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.

Pharmacological studies on Wuzi Yanzong Pills have mostly focused on the reproductive system, nervous system, immune system, etc. It has the ability to regulate the hypothalamic-pituitary-gonadal axis, regulate sex hormones, anti-oxidative stress, anti-inflammation, anti-apoptosis, regulate autophagy, and improve the body's immune system, etc., which involves the effects of NOD, TGF-β1/Smads, RAS, Keap-1/Nrf2/HO-1, TLR4/MyD88/NF-κB, PI3K/Akt, Bcl-2/Bax, SIRT1 and other signaling pathways. At present, most of the studies focus on the mechanism of the whole prescription of Wuzi Yanzong Pills, and the research on the disassembled prescriptions needs to be improved, and the different mechanisms of the same signaling pathway need to be further explored.

Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.

OBJECTIVE: To investigate the association between long-term glycemic control and cerebral infarction risk in patients with diabetes through a large-scale cohort study. METHODS: This prospective, community-based cohort study included 12,054 patients with diabetes. From 2006 to 2012, 38,272 fasting blood glucose (FBG) measurements were obtained from these participants. FBG trajectory patterns were generated using latent mixture modelling. Cox proportional hazards models were applied to assess the subsequent risk of cerebral infarction associated with different FBG trajectory patterns. RESULTS: At baseline, the mean age of the participants was 55.2 years. Four distinct FBG trajectories were identified based on FBG concentrations and their changes over the 6-year follow-up period. After a median follow-up of 6.9 years, 786 cerebral infarction events were recorded. Different trajectory patterns were associated with significantly varied outcome risks (Log-Rank P < 0.001). Compared with the low-stability group, Hazard Ratio ( HR) adjusted for potential confounders were 1.37 for the moderate-increasing group, 1.23 for the elevated-decreasing group, and 2.08 for the elevated-stable group. CONCLUSION Sustained high FBG levels were found to play a critical role in the development of ischemic stroke among patients with diabetes. Controlling FBG levels may reduce the risk of cerebral infarction.
Humans ; Cerebral Infarction/blood* ; Middle Aged ; Male ; Female ; Blood Glucose/analysis* ; Fasting/blood* ; Aged ; Prospective Studies ; Risk Factors ; Diabetes Mellitus/blood* ; Adult ; Proportional Hazards Models

Parkinson's disease(PD)is one of the most common neurodegenerative disorders.Recent evidence implicates pyroptosis as one of the pathogenic mechanisms in central nervous system disorders,although its specific mechanisms remain unclear.In this study,SH-SY5Y cells were transfected with py-roptosis-related proteins GSDME full-length(GSDME-F)or GSDME-N terminal(GSDME-N)plasmids revealed that GSDME-N significantly reduced mitochondrial membrane potential(P＜0.0001).To inves-tigate the mechanism by which GSDME mediates mitochondrial dysfunction,Western blotting analysis demonstrated that transfection with GSDME-N plasmids significantly increased BAX expression and en-hanced its translocation to mitochondria in both HEK 293T and SH-SY5Y cells(P＜0.05).SH-SY5Y cells treated with varying concentrations of rotenone(ROT)exhibited GSDME cleavage,elevated BAX expression(P＜0.05),increased mitochondrial BAX aggregation(P＜0.05),and reduced mitochondrial membrane potential(P＜0.01),as confirmed by Western blotting and JC-1 staining.Concurrently,MTT assays assessing cell viability and lactate dehydrogenase(LDH)release assays indicated that ROT in-duced these processes prior to pyroptosis.Furthermore,in a ROT-induced mouse PD model,ROT trig-gered GSDME cleavage,enhanced BAX expression,caused dopaminergic neuronal damage,and induced motor deficits.In summary,this study demonstrates that GSDME-N exacerbates mitochondrial damage and increases cytotoxicity by upregulating BAX expression and facilitating its mitochondrial translocation.This study provides novel insights into the role of GSDME in PD pathogenesis and suggests potential avenues for therapeutic intervention.

Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model's applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.

OBJECTIVE To evaluate the efficacy， safety and cost-effectiveness of toripalimab （Tor） combined with chemotherapy （CT） in the treatment of locally advanced or metastatic non-small cell lung cancer （NSCLC）. METHODS PubMed， the Cochrane Library， Embase， Web of Science， CBM， CNKI， Wanfang Data， and Health Technology Assessment （HTA） related websites were searched to collect the HTA reports， systematic reviews/meta-analyses and pharmacoeconomic studies of Tor+CT in the treatment of locally advanced or metastatic NSCLC from database/website inception to March 31， 2025. After data extraction and quality evaluation， the results of the included studies were analyzed descriptively. RESULTS A total of eleven studies were included， involving five systematic reviews/meta-analyses， and six pharmacoeconomic studies. Among the five systematic reviews/ meta-analyses， two were of high quality， while there was one each of moderate， low， and very low quality. All six pharmacoeconomic studies were of good quality. In terms of efficacy， compared with CT， Tor+CT significantly improved patients’ progression-free survival （PFS） and overall survival （P＜0.05）. In addition， compared with ipilimumab+CT， durvalumab， durvalumab+tremelimumab and sugemalimab+CT， Tor+CT could also improve the PFS （P＜0.05）. In terms of safety， there was no significant difference in the incidence of grade≥3 adverse events between patients receiving Tor+CT and CT （P＞0.05）； while Tor+CT had a lower incidence of grade≥3 adverse E-mail： events， compared with camrelizumab+CT， pembrolizumab+ 3233255290@qq.com ipilimumab， nivolumab+CT and atezolizumab+CT （P＜0.05）.In terms of cost-effectiveness， Tor+CT treatment had certain cost-effectiveness advantages， compared with CT. CONCLUSIONS Compared with CT， other programmed death-1/programmed death-ligand 1 inhibitors alone， or their combination with CT， Tor+CT for the treatment of locally advanced or metastatic NSCLC has good efficacy， safety and cost-effectiveness.

Osteoarthritis is a chronic degenerative joint disorder that adversely affects the quality of life of patients.Its pathological characteristics mainly involve the degeneration,destruction,and functional loss of articular cartilage.As one of the most crucial structures in the joint,articular cartilage is an avascular tissue composed of extracellular matrix synthesized by sparsely distributed chondrocytes.Chondrocyte apoptosis is one of the programmed cell death modalities of chondrocytes.During the normal physiological process,the proliferation and apoptosis of chondrocytes maintain a dynamic equilibrium,jointly safeguarding the homeostasis of articular cartilage.However,in the osteoarthritis process,the stimulation of related cytokines after tissue damage induces an imbalance between chondrocyte proliferation and apoptosis,resulting in excessive chondrocyte apoptosis,and subsequently leading to the degradation and degeneration of cartilage.Clinically,traditional Chinese medicine and its related therapeutic approaches remain one of the principal means for treating osteoarthritis in China.The traditional Chinese medicine,which are bioactive compounds extracted from traditional herbal medicines,has gradually gained recognition thanks to their convenient to take and remarkable therapeutic effect.Therefore,this review takes the signaling pathways involved in the inhibition of chondrocyte apoptosis by traditional Chinese medicine as the entry point,systematically retrieves and screens and summarizes relevant studies at home and abroad in the past five years,and analyzes the mechanism of action of related signaling pathways in the treatment of chondrocyte apoptosis in osteoarthritis by traditional Chinese Medicine,providing new options for the pharmacological treatment of osteoarthritis.

Objective To design a VR-based air evacuation training system for simulating the on-board medical treatment process during air evacuation.Methods A VR-based air evacuation training system was developed which used 3D modeling technology to construct models of the medical aircraft cabin,medical devices and virtual characters to achieve scene interaction.The hardware part of the system included server computers,training terminal computers,VR equipment,3D fusion projection equipment,motion capture equipment,etc.The software of the system was developed using C++,UE4 Blueprint and C# programming languages,including two modules for medical treatment unit and medical treatment training process evaluation.The efficacy of the system was verified by the trials in air evacuation.Results The system developed successfully simulated the scenarios of tracheal tube dislodgement and increased intracranial pressure in the scenario model of open severe craniocerebral injury.The expert evaluation showed that the system gained advantages in training efficiency,low cost,safety,sense of immersion and recorded the operation data in real time to optimize the follow-up training.Conclusion The system developed delivers a virtual training environment with high-fidelity replication of real-mission conditions,enabling whole-course and immersive air evacuation drills.[Chinese Medical Equipment Journal,2025,46(3):15-20]