Objective To investigate the expression of carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6) in esophageal squamous cell carcinoma (ESCC) and analyze its correlation with immune cell infiltration and patient prognosis. Methods Three ESCC datasets (GSE161533, GSE26886, and GSE23400) from the GEO database were analyzed to identify differentially expressed genes. CEACAM6 was identified as a key gene through survival analysis. Its expression, prognostic value, and relationship with immune cell infiltration were further explored using databases, such as TIMER. Tissue samples were collected from 162 patients with ESCC. Immunohistochemistry was performed to detect the expression of CEACAM6, immune cell markers (CD4, CD8, CD20, and CD56), and immune checkpoint molecules (HHLA2 and CD40LG). Correlations between CEACAM6 expression and clinicopathological features, immune cell infiltration, and immune checkpoints were analyzed. Results Bioinformatic analysis and clinical sample validation confirmed that CEACAM6 expression was significantly upregulated in ESCC tissues compared with adjacent nontumor tissues (P<0.05). High CEACAM6 expression was closely associated with advanced clinical stage (AJCC Ⅲ-Ⅳ), high T stage (T3-T4), lymph node metastasis, nonulcerative type, and poor prognosis. Furthermore, CEACAM6 expression levels were positively correlated with the infiltration density of CD8⁺ T cells, CD4⁺ T cells, and CD20⁺ B cells within the tumor microenvironment and with the expression of the immune checkpoint molecules HHLA2 and CD40LG (all P<0.05). Conclusion CEACAM6 serves as an independent poor prognostic factor for ESCC. Its high expression is implicated in the modulation of the tumor immune microenvironment by correlating with specific immune cell infiltration and immune checkpoint molecules, suggesting its potential as a novel prognostic biomarker and immunotherapeutic target for ESCC.

ObjectiveTo observe the short-term and long-term efficacy of traditional Chinese medicine （TCM） surgical operations in treating mixed hemorrhoids. MethodsA multi-center retrospective cohort study was conducted， collecting clinical data from 17，831 mixed hemorrhoid surgery patients in 8 top-tier TCM hospitals in Jiangsu Province. Standardized and structured datasets were obtained through artificial intelligence models. Patients who underwent western surgical treatment were categorized into the western surgery group （11,646 cases）， and those receiving TCM surgical operations were categorized into the TCM surgery group （6185 cases）. Propensity score matching （1∶1 matching） was used to balance baseline data between groups. The primary outcome was the one-year recurrence rate， and secondary outcomes included the main symptoms （rectal bleeding， degree of prolapse） and secondary symptoms （anal distension， anal edema， wound secretion and exudation， anal stenosis， residual skin tags， perianal itching， and anal pain） measured on days 7， 28， and 60 after discharge. ResultsAfter matching， 2194 patients were included in each group. Symptom scores showed that at 28 days after discharge， the TCM surgical group had superior improvement in rectal bleeding ［OR=5.786， 95%CI （3.092，10.827）］， degree of prolapse ［OR=4.510， 95%CI （1.649，12.333）］， and anal edema ［OR=3.188， 95%CI （1.295，7.845）］ compared to the western surgical group. At 60 days post-discharge， the TCM group still showed advantages in improving rectal bleeding ［OR=5.237， 95%CI （1.077，25.464）］ and anal pain ［OR=11.697， 95%CI （1.186，115.336）］ （P＜0.05）. Long-term follow-up showed that the one-year recurrence rate in the TCM surgery group was 1.1% （8/726）， while that in the western surgery group was 2.3% （10/444）， with no statistically significant difference between the two groups （P＞0.05）. ConclusionBased on real-world data， TCM surgical treatment for mixed hemorrhoids shows significant short-term symptom improvement， particularly in terms of hemostasis， reducing swelling， and alleviating prolapse of anal masses.

ObjectiveTo analyze the characteristics of 150 patients with spinal cord injury complicated with spasticity. MethodsA cross-sectional survey was conducted on 150 patients with spinal cord injury accompanied by spasticity from September, 2019 to December, 2024. Their age, gender, cause of injury, injury site, severity of injury, spasticity severity and other indicators were recorded. The relationships between different characteristics were analyzed, and a correlation analysis of disease duration, spasticity grade, injury level, injury severity and age were conducted. ResultsThere was no significant difference in age distribution between patients with tetraplegia and paraplegia (Z = 0.806, P = 0.420). The proportions of trauma (χ² = 3.982, P = 0.046) and tetraplegia (χ² = 10.559, P = 0.010) were higher in males than in females. Trauma was the main cause of injury in both tetraplegia and paraplegia patients; the proportion of tetraplegia was higher than paraplegia in trauma patients, while paraplegia was higher than tetraplegia in non-trauma patients (χ² = 11.885, P < 0.001). Patients with tetraplegia was dominated by incomplete injury, whereas patients with paraplegia was dominated by complete injury (χ² = 10.885, P = 0.012). Grade A injury was predominant in trauma patients (P = 0.003). Spasticity grade showed a very weak positive correlation with disease duration (r = 0.175, P = 0.032) and age (r = 0.168, P = 0.040). Injury severity showed a very weak positive correlation with age (r = 0.183, P = 0.025). ConclusionCharacteristics of patients with spinal cord injury complicated with spasticity is different with gender, cause of injury, injury level, injury severity.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

BACKGROUND:Alzheimer's disease has been associated with sarcopenia,but a causal relationship has not been established.Exploring the causal relationship between the two most common disability-burdening diseases in the aging population-Alzheimer's disease and sarcopenia-and their potential mediating factors holds certain implications for further alleviating the healthcare costs and socioeconomic burden for older adults in China.OBJECTIVE:To explore the potential causal relationship between Alzheimer's disease and sarcopenia in the general population using a Mendelian randomization study and to explore the role of body mass index in this context.METHODS:Two-sample Mendelian randomization analysis based on published genome-wide association studies(GWAS)were used to infer causality,and univariate Mendelian randomization and mediation analyses were used in the study design.Through the Integrative Epidemiology Unit(IEU)database,ieu-b-2 was selected as the Alzheimer's disease dataset(sample size:63 926),ieu-b-4816 as the body mass index dataset(99 998),ebi-a-GCST90000027 as the appendicular lean mass dataset(244 730),ukb-b-7478 as the left hand grip strength dataset(461 026),ukb-b-10215 as the right hand grip strength dataset(461 089)and ukb-b-4711 as the walking pace dataset(459 915).Inverse-variance weighting was used as the primary analysis method,and the results were validated by pleiotropy and heterogeneity analysis.The Steiger Directionality Test was performed to validate the reasonableness of the causal direction.RESULTS AND CONCLUSION:(1)The Mendelian randomization analyses provided evidence that Alzheimer's disease predicted the risk of appendicular lean mass[odds ratio(OR)=1.009;95％confidence interval(Cl),1.001-1.017;P=0.023),and walking pace(OR=1.010;95％Cl,1.003-1.017;P=0.008).No correlation with hand grip strength was observed.(2)Alzheimer's disease was negatively correlated with body mass index(OR=0.893;95％Cl,0.811-0.984;P=0.022);body mass index was positively correlated with appendicular lean mass(OR=1.084;95％Cl,1.031-1.141;P=0.002)and negatively correlated with walking pace(OR=0.975;95％Cl,0.969-0.980;P＜0.001).(3)Mediation analyses showed that the causal relationship between Alzheimer's disease and appendicular lean mass and walking pace was partially mediated by body mass index,with the proportion of mediations being 50.25％and 32.11％,respectively.(4)The results of this study suggest that based on large-scale population studies,genetic prediction of Alzheimer's disease is a potential risk factor for sarcopenia,in which body mass index plays an important mediating role.This suggests that in clinical practice,attention should be paid to the muscle condition of patients with Alzheimer's disease,and weight management should be implemented,as maintaining a body mass index within the normal high range may have a preventive effect on the occurrence of sarcopenia in patients with Alzheimer's disease.However,further research is needed to verify the applicability of this conclusion to other ethnic groups.This study utilized an international public database for analysis,providing a reference for research on the correlation between Alzheimer's disease and sarcopenia in the Chinese population.It also highlights the significant mediating role of body mass index,offering insights for further prevention and treatment of sarcopenia among Chinese individuals.

BACKGROUND:Alzheimer's disease has been associated with sarcopenia,but a causal relationship has not been established.Exploring the causal relationship between the two most common disability-burdening diseases in the aging population-Alzheimer's disease and sarcopenia-and their potential mediating factors holds certain implications for further alleviating the healthcare costs and socioeconomic burden for older adults in China.OBJECTIVE:To explore the potential causal relationship between Alzheimer's disease and sarcopenia in the general population using a Mendelian randomization study and to explore the role of body mass index in this context.METHODS:Two-sample Mendelian randomization analysis based on published genome-wide association studies(GWAS)were used to infer causality,and univariate Mendelian randomization and mediation analyses were used in the study design.Through the Integrative Epidemiology Unit(IEU)database,ieu-b-2 was selected as the Alzheimer's disease dataset(sample size:63 926),ieu-b-4816 as the body mass index dataset(99 998),ebi-a-GCST90000027 as the appendicular lean mass dataset(244 730),ukb-b-7478 as the left hand grip strength dataset(461 026),ukb-b-10215 as the right hand grip strength dataset(461 089)and ukb-b-4711 as the walking pace dataset(459 915).Inverse-variance weighting was used as the primary analysis method,and the results were validated by pleiotropy and heterogeneity analysis.The Steiger Directionality Test was performed to validate the reasonableness of the causal direction.RESULTS AND CONCLUSION:(1)The Mendelian randomization analyses provided evidence that Alzheimer's disease predicted the risk of appendicular lean mass[odds ratio(OR)=1.009;95％confidence interval(Cl),1.001-1.017;P=0.023),and walking pace(OR=1.010;95％Cl,1.003-1.017;P=0.008).No correlation with hand grip strength was observed.(2)Alzheimer's disease was negatively correlated with body mass index(OR=0.893;95％Cl,0.811-0.984;P=0.022);body mass index was positively correlated with appendicular lean mass(OR=1.084;95％Cl,1.031-1.141;P=0.002)and negatively correlated with walking pace(OR=0.975;95％Cl,0.969-0.980;P＜0.001).(3)Mediation analyses showed that the causal relationship between Alzheimer's disease and appendicular lean mass and walking pace was partially mediated by body mass index,with the proportion of mediations being 50.25％and 32.11％,respectively.(4)The results of this study suggest that based on large-scale population studies,genetic prediction of Alzheimer's disease is a potential risk factor for sarcopenia,in which body mass index plays an important mediating role.This suggests that in clinical practice,attention should be paid to the muscle condition of patients with Alzheimer's disease,and weight management should be implemented,as maintaining a body mass index within the normal high range may have a preventive effect on the occurrence of sarcopenia in patients with Alzheimer's disease.However,further research is needed to verify the applicability of this conclusion to other ethnic groups.This study utilized an international public database for analysis,providing a reference for research on the correlation between Alzheimer's disease and sarcopenia in the Chinese population.It also highlights the significant mediating role of body mass index,offering insights for further prevention and treatment of sarcopenia among Chinese individuals.

Objective: To develop a convenient, direct, and highly sensitive method for screening trace chemical additives in complex Chinese patent medicines, thereby addressing core technological bottlenecks in pharmaceutical analysis and quality control. Methods: A brush ionization probe device was independently designed and constructed, and an efficient detection method was established through systematic optimization of key parameters. Twenty-three Chinese patent medicine samples, representing 6 dosage forms (capsules, tablets, pills, granules, powders, and liquid preparations), were analyzed using 10 common chemical additives as target analytes. Results: All samples were successfully analyzed without complex pretreatment, and 5 chemical additives were detected in 7 Chinese patent medicines. The brush ionization probe device exhibited cost-effectiveness (~0.2 USD per probe), operational simplicity, rapid analysis (~10s per sample), high efficiency, and minimal reagent consumption (~10 μL per sample). Conclusion: This advancement is expected to provide an innovative scientific tool for improving the generality and convenience of on-site quality control, while promoting technological progress in disciplines such as pharmacology and traditional Chinese medicine.

ObjectiveTo construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings. MethodsData on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement. ResultsA total of 15,444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11,144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-rays in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （P＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （P＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively. ConclusionAn automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.