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.

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.

Background and aims The mechanism of cholestatic liver injury (CLI) is unclear, and effective therapies are lacking. While peroxisome proliferator-activated receptor alpha (PPARα) agonists show potential hepatoprotective effect and pyroptosis is implicated in hepatocellular damage, how PPARα activation mitigates lithocholic acid (LCA)-induced pyroptosis remains unknown. Methods The hepatoprotective effect of PPARα agonists was evaluated in a mouse model of intrahepatic cholestasis induced by LCA. Liver injury was assessed via serum biochemistry, hematoxylin and eosin and TUNEL staining, and electron microscopy. Pyroptosis pathways were analyzed using real-time quantitative polymerase chain reaction, Western blot, and co-immunoprecipitation. Results Combined morphological, histopathological, and biochemical analyses confirmed that PPARα activation protects against CLI. Compared with LCA treatment alone, PPARα activation significantly attenuated the elevation of serum lactate dehydrogenase (LDH), the increased TUNEL-positive cells, and the formation of hepatocyte membrane pores. Mechanistically, PPARα activation suppressed both NOD-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis and apoptosis protease-activating factor-1 (APAF-1)/CASPASE-3/GSDME-mediated pyroptosis. Furthermore, PPARα agonist pretreatment inhibited activation of the nuclear factor-kappa B (NF-κB) and forkhead box O1 (FOXO1) signaling pathways. Conclusions PPARα protects against LCA-induced CLI by inhibiting both NLRP3 inflammasome-mediated pyroptosis associated with NF-κB and APAF-1/CASPASE-3/GSDME-mediated pyroptosis associated with the FOXO1 signaling pathway.

Ischemic stroke accounts for approximately 80% of all stroke cases worldwide and remains a leading cause of long‑term neurological disability and mortality. Intravenous thrombolysis and endovascular thrombectomy constitute the mainstay of acute reperfusion therapies, yet their clinical utility is severely constrained by narrow therapeutic time windows. Even when large‑vessel recanalization is successfully achieved, a considerable proportion of patients still suffer poor functional outcomes due to futile reperfusion and ischemia‑reperfusion injury. Historically, preclinical studies have largely focused on direct neuronal protection against calcium overload, oxidative stress and ferroptosis. Unfortunately, most promising neuroprotective agents that exhibited robust benefits in rodent models have failed to demonstrate satisfactory efficacy in phase‑Ⅱ and phase‑Ⅲ clinical trials. One critical contributor to such translational failure is the disregard for the pathological disturbance within the cerebral vascular microenvironment. Instead of merely regarding neuronal death as an autonomous cellular event, accumulating evidence indicates that sequential biochemical disruption of perivascular microenvironment acts as an initiating amplifier that propagates damage toward brain parenchyma after ischemic insult. The cerebral vascular microenvironment centers on brain microvessels and comprises endothelial cells, endothelial glycocalyx, basement membrane, pericytes, astrocytic end‑feet, perivascular microglia, as well as interstitial ions, inflammatory mediators, reactive oxygen species and extracellular matrix components. Distinct from the concept of neurovascular unit which emphasizes functional coupling between neurons, glia and vessels, the vascular microenvironment highlights dynamic biochemical homeostasis in perivascular interstitial space. Following cerebral ischemia, injured brain tissue can be spatially divided into the irreversibly damaged infarct core, metabolically‑stressed ischemic penumbra and mildly‑affected peripheral regions. These three zones lack rigid anatomical boundaries and evolve dynamically depending on reperfusion timing, presenting divergent features in blood perfusion, blood‑brain barrier integrity, cellular viability and immune‑inflammatory profiles. Four interrelated pathological cascades jointly disrupt microenvironmental homeostasis: calcium‑overload‑triggered cytotoxic cascades; blood‑brain barrier breakdown resulting from glycocalyx shedding, tight‑junction dissociation and matrix metalloproteinase‑driven basement‑membrane degradation; inflammatory‑immune dysregulation dominated by microglial phenotypic reprogramming; and mutual crosstalk between coagulation disturbance and neuroinflammation that exacerbates secondary brain injury. Multiple molecular layers orchestrate these pathological alterations. Excessive activation of HIF‑1α, NF‑κB and MAPK/ERK signaling pathways promotes barrier leakage, pro‑inflammatory factor secretion and extracellular‑matrix remodeling. Epigenetic modulation mediated by non‑coding RNAs including miRNAs and lncRNAs reshapes vascular microenvironment via three major patterns: modulating endothelial‑pericyte barrier integrity, switching microglia‑astrocyte inflammatory phenotypes, and driving metabolic reprogramming among multiple cell populations within microdomains. In addition, metabolic shift toward glycolysis among endothelial cells, microglia, astrocytes and compromised neurons establishes self‑amplifying vicious cycles of energy depletion and oxidative stress in perivascular niches. Targeted theranostic approaches oriented toward vascular microenvironment bring new opportunities for precision medicine of ischemic stroke. Magnetic nanoprobes and near‑infrared fluorescent probes respond to elevated reactive oxygen species, pH shift and inflammatory signals, enabling in situ visualization of pathological signatures of microenvironment and risk evaluation of hemorrhagic transformation. Function‑tailored nanodelivery systems exert multi‑modal protective effects: reactive oxygen species‑responsive polymeric micelles deliver calcium chelators to block calcium‑overload‑initiated injury cascades; nanozymes scavenge reactive oxygen/nitrogen species and revert microglial pro‑inflammatory polarization; biomimetic membrane‑coated nanoparticles accumulate at micro‑thrombosis sites to break thrombo‑inflammatory loops and mitigate no‑reflow phenomenon; ferroptosis‑inhibitor‑loaded nanocarriers eliminate excessive labile iron within perivascular toxic microdomains. During subacute and recovery phases, spatiotemporally controlled release of angiogenic factors by nanocarriers facilitates maturation of newly‑formed microvessels rather than blind vascular sprouting, which helps reconstruct intact blood‑brain barrier and restore microenvironmental homeostasis. Nevertheless, substantial translational bottlenecks still impede clinical transformation of these nanomedicines, including potential biosafety risks caused by in vivo material accumulation, protein‑corona‑induced impairment of targeting capacity, intrinsic blood brain barrier transport obstacles, lack of unified quality‑control criteria for large‑scale manufacture, reliance on high‑end clinical imaging instruments, and prominent interspecies discrepancies between experimental animals and human patients. On the basis of summarizing spatiotemporal pathological characteristics, multi‑level regulatory mechanisms and cutting‑edge microenvironment‑oriented diagnostic and interventional strategies, the present review advocates a conceptual shift from isolated neuronal protection to comprehensive reconstruction of perivascular vascular microenvironment homeostasis. Such perspective provides theoretical foundations for expanding therapeutic time windows and inspires future development of novel therapeutic modalities for ischemic stroke.

[Objective] To study the molecular biological mechanism for a case of ABO blood group B subtype, and perform three-dimensional modeling of the mutant enzyme. [Methods] The ABO phenotype was identified by the tube method and microcolumn gel method; the ABO gene of the proband was detected by sequence-specific primer polymerase chain reaction (PCR-SSP), and the exon 6 and 7 of the ABO gene were sequenced and analyzed. Homologous modeling of Bw.03 glycosyltransferase (GT) was carried out by Modeller and analyzed by PyMOL2.5.0 software. [Results] The weakening B antigen was detected in the proband sample by forward typing, and anti-B antibody was detected by reverse typing. PCR-SSP detection showed B, O gene, and the sequencing results showed c.721 C>T mutation in exon 7 of the B gene, resulting in p. Arg 241 Trp. Compared with the wild type, the structure of Bw.03GT was partially changed, and the intermolecular force analysis showed that the original three hydrogen bonds at 241 position disappeared. [Conclusion] Blood group molecular biology examination is helpful for the accurate identification of ambiguous blood group. Homologous modeling more intuitively shows the key site for the weakening of Bw.03 GT activity. The intermolecular force analysis can explain the root cause of enzyme activity weakening.

Hypertrophic scar is a fibrous hyperplastic disorder that arises from skin injuries. The current therapeutic modalities are constrained by the dense and rigid scar tissue which impedes effective drug delivery. Additionally, insufficient autophagic activity in fibroblasts hinders their apoptosis, leading to excessive matrix deposition. Here, we developed an active microneedle (MN) system to overcome these challenges by integrating micromotor-driven drug delivery with autophagy regulation to remodel the scar microenvironment. Specifically, sodium bicarbonate and citric acid were introduced into the MNs as a built-in engine to generate CO₂ bubbles, thereby enabling enhanced lateral and vertical drug diffusion into dense scar tissue. The system concurrently encapsulated curcumin (Cur), an autophagy activator, and triamcinolone acetonide (TA), synergistically inducing fibroblast apoptosis by upregulating autophagic activity. In vitro studies demonstrated that active MNs achieved efficient drug penetration within isolated scar tissue. The rabbit hypertrophic scar model revealed that TA-Cur MNs significantly reduced the scar elevation index, suppressed collagen I and transforming growth factor-β1 (TGF-β1) expression, and elevated LC3 protein levels. These findings highlight the potential of the active MN system as an efficacious platform for autonomous augmented drug delivery and autophagy-targeted therapy in fibrotic disorder treatments.

OBJECTIVES: To explore the bioactive components in Jiawei Xiaoyao Pills (JWXYP) and their mechanisms for alleviating depression-like behaviors. METHODS: The active compounds, key targets, and pathways of JWXYP were identified using TCMSP and TCMIP databases. Thirty-six SD rats were randomized equally into 6 groups including a control group and 5 chronic unpredictable mild stress (CUMS)-induced depression groups. After modeling, the 5 model groups were treated with daily gavage of normal saline, 1.8 mg/kg fluoxetine hydrochloride (positive control drug), or JWXYP at 1.44, 2.88, and 4.32 g/kg. The depression-like behaviors of the rats were evaluated using behavioral tests, and pathological changes in the liver and hippocampus were examined with HE staining. The biochemical indicators in the serum and brain tissues were detected using ELISA. Serum metabolomics analysis was performed to identify the differential metabolites using OPLS-DA, and gut microbiota changes were analyzed using 16S rDNA sequencing. RESULTS: Network pharmacology revealed that menthone and paeonol in JWXYP were capable of penetrating the blood-brain barrier to regulate inflammatory pathways and protect the nervous system. In the rat models subjected to CUMS, treatment with JWXYP significantly improved body weight loss, sucrose preference and open field activities, reduced liver inflammation, alleviated structural changes in the hippocampal neurons, decreased serum levels of TNF‑α, IL-1β, IL-6 and LBP, and increased 5-HT and VIP concentrations in the serum and brain tissue, and these effects were the most pronounced in the high-dose group. Metabolomics analysis showed changes in such metabolites as indole-3-acetamide and acetyl-L-carnitine in JWXYP-treated rats, involving the pathways for bile acid biosynthesis and amino acid metabolism. 16S rDNA analysis demonstrated increased gut microbiota diversity and increased abundance of Lactobacillus species in JWXYP-treated rats. CONCLUSIONS JWXYP alleviates depression-like symptoms in rats by regulating the neurotransmitters, inhibiting inflammation and oxidation, and modulating gut microbiota.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Gastrointestinal Microbiome/drug effects* ; Rats, Sprague-Dawley ; Depression/drug therapy* ; Neurotransmitter Agents/metabolism* ; Rats ; Inflammation ; Male ; Hippocampus ; Behavior, Animal/drug effects*

OBJECTIVE: Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection frequently develop central nervous system damage, yet the mechanisms driving this pathology remain unclear. This study investigated the primary pathways and key factors underlying brain tissue damage induced by the SARS-CoV-2 beta variant (lineage B.1.351). METHODS: K18-hACE2 and C57BL/6 mice were intranasally infected with the SARS-CoV-2 beta variant. Viral replication, pathological phenotypes, and brain transcriptomes were analyzed. Gene Ontology (GO) analysis was performed to identify altered pathways. Expression changes of host genes were verified using reverse transcription-quantitative polymerase chain reaction and Western blot. RESULTS: Pathological alterations were observed in the lungs of both mouse strains. However, only K18-hACE2 mice exhibited elevated viral RNA loads and infectious titers in the brain at 3 days post-infection, accompanied by neuropathological injury and weight loss. GO analysis of infected K18-hACE2 brain tissue revealed significant dysregulation of genes associated with innate immunity and antiviral defense responses, including type I interferons, pro-inflammatory cytokines, Toll-like receptor signaling components, and interferon-stimulated genes. Neuroinflammation was evident, alongside activation of apoptotic and pyroptotic pathways. Furthermore, altered neural cell marker expression suggested viral-induced neuroglial activation, resulting in caspase 4 and lipocalin 2 release and disruption of neuronal molecular networks. CONCLUSION These findings elucidate mechanisms of neuropathogenicity associated with the SARS-CoV-2 beta variant and highlight therapeutic targets to mitigate COVID-19-related neurological dysfunction.
Animals ; COVID-19/genetics* ; Mice ; Brain/metabolism* ; Apoptosis ; Mice, Inbred C57BL ; SARS-CoV-2/physiology* ; Pyroptosis ; Gene Expression Profiling ; Transcriptome ; Male ; Female

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.