ObjectiveThis paper aims to conduct the feature analysis and correlation analysis on the ocular collateral features and differential metabolites in patients with chronic hepatitis B （CHB） complicated by glucose metabolism disorder （GMD），particularly those with the damp-heat syndrome type，by integrating shadowless scleral imaging and metabolomics technologies. MethodsA total of 313 patients were recruited from the Hepatology and Endocrinology Outpatient Departments of Public Health Clinical Center of Chengdu according to the inclusion/exclusion criteria，and they were divided into a CHB group and a CHB complicated by GMD groups （damp-heat syndrome group and non-damp-heat syndrome group）. All patients underwent high-definition ocular image acquisition and feature extraction using an intelligent analysis system for shadowless scleral imaging to analyze the differences in the counting of morphological feature scores of ocular collaterals among groups. By using a digital sampling method，24 patients from each group were randomly selected，along with 20 healthy volunteers，for untargeted metabolomic analysis of peripheral serum. Differential metabolites were identified，statistically analyzed，and subjected to potential biomarker analysis and pathway enrichment. Spearman method was performed to conduct the correlation analysis on the differential ocular collateral features and differential metabolites，followed by correlation network construction. ResultsCompared with those in the CHB group，patients with CHB complicated by GMD showed significant changes in ocular collateral feature scores such as "hillock"，"blood vessels"，and "pale dusky coloration" （P<0.05）. In comparison with those in the healthy group，metabolites including N-acetylglucosamine，acetylhomoserine，and myo-inositol （AUC>0.7） were identified as potential biomarkers for the disease. Compared with those in the CHB complicated by GMD group with non-damp-heat syndrome，patients with damp-heat syndrome exhibited significant changes in feature scores of "plaques"，"yellow coloration"，"spleen"，and "gallbladder" （P<0.05）. In comparison with those in the healthy group，metabolites such as O²′-4a-cyclic tetrahydrobiopterin，theobromine，xanthurenic acid，and L-glutamic acid 5-phosphate （AUC>0.7） were identified as potential biomarkers for the damp-heat syndrome type. The Spearman correlation analysis reveals weak to moderate linear correlations between the differential scleral collateral features and metabolites. By constructing a "disease-syndrome" network of ocular diagnosis and metabolites，"xanthurenic acid-gallbladder" and "theobromine-plaque/yellow coloration" were identified as specific molecular-phenotypic correlated biomarker clusters for CHB complicated by GMD with dampness-heat syndrome. ConclusionPatients with CHB complicated by GMD demonstrate differential ocular diagnostic features and serum metabolites corresponding to disease states and dampness-heat syndrome. These objective biomarkers can guide both clinical syndrome differentiation and medication. The macro-micro integration based on ocular feature clusters and potential metabolic biomarkers offers an innovative approach to a combined traditional Chinese and Western medicine diagnosis and treatment model for this disease.

Purpose: In this study, the neural communication patterns between hypothalamic structures and cortical areas in girls diagnosed with central precocious puberty (CPP) were explored. Endocrine profiles were incorporated to clarify the pathophysiological interactions between cerebral networks and hormonal regulation. The hypothalamus was designated as the key focus area for connectivity analysis. Methods: Fifty-seven girls (37 CPP, 20 non-CPP) were recruited from the Pediatric Development Clinic at the Second Affiliated Hospital of Anhui Medical University. The collected data included demographic information, gonadotropin-releasing hormone stimulation tests, and magnetic resonance imaging scans. Hypothalamic functional connectivity (FC) was analyzed using predefined region of interest coordinates, and correlations between hormone levels and FC values were assessed. Results: Compared to the non-CPP group, the CPP group exhibited elevated baseline follicle-stimulating hormone (FSH), baseline luteinizing hormone (LH), peak FSH, peak LH, and peak LH/FSH ratios. Patients with CPP exhibited enhanced neural synchronization linking the right lateral hypothalamic effector zone to the right superior frontal gyrus (displaying a borderline significant correlation with peak LH concentrations), concurrent with diminished functional coupling of the right lateral hypothalamic efferent to the right fusiform/supramarginal gyri. The left lateral hypothalamic projections demonstrated amplified connectivity with the right cuneus. No FC differences were observed in the medial hypothalamus. Conclusion Abnormal lateral hypothalamus FC patterns were identified in CPP girls and were particularly linked to peak LH levels. The findings offer novel insights into the neuroendocrine mechanisms underlying CPP.

OBJECTIVE:Elevated blood pressure increases the risk of cardiovascular diseases.Isometric exercise training has been shown to significantly reduce resting blood pressure,but the factors influencing its effectiveness remain unclear,and specific application guidelines are yet to be established.This study aims to evaluate the impact of isometric exercise training on resting blood pressure through meta-analysis,explore its moderating factors,and provide evidence-based recommendations based on its dose-response relationship.METHODS:Following the PRISMA guidelines,a systematic search was conducted in PubMed,Embase,Cochrane Library,Scopus,and Web of Science databases using keywords"Isometric exercise training,""Systolic blood pressure,"and"Diastolic blood pressure,"covering literature up to September 2024.Randomized controlled trials involving isometric exercise training and resting blood pressure were included.Three independent researchers performed literature screening and data extraction,assessing bias risk and quality grades using the Risk of Bias 2.0 tool and GRADE framework.Main effect pooling,publication bias assessment,subgroup,and regression analysis were conducted using R software(version 4.3.4).RESULTS:A total of 28 articles(comprising 32 randomized controlled trials)involving 977 participants were included.(1)Meta-analysis results indicated that isometric exercise training significantly reduced resting systolic blood pressure(MD=-8.01,95％CI=-9.22 to-6.80,P＜0.01,I2=18.20％,low evidence grade)and diastolic blood pressure(MD=-3.46,95％CI=-4.64 to-2.28,P＜0.01,I2=0％,moderate evidence grade)compared to no exercise.(2)Subgroup analysis results revealed significant influences of gender,health status,exercise modality,frequency,intensity,duration,sets per session,rest duration,and baseline blood pressure on the main effects for both systolic(P＜0.01)and diastolic blood pressure(P＜0.05).(3)Regression analysis results did not show any significant influencing factors,but body mass index(β=-4.11,P=0.091)showed a significant negative trend on the main effect for systolic blood pressure.(4)No significant publication bias was observed in the meta-analysis results(P＞0.05).CONCLUSION:(1)Isometric exercise training significantly lowers systolic(low evidence grade)and diastolic(moderate evidence grade)blood pressure with clinically meaningful thresholds.(2)Participant characteristics(gender,health status,baseline blood pressure,and body mass index)and isometric exercise training protocols(modality,frequency,intensity,duration,cycle,sets per session,and rest duration)influence its antihypertensive effects.(3)The article recommends the optimal blood pressure management prescription:three sessions per week,with four sets per session,each set lasting 2 minutes with a 2-minute rest,at an intensity of 95％HRpeak using isometric wall squat exercises;the intervention period can be adjusted around a 6-week node.Future high-quality research is urgently needed to further validate and support these conclusions.

OBJECTIVE:Elevated blood pressure increases the risk of cardiovascular diseases.Isometric exercise training has been shown to significantly reduce resting blood pressure,but the factors influencing its effectiveness remain unclear,and specific application guidelines are yet to be established.This study aims to evaluate the impact of isometric exercise training on resting blood pressure through meta-analysis,explore its moderating factors,and provide evidence-based recommendations based on its dose-response relationship.METHODS:Following the PRISMA guidelines,a systematic search was conducted in PubMed,Embase,Cochrane Library,Scopus,and Web of Science databases using keywords"Isometric exercise training,""Systolic blood pressure,"and"Diastolic blood pressure,"covering literature up to September 2024.Randomized controlled trials involving isometric exercise training and resting blood pressure were included.Three independent researchers performed literature screening and data extraction,assessing bias risk and quality grades using the Risk of Bias 2.0 tool and GRADE framework.Main effect pooling,publication bias assessment,subgroup,and regression analysis were conducted using R software(version 4.3.4).RESULTS:A total of 28 articles(comprising 32 randomized controlled trials)involving 977 participants were included.(1)Meta-analysis results indicated that isometric exercise training significantly reduced resting systolic blood pressure(MD=-8.01,95％CI=-9.22 to-6.80,P＜0.01,I2=18.20％,low evidence grade)and diastolic blood pressure(MD=-3.46,95％CI=-4.64 to-2.28,P＜0.01,I2=0％,moderate evidence grade)compared to no exercise.(2)Subgroup analysis results revealed significant influences of gender,health status,exercise modality,frequency,intensity,duration,sets per session,rest duration,and baseline blood pressure on the main effects for both systolic(P＜0.01)and diastolic blood pressure(P＜0.05).(3)Regression analysis results did not show any significant influencing factors,but body mass index(β=-4.11,P=0.091)showed a significant negative trend on the main effect for systolic blood pressure.(4)No significant publication bias was observed in the meta-analysis results(P＞0.05).CONCLUSION:(1)Isometric exercise training significantly lowers systolic(low evidence grade)and diastolic(moderate evidence grade)blood pressure with clinically meaningful thresholds.(2)Participant characteristics(gender,health status,baseline blood pressure,and body mass index)and isometric exercise training protocols(modality,frequency,intensity,duration,cycle,sets per session,and rest duration)influence its antihypertensive effects.(3)The article recommends the optimal blood pressure management prescription:three sessions per week,with four sets per session,each set lasting 2 minutes with a 2-minute rest,at an intensity of 95％HRpeak using isometric wall squat exercises;the intervention period can be adjusted around a 6-week node.Future high-quality research is urgently needed to further validate and support these conclusions.

ObjectiveTo systematically quantify the multidimensional effect of virtual reality-based exergaming (VREG) on health outcomes among individuals with cognitive impairment under the framework of the International Classification of Functioning, Disability and Health (ICF). MethodsMeta-analysis was conducted in accordance with the PRISMA statement. PubMed, Cochrane Library, Embase, Scopus, Web of Science, CNKI, Wanfang Data and VIP were researched for randomized controlled trials (RCTs) investigating VREG for cognitive impairment, with the search deadline set on February 1, 2026. The Cochrane Risk of Bias 2.0 tool was adopted to evaluate the methodological quality of included studies. A three-level meta-analytic model was implemented with R 4.4.2. The GRADE framework was further used to rate the certainty of pooled evidence. ResultsA total of 22 RCTs were included. VREG might improve global cognitive function (SMD = 0.69, 95%CI 0.11 to 1.28, P = 0.022), memory (SMD = 0.47, 95%CI 0.07 to 0.87, P = 0.023), attention (SMD = 0.62, 95%CI 0.23 to 1.01, P = 0.004), executive function (SMD = 0.42, 95%CI 0.09 to 0.75, P = 0.013), balance (SMD = 0.54, 95%CI 0.07 to 1.00, P = 0.027), muscle strength (SMD = 0.63, 95%CI 0.37 to 0.88, P = 0.001), mobility (SMD = 0.29, 95%CI 0.04 to 0.54, P = 0.024) and quality of life (SMD = 0.29, 95%CI 0.03 to 0.56, P = 0.029). No significant effect was observed for depressive symptoms, comprehensive physical fitness and fall risk (all P > 0.05). Subgroup and meta-regression analyses indicated that session duration < 45 minutes produced the largest improvement in attention (SMD = 0.95, 95%CI 0.55 to 1.34, P < 0.001); intervention cycles of twelve to 24 weeks were optimal for mobility (SMD = 0.85, 95%CI 0.44 to 1.25, P < 0.001); age negatively correlated to balance improvement (β = -0.11, 95%CI -0.20 to -0.02, P = 0.020), suggesting less intervention benefits among older participants. ConclusionVREG is an effective non-pharmacological intervention to improve multidimensional physical and mental health in people with cognitive impairment. Protocols featuring short single sessions with long-term duration should be prioritized.

Objective To analyze the influencing factors of poor incision healing in postoperative patients with breast cancer.Methods The clinical data of 150 patients with breast cancer diagnosed by the Department of Nail Milk surgery of the Fifth affiliated Hospital of Xinjiang Medical University from January 2016 to December 2023 were retrospectively analyzed.According to the postoperative wound healing,the patients were divided into two groups:good healing group(n=94)and poor healing group(n=56).The general data,operation-related data and clinicopathological characteristics of the two groups were collected for univariate analysis,and the single-factor indexes with statistical significance were analyzed by multivariate Logistic analysis to screen the risk factors of poor incision healing after operation.The statistically significant indexes of regression analysis were analyzed by receiver operating characteristic(ROC)curve to further explore its predictive value in poor incision healing after breast cancer operation.Results The data of the two groups were compared and analyzed.Multivariate Logistic regression analysis showed that BMI,diabetes,age,axillary lymph node dissection and hemoglobin were independent risk factors for poor incision healing(P＜0.05).ROC curve analysis of independent risk factors showed that when the age was more than 57.5,the area under the curve(AUC)was 0.635,the sensitivity was 55.4％,the specificity was 68.1％,and the critical value was 57.5.When BMI＞24.9 kg/m2,the area under the curve(AUC)was 0.735,the sensitivity was 87.5％,the specificity was 61.7％,and the critical value was 24.9(P＜0.001).When hemoglobin＜101.5 g/L,the area under the curve(AUC)was 0.829,the sensitivity was 57.1％,the specificity was 94.7％,and the critical value was 101.5 g/L(P＜0.001).Conclusion BMI,diabetes,hemoglobin,age and axillary lymph node dissection are independent risk factors for poor wound healing after breast cancer operation.When BMI＞24.9 kg/m2,age＞57.5 years old and hemoglobin＜101.5 g/L,it can predict the occurrence of poor incision healing in patients with breast cancer.

Objective:To construct fused cancer cell/neutrophil membrane-coated polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNP@FMs) and explore the potential for targeted pancreatic cancer fluorescence imaging and MRI.Methods:Cancer cell membranes fused with neutrophil membranes were encapsulated on the surface of polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNPs) to prepare PMNP@FMs. The morphology, structure, and MRI performance of the product were characterized. The cytotoxicity of PMNP@FMs towards human pancreatic cancer cells (PANC-1) and normal human pancreatic ductal epithelial cells (hTERT-HPNE) was evaluated using cell counting kit (CCK)-8, and in vivo toxicity was assessed in healthy mice. PANC-1 pancreatic cancer xenograft nude mouse models were established for in vivo fluorescence imaging and MRI. Data were analyzed using the independent-sample t test, repeated measures analysis of variance and the least significance difference method. Results:PMNP@FMs exhibited a core-shell structure with a diameter of (112.81±8.64) nm, negative surface charge, and good dispersibility. The T 1 relaxivity of PMNPs was 18.81±0.22, which was 4.1 times higher than that of gadopentetate dimeglumine (Gd-DTPA) (4.55±0.24; t=75.54, P<0.001). Co-culture of PMNPs and PMNP@FMs with hTERT-HPNE and PANC-1 cells for 24 h resulted in cell viability above 90% within the concentration range of 0-500 μg/ml. PMNP@FMs did not affect mouse survival and showed no apparent organ damage. In vivo fluorescence imaging and MRI revealed that PMNP@FMs accumulated highly in tumors and reached the peak 24 h post intravenous administration (relative MR signal: 1.35±0.01, fluorescence intensity: (1.20±0.25)×10 10), surpassing the peak observed in the control group (1.22±0.01, (3.87±0.50)×10 9;F values: 11.03-188.01, t values: 18.20, 5.64, all P<0.05), with hepatic metabolism being the primary route of clearance. Conclusion:PMNP@FMs demonstrate a potential for targeted pancreatic cancer fluorescence imaging and MRI, offering promising prospect for precise diagnosis of early-stage pancreatic cancer.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

ObjectiveTo study the clinical characteristics and influencing factors of rheumatoid arthritis （RA） in the patients with cold dampness obstruction syndrome. MethodsThe RA patients treated in the Department of Traditional Chinese Medicine and Rheumatology of the China-Japan Friendship Hospital from August 2022 to June 2024 were selected. The demographic information， clinical data， laboratory test results， and traditional Chinese medicine （TCM） symptom information were collected for syndrome differentiation， on the basis of which the characteristics and influencing factors of cold dampness obstruction syndrome were analyzed. ResultsA total of 258 RA patients were selected in this study， including 88 （34.1%） patients with cold dampness obstruction syndrome， 53 （20.5%） patients with dampness and heat obstruction syndrome， 31 （12.0%） patients with wind dampness obstruction syndrome， 29 （11.2%） patients with liver-kidney deficiency syndrome， 19 （7.4%） patients with Qi-blood deficiency syndrome， 14 （5.4%） patients with phlegm-stasis obstruction syndrome， 15 （5.8%） patients with stasis obstructing collateral syndrome and 9 （3.5%） patients with Qi-Yin deficiency syndrome. The patients were assigned into two groups of cold dampness obstruction syndrome and other syndromes. The group of cold dampness obstruction syndrome had lower joint fever， 28-tender joint count （TJC28）， and 28-joint disease activity score （DAS28）-C-reactive protein （CRP） and higher central sensitization， cold feeling of joints， fear of wind and cold， cold limbs， and abdominal distention than the group of other syndromes （P<0.05）. The binary logistic regression analysis showed that central sensitization （OR 5.749， 95%CI 2.116-15.616， P<0.001） and DAS28-CRP （OR 0.600， 95% CI 0.418-0.862， P=0.006） were the independent factors influencing cold dampness obstruction syndrome in RA. ConclusionCold dampness obstruction syndrome is a common syndrome in RA patients. It is associated with central sensitization， cold feeling of joints， abdominal distension and may be a clinical syndrome associated with central sensitization.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.