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.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

AIM To explore the clinical effects of Supplemented Buyang Huanwu Decoction on postoperative patients with lumbar vertebral fracture complicated with spinal cord injury due to Qi Deficiency and Blood Stasis Pattern.METHODS One hundred and twenty patients were randomly assigned into control group(60 cases)for 6-week intervention of conventional treatment,and observation group(60 cases)for 6-week intervention of both Supplemented Buyang Huanwu Decoction and conventional treatment.The changes in clinical effects,TCM syndrome scores,spinal cord conduction signals(SEP amplitude,MEP amplitude),serum neurotrophic factors(NGF,IGF-1,BDNF),coagulation and inflammatory indices(PT,APTT,TNF-α,IL-1 β)and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,TNF-α,IL-1β(P＜0.05),increased spinal cord conduction signals,coagulation and inflammatory indices(P＜0.05),and shortened PT,APTT(P＜0.05),especially for the observation group(P＜0.05).No significant difference in incidence of adverse reactions was found between the two groups(P＞0.05).CONCLUSION For the patients with lumbar vertebral fracture complicated with spinal cord injury due to Qi Deficiency and Blood Stasis Pattern,Supplemented Buyang Huanwu Decoction can safely and effectively promote neurological function recovery.

OBJECTIVE: To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases, and to develop deep learning-based models for daily hospital admission prediction. METHODS: A multi-city study was conducted in Tokyo's 23 wards, Osaka City, and Nagoya City. Random forest models were employed to assess the synergistic short-term lag effects (lag0, lag3, and lag7) of climate and air pollutants on hospitalization for five cardiovascular diseases (CVDs) and two respiratory diseases (RDs). Furthermore, we developed hybrid deep learning models that integrated an autoencoder (AE) with a Long Short-Term Memory network (AE+LSTM) to predict daily hospital admissions. RESULTS: On the day of exposure (lag0), air pollutants, particularly nitrogen oxides (NO x), exhibited the strongest influence on hospital admissions for CVD and RD, with pronounced effects observed for hypertension (I10-I15), ischemic heart disease (I20), arterial and capillary diseases (I70-I79), and lower respiratory infections (J20-J22 and J40-J47). At longer lags (lag3 and lag7), temperature and precipitation were more influential predictors. The AE+LSTM model outperformed the standard LSTM, improving the prediction accuracy by 32.4% for RD in Osaka and 20.94% for CVD in Nagoya. CONCLUSION Our findings reveal the dynamic, time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions. This framework can inform early warning systems, enhance healthcare resource allocation, and support climate-adaptive public health strategies.
Humans ; Hospitalization/statistics & numerical data* ; Cardiovascular Diseases/epidemiology* ; Japan/epidemiology* ; Air Pollutants/analysis* ; Air Pollution/adverse effects* ; Cities/epidemiology* ; Climate ; Respiratory Tract Diseases/epidemiology* ; Deep Learning ; Male

Objectives:QT interval prolongation during treadmill test exercise is one of the clinical feature of patients with long QT syndrome(LQTS).This study aimed to explore the feasibility and efficacy of treadmill exercise testing as an auxiliary diagnosis tool for LQTS in clinical practice.Methods:We enrolled normal healthy individuals,common cardiovascular disease patients,and clinically diagnosed or suspected LQTS patients,who underwent treadmill exercise test from July 2023 to July 2024 at Fuwai Hospital.A special treadmill exercise testing procedure was designed to record the QT interval correction(QTc)intervals of the twelve lead electrocardiogram at 6 time points when performing the exercise tablet,including supine,sitting,standing,peak exercise,and recovery at 1-minute and 4-minute.The differences in QTc intervals among healthy group,cardiovascular diseases group,and suspected LQTS group were compared.Results:A total of 80 cases were consecutively enrolled,including 37 normal healthy controls,25 patients with common cardiovascular disease,and 18 patients with suspected LQTS.The QTc intervals at 6 points did not differ significantly between normal healthy controls and patients with cardiovascular disease,with QTc intervals less than 480 ms at all measurement.For patients with suspected LQTS,67.7%(12/18)of these patients presented a QTc interval≥480 ms at the 4-minute during recovery period.Among them,5 cases were confirmed to have pathogenic gene mutations of LQTS by genetic testing(including 1 case with a lying electrocardiogram QTc interval of 489 ms diagnosed with LQTS 1 type and a QTc interval of 636 ms during the 4-minute recovery period after exercise);5 clinically diagnosed patients(negative or undetectable in genetic testing)with a Schwartz score≥4,and the remaining 2 patients had a Schwartz score of 3.The remaining 5/18 patients,include 2 patients with clinical Schwartz scores≥4 and 3 patients with clinical suspicion(Schwartz scores 2-3)had a 4 min QTc interval of 445-480 ms during exercise recovery.Another patient with clinical suspicion(Schwartz score 3)had a 4 min QTc interval of<445 ms during exercise recovery and a negative genetic test at a later stage.Receiver operating characteristic curve analysis showed a sensitivity of 83.3%and specificity of 98.4%for QTc interval≥482 ms during the 4-minute recovery period of exercise as the LQTS diagnostic cutoff.Conclusions:This study results suggest that this special treadmill exercise testing protocol is effective in identifying LQTS and has strong feasibility and generalizability for clinical practice.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To explore the level of energy balance related protein antibody,adropin,and its correlation with collateral circulation status in elderly patients with AMI.Methods A total of 193 elderly AMI patients admitted to our department from February 2022 to February 2024 were enrolled,and based on their collateral circulation status,they were divided into a good circulation group(121 cases)and a poor circulation group(72 cases).The level of adropin was determined.Multivariate logistic regression analysis was employed to determine the influencing factors of poor collateral circulation in elderly AMI patients.Results The poor circulation group had significantly larger proportion of hypertension,higher fasting blood glucose(FBG),and larger red blood cell distribution width(RDW),but lower adropin level and mean platelet volume(MPV)when com-pared with the good circulation group(P＜0.05,P＜0.01).Multivariate logistic regression analy-sis showed that adropin,FBG,RDW,MPV,and hypertension were all influencing factors for poor collateral circulation in elderly AMI patients(P＜0.05,P＜0.01).The AUC value of adropin,FBG,RDW,MPV,and hypertension was 0.810,0.762,0.761,0.715 and 0.563,respectively in pre-dicting poor collateral circulation.Among them,adropin level had the highest predictive value(P＜0.01).Conclusion The decrease in adropin level in elderly AMI patients is closely associated with poor collateral circulation,and it is a predictive factor for collateral circulation.

Objective:To investigate exercise fear condition and its influencing factors in patients with coronary heart dis-ease(CHD)involving in phase Ⅱ cardiac rehabilitation after percutaneous coronary intervention(PCI).Methods:Conven-ience sampling was performed among cardiac rehabilitation patients from a Shanxi grade-A tertiary hospital between Janu-ary 2023 and June 2023.General data questionnaire,Tampa Scale for Kinesiophobia-Short Version for Heart patients(TSK-SV H),the Multidimensional Scale of Perceived Social Support(MSPSS)and the Post Percutaneous Coronary In-tervention Health Literacy Scale(PPCIHLS)were used for assessment.Pearson and Spearman rank correlation analyses were applied to investigate the relationships between social support factors and health literacy factors with patients'fear of exercise;multivariate linear regression models and quantile regression models were used to analyze the influencing factors of fear of exercise.Results:Among the 118 patients,average TSK-SV H score was(33.78±3.79)points;10.2％patients showed significant fear.The correlation analysis showed that PPCIHLS score was negatively correlated with TSK-SV H score(r=-0.423,P＜0.001).Results from multivariate regression analysis indicated that health literacy,cardiac func-tion class and drinking status affected the average score of exercise fear(P＜0.05 all).The quantile regression model pro-vided additional insights,showing that the impact of factors such as living situation,drinking status,cardiac function class and health literacy on exercise fear varied across different quantiles.Notably,cardiac function class had a consistently posi-tive effect on TSK-SV H score at various quantiles.Conclusion:Improving health literacy and tailored rehabilitation plans are vital to reduce exercise fear and improve cardiac rehabilitation effect.

With the establishment of bio-psycho-social medical model,both social and psychological factors play an important role in the occurrence,development and treatment of diseases.Hypertension is a common chronic multiple disease in China,and patients are often complicated with depression and other e-motional disorders.The interaction between hypertension and depression significantly increases the risk of poor prognosis.Current studies have shown a bidirectional promoting relationship between hypertension and depression,and they have some com-mon pathogenesis.However,the specific mechanism of their co-morbidity has not been fully elucidated.Renin-angiotensin sys-tem(RAS)plays an important role in the regulation of hyperten-sion and depression and other emotions.It is composed of two antagonistic pathways.The balance is maintained by angioten-sin-converting enzyme 2(ACE2).Therefore,this article reviews the relationship and mechanism of RAS in hypertension,depres-sion and comorbid states,in order to provide new treatment ide-as for hypertension and depression.