ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

Objective To establish a performance verification scheme for the metagenomic next-generation sequencing(mNGS)DNA workflow.Methods Reference materials and clinical samples were used for conducting experiments.The mNGS detection results were evaluated in terms of limit of detection(LOD),repeatability,robustness,anti-interference ability,specificity and accuracy,as well as the patterns of library construction and the performance of sequencers.Results All species in the reference materials were stably detected,and the LOD of mNGS was 5.0E+02 CFU/mL(copies/mL).The repeatability was 100％and the within-batch(coefficient of variation)CV ranged from 8.53％to 38.73％.The linear correlation coefficient|r|＞0.9 was found between the input pathogenic microorganism concentration and the read count.Meanwhile,the experimental robustness was found to be good.The results of the anti-interference test showed that the higher concentration of human DNA inputted,the fewer pathogenic microorganism read counts detected by mNGS.Meanwhile,the read counts of related species presented a proportional relationship with the corresponding pathogenic microorganisms concentration inputted,which meant the validation of the cross-interference test had been passed.Furthermore,the detection result of D0 was negative.The accuracy of clinical samples testing was 90.9％(10/11).In addition,the library quality control results obtained by the automatic liquid handling workstation and manually operation were all acceptable.The performance of the three Illumina sequencers met or were better than the factory standards.Conclusion The clinical laboratory performance verification scheme for mNGS detection was established,which included the design for reference materials,comparison of different patterns for library construction,and performance evaluation of the sequencers.More importantly,the performance verification scheme can be used to evaluate and ensure the quality of mNGS DNA workflow detection process.

Objective:To investigate the risk factors for patients with hypertriglyceridemia-related acute pancreatitis (HTG-AP) developing into severe acute pancreatitis or experiencing organ failure.Methods:This retrospective cohort study collected clinical data from 2 429 patients diagnosed with acute pancreatitis from five hospitals in China between January 2019 and December 2023 using a pre-designed data collection form. The cohort included 1 516 males and 913 females,with an age of (50.2±16.5)years(range: 11 to 99 years). Among them,353 patients (16.1%) had HTG-AP,while 1 846 (83.9%) had non-HTG-AP. HTG-AP was defined as serum triglyceride levels>500 mg/dl with other etiologies excluded. Intergroup comparisons were performed using t-tests,Mann-Whitney U test or χ2 tests,respectively. Univariate and multivariate logistic regression analyses were conducted to assess risk factors for severe acute pancreatitis after adjusting for potential confounders,and a predictive model was developed and validated. Results:Compared with other etiologies,HTG-AP patients had a higher risk of progressing to SAP ( OR=1.415,95% CI: 0.866 to 2.312, P=0.017) and organ failure ( OR=1.256,95% CI: 1.015 to 1.554, P=0.036). Among HTG-AP patients,risk factors for SAP included body mass index ( OR=1.856,95% CI: 1.742 to 1.987, P=0.033),fasting blood glucose ( OR=1.128,95% CI: 1.036 to 1.229, P=0.006),white blood cell count( OR=1.162,95% CI: 1.055 to 1.281, P=0.002),and the presence of pleural effusion ( OR=13.151,95% CI: 4.330 to 19.946, P<0.01). A nomogram prediction model for SAP in HTG-AP was constructed based on these risk factors,demonstrating good discriminative ability with area under the curve values of 0.877 in the training set and 0.894 in the validation set,along with satisfactory calibration. Conclusions:HTG-AP patients are at higher risk of developing SAP and organ failure. The risk prediction model incorporating body mass index,fasting blood glucose,white blood cell count,and pleural effusion shows good predictive value for SAP.

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

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

Objective To screen out the indexes of medical insurance fund under China Healthcare Security Diagno-sis Related Groups(CHS-DRG)payment based on Delphi method and improve the efficiency of medical insurance fund supervision.Methods Using the Delphi method,32 experts in the medical insurance field were selected and 2 rounds of expert questionnaires were conducted to design an evaluation scale for the monitoring indicators of medi-cal insurance funds under CHS-DRG payment.The consultation results were evaluated from three aspects of motiva-tion,authority,and coordination.Results A medical insurance fund supervision index system based on three dimen-sions of medical insurance fund use quality,efficiency and safety supervision is constructed,including 3 first-level indicators,7 second-level indicators and 44 third-level indicators.Results The construction ofmedical insurance fund supervision index system under CHS-DRG payment should respect the actual clinical diagnosis and treatment,strengthen the intelligent supervision method,and timely intervene in medical insurance fund management risks.

Objective:To establish a preliminary performance validation protocol for the bioinformatics procedure of metagenomic next-generation sequencing (mNGS) in clinical laboratories.Methods:Three types of simulated datasets were designed and the CatⅠ dataset mainly consisted of pathogen reference genomes and human sequences. CatⅠA was a dataset composed of common pathogens mixed with human sequences and was used to evaluate the inclusiveness, accuracy, recall rates, precision, F1-Score, and other indicators of the mNGS bioinformatics procedure. CatⅠB was a dataset composed of closely related species of common pathogens mixed with human sequences, which was used to evaluate the discriminating ability of closely related species of bioinformatics procedure by calculating the detection rates and the relative abundance ratio of closely related species. The real data of 200 clinical samples was selected to construct CatⅡ and the simulated dataset consisted of colonized bacteria, experimental environment bacteria, reagent engineering bacteria, pathogen reference genomes, and human sequences, which was used to evaluate the sensitivity, specificity, and accuracy of bioinformatics pipeline for pathogens detection. The CatⅢ dataset was obtained from the negative bronchoalveolar lavage fluid BALF sequencing data mixed with 20 rare pathogens sequences in order to evaluate the positive detection rates and recall rates of rare pathogens in the bioinformatics analysis.Results:The analysis of the CatⅠA dataset showed that the positive consistency rate, inclusiveness, precision and accuracy of the bioinformatics peocedure under three sequence gradients were all greater than 99%, with a recall rate of 72.31% (95% CI 69.61%-75.01%) and a F1 Score of 82.00% (95% CI 79.77%-84.22%). In the CatⅠB dataset, the closely related species could be effectively detected at all sequence and proportion gradients, and the relative abundance ratio of closely related species was within 2 times of the design ratio except for the coronavirus, haemophilus, primate bocaparvovirus, human respiratory syncytial virus, and eimeria, indicating good ability to identify the closely related species. All the 24 species of pathogens included in CatⅡ dataset were effectively detected, with the sensitivity, specificity, and accuracy all greater than 90%. All rare pathogens were detected in the CatⅢ dataset, with a detection rate of 100%. Conclusions:With the simulated datasets, the performance validation scheme for the mNGS bioinformatics analysis was preliminary established and could evaluate the accuracy of sequence classification, the ability to identify the closely related species, and detection ability of common and rare pathogens, which may provide some references for the construction of mNGS process.

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.