ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

BACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning. METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of the two,three core genes were identified,which were germinal center associated signaling and motility like,integrin beta-8,and carboxypeptidase A3.The areas under the ROC curve of the three core genes were 0.744,0.739,and 0.734,respectively,indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome.The results of immune infiltration analysis showed that memory B cells,CD56 bright NK cells,and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group(P<0.05),and were significantly positively correlated with the above three biomarkers(P<0.05).The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways,mainly related to olfactory transduction pathway,neuroactive ligand-receptor interaction,and infection pathway.The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes,abnormal immune regulation,and multiple pathways imbalance.However,the interactions between these genes and immune cells,as well as their relationships with various pathways need to be further investigated.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

OBJECTIVE To evaluate the cost-effectiveness of finerenone combined with standard treatment regimen in the treatment of diabetic nephropathy （DN）. METHODS From the perspective of healthcare service providers， a Markov model was established to simulate the dynamic changes of each stage in DN patients who received finerenone combined with the standard treatment regimen or the standard treatment regimen alone based on the phase Ⅲ clinical trial study of finerenone for DN. Markov model was used to perform the cost-effectiveness of long-term effects and the costs of the two therapies with a simulation cycle of 4 months， a simulation period of 15 years and an annual discount rate of 5%. At the same time， one-way sensitivity analysis and probability sensitivity analysis were performed， and the stability of the results was validated. RESULTS Accumulative cost of the standard treatment regimen was 579 329.54 yuan， and the accumulative utility was 8.052 4 quality-adjusted life year （QALYs）； the accumulative cost of finerenone combined with the standard treatment regimen was 332 520.61 yuan， and the accumulative utility was 8.187 4 QALYs. Finerenone combined with the standard treatment regimen was more cost-effective. The results of one-way sensitivity analysis showed that dialysis status utility value， DN stage 3 utility value and DN stage 4 utility value had a great influence on the incremental cost-effectiveness ratio， but did not affect the robustness of the model. The results of probability sensitivity analysis showed that finerenone combined with the standard treatment regimen was more cost-effective with 100% probability. CONCLUSIONS For DN patients， finerenone combined with the standard treatment regimen is more cost-effective as an absolute advantage option.

ObjectiveTo investigate the mechanism of action of modified Shaofu Zhuyutang in antagonizing cellular pyroptosis and fibrosis in ectopic endometrial tissues of endometriosis through the Toll-like receptor 4/nuclear factor-κB/NOD-like receptor protein 3 （TRL4/NF-κB/NLPR3） signaling pathway. MethodsSeventy-two SPF-grade female SD rats were randomly divided into a sham-operated group （n = 12） and a modeling group （n = 60）. The rats in the sham-operated group underwent a caesarean section， while the rats in the modeling group were used to establish an endometriosis model through the auto-transplantation method. After successful modeling， the animals were randomly divided into the model group， progesterone group （0.25 mg·kg^-1）， and modified Shaofu Zhuyutang low-， medium-， and high-dose groups （7.5， 15， 30 g·kg^-1）， with 12 animals in each group. After 4 weeks of drug administration， voluntary activity and heat pain latency were observed. The rats were sacrificed for tissue collection， and Masson staining were used to observe histopathological changes in the endometrial tissues. Enzyme-linked immunosorbent assay （ELISA） was used to measure serum levels of interleukin-18 （IL-18）， interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， and transforming growth factor-β （TGF-β）. Immunohistochemistry （IHC） was used to detect the protein expression area of tumor necrosis factor-related factor 6 （TRAF6） and NLPR3 in the endometrial tissues. Immunofluorescence （IF） was used to detect the relative fluorescence intensity of Caspase-1 and gasdermin D （GSDMD） in the endometrial tissues. Western blot was employed to measure the relative expression of TRL4， myeloid differentiation factor 88 （MyD88）， TRAF6， NF-κB p65， phosphorylated NF-κB p65 （p-NF-κB p65）， and NLPR3 proteins in endometrial tissues. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of TRL4， MyD88， TRAF6， NF-κB， and NLPR3 in the endometrial tissues. ResultsCompared with the sham-operated group， rats in the model group showed reduced voluntary activity and shorter heat pain latency. Serum levels of IL-18， IL-1β， TNF-α， and TGF-β were elevated. The relative expression areas of TRAF6 and NLPR3 proteins were increased， and the relative fluorescence intensity of Caspase-1 and GSDMD was enhanced. The relative expression of TRL4， MyD88， TRAF6， NF-κB p65， p-NF-κB p65， and NLPR3 proteins， along with the expression of TRL4， MyD88， TRAF6， NF-κB， and NLPR3 mRNA， were significantly increased （P<0.01）. Compared with the model group， rats in the progesterone group and the modified Shaofu Zhuyutang medium- and high-dose groups exhibited improved voluntary activity， longer heat pain latency， the fibrosis of endometrial tissue is alleviated. Serum levels of IL-18， IL-1β， TNF-α， and TGF-β were decreased. The relative expression areas of TRAF6 and NLPR3 proteins decreased， and the relative fluorescence intensity of Caspase-1 and GSDMD weakened. The relative expression of TRL4， MyD88， TRAF6， p-NF-κB p65， NLPR3 proteins， and TRL4， MyD88， TRAF6， NF-κB， and NLPR3 mRNA expression were reduced （P<0.05， P<0.01）. ConclusionModified Shaofu Zhuyutang may play a therapeutic role in endometriosis by interfering with key proteins in the TRL4/NF-κB/NLPR3 signaling pathway， reducing NLRP3 inflammasome-induced cellular pyroptosis， antagonizing the fibrosis process in ectopic endometrial tissues， improving the inflammatory microenvironment in the pelvic cavity， and alleviating pain.

The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Background/Aims: Nonalcoholic fatty liver disease (NAFLD) is a common disease with severe inflammatory processes associated with numerous gastrointestinal diseases, such as inflammatory bowel disease (IBD). Therefore, we investigated the relationship between NAFLD and IBD and the possible risk factors associated with the diagnosis of IBD. Methods: This longitudinal nationwide cohort study investigated the risk of IBD in patients with NAFLD alone. General characteristics, comorbidities, and incidence of IBD were also compared. Results: Patients diagnosed with NAFLD had a significant risk of developing IBD compared to control individuals, who were associated with a 2.245-fold risk of the diagnosis of IBD and a 2.260- and 2.231-fold of increased diagnosis of ulcerative colitis and Crohn’s disease, respectively (P< 0.001). The cumulative risk of IBD increased annually during the follow-up of patients with NAFLD (P< 0.001). Conclusions Our results emphasize that NAFLD significantly impacts its incidence in patients with NAFLD. If patients with NAFLD present with risk factors, such as diabetes mellitus and dyslipidemia, these conditions should be properly treated with regular follow-ups. Furthermore, we believe that these causes may be associated with the second peak of IBD.

Objective: Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA). Methods: The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox. Results: Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%. Conclusion This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.