Objective:To investigate the current status and influencing factors of barriers to going out among elderly patients with chronic obstructive pulmonary disease (COPD), and to provide a reference for implementing targeted preventive measures.Methods:Elderly COPD patients from the People's Hospital of Xinjiang Uygur Autonomous Region were selected between January and May 2024 by convenience sampling. A cross-sectional survey was conducted using a general information questionnaire, Scale on Barriers to Going Out for the Elderly, Perceived Isolation Scale and COPD Assessment Test (CAT).Results:A total of 270 questionnaires were distributed, with 250 valid responses, the effective response rate was 92.6%. Among the 250 elderly COPD patients, there were 124 males and 126 females, with age distributions: 116 patients aged 60-69 years, 94 aged 70-79 years, and 40 aged ≥80 years. The total score for Scale on Barriers to Going Out for the Elderly was (20.01 ± 4.09). The score on barriers to going out in elderly COPD patients were positively correlated with the Perceived Isolation Scale and CAT scores ( r = 0.456 and 0.625, both P <0.05). Multiple linear regression analysis revealed that age, oxygen therapy, exercise habits,Perceived Isolation Scale, and CAT score classification were the main influencing factors for barriers to going out ( t values were -2.85 to 8.93, all P<0.05), explaining 63.0% of the total variance. Conclusions:The level of barriers to going out in elderly COPD patients is moderate-to-high level. Healthcare professionals should emphasize the assessment of barriers to going out, closely monitor high-risk groups, and develop and implement interventions to prevent such barriers.

Objective:To present an evaluation indicator system for access to cancer screening services.Methods:The evaluation indicator pool was constructed through a scoping review. The theoretical framework was constructed based on the multi-source indicators, and the qualitative expert consultation method was employed to form the initial version of the three-level evaluation indicator system. Delphi expert consultation method was conducted in two rounds to evaluate the relevance, importance, and availability of the proposed evaluation indicator system. The expert positive coefficient, authority coefficient, coordination degree of expert opinions, and concentration of expert opinions were subjected to analysis. Subsequently, the three-level evaluation indicator system for access to cancer screening services was adjusted and determined based on the boundary value method and the open opinions of experts. Finally, the combination weight method was employed to determine the weight.Results:The initial version of the indicator system comprised 3 primary (first-level) indicators, 11 secondary (second-level) indicators, and 46 tertiary (third-level) indicators. Delphi expert consultation was conducted for the initial version, and 17 experts ultimately completed it, exhibiting a positive coefficient of 100% and an authority coefficient of 0.87. In comparison to the initial round of consultation, Kendall's W coefficient ranges (0.15-0.43, all P<0.05) of relevance, importance, and availability scores for each tertiary indicator in the second round exhibited an improvement. The analysis of the importance dimension indicates that expert opinions are also more concentrated, as evidenced by an increase of 8.5% and 7.0% in the proportion of the tertiary indicators with an arithmetic mean above 8 and a full mark ratio above 0.5, respectively. The final evaluation indicator system comprises three primary indicators, with the weights of structure evaluation, process evaluation, and outcome evaluation being 0.338, 0.378, and 0.285, respectively. It also comprises 11 secondary indicators and 45 tertiary indicators. Conclusions:The evaluation indicator system developed in this article can be an effective evaluation tool for quantitative comparison of access to cancer screening services across different populations, cancer types, and before and after intervention. Furthermore, it is recommended that the system undergo continuous optimization concerning its application.

Objective:To screen and extract relevant evidence on the management of malnutrition in pediatric cancer patients and provide a best evidence summary.Methods:A systematic search was conducted across multiple websites and databases, including UpToDate, BMJ Best Practice, WHO website, Guidelines International Network, National Institute for Health and Care Excellence, National Guideline Clearinghouse, PubMed, Web of Science Core Collection, Embase, Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disc, Wanfang Data and others, for evidence on nutritional management of malnutrition in pediatric cancer patients. The search included literature from inception to August 31, 2023. Literature was selected following strict inclusion and exclusion criteria by researchers trained in evidence-based nursing courses. The quality of the selected literature was evaluated, and evidence was extracted and summarized.Results:A total of 11 articles were included, comprising two clinical decision papers, three guidelines, one evidence summary, two systematic reviews, and three expert consensus documents. The evidence was summarized into 24 evidence across five main areas: multidisciplinary team approach, nutritional risk screening and assessment, nutrient intake, dietary and nutritional education, and enteral and parenteral nutrition support.Conclusions:This study provides a best evidence summary for the nutritional management of malnutrition in pediatric cancer patients, offering evidence-based support for clinical practice among healthcare professionals.

Objective:To observe the effects of auricular point compression and exercise therapy on glycolipid metabolism,oxidative stress and sleep quality in type 2 diabetes mellitus(T2DM)patients with poor glycemic control.Methods:This study was a single-center randomized controlled study,86 T2DM patients with poor glycemic control who were treated in our hospital from June 2022 to June 2024 were divided into control group(received conventional treatment,n=43)and observation group(received auricular point compression and exercise therapy,n=43)by using the random number table method.The clinical efficacy,blood lipid indexes,blood glucose indexes,oxidative stress indexes and sleep quality were compared between the two groups.Results:The total clinical effective rate,high-density lipoprotein cholesterol(HDL-C),superoxide dismutase(SOD)and Glutathione peroxidase(GSH-Px)of the observation group after treatment were higher than those of the control group,and the fasting blood glucose(FBG),glycated hemoglobin(HbA1c),Homeostatic Model Assessment of Insulin Resistance(HOMA-IR),total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C),triglycerides(TG),reactive oxygen species(ROS),malondialdehyde(MDA)and Pittsburgh sleep Quality Index(PSQI)score were lower than those of the control group(P＜0.05).Conclusion:Auricular point compression and exercise therapy can regulate glycolipid metabolism in T2DM patients with poor glycemic control,improve the degree of oxidative stress and sleep quality.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Objective:To systematically synthesize the illness stigma experiences of stroke patients.Methods:A systematic search was conducted in PubMed, Cochrane Library, Embase, Web of Science, China Biology Medicine disc, China National Knowledge Infrastructure, Wanfang Data, and VIP for qualitative studies exploring illness stigma in stroke patients. The search period extended from database inception to September 30, 2024. A Meta-synthesis approach was employed to integrate the findings.Results:A total of fourteen articles were included and ten new categories were extracted and synthesized into four integraion results: sources of illness stigma, key influencing factors of illness stigma, manifestations and impacts of illness stigma, and coping strategies illness stigma.Conclusions:Illness stigma is a significant barrier to stroke rehabilitation. Nurses should be aware of the characteristics of illness stigma, conduct early identification, and develop individualized strategies for prevention and intervention. It is essential to help patients build multi-dimensional support systems involving society and family, and to guide them in gradually accepting and adapting to their new self.

Objectives:To analyze factors associated with physical fitness and health in children and adolescents from the perspective of cohort and urban-rural differences in order to provide evidence for optimizing health intervention policies.Methods:Using data from the China Family Panel Studies(CFPS)from 2012 to 2020,this study examined trends in the health of children and adolescents in terms of height and weight.A hierarchical mixed-effects model was used to examine the impact of socioeconomic factors such as household income,health insurance and regional public health expenditure on physical fitness and health.Interaction models were also used to assess heterogeneous effects across birth cohorts and urban-rural contexts.Results:The physical fitness and health of children and adolescents in China have improved significantly,but urban-rural disparities persist.Household income,parental education and health insurance have protective effects on health,but the strength of these effects varies by cohort and between urban and rural areas.Height and weight outcomes for rural children were more closely associated with economic development and health insurance coverage.Conclusions:The factors associated with physical fitness and health in children and adolescents are dynamic.It is necessary to establish a tiered and targeted health promotion system,strengthening health insurance coverage and interventions in rural areas,while prioritizing family-based healthy lifestyle counselling in urban areas.

Objective To analyze the efficacy of autologous tissue repair for rectocele through the perineal approach in treating constipation and fecal incontinence in patients.Methods From January 2021 to November 2022,23 female patients with symptomatic rectal protrusion were treated with perineal autologous tissue repair.Preoperatively and at 12 months postoperatively,the Cleveland Clinic Constipation Score(CCCS),Cleveland Clinic Incontinence Score(CCIS),and Patient Assessment of Constipation Quality of Life(PAC-QOL)questionnaires were used to assess postoperative outcomes and quality of life.Results Among the 23 patients,with a median follow-up time of 12.6 months,CCCS decreased from 17.09±1.68 to 3.96±2.08(P＜0.05);CCIS decreased from 1.52±4.15 to 0.52±1.41(P＞0.05);PAC-QOL:physical discomfort decreased from 13.00±1.51 to 4.74±1.98;psychological discomfort decreased from 20.96±3.27 to 5.74±2.67;concern and anxiety decreased from 26.13±4.37 to 8.78±3.14;satisfaction decreased from 15.39±2.35 to 4.60±1.59(P＜0.05).All patients showed significant improvement in constipation and incontinence symptoms postoperatively,with no serious postoperative complications and a marked improvement in postoperative quality of life.Conclusion Rectocele repair with perineal approach using autologous tissue is an effective and safe method,avoiding potential potential complications associated with grafts.

Processing for deodorization is widely used in the production of animal-derived Chinese medicinal materials. In this study, Heracles Neo ultra-fast gas-phase electronic nose combined with chemometrics was employed to analyze the overall odor difference of Cervi Cornu Pantotrichum(focusing on that derived from Cervus nippon Temminck in this study) before and after processing. The results showed that the electronic nose effectively distinguished between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. HS-GC-MS was used to identify and quantify the volatile components in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum, and 35 and 37 volatile components were detected in the medicinal materials and decoction pieces, respectively. The medicinal materials and decoction pieces contained 28 common volatile components contributing to the odor of Cervi Cornu Pantotrichum. The odor activity value(OAV) of each volatile component was calculated based on the olfactory threshold and relative content. The results showed that there were 17 key odor substances such as isovaleraldehyde, 2-methylbutanal, isobutyraldehyde, hexanal, and methanethiol in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. All of them had bad odor and were the main source of the odor of Cervi Cornu Pantotrichum. The results of principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) showed that there were significant differences in volatile components between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. Based on the thresholds of P<0.05 and Variable Importance in Projection(VIP)>1, 21 differential volatile odor components were screened out. Among them, isopentanol, isovaleraldehyde, 2-methylbutanal, n-nonanal, and dimethylamine were the key differential odor compounds between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. The odor compounds and their relative content reduced, and some flavor substances such as esters were produced after processing with wine, which was the main reason for the reduction of the odor after processing of Cervi Cornu Pantotrichum.
Odorants/analysis* ; Electronic Nose ; Gas Chromatography-Mass Spectrometry/methods* ; Animals ; Volatile Organic Compounds/analysis* ; Deer ; Drugs, Chinese Herbal/chemistry*

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans