Objective: To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine (TCM) syndrome differentiation within real clinical settings, we propose a data-efficient learning framework enhanced by knowledge graphs. Methods: We developed Agent-GNN, a three-stage decoupled learning framework, and validated it on the Traditional Chinese Medicine Syndrome Diagnosis (TCM-SD) dataset containing 54 152 clinical records across 148 syndrome categories. First, we constructed a comprehensive medical knowledge graph encoding the complete TCM reasoning system. Second, we proposed a Functional Patient Profiling (FPP) method that utilizes large language models (LLMs) combined with Graph Retrieval-Augmented Generation (RAG) to extract structured symptom-etiology-pathogenesis subgraphs from medical records. Third, we employed heterogeneous graph neural networks to learn structured combination patterns explicitly. We compared our method against multiple baselines including BERT, ZY-BERT, ZY-BERT + Know, GAT, and GPT-4 Few-shot, using macro-F1 score as the primary evaluation metric. Additionally, ablation experiments were conducted to validate the contribution of each key component to model performance. Results: Agent-GNN achieved an overall macro-F1 score of 72.4%, representing an 8.7 percentage points improvement over ZY-BERT + Know (63.7%), the strongest baseline among traditional methods. For long-tail syndromes with fewer than 10 samples, Agent-GNN reached a macro-F1 score of 58.6%, compared with 39.3% for ZY-BERT + Know and 41.2% for GPT-4 Few-shot, representing relative improvements of 49.2% and 42.2%, respectively. Ablation experiments confirmed that the explicit modeling of etiology-pathogenesis nodes contributed 12.4 percentage points to this enhanced long-tail syndrome performance. Conclusion This study proposes Agent-GNN, a knowledge graph-enhanced framework that effectively addresses the long-tail distribution challenge in TCM syndrome differentiation. By explicitly modeling manifestation-mechanism-essence patterns through structured knowledge graphs, our approach achieves superior performance in data-scarce scenarios while providing interpretable reasoning paths for TCM intelligent diagnosis.

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 analyze the epidemiological characteristics of varicella among nursery children in Shenzhen from 2015 to 2024, and to evaluate the impact of optimizing varicella vaccine (VarV) immunization strategies on varicella incidence. Methods: Varicella incidence data for nursery children in Shenzhen from 2015 to 2024 were obtained from the China Disease Prevention and Control Information System. The study period was divided into three phases:one dose self pay VarV (January 2015 to October 2017), two dose self pay VarV (November 2017 to October 2019), and two dose free VarV (November 2019 to December 2024). Interrupted time series (ITS) analysis was conducted to assess changes in the level and trend of varicella incidence associated with each phase of policy implementation. Results: A total of 27 517 varicella cases was reported among nursery children from 2015 to 2024, with an average annual incidence of 514.01/100 000. During the same period, 136 clustered outbreaks were reported in nursery institutions, involving a cumulative total of 1 091 cases. ITS analysis showed that during the self pay 1 dose stage, the varicella incidence among nursery children showed an upward trend, with an average monthly increase of 2.58/100 000 (95% CI =2.21/ 100 000 -2.95/100 000, P <0.01). After the implementation of the self pay 2 dose strategy, the incidence decreased, with a change in incidence of -26.12/100 000 (95% CI =-37.30/100 000 to -14.94/100 000) and a change in slope of -2.65/100 000 (95% CI = -3.38/100 000 to -1.93/100 000)(all P <0.01). After the implementation of the free 2 dose strategy, the incidence decreased further, with a change in incidence of -40.03/100 000 (95% CI =-50.39/100 000 to -29.66/100 000, P <0.01) and a change in slope of -0.56/100 000 (95% CI =-1.20/100 000-0.08/100 000, P =0.09). Conclusion The gradual optimization of the VarV vaccination strategy in Shenzhen from self pay 1 dose to free 2 dose has significantly reduced the varicella incidence among nursery children, demonstrating good short term control and long term intervention effectiveness.

ObjectiveJunctophilin-2 (JPH2) is an essential structural protein that maintains junctional membrane complexes (JMCs) in cardiomyocytes by tethering the plasma membrane to the sarcoplasmic reticulum, thereby facilitating excitation-contraction (E-C) coupling. Mutations in JPH2 have been associated with hypertrophic cardiomyopathy (HCM), but the molecular mechanisms governing its membrane-binding properties and the functional relevance of its membrane occupation and recognition nexus (MORN) repeat motifs remain incompletely understood. This study aimed to elucidate the structural basis of JPH2 membrane association and its implications for HCM pathogenesis. MethodsA recombinant N-terminal fragment of mouse JPH2 (residues1-440), encompassing the MORN repeats and an adjacent helical region, was purified under near-physiological buffer conditions.X-ray crystallography was employed to determine the structure of the JPH2 MORN-Helix domain. Sequence conservation analysis across species and junctophilin isoforms was performed to assess the evolutionary conservation of key structural features. Functional membrane-binding assays were conducted using liposome co-sedimentation and cell-based localization studies in COS7 and HeLa cells. In addition, site-directed mutagenesis targeting positively charged residues and known HCM-associated mutations, including R347C, was used to evaluate their effects on membrane interaction and subcellular localization. ResultsThe crystal structure of the mouse JPH2 MORN-Helix domain was resolved at 2.6 Å, revealing a compact, elongated architecture consisting of multiple tandem MORN motifs arranged in a curved configuration, forming a continuous hydrophobic core stabilized by alternating aromatic residues. A C-terminal α-helix further reinforced structural integrity. Conservation analysis identified the inner groove of the MORN array as a highly conserved surface, suggesting its role as a protein-binding interface. A flexible linker segment enriched in positively charged residues, located adjacent to the MORN motifs, was found to mediate direct electrostatic interactions with negatively charged phospholipid membranes. Functional assays demonstrated that mutation of these basic residues impaired membrane association, while the HCM-linked R347C mutation completely abolished membrane localization in cellular assays, despite preserving the overall MORN-Helix fold in structural modeling. ConclusionThis study provides structural insight into the membrane-binding mechanism of the cardiomyocyte-specific protein JPH2, highlighting the dual roles of its MORN-Helix domain in membrane anchoring and protein interactions. The findings clarify the structural basis for membrane targeting via a positively charged linker and demonstrate that disruption of this interaction—such as that caused by the R347C mutation—likely contributes to HCM pathogenesis. These results not only enhance current understanding of JPH2 function in cardiac E-C coupling but also offer a structural framework for future investigations into the assembly and regulation of JMCs in both physiological and disease contexts.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective To investigate the effect of Gd-hydroxyapatite(Gd-HA)stents with adipose mesenchymal cells(ADSCs)on the repair of knee articular cartilage defects.Methods To isolate,culture,and identify rabbit ADSCs by establishing a rabbit knee joint full-thickness cartilage defect model,a total of 18 rabbits were randomly divided into blank control group,Gd-HA scaffold group,and ADSCs+Gd-HA scaffold group.At week 12 and 24 after surgery,International Curtilage Repair Society(ICRS)score,HE,toluidine blue,modified red O bright green and ColⅡ were detected by immunohistochemical staining,then ColⅡand GAG mRNA expression levels were detected by O'Driscoll and Real-time PCR.ColⅡ protein expression was detected by Western blotting,GAG content was detected by DMMB,biomechanical strength was detected by indentation test,and PKH26 labeled ADSCs was used to trace the tissue engineering scaffold with Gd-HA composite ADSCs to evaluate the repair effect of rabbit knee cartilage defects.Results The ADSCs isolated and cultured in vitro showed good growth,stable phenotype and good directional differentiation through macroscopic observation and histological staining,it could be seen that the repair degree and effect of the knee joint full-thickness cartilage defect model implanted with Gd-HA scaffold group were better than those of the blank control group,while the cartilage repair situation of the ADSCs+Gd-HA scaffold group was better than that of the Gd-HA scaffold group(P＜0.05);The ICRS and improved O'Driscoll scores were higher than the other two groups(P＜0.05).Compared with the Gd-HA group,the ADSCs+Gd-HA group could produce ColⅡ and GAG during the process of cartilage repair,with stronger mechanical strength of the repaired tissue(P＜0.05);PKH26 labeled ADSCs were found in the repaired tissues of the ADSCs+Gd-HA group,and they were involved in the composition of newly formed tissues.Conclusion Gd-HA scaffold material combined with ADSCs has a good repair effect on full-thickness cartilage defects in the knee joint as a new type of biological material for repairing joint cartilage defects.

[This corrects the article DOI: 10.1016/j.apsb.2018.08.009.].

Stellate ganglion block (SGB) is a specific type of peripheral nerve block in which local anesthetics and/or steroids are injected around the stellate ganglia.In the past,SGB was mainly used to alleviate pain-related syndromes.With the development of ultrasound technology,SGB has been widely used in non-analgesic fields,demonstrating significant therapeutic effects on arrhythmias,hot flashes,psychiatric disorders,cerebrovascular diseases,insomnia,and post coronavirus disease-2019 conditions in recent years.This study reviews the progress in the application of SGB in the non-analgesic fields.
Stellate Ganglion ; Humans ; Autonomic Nerve Block/methods* ; Anesthetics, Local/administration & dosage* ; COVID-19

INTRODUCTION: Hydroxychloroquine (HCQ), originally an antimalarial drug, is currently used to treat multiple disorders, especially rheumatic diseases. Given its good efficacy and safety, HCQ is widely administered in pregnant patients. However, the safety profile of HCQ during pregnancy remains controversial due to limited research. In addition, HCQ has been reported to reduce preeclampsia in patients with systemic lupus erythematosus (SLE) and could potentially alleviate the symptom of preeclampsia. However, the clinical profile and molecular mechanism of HCQ in preeclampsia is yet to be fully understood. METHOD: We reviewed the literature on HCQ treatment in pregnancy with rheumatic diseases and preeclamp-sia in PubMed and Web of Science. We also discussed the safety of long-term therapy with HCQ during pregnancy. RESULTS: HCQ mainly modulates autoimmune response through inhibition of lysosomal function, toll-like receptor (TLR) signalling, nicotinamide adenine dinucleotide phosphate-mediated oxidative stress and autophagy. Benefits of HCQ in treating rheumatic diseases, including antiphospholipid syndrome, rheumatoid arthritis and Sjogren's syndrome during pregnancy, has been demonstrated in clinics. In particular, multiple clinical guidelines recommend HCQ as an indispensable therapeutic drug for pregnant patients with SLE. Additionally, it may potentially function in preeclampsia to improve clinical symptoms. CONCLUSION HCQ is effectively used for rheumatic diseases during pregnancy. The benefits of HCQ treatment in rheumatic diseases outweigh the risk of adverse reactions it induces in pregnant women.
Humans ; Hydroxychloroquine/pharmacology* ; Pregnancy ; Female ; Antirheumatic Agents/pharmacology* ; Rheumatic Diseases/drug therapy* ; Pregnancy Complications/drug therapy* ; Pre-Eclampsia/prevention & control* ; Lupus Erythematosus, Systemic/drug therapy* ; Arthritis, Rheumatoid/drug therapy* ; Antiphospholipid Syndrome/drug therapy* ; Sjogren's Syndrome/drug therapy*