Prostate-specific membrane antigen (PSMA) is expressed in the neovasculature of various malignancies, such as colorectal cancer (CRC) and hepatocellular carcinoma (HCC). However, PSMA expression in hepatic CRC metastasis has not been studied in detail. Methods: The PSMA expression in primary CRC and corresponding hepatic metastasis was evaluated by immunohistochemistry in a metastatic CRC cohort (n = 56), which was divided into subgroups according to treatment history and timing of metastasis. Demographic and histological characteristics of primary CRC were collected and their relationships with PSMA expression were examined. Additionally, the PSMA expression in resected HCC (n = 76) was compared with that of hepatic CRC metastasis. Results: In primary CRC, PSMA level showed a positive association with tumor size. Lower PSMA expression in hepatic metastasis was associated with higher primary CRC grade, advanced pTNM stage at the time of CRC resection, presence of tumor deposit, and unresectability of metastatic lesion. PSMA expression in primary CRC correlated with that in hepatic metastasis only in concurrent and untreated metastasis subgroup. PSMA expression in primary CRC and hepatic metastasis, regardless of treatment history and timing of metastasis, was not significantly different from that of HCC. Conclusions: Several adverse pathological features of primary CRC were associated with a lower PSMA expression in hepatic metastasis. PSMA expression in hepatic metastasis correlated with that of primary CRC only in concurrent and untreated subgroup. Primary HCC and hepatic CRC metastasis show comparable levels of PSMA expression.

ObjectiveTo investigate the value of predictive models established based on machine learning methods in predicting the risk of metabolic associated fatty liver disease （MAFLD）， and to analyze its key risk factors. MethodsA retrospective analysis was performed for the 50 variables of 2 168 healthy individuals who underwent physical examination in Department of Health Assessment， Xiyuan Hospital， China Academy of Chinese Medical Sciences， from January 2021 to December 2024， including body composition， past history， and laboratory tests， and according to whether they were diagnosed with MAFLD or not， they were divided into MAFLD group with 265 individuals and non-MAFLD group with 1 903 individuals. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Randomly split the research data into a training set and a validation set in a 70% to 30% ratio. Predictive factors were screened from the training set data using univariate analysis， LASSO regression， and multivariate Logistic regression analysis. Predictive models were then constructed using seven machine learning methods： Logistic regression， decision tree， random forest （RF）， eXtreme gradient boosting， light gradient boosting machine， support vector machine， and artificial neural network. Model performance was evaluated by plotting receiver operating characteristic curve for the validation set and calculating the area under the curve （AUC）， sensitivity， specificity， and Youden index for each model. Furthermore， the SHapley Additive exPlanation （SHAP） method was used to analyze the contribution of variables in the optimal model. ResultsThe prevalence rate of MAFLD among the 2 168 subjects was 12.22% （265/2 168）. Smoking， diastolic blood pressure， phase angle， visceral fat area， muscle fat ratio， waist-to-hip ratio， aspartate aminotransferase， non-HDL-C/HDL-C ratio， triglyceride-glucose index， and gallstones were independent risk factors for MAFLD （all P<0.05）. The seven predictive models of support vector machine， eXtreme gradient boosting， decision tree， light gradient boosting machine， artificial neural network， RF， and Logistic regression had an AUC of 0.738， 0.754， 0.757， 0.786， 0.795， 0.796， and 0.815， respectively， in the validation set， among which the RF model had the best discriminatory ability （AUC=0.796， 95% confidence interval： 0.754 — 0.839）， with a sensitivity of 81.01%， a specificity of 63.16%， and a Youden index of 44.17%. The SHAP analysis showed that visceral fat area， waist-to-hip ratio， and diastolic blood pressure were the top three predictive factors in terms of importance. ConclusionThe RF model， constructed based on body composition and clinical indicators， has a good performance in predicting the risk of MAFLD， and its interpretability can help to identify high-risk individuals in the early stage in clinical practice.

Objective:To analyze the evaluation of the “1+1+2M” healthcare management model (outpatient core medical staff+ward core medical staff+multi interdisciplinary team+multi department coordination) by healthcare workers in a large public hospital and its influencing factors.Methods:It was a cross-sectional study, using the judgment sampling method to select 70 healthcare workers in the hospital healthcare department in November 2024. A self-designed public hospital healthcare management model evaluation questionnaire was used to investigate the “1+1+2M” healthcare management model. A total of 70 questionnaires were distributed, and 70 valid questionnaires were collected (100%). The basic information, team integration, human resource allocation, and evaluation of healthcare management models in the healthcare workers were collected. The factors affecting healthcare worker evaluation were identified with Kruskal Wallis test and multiple logistic regression analysis.Results:Among the 70 healthcare workers included in the analysis, there were 30 doctors, 30 nursing staff, and 10 management/technical personnel; 19 males and 51 females; more than 70% of the participants were aged 50 years or younger; the length of service was (19.11±9.44) years, and the length of service for healthcare was (9.79±6.06) years. Healthcare workers showed high satisfaction with the “1+1+2M” healthcare management model, with an overall satisfaction rate of 87.14% (61/70). The evaluation of work collaboration, business norms, and mode configuration by healthcare workers was positively correlated with the evaluation of healthcare management mode [very satisfied with health management mode: relatively satisfied with work collaboration ( OR=0.064, 95% CI: 0.004-0.965), satisfied with business norms ( OR=0.018, 95% CI: 0.001-0.312), relatively satisfied with business norms ( OR=0.061, 95% CI: 0.004-0.938), satisfied with mode configuration ( OR=0.006, 95% CI: 0.000-0.111); satisfied with health management mode: satisfied with business norms ( OR=0.049, 95% CI: 0.005-0.498), satisfied with mode configuration ( OR=0.074, 95% CI: 0.006-0.943)] (all P<0.05). Conclusions:The health care staff in a large public hospital have a good evaluation of the operation effect of the “1+1+2M”health management model. The higher the evaluation of work collaboration, business norms, and model configuration by the health care staff, the higher the overall satisfaction with the health management model.

This paper reviews the published articles of recent years on the application of deep learning methods in automatic localization of acupoint, and summarizes it from 3 key links, i.e. the dataset construction, the neural network model design, and the accuracy evaluation of acupoint localization. The significant progress has been obtained in the field of deep learning for acupoint localization, but the scale of acupoint detection needs to be expanded and the precision, the generalization ability, and the real-time performance of the model be advanced. The future research should focus on the support of standardized datasets, and the integration of 3D modeling and multimodal data fusion, so as to increase the accuracy and strengthen the personalization of acupoint localization.
Deep Learning ; Acupuncture Points ; Humans ; Neural Networks, Computer

Objective To comprehensively assess the predictive value of C-reactive protein(CRP)/D-dimer(D-D)and albumin/fibrinogen(FAR)in predicting major adverse cardiovascular events(MACE)after percutaneous coronary intervention(PCI)in patients with coronary heart disease(CHD)and to construct a nomogram model for predicting post-procedural MACE in CHD patients.Methods A retrospective study was conducted on 201 CHD patients who underwent PCI at the First Affiliated Hospital of Kunming Medical University between June 2022 and March 2025.These patients were divided into MACE group(n=77)and non-MACE group(n=124)based on whether MACE occurred or not.84 CHD patients from another medical center were also collected as the validation set.The expression levels of CRP/D-D and FAR were compared between the two groups;independent predictors of postoperative MACE in CHD patients were screened by univariate and multivariate logistic regression analyses;the predictive value of CRP/D-D and FAR for the occurrence of postoperative MACE in CHD patients was assessed using ROC curves;A nomogram model was established integrating indicators such as CRP/D-D and FAR,and internal and external validations of the nomogram model were conducted using ROC curves,calibration curves,and decision curve analysis(DCA)curves.Results Compared with CHD patients in the non-MACE group,CRP/D-D and FAR levels were significantly higher in the MACE group(P<0.05).Multivariate analysis showed that age,NTproBNP,WBC,CRP/D-D,and FAR were independent risk factors for postoperative MACE in CHD patients(P<0.05).ROC curve analysis indicated that the AUC predicted by CRP/D-D combined with FAR was higher than that of CRP/D-D alone(Z=3.473,P<0.001),and FAR alone(Z=2.812,P<0.05).The Nomogram model constructed based on the aforementioned factors was validated internally and externally,and the results showed that the Nomogram model had good calibration,excellent discriminative ability,and reliable clinical utility,accurately predicting the risk of postoperative MACE.Conclusion The CRP/D-D ratio and FAR,as emerging composite biomarkers,showed significant predictive ability in predicting the risk of MACE after PCI in patients with CHD,providing a new reliable tool for clinical risk stratification.

Mucoceles, commonly observed in the appendix, are mucin-filled, dilated structures arising from a range of etiologies. Cases associated with dysplastic or neoplastic epithelium can rupture and disseminate within the abdominopelvic cavity. Similar lesions in other parts of the colon are exceedingly rare, with only 16 colonic mucoceles having been reported. The first case of a colonic mucinous neoplasm with dysplasia resembling a low-grade appendiceal mucinous neoplasm involving rectal stump was described in 2016. Here, we present the second such case arising in the rectal stump, identified in a 44-year-old male with extensive surgical history. Microscopic examination revealed low-grade dysplastic epithelium lining the cyst and mucin dissecting into the stroma, without evidence of rupture or extramural mucin. The patient was followed for 16 months without recurrence or peritoneal disease. The exact etiology and outcome of these rare lesions remain unknown, requiring close follow-up.

ObjectiveTo explore the data standard system of electronic medical records in the field of rehabilitation, focusing on the terminology and coding standards, data structure, and key content categories of rehabilitation electronic medical records. MethodsBased on the Administrative Norms for the Application of Electronic Medical Records issued by the National Health Commission of China, the electronic medical record standard architecture issued by the International Organization for Standardization and Health Level Seven (HL7), the framework of the World Health Organization Family of International Classifications (WHO-FICs), Basic Architecture and Data Standards of Electronic Medical Records, Basic Data Set of Electronic Medical Records, and Specifications for Sharing Documents of Electronic Medical Records, the study constructed and organized the data structure, content, and data standards of rehabilitation electronic medical records. ResultsThe data structure of rehabilitation electronic medical records should strictly follow the structure of electronic medical records, including four levels (clinical document, document section, data set and data element) and four major content areas (basic information, diagnostic information, intervention information and cost information). Rehabilitation electronic medical records further integrated information related to rehabilitation needs and characteristics, emphasizing rehabilitation treatment, into clinical information. By fully applying the WHO-FICs reference classifications, rehabilitation electronic medical records could establish a standardized framework, diagnostic criteria, functional description tools, coding tools and terminology index tools for the coding, indexing, functional description, and analysis and interpretation of diseases and health problems. The study elaborated on the data structure and content categories of rehabilitation electronic medical records in four major categories, refined the granularity of reporting rehabilitation content in electronic medical records, and provided detailed data reporting guidance for rehabilitation electronic medical records. ConclusionThe standardization of rehabilitation electronic medical records is significant for improving the quality of rehabilitation medical services and promoting the rehabilitation process of patients. The development of rehabilitation electronic medical records must be based on the national and international standards. Under the general electronic medical records data structure and standards, a rehabilitation electronic medical records data system should be constructed which incorporates core data such as disease diagnosis, functional description and assessment, and rehabilitation interventions. The standardized rehabilitation electronic medical records scheme constructed in this study can support the improvement of standardization of rehabilitation electronic medical records data information.

ObjectiveTo analyze the data structure and standards of rehabilitation outpatient medical records, to provide data support for improving the quality of rehabilitation outpatient care and developing medical insurance payment policies. MethodsBased on the normative documents issued by the National Health Commission, Basic Standards for Medical Record Writing and Standards for Electronic Medical Record Sharing Documents, in accordance with the Quality Management Regulations for Outpatient (Emergency) Diagnosis and Treatment Information Pages (Trial), reference to the framework of the World Health Organization Family of International Classifications (WHO-FICs), the data framework and content of rehabilitation outpatient medical records were determined, and the data standards were discussed. ResultsThis study constructed a data framework for rehabilitation outpatient medical records, including four main components: patient basic information, visit process information, diagnosis and treatment information, and cost information. Three major reference classifications of WHO-FICs, International Classification of Diseases, International Classification of Functioning, Disability and Health, and International Classification of Health Interventions,were used to establish diagnostic standards and standardized terminology, as well as coding disease diagnosis, functional description, functional assessment, and rehabilitation interventions, to improve the quality of data reporting, and level of quality control in rehabilitation. ConclusionThe structuring and standardization of rehabilitation outpatient medical records are the foundation for sharing of rehabilitation data. The using of the three major classifications of WHO-FICs is valuable for the terminology and coding of disease diagnosis, functional description and assessment, and intervention in rehabilitation outpatient medical records, which is significant for sharing and interconnectivity of rehabilitation outpatient data, as well as for optimizing the quality and safety of rehabilitation medical services.

ObjectiveTo explore the standardization of inpatient rehabilitation medical record summary sheet, encompassing its structure, content and data standards, to enhance the standardization level of inpatient rehabilitation medical record summary sheet, improve data reporting quality, and provide accurate data support for medical insurance payment, hospital performance evaluation, and rehabilitation discipline evaluation. MethodsBased on the relevant specifications of the National Health Commission's Basic Norms for Medical Record Writing, Specifications for Sharing Documents of Electronic Medical Records, and Quality Management and Control Indicators for Inpatient Medical Record Summary Sheet (2016 Edition), this study analyzed the structure and content of the inpatient rehabilitation medical record summary sheet. The study systematically applied the three major reference classifications of the World Health Organization Family of International Classifications, International Classification of Diseases (ICD-10/ICD-11, ICD-9-CM-3), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), for disease diagnosis, functional description and assessment, and rehabilitation intervention, forming a standardized terminology system and coding methods. ResultsThe inpatient rehabilitation medical record summary sheet covered four major sections: inpatient information, hospitalization information, diagnosis and treatment information, and cost information. ICD-10/ICD-11 were the standards and coding tools for admission and discharge diagnoses in the inpatient rehabilitation medical record summary sheet. The three functional assessment tools recommended by ICD-11, the 36-item version of World Health Organization Disability Assessment Schedule 2.0, Brief Model Disability Survey and Generic Functioning domains, as well as ICF, were used for rehabilitation functioning assessment and the coding of outcomes. ICHI Beta-3 and ICD-9-CM-3 were used for coding surgical procedures and operations in the medical record summary sheet, and also for coding rehabilitation intervention items. ConclusionThe inpatient rehabilitation medical record summary sheet is a summary of the relevant content of the rehabilitation medical record and a tool for reporting inpatient rehabilitation data. It needs to be refined and optimized according to the characteristics of rehabilitation, with necessary data supplemented. The application of ICD-11/ICD-10, ICF and ICHI Beta-3/ICD-9-CM-3 classification standards would comprehensively promote the accuracy of inpatient diagnosis of diseases and functions. Based on ICD-11 and ICF, relevant functional assessment result data would be added, and ICHI Beta-3/ICD-9-CM-3 should be used to code rehabilitation interventions. Improving the quality of rehabilitation medical records and inpatient rehabilitation medical record summary sheet is an important part of rehabilitation quality control, and also lays an evidence-based data foundation for the analysis and application of inpatient rehabilitation medical record summary sheet.

Mucoceles, commonly observed in the appendix, are mucin-filled, dilated structures arising from a range of etiologies. Cases associated with dysplastic or neoplastic epithelium can rupture and disseminate within the abdominopelvic cavity. Similar lesions in other parts of the colon are exceedingly rare, with only 16 colonic mucoceles having been reported. The first case of a colonic mucinous neoplasm with dysplasia resembling a low-grade appendiceal mucinous neoplasm involving rectal stump was described in 2016. Here, we present the second such case arising in the rectal stump, identified in a 44-year-old male with extensive surgical history. Microscopic examination revealed low-grade dysplastic epithelium lining the cyst and mucin dissecting into the stroma, without evidence of rupture or extramural mucin. The patient was followed for 16 months without recurrence or peritoneal disease. The exact etiology and outcome of these rare lesions remain unknown, requiring close follow-up.