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:This study aims to construct a quality evaluation index system for specialized disease databases. Through systematic assessment and optimization, it seeks to comprehensively enhance the quality and standardization of specialized disease cohort data. This initiative will provide more precise and reliable data support for disease research, the development of innovative drugs and medical devices, as well as policy formulation.Methods:By conducting a thorough analysis of domestic and international literature and policies related to clinical research data quality evaluation systems, preliminary quality evaluation indicators for specialized disease databases were established. Utilizing the Delphi method in two rounds, a quality evaluation system for specialized disease databases was constructed. The Analytic Hierarchy Process (AHP) and YAAHP 7.5 software were then employed to calculate the relative weights of indicators at various levels and their composite weights.Results:The two rounds of expert consultation achieved a 100.00% valid response rate, with an expert authority coefficient of 0.81 in both rounds. In the second round, the Kendall′s coordination coefficients for the first-level and second-level indicators reached 0.311 and 0.218, respectively ( P<0.05), indicating a good level of consensus among experts. The final specialized disease database quality evaluation system consists of 3 first-level indicators, 10 second-level indicators, and 32 third-level indicators. The first-level indicators include database construction, data quality, and cohort development, with weight coefficients of 31.82%, 41.49%, and 26.69%, respectively. The scientific validity of the indicator system was confirmed through reliability and validity analyses. When applied to assessing 58 specialized disease database projects from 36 medical institutions in a certain city, the results showed significant improvements in scores for database construction, data quality, and cohort development, with the most notable improvement observed in database construction. Conclusions:This study successfully developed a scientific, practical, and rationally weighted quality evaluation system for specialized disease databases, demonstrating high expert consensus and broad applicability.Validation studies have shown that this system effectively enhances the standardization and data quality of databases, providing robust technical support and assurance for specialized disease research and data resource sharing.

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 corrects the article DOI: 10.1016/j.apsb.2019.01.013.].

OBJECTIVES: To explore the values of materialism in medical postgraduates and its relationship with depression. METHODS: We conducted a survey among 592 postgraduates in clinical medicine using Materialism Tendency Scale (MTS) and Patient Health Questionnaire-9 (PHQ-9). Descriptive analysis, mean comparison, and correlation analysis were used for data analysis. RESULTS: The postgraduates had a mean total score of materialism of 56.36±10.44, and 11.8%, 54.1%, and 34.1% of them reported high, medium, and low levels of materialism, respectively. No significant differences were found in the total score of materialism among the postgraduates of different genders or with different family economic levels, but the postgraduates from urban families had a significantly higher level of materialism than those from rural families (P<0.001). 18.4% of the postgraduates screened positive for moderate to severe depressive symptoms. The total score of MTS was positively correlated with depression (r=0.289, P<0.001). The dimension scores of MTS for materialism obsessions, material vanity, material interest, and material pursuit were all positively correlated with depression (r=0.183-0.289, P<0.001). The depressive symptoms and their overall levels differed significantly among individuals with different levels of materialism, and higher levels of materialism were associated with severer depressive symptoms (F=18.792, P<0.001) and a higher positive rate of depression (χ²=27.528, P<0.001). CONCLUSIONS Materialistic values may increase the risk of depression among medical postgraduates.
Humans ; Depression/epidemiology* ; Surveys and Questionnaires ; Female ; Adult ; Male ; Middle Aged ; Risk Factors

This study investigated the impact of metabolic syndrome (MetS) and its components on prostate volume (PV) in the general Chinese population. In total, 43 455 participants in The First Medical Center of the Chinese PLA General Hospital (Beijing, China) from January 1, 2012, to December 31, 2022, undergoing health examinations were included in the study. Participants were categorized into four groups according to PV quartiles: Q1 (PV ≤24.94 ml), Q2 (PV >24.94 ml and ≤28.78 ml), Q3 (PV >28.78 ml and ≤34.07 ml), and Q4 (PV >34.07 ml), with Q1 serving as the reference group. Logistic regression analyses were used to examine the association between MetS and PV, with subgroup analyses conducted by age. Among the participants, 18 787 (43.2%) were diagnosed with MetS. In the multivariate analysis model, a significant correlation between MetS and PV was observed, with odds ratios (ORs) increasing as PV increased (Q2, OR = 1.203, 95% confidence interval [CI]: 1.139-1.271; Q3, OR = 1.300, 95% CI: 1.230-1.373; and Q4, OR = 1.556, 95% CI: 1.469-1.648). Analysis of MetS components revealed that all components were positively associated with PV, with abdominal obesity showing the most significant effect. The number of MetS components was identified as a dose-dependent risk factor for elevated PV. The impact of MetS, its components, and component count on PV exhibited a decreasing trend with advancing age. Overall, the influence of MetS, its components, and component count on PV was predominantly observed in the age groups of 40-49 years and 50-59 years. Early intervention targeting MetS can significantly alleviate the increase in PV, particularly benefiting individuals aged 40-59 years who have abdominal obesity.
Humans ; Male ; Metabolic Syndrome/complications* ; Middle Aged ; Cross-Sectional Studies ; Aged ; Prostate/diagnostic imaging* ; Adult ; Age Factors ; Organ Size ; China/epidemiology* ; Obesity, Abdominal ; Risk Factors

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:This study aims to construct a quality evaluation index system for specialized disease databases. Through systematic assessment and optimization, it seeks to comprehensively enhance the quality and standardization of specialized disease cohort data. This initiative will provide more precise and reliable data support for disease research, the development of innovative drugs and medical devices, as well as policy formulation.Methods:By conducting a thorough analysis of domestic and international literature and policies related to clinical research data quality evaluation systems, preliminary quality evaluation indicators for specialized disease databases were established. Utilizing the Delphi method in two rounds, a quality evaluation system for specialized disease databases was constructed. The Analytic Hierarchy Process (AHP) and YAAHP 7.5 software were then employed to calculate the relative weights of indicators at various levels and their composite weights.Results:The two rounds of expert consultation achieved a 100.00% valid response rate, with an expert authority coefficient of 0.81 in both rounds. In the second round, the Kendall′s coordination coefficients for the first-level and second-level indicators reached 0.311 and 0.218, respectively ( P<0.05), indicating a good level of consensus among experts. The final specialized disease database quality evaluation system consists of 3 first-level indicators, 10 second-level indicators, and 32 third-level indicators. The first-level indicators include database construction, data quality, and cohort development, with weight coefficients of 31.82%, 41.49%, and 26.69%, respectively. The scientific validity of the indicator system was confirmed through reliability and validity analyses. When applied to assessing 58 specialized disease database projects from 36 medical institutions in a certain city, the results showed significant improvements in scores for database construction, data quality, and cohort development, with the most notable improvement observed in database construction. Conclusions:This study successfully developed a scientific, practical, and rationally weighted quality evaluation system for specialized disease databases, demonstrating high expert consensus and broad applicability.Validation studies have shown that this system effectively enhances the standardization and data quality of databases, providing robust technical support and assurance for specialized disease research and data resource sharing.

Objective To investigate the predictive value of procalcitonin(PCT),interleukin-6(IL-6),heparin binding protein(HBP),and C-reactive protein(CRP)in cerebral infarction(CI)complicated with pulmonary infection.Methods 145 CI patients treated in Jiangning Hospital Affiliated to Nanjing Medical University from July 2022 to June 2023 were selected as the CI group.Ac-cording to whether combined with pulmonary infection,they were divided into the co-infected group(n=76)with pulmonary infection and the simple CI group(n=69)without pulmonary infection.61 healthy volunteers performed physical examination in our hospital at the same time were enrolled as the control group.The general clinical data from each group were collected and their serum biochemical indicators were compared.The multivariate Logistic regression was used to analyze the risk factors of CI complicated with pulmonary in-fection and the receiver operating characteristic(ROC)curve was constructed.The area under the ROC curve(AUCROC)was used to evaluate the diagnostic value of each index for CI complicated with pulmonary infection.Results Age,PCT,IL-6,HBP,and CRP were the independent risk factors of CI complicated with pulmonary infection(P＜0.05).The ROC curve analysis showed that each in-dex of PCT,IL-6,HBP,and CRP had a certain value in predicting CI combined with pulmonary infection.The sensitivity and specific-ity of the combination detection of PCT,IL-6,HBP,and CRP were 85.47%and 75.36%,respectively,which were significantly high-er than that of the single detection of PCT,IL-6,HBP,and CRP(P＜0.05).Conclusion The levels of serum PCT,IL-6,HBP,and CRP are highly expressed in CI patients combined with pulmonary infection.They can be used for the diagnosis of CI combined with pulmonary infection.The combination detection of the four indexes can effectively improve the diagnosis accuracy of CI complicated with pulmonary infection.

The comparison between traditional Chinese medicine Jinzhen oral liquid (JZOL) and Western medicine in treating children with acute bronchitis (AB) showed encouraging outcomes. This trial evaluated the efficacy and safety of the JZOL for improving cough and expectoration in children with AB. 480 children were randomly assigned to take JZOL or ambroxol hydrochloride and clenbuterol hydrochloride oral solution for 7 days. The primary outcome was time-to-cough resolution. The median time-to-cough resolution in both groups was 5.0 days and the antitussive onset median time was only 1 day. This randomized controlled trial showed that JZOL was not inferior to cough suppressant and phlegm resolving western medicine in treating cough and sputum and could comprehensively treat respiratory and systemic discomfort symptoms. Combined with clinical trials, the mechanism of JZOL against AB was uncovered by network target analysis, it was found that the pathways in TRP channels like IL-1β/IL1R/TRPV1/TRPA1, NGF/TrkA/TRPV1/TRPA1, and PGE2/EP/PKA/TRPV1/TRPA1 might play important roles. Animal experiments further confirmed that inflammation and the immune regulatory effect of JZOL in the treatment of AB were of vital importance and TRP channels were the key mechanism of action.