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.

Objective To explore the method for establishing a pig left lung orthotopic transplantation model. Methods A total of 4 male Yorkshire pigs weighing (40.0±2.5) kg underwent surgical procedures, including anesthesia, tracheal intubation, donor lung procurement, and recipient lung transplantation. Histological morphology and blood gas analysis of the transplanted lungs were assessed 2 hours after reperfusion to evaluate the histological changes and function of the transplanted lungs. Results The pigs underwent left lung in situ transplantation, effectively simulating conditions relevant to human lung transplantation. Two hours after the transplantation, arterial blood gas analysis showed that arterial partial pressure of oxygen was 155.4-178.6 mm Hg, arterial partial pressure of carbon dioxide was 53.1-62.4 mm Hg, and the oxygenation index was 310.8-357.2 mm Hg. Hematoxylin and eosin staining indicated a low degree of pulmonary edema and minimal cellular infiltration. Conclusion The pig left lung orthotopic transplantation model possesses strong operability and stability. Researchers can replicate this model according to the described methods and further conduct basic research and explore clinical translational applications.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

This paper reports the case of a 10-month-old male infant with Beckwith-Wiedemann syndrome (BWS) who presented with a reducible right inguinal mass and an empty scrotum for 10 months and was admitted for elective surgery. Preoperative ultrasonography revealed a right adrenal mass, which was pathologically diagnosed as ganglioneuroblastoma (GNB) after surgical excision. The patient exhibited characteristic features of BWS, including omphalocele, flame-shaped nevus on the forehead, bilateral earlobe creases, and embryonal tumor. Next-generation sequencing identified a heterozygous mutation in the CDKN1C gene (chr11:2905365), confirming the diagnosis of BWS. Early diagnosis, standardized management, and tumor surveillance are crucial for improving prognosis in children with BWS. Ultrasonography enables early detection of tumors and informs clinical decision-making regarding intervention.
Humans ; Beckwith-Wiedemann Syndrome/genetics* ; Male ; Ganglioneuroblastoma/complications* ; Infant ; Cyclin-Dependent Kinase Inhibitor p57/genetics* ; Mutation

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

Objective: To determine the expression of melanoma-associated antigens D4(MAGED4) and SIRT7 in human glioma, and to analyze the potential effects of MAGED4 and SIRT7 on glioma cell proliferation. Methods: The MAGED4 and SIRT7 expression levels and their correlation were compared by the China glioma genome atlas(CGGA), human protein atlas(HPA), and UALCAN databases. Survival analysis, ROC curve analysis, and Cox regression analysis were used to predict the outcome of MAGED4 and SIRT 7 in glioma patients. Gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG) signaling pathway enrichment analysis were used to explore the biological functions of MAGED4 and SIRT7 in glioma. Western blot experiment was used to investigate whether MAGED4 protein exerted its regulatory effects on the activity of the PI3K/AKT signaling pathway via SIRT7. The effect of MAGED4 on cell proliferation in glioma through SIRT7 was explored by CCK-8. Results: The analysis results of CGGA, UALCAN, and HPA databases showed that the expression levels of MAGED4 and SIRT7 in glioma tissues were higher than those in normal brain tissue, and the expression were positively correlated. Results of survival, ROC, and Cox analysis showed that high expression of MAGED4 and SIRT7 mRNA were risk factors for poor prognosis in glioma. Results of KEGG enrichment analysis showed that MAGED4 and SIRT7 were associated with the PI3K/AKT signaling in glioma, and Western blot results showed that MAGED4 activated the PI3K/AKT signaling pathway by regulating SIRT7. The CCK-8 results showed that MAGED4 promotes the proliferation of glioma cells through SIRT7. Conclusion MAGED4 and SIRT7 are highly expressed in glioma and associated with poor prognosis, and MAGED4 promotes glioma cell proliferation through activation of the PI3K/AKT signaling pathway by SIRT7.