This study employed bioinformatics to screen the feature genes related to efferocytosis in diabetic kidney disease(DKD) and explores traditional Chinese medicine(TCM) regulating these feature genes. The GSE96804 and GSE30528 datasets were integrated as the training set, and the intersection of differentially expressed genes and efferocytosis-related genes(ERGs) was identified as DKD-ERGs. Subsequently, correlation analysis, protein-protein interaction(PPI) network construction, enrichment analysis, and immune infiltration analysis were performed. Consensus clustering was conducted on DKD patients based on the expression levels of DKD-ERGs, and the expression levels, immune infiltration characteristics, and gene set variations between different subtypes were explored. Eight machine learning models were constructed and their prediction performance was evaluated. The best-performing model was evaluated by nomograms, calibration curves, and external datasets, followed by the identification of efferocytosis-related feature genes associated with DKD. Finally, potential TCMs that can regulate these feature genes were predicted. The results showed that the training set contained 640 differentially expressed genes, and after intersecting with ERGs, 12 DKD-ERGs were obtained, which demonstrated mutual regulation and immune modulation effects. Consensus clustering divided DKD into two subtypes, C1 and C2. The support vector machine(SVM) model had the best performance, predicting that growth arrest-specific protein 6(GAS6), S100 calcium-binding protein A9(S100A9), C-X3-C motif chemokine ligand 1(CX3CL1), 5'-nucleotidase(NT5E), and interleukin 33(IL33) were the feature genes of DKD. Potential TCMs with therapeutic effects included Astragali Radix, Trionycis Carapax, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma, which mainly function to clear heat, replenish deficiency, activate blood, resolve stasis, and promote urination and drain dampness. Molecular docking revealed that the key components of these TCMs, including β-sitosterol, quercetin, and sitosterol, exhibited good binding activity with the five target genes. These results indicated that efferocytosis played a crucial role in the development and progression of DKD. The feature genes closely related to both DKD and efferocytosis, such as GAS6, S100A9, CX3CL1, NT5E, and IL33, were identified. TCMs such as Astragali Radix, Trionycis Carapa, Sargassum, Rhei Radix et Rhizoma, Curcumae Radix, and Alismatis Rhizoma may provide a new therapeutic strategy for DKD by regulating efferocytosis.
Humans ; Computational Biology ; Diabetic Nephropathies/physiopathology* ; Protein Interaction Maps ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Phagocytosis/genetics* ; Efferocytosis

With the rise of data-intensive research, data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence (AI) readiness. In the biomedical domain, data are characterized by high complexity and privacy sensitivity, calling for robust and systematic data management skills. This paper reviews current trends in scientific data governance and the evolving policy landscape, highlighting persistent challenges such as inconsistent standards, semantic misalignment, and limited awareness of compliance. These issues are largely rooted in the lack of structured training and practical support for researchers. In response, this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive, lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness. Furthermore, it outlines a tiered training strategy tailored to different research stages-undergraduate, graduate, and professional, offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
Artificial Intelligence ; Humans ; Biomedical Research

OBJECTIVE: To explore the feasibility and effectiveness of repairing surgical defect in pharyngo-laryngeal malignant tumor with free rectus femoris flap. METHODS: The clinical data of 34 patients with surgical defects in pharyngo-laryngeal malignant tumor who met the selection criteria between July 2014 and August 2024 were retrospectively analyzed. There were 25 males and 9 females, aged 25-82 years, with a median age of 54 years. The disease duration ranged from 2 months to 2 years, with a median of 7 months. The tumor locations included the oropharynx, hypopharynx, cervical esophagus, and larynx. Pathological types included squamous cell carcinoma (29 cases), myoepithelial carcinoma (2 cases), adenoid cystic carcinoma (1 case), and diffuse large B-cell lymphoma (2 cases). TNM staging: 16 cases of T ₄N ₁M ₀, 3 cases of T ₄N ₂M ₀, 3 cases of T ₄N ₀M ₀, 10 cases of T ₃N ₁M ₀, and 2 cases of T ₃N ₀M ₀. The 2017 American Joint Committee on Cancer (AJCC) staging was stage Ⅲ in 2 cases and stage Ⅳ in 32 cases. The blood supply of the proximal rectus femoris muscle was observed by enhanced CT of the lower limb vessels before operation, and the surgical defects ranged from 3.0 cm×2.0 cm to 12.0 cm×8.5 cm. The blood supply and perforators of rectus femoris muscle were explored during operation, and the free rectus femoris flap pedicled with the direct vascular stem of rectus femoris muscle was used to repair the defect. For the patients with pharyngeal fistula or obvious neck swelling after operation, the blood supply of the flap was analyzed by vascular enhanced CT to determine the corresponding strategies of nutritional support, anti-infection, dressing change and drainage. Radiotherapy and chemotherapy were supplemented in 27 patients with lymph node metastasis after operation. RESULTS: All the 34 patients were followed up 1-10 years, with an average of 3 years. The flap was found to be necrotic by fibrolaryngoscopy at 1 week after operation in 2 cases, and the incision healed after dressing change and nutritional support, and no reoperation was performed. The flap was in good condition at 1 week after operation in 4 cases, and the signs of gradual necrosis of the flap were found within 1 month after operation, of which 2 cases were healed after dressing change, 1 case was removed the necrotic tissue by reoperation, and 1 case was healed after pectoralis major myocutaneous flap was used to repair the pharyngeal tissue defect. The flaps survived in 28 cases, including 4 cases of pharyngeal fistula, which healed by dressing change. Twenty-two cases achieved satisfactory results in swallowing or phonation. Two patients with total laryngectomy and voice reconstruction underwent reoperation to seal the voice tube because of postoperative aspiration. During the follow-up, 1 case had tracheal stomal recurrence, 2 cases had bone metastasis, and 1 case had bone and lung metastasis. CONCLUSION The free rectus femoris flap has good flexibility, the volume of the flap is easy to adjust, and the incision of the donor site is concealed, which is expected to become a new choice for the repair of the surgical defect in pharyngo-laryngeal malignant tumor.
Humans ; Male ; Middle Aged ; Female ; Aged ; Adult ; Plastic Surgery Procedures/methods* ; Retrospective Studies ; Laryngeal Neoplasms/pathology* ; Aged, 80 and over ; Pharyngeal Neoplasms/pathology* ; Free Tissue Flaps/blood supply* ; Quadriceps Muscle/transplantation* ; Surgical Wound/surgery* ; Treatment Outcome

The choice of biopsy method is critical in diagnosing prostate cancer (PCa). This retrospective cohort study compared systematic biopsy (SB) or cognitive fusion-targeted biopsy combined with SB (CB) in detecting PCa and clinically significant prostate cancer (csPCa). Data from 2572 men who underwent either SB or CB in Fudan University Shanghai Cancer Center (Shanghai, China) between January 2019 and December 2023 were analyzed. Propensity score matching (PSM) was used to balance baseline characteristics, and detection rates were compared before and after PSM. Subgroup analyses based on prostate-specific antigen (PSA) levels and Prostate Imaging-Reporting and Data System (PI-RADS) scores were performed. Primary and secondary outcomes were the detection rates of PCa and csPCa, respectively. Of 2572 men, 1778 were included in the PSM analysis. Before PSM, CB had higher detection rates for both PCa (62.9% vs 52.4%, odds ratio [OR]: 1.54, P < 0.001) and csPCa (54.9% vs 43.3%, OR: 1.60, P < 0.001) compared to SB. After PSM, CB remained superior in detecting PCa (63.1% vs 47.9%, OR: 1.86, P < 0.001) and csPCa (55.0% vs 38.2%, OR: 1.98, P < 0.001). In patients with PSA 4-12 ng ml -1 (>4 ng ml -1 and ≤12 ng ml -1 , which is also applicable to the following text), CB detected more PCa (59.8% vs 40.7%, OR: 2.17, P < 0.001) and csPCa (48.1% vs 27.7%, OR: 2.42, P < 0.001). CB also showed superior csPCa detection in those with PI-RADS 3 lesions (32.1% vs 18.0%, OR: 2.15, P = 0.038). Overall, CB significantly improves PCa and csPCa detection, especially in patients with PSA 4-12 ng ml -1 or PI-RADS 3 lesions.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Propensity Score ; Retrospective Studies ; Middle Aged ; Aged ; Image-Guided Biopsy/methods* ; Prostate-Specific Antigen/blood* ; Prostate/diagnostic imaging*

Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

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 investigate the correlation of serum human epididymis protein 4(HE4)and lung injury Prediction(LIPS)score with sepsis-associated acute respiratory distress syndrome(ARDS)and 28 d mortality.Methods A total of 207 patients with sepsis admitted to the hospital from January 2021 to January 2024 were selected.According to the presence or absence of ARDS,the patients were divided into ARDS group(73 cases)and non-ARDS group(134 cases).According to the 28 d survival,the ARDS group was further di-vided into death group(26 cases)and survival group(47 cases).HE4 level was detected by electrochemilumi-nescence,and LIPS score was calculated.Multivariate Logistic regression model was used to analyze the influ-encing factors of death in ARDS patients.Point biserial correlation was used to analyze the correlation between serum HE4,LIPS score and 28 d death in ARDS patients.The receiver operating characteristic(ROC)curve was used to evaluate the value of serum HE4 and LIPS score in the diagnosis of sepsis-associated ARDS and predicting 28 d mortality.Results Compared with non-ARDS group,the serum HE4 level and LIPS score were significantly increased in ARDS group(P<0.05).The independent risk factors for sepsis-associated ARDS were increased SOFA score,increased blood lactic acid,increased HE4 and increased LIPS score(P<0.05).The 28 d mortality of 73 patients with sepsis-associated ARDS was 35.62%(26/73).Compared with the survival group,the serum HE4 and LIPS scores were increased in the death group(P<0.05).Serum HE4 and LIPS scores were positively correlated with 28 d mortality in patients with sepsis-associated ARDS(r=0.605,0.579,P<0.05).The area under the curve(AUC)of serum HE4 combined with LIPS score in the di-agnosis of sepsis-associated ARDS was 0.909,which was larger than 0.828 and 0.806 of serum HE4 and LIPS score alone(Z=3.360,3.387,all P<0.05).The AUC of serum HE4 combined with LIPS score in predicting 28 d mortality of patients with sepsis-associated ARDS was 0.891,which was larger than 0.808 and 0.779 of by serum HE4 and LIPS score alone(Z=2.028,2.017,all P<0.05).Conclusion The elevated serum HE4 level and LIPS score are independent risk factors for sepsis-associated ARDS,and are closely related to 28 d mortality.The combination of serum HE4 and LIPS score has a high value in the diagnosis of sepsis-associated ARDS and the prediction of 28 d mortality.

Abdominal war trauma is a common and high-risk type of injury in the modern battlefield,with rapid changes in condition and a high mortality rate.There is an urgent need for emerging medical technologies to improve the efficiency and success rate of first aid for military casualties.With the development of artificial intelligence(AI),5G,and other emerging technologies,the concept of intelligent medical treatment is gradually forming and can assist in the diagnosis and treatment of abdominal trauma.This paper reviews the characteristics of abdominal war trauma in modern wars,discusses the application of intelligent medical treatment for abdominal war trauma and its drawbacks to be solved,aiming to provide reference for research related to abdominal war trauma.