Objective To systematically evaluate the risk prediction models for postoperative delirium in adults with cardiac surgery. Methods The SinoMed, CNKI, Wanfang, VIP, PubMed, EMbase, Web of Science, and Cochrane Library databases were searched to collect studies on risk prediction models for postoperative delirium in cardiac surgery published up to January 29, 2025. Two researchers screened the literature according to inclusion and exclusion criteria, used the PROBAST bias tool to assess the quality of the literature, and conducted a meta-analysis of common predictors in the model using Stata 17.0 software. Results A total of 21 articles were included, establishing 45 models with 28733 patients. Age, cardiopulmonary bypass time, history of diabetes, history of cerebrovascular disease, and gender were the top five common predictors. The area under the curve (AUC) of the 45 models ranged from 0.544 to 0.98. Fourteen out of the 21 studies had good applicability, while the applicability of the remaining seven was unclear; 20 studies had a high risk of bias. Meta-analysis showed that the incidence of postoperative delirium in adults with cardiac surgery was 18.6% [95%CI (15.7%, 21.6%)], and age [OR=1.045 (1.036, 1.054), P<0.001], history of cerebrovascular disease [OR=1.758 (1.459, 2.057), P<0.001], gender [OR=1.732 (1.430, 2.034), P<0.001], mini-mental state examination score [OR=3.930 (1.859, 8.309), P<0.001], and length of ICU stay [OR=5.586 (4.289, 6.883), P<0.001] were independent influencing factors for postoperative delirium after cardiac surgery. Conclusion The risk prediction models for postoperative delirium after cardiac surgery have good predictive performance, but there is a high overall risk of bias. In the future, large-sample, multicenter, high-quality prospective clinical studies should be conducted to construct the optimal risk prediction model for postoperative delirium in adults with cardiac surgery, aiming to identify and prevent the occurrence of postoperative delirium as early as possible.

The patient was a 56-year-old man who presented with abnormal urinalysis findings of over one month’s duration. Serological tests revealed an elevated serum free light chain κ/λ ratio, and immunofixation electrophoresis detected an IgG-κ monoclonal band. Renal biopsy demonstrated membranous nephropathy, with electron-dense deposits observed in the subepithelial space and mesangium on electron microscopy. Immunofluorescence confirmed glomerular deposits of IgG_1/IgG₃-κ. Based on clinical manifestation, bone marrow flow cytology, and lymph node pathology, the patient was diagnosed with small lymphocytic lymphoma (SLL), and membranous nephropathy with light chain-restricted deposition (IgG₁/IgG₃-κ) secondary to SLL. After receiving obinutuzumab treatment, the patient’s proteinuria partially remitted, the lymph nodes significantly shrank, and clinical remission was achieved.

Background: Advances in intensive care have markedly improved survival after severe brain injury, leading to a growing population of patients with prolonged disorders of consciousness (pDOC). Current management of pDOC remains largely supportive, and evidence-based neuromodulatory interventions are limited; moreover, existing guidelines provide insufficiently explicit recommendations regarding mechanisms of action and objective biomarkers of treatment response. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential noninvasive intervention; however, its modulatory effects on brain function in pDOC are not yet well characterized, and the paucity of integrative mechanistic evidence has constrained its translation into routine clinical practice. Objectives: Within a multimodal assessment framework, this study aims to systematically elucidate the neurobiological mechanisms by which taVNS modulates brain function and autonomic activity in patients with pDOC, and to evaluate its clinical potential to enhance levels of consciousness. Methods: In this randomized controlled trial, 60 patients with vegetative state/minimally conscious state will be enrolled and randomly allocated to a taVNS group, a transcutaneous nonauricular vagus nerve stimulation group (sham), or a control group (n = 20 per group) for a 4-week intervention. The primary outcome will be changes in the Coma Recovery Scale-Revised scores from baseline to weeks 1, 2, and 4 of treatment. Secondary outcomes will include functional brain activity assessed by electroencephalography and functional near-infrared spectroscopy, as well as autonomic modulation indexed by heart rate variability. Functional prognosis will be evaluated using the Glasgow Outcome Scale-Extended at the end of treatment and at a 6-month follow-up. Safety will be assessed by continuous monitoring and documentation of adverse events throughout the study period. Results and discussion: By integrating electroencephalography–functional near-infrared spectroscopy with heart rate variability, this study will characterize the effects of taVNS on functional brain networks and consciousness recovery in pDOC across complementary behavioral, electrophysiological, hemodynamic, and autonomic domains, while interrogating potential sources of clinical and neurobiological heterogeneity. The findings are expected to provide a mechanistic and evidence-based foundation for the mechanism-driven clinical implementation of taVNS and the optimization of stimulation protocols in pDOC. Clinical trial registration: International Traditional Medicine Clinical Trial Registry, ITMCTR20250021041, https://itmctr.ccebtcm.org.cn.

A VLi-net based cell nucleus segmentation method integrating convolutional neural networks(CNN)and Vision Transformer(ViT)is proposed to address the limitation that the U-Net with CNN as its backbone is only proficient in capturing local features and has a restricted receptive field.Firstly,to mitigate challenges such as high cost of data annotation and insufficient annotated data,text annotations are introduced to enhance the network's understanding of image information.Secondly,to improve the segmentation performance of VLi-net,ViT and CNN are combined to fully extract global and local features,with multi-receptive field convolution features incorporating into the ViT structure for effectively mitigating the issues of limited local information interaction and single feature representation in ViT.Finally,an interactive fusion module(ViFusion)is used to efficiently fuse the multi-level features from the CNN and ViT branches.Experimental results show that VLi-net achieves a Dice coefficient of 80.85%and a mean intersection over union(MIoU)of 66.83%on the MoNuSeg dataset,obtains a Dice coefficient of 80.53%and a MIoU of 67.54%on the DSB-2018 dataset,and has a Dice coefficient of 86.87%and a MIoU of 77.44%on the TNBC dataset.These findings confirm that VLi-net outperforms other methods across multiple experimental metrics.

Myasthenia gravis(MG)is an autoimmune disease characterized primarily by skeletal muscle weakness and,in severe cases,respiratory involvement.Western medical treatment predominantly relies on immunosuppressants,but long-term administration often leads to notable side effects.In contrast,traditional Chinese medicine(TCM)offers the advantage of multi-target interventions.However,the pathogenesis of MG has not been fully elucidated,and the establishment of animal models that accurately reflect the clinical characteristics of both Chinese and Western medicine is essential for mechanism research and new drug development.This paper systematically reviews the etiology and pathogenesis,diagnostic criteria,and progress of animal model research for MG from both Chinese and Western medicine perspectives.In Western medicine,the pathogenesis of MG is closely related to genetic susceptibility,environmental factors,and autoantibody-mediated postsynaptic membrane damage.In TCM,MG is classified under the category of"flaccidity syndrome",attributed to congenital deficiencies and acquired malnourishment.Western diagnostic criteria involve a combination of clinical symptoms,fatigue testing,serum antibody assays,and electrophysiological evaluation.In contrast,TCM diagnosis emphasizes the integration of primary and secondary symptoms with tongue and pulse pattern differentiation.Currently available animal models mainly include experimental autoimmune myasthenia gravis(EAMG)and passive transfer myasthenia gravis(PTMG).The Toredo acetylcholine receptor(AChR)induced EAMG model aligns well with Western diagnostic criteria,but poorly matches secondary symptoms in TCM.The synthetic AChR peptide model is widely used,but shows low conformity with TCM syndromes.Models induced by muscle-specific tyrosine kinase(MuSK),low-density lipoprotein receptor-related protein 4(LRP4),and transgenic models demonstrate high innovation but exhibit low clinical conformity.Evaluation of these models requires integration of behavioral,electrophysiological,and immunological indicators.However,a systematic framework for modelling TCM syndromes is still lacking.Future research should integrate TCM-based etiological modelling methods with the Western pathological mechanisms to construct disease-syndrome combination models.Additionally,it is crucial to establish a TCM syndrome evaluation system based on"validation by prescription",as well as to improve the scientific rigor and practicality of animal models by the incorporation of emerging technologies.This review provides a theoretical foundation for optimizing MG animal model design,advancing the research on the combination of Chinese and Western medicine,and supporting efficacy assessment and mechanism exploration of Chinese herbal prescriptions.

Objective To propose a classification model based on a pseudo-bag strategy and feature adjustment for whole slide imaging in pathology.Methods A pseudo-bag generator was constructed to divide a parent bag into 3 pseudo-bags for increasing the number of training bags.Then,a pseudo-bag learning method based on Nystr?m-based algorithm for approximating self-attention and a selective feature fusion method were employed to process the pseudo-bags.Specifically,the pseudo-bag learning method based on Nystr?m-based algorithm for approximating self-attention reduced computational complexity through an improved multi-head self-attention mechanism while deeply extracting instance features to obtain pseudo-bag classification predictions,thereby enhancing pseudo-bag classification accuracy;and the selective feature fusion method refined pseudo-bag features by filtering and extracting relevant instances.Finally,the model adjusted bag features by extracting confounding factors to avoid interference from irrelevant information and further improve classification accuracy.Results The proposed model was evaluated on two datasets(CAMELYON-16 and TCGA-NSCLC)and compared with 10 other methods,and the results demonstrated that the proposed model achieved the best performance.The proposed method reached an accuracy of 0.943 on the CAMELYON-16 dataset and 0.906 on the TCGA-NSCLC dataset.Conclusion The proposed model can significantly improve the accuracy of whole-slide pathological image classification by effectively mitigating the overfitting and avoiding interference from irrelevant information.

Objective To analyze the relationship between article content and user feedback on WeChat public numbers in hospitals,and to provide data support and theoretical basis for optimizing public number content and health communication strategy.Methods 1 279 tweets of WeChat public number of a tertiary public hospital from 2023 to 2024 were selected to ana-lyze the relationship between article categories and user feedback behavior,interactive behavior and article dissemination effect,and to assess the important influencing factors of user attention conversion behavior.Results The articles in the health science popularization category show significant advantages in the indicators of users' reading and sharing behaviors;the interactive indica-tors of users' liking and commenting have a significant positive effect on the dissemination effect of the articles,while the number of"watching"hurts the number of readings;the assessment of the random forest model shows that the number of readings genera-ted by sharing and the number of readings brought by each sharing are the most important factors influencing the users' attention.The random forest model assessment showed that the number of reads generated by sharing and the number of reads per share were the most important factors influencing users' attention.Conclusion Health science articles dominate users' reading and sharing behaviors,user interaction significantly improves the dissemination effect and attention conversion of the articles,and the number of reads generated by sharing is the key factor determining users' attention.This suggests that content dissemination strategies need to be combined with the content characteristics of the article,focusing on improving the interaction and sharing guidance of con-tent with high dissemination potential.

Recent research progress into the use of Chinese medicine has demonstrated good therapeutic effects for increasing numbers of Chinese medicines for immune system diseases.Atopic dermatitis(AD)is an inflammatory disease characterized by type 2 immunity,and research into its pathogenesis and therapeutic immunopharmaceuticals has result ed in various different types of animal models.This review summarizes the existing animal models of AD and their immune-related characteristics,with the aim of providing appropriate references for the selection of future research models related to AD.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

A tertiary public general hospital in Guangdong has innovated its inpatient bed scheduling system by integra-ting multiple models,including"Hospital-Wide Bed Pooling,"outpatient chemotherapy,day surgery,pre-admission,and pre-discharge programs.Supported by policy guidance,this initiative optimizes clinical operations,enhances patient admission struc-tures and processes,and improves bed utilization efficiency through a multi-dimensional centralized bed management approach.By rationally allocating hospital-wide bed resources and maximizing their operational effectiveness,the hospital advances high-quality development in healthcare delivery.