Nano/microplastics(NMPs),due to their environmental persistence and resistance to degradation,have emerged as a major contributor to global pollution.NMPs are capable of adsorbing various hazardous chemicals and heavy metals,thereby posing threats to aquatic ecosystem health,which may ultimately cause potential risks to human health.Conventional analytical methods suffered from limited resolution,insufficient chemical information,or destruction of sample,invalidating these assays for on-site detection of NMPs.Surface-enhanced Raman scattering(SERS)offers distinct advantages such as high sensitivity,superior specificity,rich fingerprint information,and non-destructive analysis,thus facilitating the on-site analysis of NMPs in complex matrices.This review summarized recent advances in SERS substrates for detection of NMPs,discussed the construction and applications of SERS-based multimodal detection strategies,and introduced the research progress of SERS detection of NMPs in food safety,environmental pollution,and bioanalysis.Moreover,the main challenges and future directions of SERS-based NMP detection were outlined.

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 clinical characteristics and prognostic factors of elderly patients with diffuse large B-cell lymphoma (DLBCL). METHODS: Clinical data of elderly DLBCL patients diagnosed pathologically between 2010 and 2015 were extracted from the SEER database. Cox proportional hazards model was used for multivariate analysis, and Kaplan-Meier survival curves were plotted to explore the prognostic factors affecting overall survival (OS). RESULTS: A total of 11 523 elderly DLBCL patients were included, of whom 58.6% had stage Ⅲ/Ⅳ disease, and 28.8% exhibited extranodal involvement. Besides lymph nodes (68.5%), common primary extranodal sites included the gastrointestinal tract (9.8%) and lip, mouth, and pharynx (4.1%). The median survival time for the entire cohort was 47 months, with a 3-year survival rate of 52.0%, and a 5-year survival rate of 47.8%. Multivariate Cox regression analysis revealed that age, sex, race, Ann Arbor stage, primary site, B symptoms, treatment modality, treatment sequence, and whether DLBCL was the first malignant primary indicator were independent prognostic factors affecting OS in elderly DLBCL patients (all P <0.05). CONCLUSION Age≥70 years, male, black race, advanced Ann Arbor stage, primary sites in the lungs, liver, or kidney, presence of B symptoms, and preoperative systemic therapy were independent risk factors for poor prognosis in elderly DLBCL patients.
Humans ; Lymphoma, Large B-Cell, Diffuse/diagnosis* ; Prognosis ; Aged ; Male ; Female ; Survival Rate ; Proportional Hazards Models ; Aged, 80 and over ; Kaplan-Meier Estimate ; Neoplasm Staging

BACKGROUND: Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors. METHODS: The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients. RESULTS: This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts. CONCLUSION The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.

BACKGROUND: The association of systemic inflammatory response index (SIRI) with prognosis of coronary artery disease (CAD) patients has never been investigated in a large sample with long-term follow-up. This study aimed to explore the association of SIRI with all-cause and cause-specific mortality in a nationally representative sample of CAD patients from United States. METHODS: A total of 3386 participants with CAD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were included in this study. Cox proportional hazards model, restricted cubic spline (RCS), and receiver operating characteristic curve (ROC) were performed to investigate the association of SIRI with all-cause and cause-specific mortality. Piece-wise linear regression and sensitivity analyses were also performed. RESULTS: During a median follow-up of 7.7 years, 1454 all-cause mortality occurred. After adjusting for confounding factors, higher lnSIRI was significantly associated with higher risk of all-cause (HR = 1.16, 95% CI: 1.09-1.23) and CVD mortality (HR = 1.17, 95% CI: 1.05-1.30) but not cancer mortality (HR = 1.17, 95% CI: 0.99-1.38). The associations of SIRI with all-cause and CVD mortality were detected as J-shaped with threshold values of 1.05935 and 1.122946 for SIRI, respectively. ROC curves showed that lnSIRI had robust predictive effect both in short and long terms. CONCLUSIONS SIRI was independently associated with all-cause and CVD mortality, and the dose-response relationship was J-shaped. SIRI might serve as a valid predictor for all-cause and CVD mortality both in the short and long terms.

Tailored lipid nanoparticles (LNPs)-mediated small interfering RNA (siRNA) nanomedicines show promise in treating liver disease, such as acute liver injury (ALI) and non-alcoholic steatohepatitis (NASH). However, constructing LNPs that address biosafety concerns, ensure efficient delivery, and target specific hepatic subcellular fractions has been challenging. To evade above obstacles, we develop three novel self-degradable "gemini-like" ionizable lipids (SS-MA, SS-DC, SS-MH) by incorporating disulfide bonds and modifying the length of ester bond and tertiary amino head. Our findings reveal that the disulfide-bond-bridged LNPs exhibit reduction-responsive drug release, improving both biosafety and siRNA delivery efficiency. Furthermore, the distance of ester bond and tertiary amino head significantly influences the LNPs' pK _a, thereby affecting endosomal escape, hemolytic efficiency, absorption capacity of ApoE, uptake efficiency of hepatocytes and liver accumulation. We also develop the modified-mannose LNPs (M-LNP) to target liver macrophages specifically. The optimized M-MH_LNP@TNFα exhibits potential in preventing ALI by decreasing tumor necrosis factor α (TNFα) levels in the macrophages, while MH_LNP@DGAT2 could treat NASH by selectively degrading diacylglycerol O-acyltransferase 2 (DGAT2) in the hepatocytes. Our findings provide new insights into developing novel highly effective and low-toxic "gemini-like" ionizable lipids for constructing LNPs, potentially achieving more effective treatment for hepatic diseases.

OBJECTIVE: Several epidemiological observational studies have related particulate matter (PM) exposure to Inflammatory bowel disease (IBD), but many confounding factors make it difficult to draw causal links from observational studies. The objective of this study was to explore the causal association between PM _2.5 exposure, its absorbance, and IBD. METHODS: We assessed the association of PM _2.5 and PM _2.5 absorbance with the two primary forms of IBD (Crohn's disease [CD] and ulcerative colitis [UC]) using Mendelian randomization (MR) to explore the causal relationship. We conducted two-sample MR analyses with aggregated data from the UK Biobank genome-wide association study. Single-nucleotide polymorphisms linked with PM _2.5 concentrations or their absorbance were used as instrumental variables (IVs). We used inverse variance weighting (IVW) as the primary analytical approach and four other standard methods as supplementary analyses for quality control. RESULTS: The results of MR demonstrated that PM _2.5 had an adverse influence on UC risk (odds ratio [ OR] = 1.010; 95% confidence interval [ CI] = 1.001-1.019, P = 0.020). Meanwhile, the results of IVW showed that PM _2.5 absorbance was also causally associated with UC ( OR = 1.012; 95% CI = 1.004-1.019, P = 0.002). We observed no causal relationship between PM _2.5, PM _2.5 absorbance, and CD. The results of sensitivity analysis indicated the absence of heterogeneity or pleiotropy, ensuring the reliability of MR results. CONCLUSION Based on two-sample MR analyses, there are potential positive causal relationships between PM _2.5, PM _2.5 absorbance, and UC.
Humans ; Mendelian Randomization Analysis ; Particulate Matter/analysis* ; Polymorphism, Single Nucleotide ; Inflammatory Bowel Diseases/genetics* ; Air Pollutants/analysis* ; Crohn Disease/genetics* ; Colitis, Ulcerative/genetics* ; Genome-Wide Association Study ; Risk Factors ; Environmental Exposure

Objective:To develop a popular science educational product aimed at enhancing home-based self-management among peritoneal dialysis (PD) patients.Methods:A preliminary framework for popular science content on home-based self-management for PD patients was constructed based on an evidence summary. Through refinement of the evidence and expert panel discussions, the final educational content was determined and used to produce a popular science video. From June 18 to June 30, 2024, the video was disseminated through both online and offline channels. A questionnaire survey was used to assess the quality of the popular science educational product.Results:The final content framework consisted of four key areas: volume management, nutrition management, exercise management, and management of common problems. A popular science video with a total duration of 5 minutes and 52 seconds was developed. A total of 158 PD patients rated the quality of the popular science educational product with an average score of (83.03±2.01) .Conclusions:The development method of the popular science educational product is scientifically sound, and the content is suitable for dissemination. It provides a practical reference for improving health literacy and self-management ability among home-based PD patients.

Lung cancer has the highest incidence and mortality among malignant tumors in China.Persistent subsolid nodules(SSNs)are closely associated with early-stage lung adenocarcinoma.Artificial intelligence(AI),as an emerging technology,is capable of performing in-depth analysis of large-scale imaging data through autonomous learning and possesses the ability to predict outcomes from new data,demonstrating great potential and application prospects in the assessment of SSNs.AI can not only effectively assist radiologists in diagnosis and treatment,but also improve work efficiency while reducing misdiagnosis and missed diagnosis rates.This review summarizes the recent applications and research progress of AI in the assessment of SSNs,to provide new insights for the diagnosis and treatment of SSNs.