The 2024 National Education Work Conference pointed out that at the current juncture of the critical period for achieving the goals and tasks of the 14th Five-Year Plan,the implementation of the Education Powerhouse Construction Plan Outline should be taken as the main line of work,and building first-class disciplines is an crucial task for a higher education powerhouse.In 2022,forensic medicine was officially listed as a first-level discipline under the medical category,presenting an un-precedented historical opportunity for the development of forensic medicine.The forensic medicine dis-cipline of Southern Medical University comprehensively improves the quality of talent cultivation and facilitates the construction of first-class disciplines as its main direction.It aims to initiate and imple-ment a high-level faculty team building plan featuring"combining recruitment and cultivation,inter-disciplinary integration";make vigorous efforts to establish a first-level doctoral program,refine advan-tageous second-level disciplines and research directions;and establish an innovative research platform from a high starting point with deep integration.The discipline adheres to moral cultivation and the Five Domains of Education simultaneous development,to build a high-quality talent joint training model.Guided by the construction of the national legal system and industry needs,the discipline will enhance social service capabilities.The forensic medicine construction in our university will continue to contribute to the rule of law in China and educational power.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective To observe the effect of dulaglutide combined with insulin aspart and metformin on blood glucose,pancreatic beta-cell status and physique in elderly patients with type 2 diabetes mellitus(T2DM)and obesity.Methods Elderly patients with T2DM and obesity were divided into the control group and the treatment group according to the queue method.Both groups were given intensive insulin therapy with insulin aspart injection at 0.4-0.6 U·kg-1·d-1 and oral administration of 0.5 g of metformin tablets,tid.A week later,the treatment of control group was switched to sequential therapy with insulin glargine injection at an initial dose of 0.4-0.6 U·kg-1·d-1,qn.The dose was adjusted according to blood glucose concentration.During this period,0.5 g of metformin tablets was administrated,tid,for 12 consecutive weeks.Meanwhile,treatment of the treatment group was switched to sequential therapy with 1.5 mg of dulaglutide injection,once a week.During this period,0.5 g of metformin tablets was administrated,tid,for 12 consecutive weeks.The two groups were compared in terms of clinical efficacy,blood glucose level[glycosylated hemoglobin(HbAlc),fasting plasma glucose(FPG)],pancreatic beta-cell status[fasting insulin(FINS),homeostasis model assessment-β(HOMA-β)and homeostasis model assessment-insulin resistance index(HOMA-IR)],and physical parameters[waist circumference and body mass index(BMI)].Safety was evaluated.Results Fifty-three cases and fifty-one cases were included in the treatment group and the control group,respectively.After treatment,the total effective rates of the treatment group and the control group were 98.11％(52 cases/53 cases)and 84.31％(43 cases/51 cases),and the difference was statistically significant(P＜0.05).After treatment,HbAlc in the treatment and the control group were(7.01±0.75)％and(7.63±0.82)％;FPG levels were(6.23±0.70)and(6.62±0.74)mmol·L-1;FINS levels were(5.25±1.06)and(6.48±1.12)mU·L-1;HOMA-β were 32.62±6.53 and 27.19±5.18;HOMA-IR were 1.31±0.25 and 1.65±0.28;waist circumference were(82.31±6.04)and(85.79±6.82)cm;BMI were(27.14±1.23)and(27.91±1.15)kg·m-2.The differences in above indicators between the treatment group and the control group were statistically significant(all P＜0.05).Adverse drug reactions in the treatment group mainly included nausea,vomiting and skin rash.Adverse drug reactions in the control group mainly included nausea and vomiting.The total incidence rates of adverse drug reactions in the treatment and the control group were 11.32％and 9.80％,without statistically significant difference(P＞0.05).Conclusion Dulaglutide combined with insulin aspart and metformin can effectively improve blood glucose,lipids,inflammation and pancreatic β-cell status in elderly patients with T2DM and obesity,reduce glycemic excursions,and promote decreases in waist circumference and BMI,with good safety.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with unclear etiology and limited treatment options. The median survival time for IPF patients is approximately 2-3 years and there is no effective intervention to treat IPF other than lung transplantation. As important components of lung tissue, endothelial cells (ECs) are associated with pulmonary diseases. However, the role of endothelial dysfunction in pulmonary fibrosis (PF) is incompletely understood. Sphingosine-1-phosphate receptor 1 (S1PR1) is a G protein-coupled receptor highly expressed in lung ECs. Its expression is markedly reduced in patients with IPF. Herein, we generated an endothelial-conditional S1pr1 knockout mouse model which exhibited inflammation and fibrosis with or without bleomycin (BLM) challenge. Selective activation of S1PR1 with an S1PR1 agonist, IMMH002, exerted a potent therapeutic effect in mice with bleomycin-induced fibrosis by protecting the integrity of the endothelial barrier. These results suggest that S1PR1 might be a promising drug target for IPF therapy.

Humans ; Consensus ; Hypersensitivity ; Desensitization, Immunologic/adverse effects* ; Immunotherapy/adverse effects* ; Injections, Subcutaneous ; Allergens

OBJECTIVE: To investigate the causes of ineffectiveness of platelet transfusion with monoclonal antibody solid phase platelet antibody test (MASPAT) matching in patients with allogeneic hematopoietic stem cell transplantation and explore the strategies of platelet transfusion. METHODS: A case of donor-specific HLA antibodies (DSA) induced by transfusion which ultimately resulted in transplantation failure and ineffective platelet transfusion with MASPAT matching was selected, and the causes of ineffective platelet transfusion and platelet transfusion strategy were retrospectively analyzed. RESULTS: The 32-year-old female patient was diagnosed as acute myeloid leukemia (high risk) in another hospital with the main symptoms of fever and leukopenia, who should be admitted for hematopoietic stem cell transplantation after remission by chemotherapy. In the course of chemotherapy, DSA was generated due to platelet transfusion, and had HLA gene loci incompatible with the donor of the first transplant, leading to the failure of the first transplant. The patient received platelet transfusion for several times before and after transplantation, and the results showed that the effective rate of MASPAT matched platelet transfusion was only 35.3%. Further analysis showed that the reason for the ineffective platelet transfusion was due to the missed detection of antibodies by MASPAT method. During the second hematopoietic stem cell transplantation, the DSA-negative donor was selected, and the matching platelets but ineffective transfusion during the primary transplantation were avoided. Finally, the patient was successfully transplanted and discharged from hospital. CONCLUSIONS DSA can cause graft failure or render the graft ineffective. For the platelet transfusion of patients with DSA, the platelet transfusion strategy with matching type only using MASPAT method will miss the detection of antibodies, resulting in invalid platelet transfusion.
Female ; Humans ; Adult ; Platelet Transfusion ; Antibodies, Monoclonal ; Retrospective Studies ; HLA Antigens ; Hematopoietic Stem Cell Transplantation