Elderly patients with primary membranous nephropathy (PMN) exhibit significant prognostic heterogeneity and poor tolerance to immunotherapy. However, there is a lack of early prognostic prediction tools specifically for this population. This study aimed to develop a prognostic prediction model applicable to elderly PMN patients.

Background/Aims: The pathophysiology of refractory gastroesophageal reflux disease (RGERD), which differs from proton pump inhibitor dependent gastroesophageal reflux disease (DGERD), remains incompletely elucidated. This study aims to compare esophageal motility patterns, transdiaphragmatic pressure gradients (TPG), and reflux profiles between RGERD and DGERD patients, and to delineate dynamic pressure gradient-esophagogastric junction (EGJ) interactions in these patients. Methods: In this retrospective study, 274 patients who underwent 24-hour impedance-pH monitoring and high-resolution manometry, along with an assessment of proton pump inhibitor responsiveness, were classified as RGERD (32.5%), DGERD (54.4%), or non-GERD (13.1%). Clinical characteristics, TPG, esophageal motility, and reflux metrics were compared between RGERD and DGERD patients. Subgroup analysis excluding hiatal hernia (HH) was conducted to investigate the pathophysiology of RGERD. Results: The RGERD group exhibited a significantly higher proportion of chest pain compared to the DGERD group. Regarding reflux profiles, RGERD patients without HH (RGERDHH- group) experienced increased weakly acidic reflux (P < 0.001) and prolonged bolus exposure (P = 0.006) compared to their counterparts (DGERDHH- group). Mechanistically, the RGERDHH- group showed reduced lower esophageal sphincter basal pressure (P = 0.010) and EGJ contractile integral (P = 0.005). Notably, following a wet-swallow, the RGERDHH- group experienced the significant elevation in gastric pressure and TPG. Correlation analyses revealed weakly acidic reflux and bolus exposure were positively correlated with gastric pressure variation, and inversely correlated with lower esophageal sphincter basal pressure. Conclusions Transient gastric pressure elevation and compromised EGJ barrier function drive weakly acidic reflux and esophageal bolus exposure. This pressure gradient-barrier mismatch underpins the refractoriness of RGERD.

ObjectiveTo compare the performance of different automatic evaluation methods for detecting pattern hallucinations generated by large language models （LLMs） in traditional Chinese medicine （TCM） syndrome differentiation and to identify a strategy with higher overall discriminative performance against expert manual judgment. MethodsA standardized TCM syndrome-differentiation dataset containing 598 cases was constructed from case reports published in Chinese core journals indexed by Peking University Core Journals or Chinese Science Citation Database. Each of the 598 cases was submitted to five Chinese LLMs， with each model generating one syndrome-pattern output per case， yielding 2990 outputs in total. The 598 outputs generated by Qwen3-Next-80B-A3B-Thinking were independently annotated for syndrome-pattern hallucinations by two licensed TCM physicians with intermediate or higher professional titles. Disagreements were adjudicated by a third licensed TCM physician with a senior associate professional title and more than 10 years of clinical experience. The resulting consensus annotations served as the reference labels for training and validating the automated evaluation method. Based on these reference labels， three categories of automated evaluation methods were developed and compared following a progressive strategy from text-level semantic similarity， to syndrome-element structural consistency， and finally multi-feature fusion. Method A employed sentence-embedding models based on semantic similarity between pattern texts. Method B adopted a rule-based threshold method using weighted Jaccard coefficients of disease-location and disease-nature syndrome elements. Method C utilized a machine-learning classifier integrating syndrome-element matching scores， missing and redundant syndrome-element counts， and multiple semantic-similarity features. Five-fold cross-validation was used to evaluate the discriminative performance of each method against manual judgment. The optimal method was then applied to uniformly assess the pattern hallucination rates of five LLMs. ResultsIn method A， text2vec-large-chinese showed best overall performance， with an area under the receiver operating characteristic curve （AUROC）， area under the precision-recall curve （PR-AUC）， F1-score， sensitivity， and specificity of 0.849， 0.884， 0.794， 0.801， and 0.691， respectively. In method B， the corresponding metrics of the rule-based syndrome-element Jaccard threshold method were 0.848， 0.851， 0.817， 0.845， and 0.684， respectively； those of the LLM-based syndrome-element Jaccard method were 0.872， 0.906， 0.795， 0.764， and 0.780， respectively. In method C， the multilayer perceptron （MLP） classifier showed the best overall performance， with AUROC， PR-AUC， F1-score， sensitivity， and specificity of 0.933， 0.951， 0.871， 0.857， and 0.846， respectively. Under the unified evaluation framework combining DeepSeek-R1 syndrome-element extraction and MLP classifier， the estimated pattern hallucination rates of the five LLMs ranged from 56.0% to 77.4%. ConclusionAmong the three automatic evaluation methods， Method C integrating syndrome-element discrepancy features and text semantic features using an MLP classifier showed the highest overall discriminative performance against manual judgment， and is more suitable for evaluating syndrome hallucinations in LLM-based TCM syndrome differentiation tasks.

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.

Objective:It aims to explore the advantages,disadvantages,opportunities and challenges of Chinese medicine practitioners participating in the"three-division co-management"service model in diabetes management,analyze its performance in policy support,resource matching and patient satisfaction,and put forward optimization strategies to enhance the role of Chinese medicine practitioners in chronic disease management.Methods:Through literature analysis,questionnaire survey and semi-structured interview,the data of six pilot units of"three divisions co-management"in Jinjiang were collected,and the internal and external environment of Chinese medicine practitioners participating in the"three divisions co-management"model was systematically evaluated by the Strengths,Weaknesses,Opportunities,Threats(SWOT)and the Politics,Economic,Society,Technology(PEST).Results:Chinese medicine practitioners' participation in the"three-division co-management"model has obvious advantages in policy support,resource matching and patient satisfaction,but it also faces key obstacles such as lack of incentive mechanism,shortage of Chinese medicine practitioners' resources and limitation of medical insurance reimbursement.The survey shows that 99.7％of the respondents are willing to choose this mode again,and 96.43％of the respondents said that the medical experience has improved significantly.However,43.92％of the subjects think that the professional knowledge and skills of doctors need to be improved,and 32.2％of the subjects think that the effect of Chinese medicine treatment on reducing the dependence and side effects of western medicine is limited.Conclusion:It is feasible and practical for Chinese medicine practitioners to participate in the"three divisions co-management"model,but it is necessary to further improve its service effect and sustainability by improving the performance appraisal mechanism,expanding the scope of medical insurance reimbursement,and strengthening the training for Chinese medicine practitioners' resources.

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.

There is significant inter-individual variation of pharmacokinetics and pharmacody-namics in patients receiving tacrolimus(TAC)for an-ti-rejection therapy,which cause the rejection or toxic action.Based on results of therapeutic drug monitoring and pathophysiological index of trans-plant patients,the individualized dosing regimen can be designed and adjusted by using model in-formed precision dosing(MIPD).The patients'clini-cal outcome can be improved.In the consensus,the different methods of MIPD used for patients re-ceived TAC for anti-rejection therapy were intro-duced,which can be used for the designing and ad-justing doing regimen,predicting adverse drug reac-tion,improving medication adherence and econom-ics during therapy.

OBJECTIVES: The risk factors and role of mother‒child gut microbiota in pediatric inflammatory bowel disease (PIBD) remain unclear. We aimed to explore the clinical risk factors associated with PIBD, analyze the characteristics of gut microbiota of children and their mothers, and examine the correlation of the microbial composition in mother‒child pairs. METHODS: We conducted a case-control study including children with PIBD and their mothers as the case group, as well as healthy children and their mothers as the control group. Questionnaires were used to collect information such as family illness history and maternal and early-life events. Fecal samples were collected from the children and mothers for microbiota 16S ribosomal RNA (rRNA) sequencing to analyze the composition and its potential association with PIBD. RESULTS: A total of 54 pairs of cases and 122 pairs of controls were recruited. A family history of autoimmune disease and antibiotic use during pregnancy were associated with an increased risk of PIBD, and a higher education level of the father was associated with a decreased risk of PIBD. Children with PIBD and mothers exhibited different gut microbiota compared to healthy children and mothers. Similarities were observed in the gut microbiota of mothers and children in the same groups. Some bacterial biomarkers of mothers discovered in this study had the power to predict PIBD in their offspring. CONCLUSIONS PIBD is influenced by maternal risk factors and has unique gut microbiota characteristics. The mother‒child gut microbiota is closely related, suggesting the transmission and influence of the gut microbiota between mothers and children. This study highlights the potential pathogenesis of PIBD and provides a basis for developing targeted interventions.
Humans ; Gastrointestinal Microbiome ; Female ; Risk Factors ; Case-Control Studies ; Male ; Child ; Inflammatory Bowel Diseases/etiology* ; Adult ; RNA, Ribosomal, 16S/genetics* ; Feces/microbiology* ; Mothers ; Pregnancy ; Child, Preschool

The incidence rate of metabolic associated fatty liver disease(MAFLD)is gradually increasing,and it has become a common chronic liver disease globally.MAFLD is closely associated with metabolic dysfunction,with dietary and exercise interventions as the primary treatment method,among which dietary control is of particular importance.This article summarizes related articles on the prevention and treatment of MAFLD through different fasting patterns in recent years,and the analysis showed that by restricting food intake and controlling calorie consumption,fasting therapy can help to reduce body weight and improve metabolic disorders.Further studies and clinical practice are needed to explore and validate the value of different fasting patterns in the prevention and treatment of MAFLD.

The incidence rate of metabolic associated fatty liver disease(MAFLD)is gradually increasing,and it has become a common chronic liver disease globally.MAFLD is closely associated with metabolic dysfunction,with dietary and exercise interventions as the primary treatment method,among which dietary control is of particular importance.This article summarizes related articles on the prevention and treatment of MAFLD through different fasting patterns in recent years,and the analysis showed that by restricting food intake and controlling calorie consumption,fasting therapy can help to reduce body weight and improve metabolic disorders.Further studies and clinical practice are needed to explore and validate the value of different fasting patterns in the prevention and treatment of MAFLD.