This study aimed to investigate the mechanism of liquiritin (LQ) in the improvement of ventricular remodeling (VR) after acute myocardial infarction (AMI). Molecular docking was used to predict the binding affinity of liquiritin to thioredoxin-interacting protein (TXNIP). After 2 weeks of modeling, the rats were randomly divided into a model group, a low-dose liquiritin group (20 mg/kg LQ), and a high-dose liquiritin group (40 mg/kg LQ). Liquiritin was administered by gavage once a day, and the sham group and model group were given the same volume of 0.5% sodium carboxymethylcellulose (CMC-Na), with intervention of 4 consecutive weeks. Echocardiography was employed to detect the cardiac function, HE staining was used to observe cardiological changes, ELISA was used to detect the activity of serum creatine kinase-MB (CK-MB) activity, and the colorimetric method was adopted to detect serum malondialdehyde (MDA), total superoxide dismutase (T-SOD) and catalase (CAT) activities. RT-qPCR was used to detect the gene expressions of TXNIP, thioredoxin (TRX) and NACHT, LRR, and PYD domains-containing protein 3(NLRP3). Western blot was used to detect the protein expressions of TXNIP, TRX and NLRP3 in rat myocardial tissue. Molecular docking results showed that liquiritin had a good binding affinity to TNXIP target. After 20 and 40 mg/kg liquiritin intervention, the levels of ejection fraction (EF) and fractional shortening (FS) were significantly increased (P<0.01), and the levels of LVIDs, LVIDd, LVESV, and LVEDV were decreased (P<0.01). The myocardial structure was significantly improved, the cell arrangement tended to be regular, and the area of inflammatory cell infiltration and necrosis was reduced. Liquiritin significantly reduced the level of CK-MB (P<0.01), decreased the activity of MDA, and increased the activities of CAT and T-SOD (P<0.01). Liquiritin effectively inhibited the overexpression of TXNIP and NLRP3 genes and proteins, and enhanced the expression of TRX genes and proteins in the myocardial tissues of AMI rats. In conclusion, liquiritin has a regulatory effect on the TXNIP/TRX signaling pathway, inhibits the activation of the NLRP3 inflammasome, and thus improves ventricular remodeling after acute myocardial infarction.

Objective To systematically evaluate the efficacy and safety of traditional Chinese medicine (TCM) compound in treating pulmonary nodules, providing evidence-based medical evidence for TCM intervention in pulmonary nodules. Methods Computer search of PubMed, CNKI, Wanfang, VIP, and SinoMed was conducted to select randomized controlled trials (RCTs) of TCM compound intervention in pulmonary nodules, with the retrieval time from the inception to November 29, 2023. The Cochrane bias risk assessment tool was used to evaluate the quality of the included studies, and Review Manager 5.4 was used for Meta-analysis. Results A total of 18 RCTs were included, covering 8 provinces across the country, with a total sample size of 1301 patients. The TCM compounds used in the included studies all incorporated the method of dissolving phlegm and dissipating nodules. There was a high risk of bias uncertainty in the included studies. Meta-analysis results suggested that TCM compound could significantly reduce the diameter of pulmonary nodules [MD=−1.41, 95%CI (−1.70, −1.13), P<0.001], decrease the number of nodules [MD=−0.37, 95%CI (−0.73, −0.01), P=0.05], alleviate clinical symptoms [MD=−4.84, 95%CI (−6.04, −3.64), P<0.001], and improve lung function [forced expiratory volume in one second (FEV1), MD=0.55, 95%CI (0.09, 1.01), P=0.02; FEV1/forced vital capacity, MD=6.12, 95%CI (4.47, 7.78), P<0.001]. However, there was no statistically significant difference in the probability of malignancy between the experimental group and the control group [MD=−0.01, 95%CI (−0.01, 0.00), P=0.09]. Conclusion TCM compound can significantly reduce the diameter of pulmonary nodules, decrease the number of nodules, alleviate clinical symptoms, and improve lung function, but future multicenter, large-sample, high-quality RCTs are still needed to further explore and verify this conclusion.

Cavernous transformation of the portal vein （CTPV） is a significant challenge in clinical practice. In recent years， transjugular intrahepatic portosystemic shunt has gradually been applied in the treatment of CTPV patients， but it had a technical success rate and a relatively high surgical risk. The scholars have adopted various modified techniques of portosystemic shunt to improve success rate and safety. However， for CTPV patients with extensive occlusion of the main portal vein， this surgery remains difficult， and individualized strategies for portosystemic shunt should be formulated. This article reports a case of CTPV treated by transjugular extrahepatic portosystemic shunt assisted by endoscopic ultrasound-guided portal vein localization， in order to explore a new pathway for the clinical management of patients with CTPV.

Gastroesophageal varices （GOV） bleeding is a severe complication of portal hypertension with a high mortality rate. Animal models are indispensable tools for investigating its pathogenesis and developing novel therapeutic strategies. This article systematically reviews the methods for establishing various GOV models， with a particular focus on their efficacy in simulating the key pathological processes such as an increase in hepatic venous pressure gradient and the risk of bleeding， and it also proposes targeted strategies for model selection. Finally， this article discusses the application prospects of emerging techniques in the era of precision medicine， such as organoids and gene editing， in order to provide model selection and a theoretical reference for exploring the mechanism and clinical translation of GOV.

ObjectiveTo construct a prediction model for high responsiveness to short-term aerobic training in patients with Parkinson's disease (PD) based on dual-task gait assessment, and to provide evidence for identifying individuals who are most likely to benefit from such interventions. MethodsA total of 42 patients with PD who visited the outpatient clinic of Tianjin Huanhu Hospital between August 29, 2023 and December 19, 2024 were enrolled. All participants completed a 4-week home-based aerobic cycling training program and underwent dual-task gait assessments pre- and post-training. Patients were classified into high-response group (n = 12) and low-response group (n = 30) according to whether the improvement in gait speed reached the minimal clinically important difference of 0.1 m/s. The clinical validity of this grouping was verified by comparing differences in cardiopulmonary exercise testing parameters and dual-task gait indicators between the two groups pre- and post-training. Based on baseline characteristics, Firth's penalized maximum likelihood logistic regression was applied to construct a predictive model for high responsiveness, and the discriminative performance of the model was assessed preliminarily. ResultsThe high-response group showed significantly greater improvement in gait speed, step length, cadence and the anaerobic threshold to maximal oxygen uptake ratio after training compared with the low-response group (P < 0.05), confirming the clinical validity of the grouping. The baseline anaerobic threshold to maximal oxygen uptake ratio (OR = 3.348), body mass index (OR = 2.229) and age (OR = 0.428) demonstrated certain discriminative ability in the present sample. The area under the receiver operating characteristic curve was 0.831, with an overall prediction accuracy of 81.0%, specificity of 83.3% and sensitivity of 75.0%. Robustness of the model was confirmed via internal validation using the Bootstrap method, which suggested that the results were informative, but further validation was still required. ConclusionThe anaerobic threshold/maximal oxygen uptake ratio, body mass index and age may predict the response to short-term aerobic training in patients with PD.

The digitization of traditional Chinese medicine (TCM) has generated vast amounts of data. However, these data are characterized by significant heterogeneity and complex semantic structures, posing substantial challenges for systematic integration and intelligent analysis, and limiting its potential for modern clinical and computational research. To address the challenges posed by the high heterogeneity and complex structure in TCM data, we designed and developed the TCM Data Hub platform, which is powered by the YiYuan large language models (LLMs). This platform aims to enhance intelligent data processing capabilities and unlock the potential for clinical application of TCM data through systematic integration and efficient utilization, thereby bridging the gap between traditional knowledge and modern computational research. This study first analyzed the heterogeneity and complexity of TCM information with respect to data types, structures, and semantics. A standardized data framework was constructed to enhance data integration and interoperability. Based on the TCM Intelligent Computing Platform of the China Academy of Chinese Medical Sciences, we trained the YiYuan LLMs to acquire domain-specific semantic understanding of TCM, thereby improving the platform’s comprehension of specialized terminology and knowledge systems. Leveraging the natural language processing capabilities of the LLMs, we developed a human-in-the-loop data processing system to enable efficient extraction, cleansing, and structured organization of TCM data. In addition, utilizing Vue and Java technologies, we developed multiple LLM-powered intelligent agents and systems, including a human-in-the-loop data processing system, as well as automated prescription mining and network pharmacology analysis agents. Task-specific agents tailored to TCM data processing were developed to enhance the model’s effectiveness in clinical knowledge discovery. System functionality and platform infrastructure were implemented using Java and Vue technologies.The TCM Data Hub platform has completed system construction and core functionality implementation. It supported integrated management and efficient access to 8 key types of TCM data: prescriptions, materia medica, ingredients, targets, diseases (Western medicine), diseases (TCM), syndromes, and therapeutic methods. The human-in-the-loop data processing system achieved an accuracy of 95.34% in structuring TCM data and supported annotation for data requiring manual labeling. The intelligent agent-driven big-data analytics module enabled 1-click, end-to-end workflows for TCM prescription mining, herb-syndrome association analysis, network pharmacology, and molecular biology research, completing a full data mining task in approximately 30 minutes. Users can interact with and manipulate data through a visual front-end interface. The system demonstrated stable performance, strong scalability, and a user-friendly experience. Empowered by the YiYuan LLMs, the TCM Data Hub platform significantly improves the accessibility, usability, and intelligence of TCM data. It effectively bridges traditional TCM knowledge with modern intelligent technologies, providing robust data support and intelligent tools for TCM research and clinical applications.

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.

By reviewing modern research and integrating clinical practice， this paper interprets the correlation between "deep heat， deep syncope" and the "macrocirculation-microcirculation" uncoupling in sepsis， alongside its clinical applications in sepsis prevention and treatment. It is considered that "deep heat" corresponds to peripheral vasodilation and the hypermetabolic state at the macrocirculation level， whereas "deep syncope" manifests as hypoperfusion and impaired oxygen utilization at the microcirculation level. These two states interact detrimentally， forming the core contradiction of macrocirculation-microcirculation uncoupling in sepsis. Based on the traditional Chinese medicine （TCM） connotations of "deep heat， deep syncope" and modern medical research， this paper elucidates the systemic pathological evolution of sepsis from the inflammatory cascade to impaired oxygen utilization. Accordingly， the following therapeutic principles are proposed. In the early stage， it is suggested to clear heat and drain fire to vent the constrained yang. In the progressive stage， the focus is put on unblocking the lower jiao （焦） and expelling stasis to dredge the vessels and collaterals. In the recovery stage， the method of regulating and harmonizing yin and yang can be used to smooth the flow of qi and blood.

ObjectiveTo investigate the current professional competency among full-time and part-time personnel of the nosocomial infection control administration in Shanghai, so as to provide a scientific basis for future training programmes. MethodsIn December 2024, a questionnaire survey was conducted by the Shanghai Nosocomial Infection Quality Control Center among full-time and part-time personnel of the nosocomial infection control administration across medical institutions at various levels and types in Shanghai using convenience sampling method. The questionnaire consisted of two parts: demographic information and professional competency assessment. The professional competency scale comprised four dimensions: fundamental cognition, basic skills, professional expertise, and personal qualities, totaling 35 items. ResultsA total of 1 179 questionnaires were distributed, with 1 144 valid responses collected, yielding an effective response rate of 97.03%. Statistically significant differences were observed among full-time and part-time personnel of the nosocomial infection control administration in terms of age (t=5.32, P=0.021), professional background (χ²=9.90, P=0.019), educational qualifications (χ²=19.10, P<0.001), professional titles (χ²=12.60, P=0.002), and the levels of medical institutions (χ²=111.08, P<0.001). The scores of full-time personnel of the nosocomial infection control administration in fundamental cognition [92 (82, 99) points] and basic skills [88 (78, 96) points] were significantly higher than those of part-time personnel(Z=-2.21, P=0.027;Z=-2.74, P=0.006). Statistically significant differences were found in fundamental cognition scores between full-time and part-time personnel of the nosocomial infection control administration regarding occupational safety protection, definition of healthcare-associated infection outbreaks, types of drug-resistant bacteria and their prevention and control strategies, and transmission routes of different infectious diseases (all P<0.05). Statistically significant differences were also observed in basic skills scores including proficient use of monitoring platforms, formulation and revision of standard operating procedures (SOPs), independent completion of targeted surveillance, guidance on basic infection control skills, guidance for key departments, and follow-up of personnel with occupational exposure (all P<0.05). However, no statistically significant differences were found in scores of professional knowledge and personal qualities (P>0.05). ConclusionThere are certain differences in professional competency between full-time and part-time personnel of the nosocomial infection control administration in Shanghai in terms of fundamental cognition and basic skills. Part-time personnel can effectively improve their professional competency through systematic training on basic infection control knowledge and practical skills, thereby comprehensively enhancing the overall quality of the nosocomial infection administration team.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.