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.

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

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.

Objective:To identify the development trajectories of self-neglect behavior in older adults and explore the associated influencing factors.Methods:A fixed cohort was constructed based on the data from three surveys of Chinese longitudinal healthy longevity survey (CLHLS) from 2011 to 2018. A total of eight variables from 4 dimensions including living environment, lifestyle, social interaction, and health care were selected to evaluate self-neglect. Group-based trajectory model was used to identify the development trajectory of self-neglect behavior in the older adults, and polynomial Logistic regression model was used to explore its influencing factors by Stata 16.1.Results:Finally, 2 754 older adults aged 60 and above were included.The development trajectory of self-neglect behavior in older adults, based on the group-based trajectory model, can be classified into stable-low group ( n=268, 9.7%), descending-moderate group ( n=2 224, 80.8%), and decreasing-high group ( n=262, 9.5%). Polynomial Logistic regression showed that, compared with stable-low group, living in rural areas ( B=1.116, OR=3.053, 95% CI= 2.278-4.091) and higher activities of daily living scores( B=0.137, OR=1.147, 95% CI=1.046-1.258) were the risk factors of descending-moderate group. Education levels with 1-6 years( B=-0.398, OR=0.672, 95% CI=0.469-0.963), >6 years( B=-1.072, OR=0.342, 95% CI=0.229-0.513), being married( B=-0.476, OR=0.621, 95% CI=0.444-0.870), self-reported good health( B=-0.808, OR=0.446, 95% CI= 0.213-0.932), improved health status( B=-0.704, OR=0.495, 95% CI=0.320-0.766), self-reported average economic status( B=-1.065, OR=0.345, 95% CI=0.148-0.802), self-reported good economic status( B=-1.634, OR=0.195, 95% CI=0.082-0.467), and a higher cognition score( B=-0.142, OR=0.867, 95% CI=0.798-0.942) served as protective factors of descending-moderate group. In addition to the above factors, being in the age group of 75-89 years( B=0.481, OR=1.617, 95% CI=1.057-2.473) was a risk factor for decreasing-high group compared to stable-low group. Conclusions:Three types of self-neglect behavior trajectories among older adults were identified in this study, suggesting that physical health and economy are the influencing factors of the development trajectory of self-neglect of the elderly.

【Objective】 To establish an effective quality indicator monitoring system, scientifically and objectively evaluate the quality management level of blood banks, and achieve continuous improvement of quality management in blood bank. 【Methods】 A quality monitoring indicator system that covers the whole process of blood collection and supply was established, the questionnaire of Quality Monitoring Indicators for Blood Collection and Supply Process with clear definition of indicators and calculation formulas was distributed to 17 blood banks in Shandong. Statistical analysis of 21 quality monitoring indicators in terms of blood donation service (10 indicators), blood component preparation (7 indicators ), and blood supply (4 indicators) from each blood bank from January to December 2022 were conducted using SPSS25.0 software The differences in quality monitoring indicators of blood banks of different scales were analyzed. 【Results】 The average values of quality monitoring indicators for blood donation service process of 17 blood banks were as follows: 44.66% (2 233/5 000) of regular donors proportion, 0.22% (11/50) of adverse reactions incidence, 0.46% (23/5 000) of non-standard whole blood collection rate, 0.052% (13/25 000) of missed HBsAg screening rate, 99.42% (4 971/5 000) of first, puncture successful rate, 86.49% (173/200) of double platelet collection rate, 66.50% (133/200) of 400 mL whole blood collection rate, 99.25% (397/400) of donor satisfaction rate, 82.68% (2 067/2 500) of use rate of whole blood collection bags with bypass system with sample tube, and 1 case of occupational exposure in blood collection.There was a strong positive correlation between the proportion of regular blood donors and the collection rate of 400 mL whole blood (P<0.05). The platelet collection rate, incidence of adverse reactions to blood donation, and non-standard whole blood collection rate in large blood banks were significantly lower than those in medium and small blood banks (P<0.05). The average quality monitoring indicators for blood component preparation process of 17 blood banks were as follows: the leakage rate of blood component preparation bags was 0.03% (3/10 000), the discarding rate of lipemic blood was 3.05% (61/2 000), the discarding rate of hemolysis blood was 0.13%(13/10 000). 0.06 case had labeling errors, 8 bags had blood catheter leaks, 2.76 bags had blood puncture/connection leaks, and 0.59 cases had non-conforming consumables. The discarding rate of hemolysis blood of large blood banks was significantly lower than that of medium and small blood banks (P<0.05), and the discarding rate of lipemic blood of large and medium blood banks was significantly lower than that of small blood banks (P<0.05). The average values of quality monitoring indicators for blood supply process of 17 blood banks were as follows: the discarding rate of expired blood was 0.023% (23/100 000), the leakage rate during storage and distribution was of 0.009%(9/100 000), the discarding rate of returned blood was 0.106% (53/50 000), the service satisfaction of hospitals was 99.16% (2 479/2 500). The leakage rate of blood components during storage and distribution was statistically different with that of blood component preparation bags between different blood banks (P<0.05). There were statistically significant differences in the proportion of regular blood donors, incidence of adverse reactions, non-standard whole blood collection rate, 400 mL whole blood collection rate, double platelet collection rate, the blood bag leakage rate during preparation process, the blood components leakage rate during storage and distribution as well as the discarding rate of lipemic blood, hemolysis blood, expired blood and returned blood among large, medium and small blood banks (all P<0.05). 【Conclusion】 The establishment of a quality monitoring indicator system for blood donation services, blood component preparation and blood supply processes in Shandong has good applicability, feasibility and effectiveness. It can objectively evaluate the quality management level, facilitate the continuous improvement of the quality management system, promote the homogenization of blood management in the province and lay the foundation for future comprehensive evaluation of blood banks.

Nasopharyngeal carcinoma is a common malignant tumor in southern China, and its occurrence and development mechanism are still not fully understood. However, a large number of studies have shown that DNA methylation has important clinical application value in the screening, diagnosis, treatment and prognosis evaluation of nasopharyngeal carcinoma. DNA methylation affects the division cycle, growth, invasion and migration of nasopharyngeal carcinoma cells by regulating the transcription and protein expression levels of genes associated with tumorigenesis and development. In addition, there are significant differences in DNA methylation expression levels in different stages of nasopharyngeal carcinoma, which provides theoretical guidance and clinical reference for the early diagnosis, timely treatment and response evaluation of nasopharyngeal carcinoma. Current studies have shown that DNA methylation detection may provide a simple and efficient early screening method for nasopharyngeal carcinoma, and can also explore new ideas for the development of non-invasive screening methods.

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.