ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Objective To investigate the correlation between sleep duration and chest pain in the Chinese population.Methods A cross-sectional analysis was conducted using data on chest pain and sleep duration from 7942 participants in the 2018 China Health and Retirement Longitudinal Study(CHARLS).Among them,1239 had chest pain and 6703 did not;2645 were aged>65 years and 5297 were aged≤65 years.All participants were categorized into three groups based on sleep duration:≤6 h(n=3123),6-8 h(n=2959),and>8 h(n=1860).Differences in characteristics such as gender,age,marital status,education level,diabetes,and hypertension were compared across these groups.Multivariate logistic regression analysis,curve fitting,and threshold effect analysis were further employed to explore the correlation between sleep duration and chest pain.Results A total of 7942 participants were finally included in the study.Among the three groups with sleep duration>8 h,6-8 h and≤6 h,the incidences of chest pain were 12.6%,13.0%,and 19.8%,respectively.Multivariate logistic regression analysis demonstrated that with the increase in sleep duration,the incidence of chest pain showed a decreasing trend[odds ratio(OR)=0.88,95%confidence interval(CI)0.86-0.91,P<0.001].Curve fitting and threshold effect analysis indicated a U-shaped relationship between sleep duration and the risk of chest pain.In the population aged≤65 years,the inflection point for sleep duration was 8.5 h,beyond which the risk of chest pain did not decrease;in the population aged>65 years,the inflection point was 7.7 h,beyond which the risk of chest pain significantly increased(P<0.05).Conclusions There is a U-shape relationship between sleep duration and the risk of chest pain.In the age group≤65 years old,8.5 h is the turning point;in the population>65 years old,7.7 h is the turning point.When the sleep duration is below the turning point,prolonging the sleep time can help reduce the risk of chest pain.It is recommended to maintain an appropriate sleep duration of 6-8 h to reduce the risk of chest pain.

Objective To explore the effect of motor imagery brain-computer interface(BCI)combined with upper limb rehabilita-tion robot on upper limb function of stroke patients.Methods From May to December,2024,45 stroke inpatients from Xuzhou Rehabilitation Hospital and Xuzhou Central Hospital were recruited and randomly divided into BCI group(n=15),robot group(n=15)and combined group(n=15).All the groups received conventional rehabilitation therapy.On this basis,the BCI group received motor imagery based BCI training,the robot group received upper extremity rehabilitation robot training,and the com-bined group received BCI combined with upper extremity rehabilitation robot training.They were assessed with Fugl-Meyer Assessment-Upper Extremities(FMA-UE),Action Research Arm Test(ARAT)and modified Barthel Index(MBI)before and four weeks after treatment,while they were measured delta-alpha ratio(DAR)using the quantitative electroencephalogram.Results The intra-group effect(F>101.870,P<0.001)and interaction effect(F>7.891,P<0.001)were significant in the scores of FMA-UE,ARAT and MBI,and DAR.Post Hoc test showed that all the indexes were better in the combined group than in BCI group and the robot group(P<0.05),and there was no significant difference in the indexes between BCI group and the robot group(P>0.05).Conclusion BCI combined with upper limb rehabilitation robot can promote the recovery of upper limb function and ac-tivities of daily living of stroke patients.

Objective To explore the clinical effect of nursing guided by Neuman's systems model on adolescent idiopathic scoliosis surgery patients.Methods This study is a randomized controlled trial.A total of 120 patients with adolescent idiopathic scoliosis who underwent surgery in our hospital from Jan.to Dec.2023 were enrolled.According to the order of enrollment,they were randomly assigned to experimental group or control group,with 60 patients in each group.The control group received routine nursing,while the experimental group received nursing guided by Neuman's systems model.Independent-sample t test and χ2 test were used to compare the surgical efficacy,postoperative recovery,quality of life,and self-management ability of the 2 groups.Results Six months post-surgery,the main curve Cobb angle in the experimental group was significantly smaller than that in the control group([14.33±0.78]° vs[16.65±1.02]°,P＜0.001).The postoperative bedtime([4.78±1.32])d vs[6.13±1.26]d),incision healing time([13.43±3.29]d vs[15.32±5.23]d),and hospital stay([13.17±5.36]d vs[16.93±3.14]d)were all significantly shorter in the experimental group than those in the control group(all P＜0.05).The overall complication rate in the experimental group was significantly lower than that in the control group(5.00%[3/60]vs 21.67%[13/60],P=0.016).Six months post-surgery,the experimental group scored better in terms of physical functioning,bodily pain,general health,vitality,social functioning,role-emotional,and mental health compared to the control group(all P＜0.05).In terms of patient self-management,the experimental group also had significantly higher scores in common management(17.53±5.98 vs 13.34±7.32)and symptom management(30.95±8.12 vs 27.32±7.87)compared to the control group(both P＜0.05).Conclusion The nursing guided by Neuman's systems model for adolescent idiopathic scoliosis surgery patients can promote their postoperative recovery and improve their quality of life and self-management capabilities.

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

AIM To investigate effects of petroleum ether fraction from Derris eriocarpa How on glucose and lipid metabolism in a mouse model of metabolic syndrome(MS).METHODS KM mice were fed a high-fat diet and administered streptozotocin intraperitoneally to establish MS models.The MS mice were then randomly assigned to the model group,the metformin hydrochloride group,the lovastatin group,the ursolic acid group,and the high-,medium-and low-dose D.eriocarpa petroleum ether fraction groups,with 10 mice in each group.Ten additional mice maitained on a normal diet served as the normal control group.After 4 weeks of intragastric administration,glucose and lipid metabolism indicators were measured.Hepatic pathological changes were assessed using HE staining and oil red O staining.Liver tissue mRNA expressions of ATF3,PEPCK,FXR,CYP7A1,HNF4ɑ,CYP8B1 and SRB1 were quantified by RT-qPCR.Hepatic protein expressions of ATF3,HNF4ɑ,PEPCK,FXR and CYP7A1 was analyzed by Western blot in MS mice.RESULTS Compared to the model group,the high-dose D.eriocarpa petroleum ether fraction group exhibited significant glucose tolerance improvement(reduced OGTT-AUC,P＜0.01);favorable serum lipid modulation in terms of increased HDL-C levels(P＜0.01)and decreased TG,TC,LDL-C(P＜0.01);reduced renal biomarkers(BUN,SCR)and hepatotoxic indicators of TBA,AST and ALT activities(P＜0.01);alleviated hepatic lipid accumulation and histopathological damage;downregulated mRNA and protein expressions of ATF3,HNF4ɑ and PEPCK,as well as CYP8B1 mRNA expression(P＜0.01);and upregulated mRNA and protein expressions of FXR and CYP7A1,along with SRB1 mRNA expression(P＜0.01).CONCLUSION D.eriocarpa petroleum ether fraction ameliorates glucose and lipid metabolism dysregulation in MS mice by modulating the ATF3/HNF4ɑ/CYP7A1 signaling pathway,consequently eliciting hypoglycemic,hypolipidemic,hepatoprotective and nephroprotective effects.

Cognitive decline is an early sign of dementia, mainly manifested as the ability to learn new knowledge and memory decline in the near future, so early detection, early diagnosis, early treatment are an important research direction to alleviate dementia population. Previous studies have shown that blood pressure variability (BPV) is an important risk factor for cognitive decline. At present, there is a lack of systematic understanding of the specific effects of BPV on different subdomains of cognitive function and the treatment standard of BPV. This paper reviews the existing clinical studies, emphasizes that abnormal BPV causes adverse cognitive outcomes in all age groups, and proposes future research directions.

Objective:Based on the background of high-quality development,we analyze the operational efficiency of traditional Chinese medicine(TCM)hospitals in China's Yangtze River Economic Belt in 2021 and explore the impact of external environmental factors on operational efficiency,so as to provide a reference for promoting the high-quality development of TCM hospitals in the Yangtze River Economic Belt.Methods:The three-stage DEA model was used to analyze the operational efficiency of TCM hospitals in 11 provinces and cities in the Yangtze River Economic Zone in China in 2021.Results:After three-stage DEA analysis,the values of comprehensive efficiency,pure technical efficiency and scale efficiency of TCM hospitals in China's Yangtze River Economic Belt are 0.976,0.986 and 0.990,respectively.5 provinces and cities,Shanghai,Jiangsu,Hunan,Chongqing and Guizhou,are efficient before and after the adjustment,and the comprehensive efficiency of Zhejiang,Anhui,Hubei,Jiangxi,Sichuan and Yunnan have increased compared with that before the adjustment.Ranking of the average value of the comprehensive efficiency of TCM hospitals operation in the three major city clusters of the Yangtze River Economic Belt after adjustment:Chengdu-Chongqing city cluster(0.998)>city cluster in the Yangtze River Delta(0.964)>city cluster in the middle reaches of the Yangtze River(0.962).Conclusion:The operational efficiency of TCM hospitals in the Yangtze River Economic Zone has been underestimated,and the comprehensive efficiency is mainly affected by scale efficiency;there are differences in the operational efficiency of TCM hospitals in the three major urban agglomerations,and balanced development is needed between regions;the operational efficiency of TCM hospitals is affected by the external environment,and it is necessary to improve the external environment;it is necessary to strengthen the construction of digital and informatization of TCM,and to pay attention to the role of talents in TCM,so as to promote the high-quality development of TCM hospitals.

Objective:To investigate the efficacy and safety of ablation index-guided radiofrequency catheter ablation (RFCA) and second-generation cryoballoon ablation (CBA) in elderly patients with atrial fibrillation (AF).Methods:This retrospective cohort study included 1 986 patients undergoing pulmonary vein isolation for AF at General Hospital of Northern Theater Command from August 2016 to May 2020, comprising 760 RFCA cases and 1 226 CBA cases. Elderly patients were defined as those aged 60 years or older, while non-elderly patients were those under 60 years of age. All patients were followed up for 3 years after the procedure to assess AF recurrence rates. Kaplan-Meier survival curves were plotted and compared by log-rank test. Multivariate logistic regression was used to analyze the influencing factors of AF recurrence.Results:Among 1 986 AF patients (aged (58.7±10.2) years; 1 307 males, 65.81%; 987 elderly patients, 49.70%), the overall AF recurrence rate was 24.37% (484/1 986). Kaplan-Meier analysis demonstrated a higher AF recurrence rate in the elderly group compared to the non-elderly group (log-rank P=0.007). In the RFCA group, AF recurrence rate was 22.50% (171/760), with no significant difference between the elderly (24.44%, 88/360) and non-elderly (20.75%, 83/400) subgroups ( P=0.223). In the CBA group, recurrence rates were 25.53% (313/1 226), with significantly higher recurrence in elderly patients (28.55%, 179/627) than non-elderly (22.37%, 134/599) ( P=0.013). Multivariate logistic regression analysis revealed that advanced age was not an independent predictor of AF recurrence ( P=0.447). Longer AF duration and larger left atrial diameter were independent risk factors for recurrence, while male sex was a protective factor (all P<0.05). Conclusion:Pulmonary vein isolation with second-generation CBA and RFCA guided by ablation index are safe and effective in the treatment of AF in elderly patients.