ObjectiveTo evaluate the anti-inflammatory activity of the novel magnolol ester derivative YW and to investigate its effects on chondrocyte senescence and preliminary mechanisms. MethodsMagnolol and p-methylbenzoic acid were used as raw materials to synthesize the magnolol ester derivative YW （Molecular Formula： C₂₆H₂₄O₃， Molecular Weight： 384.17， HPLC Purity >96%） via DCC/DMAP-catalyzed esterification. Cytotoxicity was assessed using the CCK-8 assay. A lipopolysaccharide （LPS）-induced RAW264.7 macrophage activation model and an interleukin-1β （IL-1β）-induced rat primary chondrocyte model were established. The release and mRNA expression of inflammatory factors including nitric oxide （NO）， IL-1β， tumor necrosis factor-alpha （TNF-α）， and IL-6 were detected by enzyme-linked immunosorbent assay （ELISA）， Griess reagent method， and quantitative real-time PCR （RT-qPCR）. The expression of senescence markers such as inducible nitric oxide synthase （iNOS）， pro-interleukin-1β （pro-IL-1β）， lysine acetyltransferase 7 （KAT7）， cyclin-dependent kinase inhibitor 1A （p21）， and cyclin-dependent kinase inhibitor 2A （p16）， as well as proteins related to chondrocyte extracellular matrix synthesis and catabolism， were analyzed by Western blot （WB）. Molecular docking was performed using Discovery Studio 2019 to validate target binding. ResultsYW exhibited no significant cytotoxicity at concentrations ≤20 μmol/L. YW concentration-dependently inhibited LPS-induced macrophage inflammatory cytokine release， significantly downregulated iNOS， Pro-IL-1β protein， and inflammatory cytokine mRNA expression （P<0.01）. YW stably bound to KAT7 protein （binding energy： -94.2 kcal/mol）； YW downregulated KAT7 and aging marker protein expression in naturally aged and IL-1β-induced chondrocyte models （P<0.01）； YW regulated chondrocyte matrix synthesis and catabolic protein expression in IL-1β-induced chondrocytes （P<0.01）. ConclusionYW inhibits macrophage activation and inflammatory cytokine release while downregulating KAT7 and senescence marker protein expression in chondrocytes， thereby blocking chondrocyte senescence.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Definitive chemoradiotherapy (dCRT) has become a cornerstone in the treatment of locally advanced esophageal cancer; however, local control remains suboptimal, and persistent lesions or locoregional recurrences after treatment are not uncommon. For patients without distant metastases but with local failure, whether surgical intervention can still offer curative potential remains a major clinical dilemma. Salvage esophagectomy (SE) offers potential long-term survival for selected patients, but this procedure is performed in the context of severe fibrosis, impaired local blood supply, and obscured anatomical planes following chemoradiotherapy, resulting in significantly higher perioperative risk compared to primary esophagectomy. Consequently, controversies exist regarding patient selection, preoperative restaging, choice of surgical approach, extent of lymphadenectomy, gastrointestinal reconstruction, and perioperative management. In recent years, with the refinement of restaging modalities such as PET/CT, the accumulation of experience in high-volume centers, and emerging evidence from clinical studies, the clinical role of SE has gradually shifted from a "high-risk salvage measure" to a "selective curative strategy aimed at achieving long-term survival in carefully selected patients". Nevertheless, standardized guidelines for patient selection, technical approaches, and perioperative management are still lacking. Based on current evidence and clinical experience, experts organized by the Integrated Esophageal Cancer Committee of Chinese Anti-Cancer Association systematically reviewed key issues regarding SE, including its definition, indications, preoperative evaluation, choice of surgical approach, lymphadenectomy, gastrointestinal reconstruction, and perioperative management, and formulated a Chinese expert consensus. This consensus aims to provide guidance for standardized assessment, appropriate referral, individualized surgical decision-making, and optimized perioperative management of patients with locoregional failure after dCRT. Ultimately, this will increase the likelihood of R0 resection, reduce the risk of severe complications, and promote the safer, more judicious, and standardized implementation of SE in high-risk scenarios.

Background/Aims: Dysregulated cholesterol metabolism is a hallmark of hepatocellular carcinoma (HCC) that drives tumor initiation and progression. However, clinical targeting of cholesterol metabolism has yielded limited benefits due to stringent feedback in tumor cells. Identifying a central mediator capable of restoring cholesterol homeostasis within the cell’s intrinsically fine-tuned regulatory framework is urgently needed. Methods: We integrated a proteomic dataset from patients with cholesterol-dysregulated HCC into a global cholesterol metabolic regulatory network to identify potential therapeutic targets for disrupted cholesterol homeostasis. The prognostic significance of the candidate targets was further validated in an independent cohort through immunohistochemistry. Functional and mechanistic studies were conducted in vitro using HCC cell lines and in vivo using mouse models. The pharmacological efficacy of the candidate agent was evaluated in both subcutaneous and orthotopic HCC mouse models. Results: ER lipid raft-associated 1 (ERLIN1), a pivotal regulator of cholesterol metabolism reprogramming, was identified as an independent favorable prognostic indicator in HCC. ERLIN1 constrains HCC progression both in vitro and in vivo by stabilizing the INSIG1–SCAP–SREBP2 axis and maintaining the metabolic balance of intracellular cholesterol. Under hypoxia, impaired factor-inhibiting hypoxia-1-dependent hydroxylation of ASB11 at asparagine residues 90 and 92 enhances ASB11-mediated ERLIN1 degradation. Pharmacological targeting of this axis using zoledronic acid (ZoA) attenuated HCC progression by weakening the ASB11–ERLIN1 interaction and restoring cholesterol homeostasis. Conclusions ERLIN1 represents a druggable metabolic vulnerability in cholesterol-dysregulated HCC. Targeting the ASB11–ERLIN1 axis with the clinically approved ZoA reestablishes cholesterol homeostasis and offers a promising therapeutic strategy to overcome the current limitations of cholesterol-targeted HCC therapies.

Objective: This study compared the effects of personalized exercise versus acupuncture on multidimensional cognitive function in individuals at high risk for stroke with mild cognitive impairment (MCI). Methods: A randomized controlled trial enrolled 200 stroke-risk adults aged 50−80 years. Ninety participants diagnosed with MCI (MMSE, HIS, CDR criteria) were randomly assigned to exercise (n = 30), acupuncture (n = 30), or control (n = 30) groups. Interventions lasted 6 months. Cognitive outcomes (MMSE, Raven’s Progressive Matrices, Digit Symbol Substitution Test, Animal Fluency, Rey-Osterrieth test, Stroop test) were assessed before and after intervention. Multivariate regression identified risk factors for MCI. ANOVA was used for group comparisons. Results: Hypertension (aOR = 1.5) and diabetes (aOR = 1.3) were significant risk factors for MCI. After intervention, the exercise group showed the largest MMSE improvement (Δ = 5.0), compared with acupuncture (Δ = 3.0) and control (Δ = 1.0) (p < 0.001). Exercise produced greater gains in non-verbal reasoning (31.8% vs. 26.1% in acupuncture, p < 0.01). Acupuncture more effectively enhanced processing speed and attention (DSST: Δ = 26 vs. Δ = 20 in exercise, p = 0.03) and executive function (Stroop interference time: Δ = 16 vs. Δ = 15 seconds in exercise, p = 0.04). Both interventions significantly improved verbal fluency (p < 0.001), with larger benefits in those with baseline MMSE ≤ 20. Conclusion Hypertension and diabetes are key risk factors for MCI in stroke-prone individuals. Exercise yields greater improvement in global cognition and memory, whereas acupuncture is more effective for enhancing attention and executive function. Both modalities benefit verbal fluency, particularly in lower-functioning participants.

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.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

OBJECTIVE To establish the UPLC fingerprint and the method for multi-component content determination in Jingangteng capsules， and to evaluate its quality by combining chemical pattern recognition analysis. METHODS An UPLC method was established. Separation was performed on a Zorbax SB-C 18 Rapid Resolution HD column， with acetonitrile-0.1% formic acid as the mobile phase for gradient elution.Using the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicines （2012 edition）， UPLC fi ngerprints were established for 10 batches of Jingangteng capsules， and similarity was evaluated. SPSS 22.0 and SIMCA 14.1 software were used to perform hierarchial-cluster analysis and orthogonal partial least squares discriminant analysis （OPLS-DA）， respectively. The same UPLC method was employed to determine the contents of chlorogenic acid， 3，5-dihydroxy-2-methylbenzoic acid-3- O -glucoside （M1）， caffeic acid， astilbin， oxyresveratrol， quercitrin and resveratrol in the 10 batches of samples. RESULTS A total of 17 common peaks were identified in UPLC fingerprints of the 10 batches of samples， of which 7 were identified as chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol. The similarities of 10 batches of samples ranged from 0.820 to 0.985. The results of hierarchial-cluster analysis showed that 10 batches of samples were grouped into four categories： S1-S4 formed one group， S5 and S6 formed another， S7， S8 and S10 formed a third， and S9 formed a fourth， consistent with the OPLS-DA results； the variable importance projection values for peaks 7， 10， 2， 16 （resveratrol）， 13 （oxyresveratrol）， 11， 6 （caffeic acid）， 5 （M1） and 15 （quercitrin） were ＞1. Quantitative analysis results showed that the contents of chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol were 1.650 8-4.213 7， 0.636 2-2.161 7， 0.031 0-0.086 5， 0.239 1-1.069 3， 0.211 9-1.104 0， 0.488 8-2.399 2， and 0.164 0-0.699 8 mg/g， respectively. CONCLUSIONS UPLC fingerprint and content determination methods established in this study are simple to operate， accurate， reliable and reproducible； when combined with chemical pattern recognition analysis， they can be used to evaluate the quality of Jingangteng capsules. Nine components， such as resveratrol， oxyresveratrol， caffeic acid， M1 and quercitrin， may serve as markers of quality variation.

ObjectiveTo investigate the role of Ras-GTPase-activating protein SH3 domain-binding protein 2 （G3BP2） in regulating the activation， proliferation， and migration of hepatic stellate cells （HSCs）. MethodsThe mouse HSCs （JS-1 cell line） were treated with 5 μg/L transforming growth factor-beta 1（TGF-β1） for 24 hours to establish an HSC activation and proliferation model. A G3BP2 knockdown system was constructed using siRNA interference technology. The experiment was divided into four groups： Control， TGF-β1 treatment， TGF-β1+si-NC， and TGF-β1+ G3BP2-siRNA. The expression levels of key fibrosis indicators， including type I collagen （Collagen I）， α-smooth muscle actin （α-SMA）， and G3BP2， were detected by Western blot and RT-qPCR. Cell proliferation activity was assessed using the CCK-8 proliferation assay kit and EdU fluorescence labeling technology. Cell migration ability was analyzed by scratch wound healing assay and Transwell migration assay. The formation level of stress granules was quantified by immunofluorescence microscopy to investigate the effects of G3BP2 on stress granule formation in activated HSCs. ResultsStimulation with TGF-β1 upregulated the expression of G3BP2 in JS-1 cells （RT-qPCR： P0.000 1； Western blot： P0.000 1）， while a downward trend in its expression was observed in the G3BP2‑silenced group （RT-qPCR： P0.01； Western blot： P0.000 1）. Compared with the control group， the TGF-β1 group exhibited increased protein expression levels of α-SMA and Collagen I （RT-qPCR： both P0.01； Western blot： P0.01 and P0.05， respectively）， concomitant with an increased number of stress granules and enhanced cell proliferation and migration capacity （all P0.001）. The experimental results demonstrated that G3BP2 knockout effectively reversed the aforementioned phenotypes， with the G3BP2-silenced group showing reduced expression of fibrotic markers （all P0.01）， decreased stress granule formation （P0.01）， and reduced cell proliferation and migration capacity （all P0.05）， compared to the negative control group. ConclusionG3BP2 enhances the activation， proliferation， and migration of HSCs by promoting the formation of stress granules， thereby accelerating the pathological progression of liver fibrosis. This suggests that stress granules may serve as important regulators in controlling the activation， proliferation， and migration of HSCs.

ObjectiveTo investigate the role of interleukin （IL）-17A in acute inhalational pneumonia induced by the highly drug-resistant and hypervirulent Staphylococcus aureus strain USA300-R in mice. MethodsAn acute inhalational pneumonia model was established in mice using an aerosolized pulmonary delivery technique. RNA sequencing （RNA-seq） and enzyme-linked immunosorbent assay （ELISA） were employed to examine the expression dynamics of Il17a mRNA and IL-17A protein， respectively， in the lungs of infected mice. Il17a knockout （Il17a^-/-） mice were generated using CRISPR/Cas9 gene editing technology. The survival rate， body weight， bacterial load in lung tissue， and histopathological changes were compared between Il17a^-/- and wild-type （WT） mice following inhalational infection with USA300-R. Results12 hours after USA300-R infection， compared to pre-infection， the expression level of Il17a mRNA in lung tissue and the level of IL-17A protein in bronchoalveolar lavage fluid （BALF） increased by approximately 50-fold （P<0.01） and 6-fold （P<0.001）， respectively. Compared to WT mice， Il17a^-/- mice exhibited approximately 10-fold higher bacterial loads in lung tissue at both 12 and 24 hours post-infection （P<0.001， P<0.05）. However， they showed significantly attenuated lung histopathological injury， reduced alveolar wall thickening， markedly decreased neutrophil infiltration， and an approximately 50% improvement in survival rate （P<0.05）. ConclusionIn acute Staphylococcus aureus USA300-R inhalational pneumonia， IL-17A contributes to bacterial clearance by recruiting neutrophils； however， excessive neutrophil infiltration exacerbates pulmonary inflammation and injury， reduces survival rates， and represents a potential therapeutic target.