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.

PURPOSE: To investigate the incidence and influencing factors of bone loss in patients with spinal cord injury (SCI). METHODS: A retrospective case-control study was conducted. Patients with SCI in our hospital from January 2019 to March 2023 were collected. According to the correlation between bone mineral density (BMD) at different sites, the patients were divided into the lumbar spine group and the hip joint group. According to the BMD value, the patients were divided into the normal bone mass group (t > -1.0 standard deviation) and the osteopenia group (t ≤ -1.0 standard deviation). The influencing factors accumulated as follows: gender, age, height, weight, cause of injury, injury segment, injury degree, time after injury, start time of rehabilitation, motor score, sensory score, spasticity, serum value of alkaline phosphatase, calcium, and phosphorus. The trend chart was drawn and the influencing factors were analyzed. SPSS 26.0 was used for statistical analysis. Correlation analysis was used to test the correlation between the BMD values of the lumbar spine and bilateral hips. Binary logistic regression analysis was used to explore the influencing factors of osteoporosis after SCI. p < 0.05 was considered statistically significant. RESULTS: The incidence of bone loss in patients with SCI was 66.3%. There was a low concordance between bone loss in the lumbar spine and the hip, and the hip was particularly susceptible to bone loss after SCI, with an upward trend in incidence (36% - 82%). In this study, patients with SCI were divided into the lumbar spine group (n = 100) and the hip group (n = 185) according to the BMD values of different sites. Then, the lumbar spine group was divided into the normal bone mass group (n = 53) and the osteopenia group (n = 47); the hip joint group was divided into the normal bone mass group (n = 83) and the osteopenia group (n = 102). Of these, lumbar bone loss after SCI is correlated with gender and weight (p = 0.032 and < 0.001, respectively), and hip bone loss is correlated with gender, height, weight, and time since injury (p < 0.001, p = 0.015, 0.009, and 0.012, respectively). CONCLUSIONS The incidence of bone loss after SCI was high, especially in the hip. The incidence and influencing factors of bone loss in the lumbar spine and hip were different. Patients with SCI who are male, low height, lightweight, and long time after injury were more likely to have bone loss.
Humans ; Spinal Cord Injuries/complications* ; Male ; Female ; Retrospective Studies ; Incidence ; Adult ; Bone Density ; Middle Aged ; Case-Control Studies ; Osteoporosis/etiology* ; Lumbar Vertebrae ; Bone Diseases, Metabolic/etiology* ; Aged ; 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:To establish an in vitro cell model simulating acute graft-versus-host disease(aGVHD)bone marrow microenvironment injury with the advantage of mouse serum of aGVHD model and explore the effect of serum of aGVHD mouse on the adipogenic differentiation ability of mesenchymal stem cells(MSCs).Methods:The 6-8-week-old C57BL/6N female mice and BALB/c female mice were used as the donor and recipient mice of the aGVHD model,respectively.Bone marrow transplantation(BMT)mouse model(n=20)was established by being injected with bone marrow cells(1 × 10'per mouse)from donor mice within 4-6 hours after receiving a lethal dose(8.0 Gy,72.76 cGy/min)of y ray general irradiation.A mouse model of aGVHD(n=20)was established by infusing a total of 0.4 ml of a mixture of donor mouse-derived bone marrow cells(1 × 107 per mouse)and spleen lymphocytes(2 × 106 per mouse).The blood was removed from the eyeballs and the mouse serum was aspirated on the 7th day after modeling.Bone marrow-derived MSCs were isolated from 1-week-old C57BL/6N male mice and incubated with 2％,5％and 10％BMT mouse serum and aGVHD mouse serum in the medium,respectively.The effect of serum in the two groups on the in vitro adipogenic differentiation ability of mouse MSCs was detected by Oil Red O staining.The expression levels of related proteins PPARy and CEBPα were detected by Western blot.The expression differences of key adipogenic transcription factors including PPARy,CEBPα,FABP4 and LPL were determined by real-time quantitative PCR(RT-qPCR).Results:An in vitro cell model simulating the damage of bone marrow microenvironment in mice with aGVHD was successfully established.Oil Red O staining showed that the number of orange-red fatty droplets was significantly reduced and the adipogenic differentiation ability of MSC was impaired at aGVHD serum concentration of 10％compared with BMT serum.Western blot experiments showed that adipogenesis-related proteins PPARy and CEBPα expressed in MSCs were down-regulated.Further RT-qPCR assay showed that the production of PPARy,CEBPα,FABP4 and LPL,the key transcription factors for adipogenic differentiation of MSC,were significantly reduced.Conclusion:The adipogenic differentiation capacity of MSCs is inhibited by aGVHD mouse serum.

ObjectiveThis study aims to explore risk factors for the development of major adverse cardiovascular and cerebrovascular events （MACCEs） in middle-aged and elderly patients with type 2 diabetes mellitus complicated with stable angina pectoris （T2DM-SAP） based on real-world clinical data in traditional Chinese medicine （TCM）， so as to develop a COX proportional risk prediction model and visualize the predicted results using a nomogram. MethodBased on the clinical scientific research information sharing system， the medical records of 586 T2DM-SAP patients （45-94 years old） were collected from January 2012 to December 2019， including age， gender， course of disease， major medical history， laboratory examination， tongue image， pulse image， TCM syndrome， and major treatment drugs. MACCE outcome indicators of patients were obtained by telephone follow-up and re-hospitalization records. The data was divided into a training set and a validation set according to 7∶3. In the training set， COX univariate analysis was used to determine the risk factors for MACCE in T2DM-SAP patients， and then variables were screened by forward-backward stepwise regression method， so as to establish a MACCE risk prediction model and construct a nomogram. The predictive efficacy of the model was reflected by the C-index， receiver operating characteristic （ROC） curve， calibration map， and clinical decision curve. ResultThe history of cerebrovascular disease ［Hazard ratio （HR）=1.983， 95% confidence interval （CI，1.314-2.993）］， low-density lipoprotein （LDL-C/mmol·L^-1）≥4.1［HR=2.683， 95%CI（1.461-4.925）］， dull red tongue ［HR=1.955， 95%CI（1.273-3.002）］， dull purple tongue ［HR=4.214， 95%CI（2.017-8.803）］， white thick coating ［HR=3.030， 95%CI（1.634-9.293）］， thin and weak pulse ［HR=2.233， 95%CI（1.283-3.888）］， and syndrome of wind-phlegm blocking collaterals ［HR=2.007， 95%CI（1.179-3.418）］ were found to be risk factors in middle-aged and elderly T2DM-SAP patients. Insulin ［HR=0.604， 95%CI（0.399-0.914）］， glycosidase inhibitor ［HR=0.627， 95%CI（0.409-0.962）］， and TCM treatment ［HR=0.328， 95%CI（0.214-0.503）］ were protective factors in middle-aged and elderly T2DM-SAP patients. The prediction model was constructed based on the above risk factors. The C-index of the model was 0.818 （95% CI 0.777 -0.859） in the training set and 0.814 （95% CI 0.773-0.855） in the validation set， and the change of C-index over time was plotted. The AUC of patients for 5， 10， 15 years in the training set was 0.71， 0.67， and 0.61. The AUC of patients for 5， 10， and 15 years in the validation set was 0.60， 0.68， and 0.63， respectively. The calibration map and clinical decision curves of 5， 10， 15 years were drawn in the training set and the validation set， respectively. The model was well calibrated and clinically effective. ConclusionThe history of cerebrovascular disease， LDL， dull red tongue， dull purple tongue， white thick coating， thin and weak pulse， and syndrome of wind-phlegm blocking collaterals are risk factors for MACCE in middle-aged and elderly T2DM-SAP patients， and insulin， glycosidase inhibitors， TCM treatment are protective factors for MACCE in middle-aged and elderly T2DM-SAP patients. A clinical prediction model is established accordingly. This model has good discrimination， calibration degree， and clinical effectiveness and provides a scientific basis for the prevention and treatment of MACCE in middle-aged and elderly T2DM-SAP patients.

Objectives It was a preliminary exploration on rare disease management of comprehensive hospital based on the"Center-Multidisciplinary Team"structure.Methods By applying interrupted time series analysis,the characteristics and related medical service of rare disease patients discharged from sample hospitals from 2021 to 2023 were measured.Results The results showed that the hospital served 19 825 rare disease patients from 2021 to 2023.The proportion of patients whose main diagnosis were rare diseases was approximately 27％.The interrupted time series technique indicated that the expected proportion of rare disease patients increased instantly by 1.89 thousand points.The capability to diagnose has significantly improved,with a growing trend on volume of bioinformatics analysis services for rare diseases patients.Conclusion The rare disease management based on the"Center-Team"structure can increase the attention of medical staff to rare diseases,strengthen the diagnostic capacity of rare diseases,and make patient management more continuous and disciplinary co-operation more efficient without significantly increasing the input burden on hospitals.

Objectives It was a preliminary exploration on rare disease management of comprehensive hospital based on the"Center-Multidisciplinary Team"structure.Methods By applying interrupted time series analysis,the characteristics and related medical service of rare disease patients discharged from sample hospitals from 2021 to 2023 were measured.Results The results showed that the hospital served 19 825 rare disease patients from 2021 to 2023.The proportion of patients whose main diagnosis were rare diseases was approximately 27％.The interrupted time series technique indicated that the expected proportion of rare disease patients increased instantly by 1.89 thousand points.The capability to diagnose has significantly improved,with a growing trend on volume of bioinformatics analysis services for rare diseases patients.Conclusion The rare disease management based on the"Center-Team"structure can increase the attention of medical staff to rare diseases,strengthen the diagnostic capacity of rare diseases,and make patient management more continuous and disciplinary co-operation more efficient without significantly increasing the input burden on hospitals.

Objectives It was a preliminary exploration on rare disease management of comprehensive hospital based on the"Center-Multidisciplinary Team"structure.Methods By applying interrupted time series analysis,the characteristics and related medical service of rare disease patients discharged from sample hospitals from 2021 to 2023 were measured.Results The results showed that the hospital served 19 825 rare disease patients from 2021 to 2023.The proportion of patients whose main diagnosis were rare diseases was approximately 27％.The interrupted time series technique indicated that the expected proportion of rare disease patients increased instantly by 1.89 thousand points.The capability to diagnose has significantly improved,with a growing trend on volume of bioinformatics analysis services for rare diseases patients.Conclusion The rare disease management based on the"Center-Team"structure can increase the attention of medical staff to rare diseases,strengthen the diagnostic capacity of rare diseases,and make patient management more continuous and disciplinary co-operation more efficient without significantly increasing the input burden on hospitals.

Objectives It was a preliminary exploration on rare disease management of comprehensive hospital based on the"Center-Multidisciplinary Team"structure.Methods By applying interrupted time series analysis,the characteristics and related medical service of rare disease patients discharged from sample hospitals from 2021 to 2023 were measured.Results The results showed that the hospital served 19 825 rare disease patients from 2021 to 2023.The proportion of patients whose main diagnosis were rare diseases was approximately 27％.The interrupted time series technique indicated that the expected proportion of rare disease patients increased instantly by 1.89 thousand points.The capability to diagnose has significantly improved,with a growing trend on volume of bioinformatics analysis services for rare diseases patients.Conclusion The rare disease management based on the"Center-Team"structure can increase the attention of medical staff to rare diseases,strengthen the diagnostic capacity of rare diseases,and make patient management more continuous and disciplinary co-operation more efficient without significantly increasing the input burden on hospitals.

Objectives It was a preliminary exploration on rare disease management of comprehensive hospital based on the"Center-Multidisciplinary Team"structure.Methods By applying interrupted time series analysis,the characteristics and related medical service of rare disease patients discharged from sample hospitals from 2021 to 2023 were measured.Results The results showed that the hospital served 19 825 rare disease patients from 2021 to 2023.The proportion of patients whose main diagnosis were rare diseases was approximately 27％.The interrupted time series technique indicated that the expected proportion of rare disease patients increased instantly by 1.89 thousand points.The capability to diagnose has significantly improved,with a growing trend on volume of bioinformatics analysis services for rare diseases patients.Conclusion The rare disease management based on the"Center-Team"structure can increase the attention of medical staff to rare diseases,strengthen the diagnostic capacity of rare diseases,and make patient management more continuous and disciplinary co-operation more efficient without significantly increasing the input burden on hospitals.