure-response curves showed that the risk of death increased rapidly with increasing concentrations of PM_2.5 and PM₁₀, while the effect of NO₂ plateaued at higher levels. No significant differences were found across age or gender subgroups. Conclusion Short-term exposure to PM_2.5, PM₁₀, and NO₂ significantly increases all-cause mortality risk in Jiading District, with effects persisting up to 7 days, highlighting the need for enhanced air pollution control measures, particularly targeting fine particulate matter.

OBJECTIVE To analyze the incidence of cyclosporine （CsA）-associated acute kidney injury （AKI） in hospitalized patients， identify influencing factors， and construct a risk prediction model. METHODS A single-center retrospective study was conducted， enrolling clinical data from hospitalized patients treated with CsA at the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital from January 2018 to July 2024. The patients were classified into AKI group and non-AKI group based on the occurrence of CsA-related AKI. Univariate analysis and multivariate Logistic regression analysis were used to identify independent risk factors for CsA-related AKI， and a risk prediction model was constructed and its performance was evaluated. RESULTS A total of 439 patients were included， of whom 54 developed CsA-related AKI， with an incidence rate of 12.30%. The occurrence of CsA-associated AKI was positively correlated with concurrent bacterial pulmonary infection， cytomegalovirus viremia， respiratory failure， renal insufficiency， gastrointestinal bleeding， and peripheral central venous catheterization （odds ratios of 763.750， 16.944， 41.933， 236.806， 17.537 and 212.789， respectively； P ＜0.05）； while uric acid， prealbumin， and calcium levels were negatively associated with it （odds ratios of 0.983， 0.967 and 0.058， respectively； P ＜0.05）. The prediction model constructed based on the above factors yielded a χ 2 value of 10.254 （ P ＞0.05） in the Hosmer-Lemeshow test. The average area under the curve （AUC） from 10-fold cross-validation was 0.885. The AUC of the receiver operating characteristic curve was 0.883， with a sensitivity of 84.3% and a specificity of 80.4%， respectively， at the optimal cutoff value of 0.1. CONCLUSIONS Six factors， including concurrent bacterial pulmonary infection and cytomegalovirus viremia， are positively associated with the occurrence of CsA-related AKI； while uric acid， prealbumin， and calcium levels are negatively associated. The Logistic regression model constructed based on these factors demonstrates good predictive performance and can assist clinic in conducting early risk assessment and personalized interventions.

Objective:To investigate the current status of treatment delay and analyze its influencing factors in patients with breast cancer-related lymphedema.Methods:Using convenience sampling, 218 patients with breast cancer-related lymphedema from The First Affiliated Hospital of Zhengzhou University between April 2024 and January 2025 were enrolled. The General Information Questionnaire, Lymphedema Self-Management Support Scale for Breast Cancer Survivors (LSMS-BCs), Brief Illness Perception Questionnaire for Breast Cancer-related Lymphedema (BIPQ-BCRL), Perceived Barriers to Health Care-Seeking Decision-Chinese (PBHSD-C), and Health Literacy Scale for Chronic Patients (HLSCP) were used to conduct a cross-sectional survey. Logistic regression identified predictors of treatment delay, with model fit assessed by the Hosmer-Lemeshow test. Discriminative ability was evaluated using receiver operating characteristic (ROC) curve analysis.Results:The study included 218 female BCRL patients, aged (58.31 ± 10.54) years. Among 218 patients, 76 experienced treatment delay, the incidence of treatment delay was 34.8% (76/218). Independent risk factors included junior high school education or below, no regular arm circumference measurement, low self-management support scores, low illness perception scores, high perceived barriers to healthcare-seeking scores, and low health literacy scores (Wald χ2 values were 7.75-15.15, all P<0.05). The Hosmer-Lemeshow test indicated good model fit ( χ2=6.21, P>0.05). The combined predictive model demonstrated significantly better discrimination than individual factors, the area under the curve of ROC was 0.846 ( P<0.01). Conclusions:The incidence of treatment delay is relatively high among breast cancer-related lymphedema patients. Nursing staff should pay special attention to patients with a junior high school education or below, no regular arm circumference measurement, low LSMS-BCs scores, low BIPQ-BCRL scores, high PBHSD-C scores and low HLSCP scores, implement timely interventions to reduce treatment delay in lymphedema patients.

Objective:To analyze the current situation and influencing factors of information need for cardiac rehabilitation of urban and rural patients with coronary heart disease, and explore the inequality of information need for cardiac rehabilitation among urban and rural patients with coronary heart disease and its influencing factors, so as to provide a scientific reference for formulating targeted cardiac rehabilitation programs.Methods:From June to July 2024, hospitalized patients with coronary heart disease from a Class A tertiary hospital in Suzhou City were selected by convenience sampling as the study subjects. A General Information Questionnaire, Information Need in Cardiac Rehabilitation Hospital Anxiety and Depression Scale, Health Literacy Management Scale, and Coronary Artery Disease Self-Management Scale were used to conduct a questionnaire survey. Multiple linear regression was used to analyze the influencing factors of the cardiac rehabilitation information needs and the oaxaca-blinder model was used to analyze the causes of unequal information needs in urban and rural patients.Results:A total of 254 hospitalized patients with coronary heart disease who met the inclusion and exclusion criteria were surveyed, included 127 males and 127 females, 182 of them were aged 60 years or older. The total cardiac rehabilitation information needs score of rural and urban patients with coronary heart disease were 168.00 (115.50, 255.00) and 213.00 (132.00, 255.00), respectively, the difference was significant ( U = 5 389.50, P<0.05). Multiple regression analysis showed that health literacy ( β = 0.871, P<0.05) was the influencing factor of cardiac rehabilitation information needs of rural coronary heart disease patients, and depression ( β = 0.719, P<0.05) and living status ( β = -0.186, P<0.05) was the influencing factor of cardiac rehabilitation information needs of urban coronary heart disease patients. Average monthly household income per capita (C = 14.50%) and health literacy (C = 88.30%) were the main causes of the difference in cardiac rehabilitation information needs between urban and rural patients with coronary heart disease. Conclusions:The information demand for cardiac rehabilitation of patients with coronary heart disease in rural area is lower than that in urban areas in Suzhou, mainly due to health literacy and monthly income. It is recommended to narrow the urban-rural gap by improving rural health literacy, optimizing medical insurance policies, and promoting a multi-level rehabilitation service network.

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.

This study aimed to investigate the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. Children and adolescents aged 7 to 18 years old in Hunan Province were recruited for questionnaire surveys and physical examinations using a multi-stage stratified cluster sampling method. Secondhand smoke exposure was evaluated according to the answer to the question, "Has someone smoked in front of you in the last 7 days?". Overweight and obesity were determined using BMI-for-age and BMI-for-sex according to the national standard WS/T586-2018. Multivariate logistic regression was used to explore the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. A total of 187 863 participants were included in this study, with a prevalence of overweight and obesity of 28.4%. The prevalence of secondhand smoke exposure at home, school and other public places was 25.5%, 12.6% and 32.3%, respectively. Children and adolescents with secondhand smoke exposure at home, school and other public places had a higher prevalence of overweight and obesity than those without exposure. After adjusting for confounding factors, secondhand smoke exposure was positively associated with overweight and obesity among children and adolescents, and exposure at home showed the closest association ( OR=1.091; 95% CI: 1.066-1.117). In conclusion, secondhand smoke exposure, especially at home, significantly increases the risk of overweight and obesity among children and adolescents. Comprehensive strategies should be implemented to avoid secondhand smoke exposure and protect children and adolescents from overweight and obesity.

Objective To systematically retrieve,assess and synthesise regarding the respiratory training for patients with spinal cord injury and to provide a reference for clinical practice.Methods According to the 6S Evidence Pyramid Model,following databases were searched for literature in relation to the respiratory training for patients with spinal cord injury:UpToDate,BMJ Best Practice,Joanna Briggs Institute Database of Systematic Reviews and Implementations,National Institute for Health and Clinical Excellence(NICE),National Guideline Clearinghouse(NGC),Registered Nurses'Association of Ontario(RNAO),Medlive,American Spinal Injury Association(ASIA),Physiotherapy Evidence Database(PEDro),The Cochrane Library,PubMed,Web of Science,CNKI,Wanfang Data,SinoMed and the websites of the Journals published by Chinese Medical Association.The types of literature included clinical decisions,practice guidelines,expert consensus,evidence summaries,systematic reviews/Meta-analyses,and original researches.The search period was from the inception of databases to 30th December,2023.Two researchers independently evaluated the quality of the literature and summarised the evidences.Results Fifteen studies were ultimately included,comprising 3 clinical decisions,3 guidelines,6 systematic reviews and 3 randomised controlled trials.A total of 21 best pieces of evidence were summarised,covering 5 aspects:pre-training assessment,training content,training equipment,training intensity and frequency,and effectiveness evaluation.Conclusion The best evidence of respiratory training for patients with spinal cord injury summarised in this study can provide the evidence-based support for healthcare professionals to formulate standardised respiratory training strategies.

Objective:To determine the detection rate of myopia among primary and secondary school students in Hunan Province in 2022 and to analyze the influencing factors at both the school and individual levels, thereby providing a scientific basis for developing myopia prevention and control strategies.Methods:From October to November 2022, a multi-stage stratified cluster random sampling method was employed to select students from Year 4 of primary school to Year 3 of senior high school across 14 prefecture-level (autonomous prefecture) cities in Hunan Province for vision screening and questionnaire surveys. A multilevel regression model was utilized to analyze the influencing factors of myopia at both the school and individual levels.Results:A total of 189 343 primary and secondary school students were included in this study. The overall myopia detection rate was 55.56%, with a significantly higher prevalence observed in female students (60.49%) compared to males (51.03%) and in urban students (59.12%) versus those from rural areas (53.50%). A marked upward trend in myopia prevalence was identified with advancing grade levels (trend test χ2=16 246.13, P<0.001). Multilevel regression analysis revealed that at the individual level, female gender, higher grade level, parental myopia history, daily homework duration ≥2 hours after school, improper reading/writing postures, and taking breaks only after more than 15 minutes of near work were associated with an increased risk of myopia. Conversely, adequate sleep duration, outdoor activity ≥2 hours, and outdoor breaks during recess demonstrated protective effects. At the school level, non-compliant blackboard illumination uniformity emerged as a significant risk factor for myopia development. Conclusions:The detection rate of myopia among primary and secondary school students in Hunan Province remains relatively high and is associated with multiple factors at both the school and individual levels. Targeted interventions should be implemented at different levels to mitigate the risk of myopia.

Hepatic encephalopathy is a difficult and critical disease with rapid progression and limited treatment methods in the field of liver disease， and it is urgently needed to make breakthroughs in its pathogenesis. Selection of appropriate prevention and treatment strategies is of great importance in delaying disease progression and reducing the incidence and mortality rates. This article reviews the theory of “turbid toxin pathogenesis” and related prevention and treatment strategies for hepatic encephalopathy in traditional Chinese medicine/Zhuang medicine， proposes a new theory of “turbid toxin pathogenesis”， analyzes the scientific connotations of “turbid”， “toxin”， and the theory of “turbid toxin pathogenesis”， and constructs the “four-step” prevention and treatment strategies for hepatic encephalopathy， thereby establishing the new clinical prevention and treatment regimen for hepatic encephalopathy represented by “four prescriptions and two techniques” and clarifying the effect mechanism and biological basis of core prescriptions and techniques in the prevention and treatment of hepatic encephalopathy， in order to provide a reference for the prevention and treatment of hepatic encephalopathy.