OBJECTIVE To analyze the differences in chemical components of Gardenia jasminoides before and after processing with ginger， and to evaluate the quality differences among different producing areas. METHODS Ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometry was used to analyze the compositional differences of G. jasminoides before and after processing with ginger. The water content， total ash， and ethanol-soluble extract content of ginger- processed G. jasminoides were determined according to the 2020 edition of Chinese Pharmacopoeia. High performance liquid chromatography was adopted to determine the contents of genipin gentiobioside， geniposide， crocin Ⅰ and crocin Ⅱ in ginger- processed G. jasminoides. RESULTS A total of 49 chemical components were identified from raw G. jasminoides and ginger- processed G. jasminoides， including 14 flavonoids， 15 iridoids， 10 organic acids， 2 alkaloids and 8 other compounds. Among them， 42 components were detected in raw G. jasminoides， 28 in ginger-processed G. jasminoides， and 21 components were common to both. After processing with ginger， raw G. jasminoides lost 21 components （including iridoids， flavonoids， alkaloids， and others）， while 7 chemical components were added （including coumarins， organic acids， organic acid esters， and flavonoids）. For the 15 batches of ginger-processed G. jasminoides， the water content ranged from 5.64% to 7.11%， total ash from 2.92% to 4.87%， and ethanol-soluble extract from 40.61% to 58.02%. The average contents of genipin gentiobioside， geniposide， crocin Ⅰ and crocin Ⅱ were 0.108 7， 0.542 2， 0.565 0， and 0.012 5 mg/g， respectively. CONCLUSIONS After processing with ginger， G. jasminoides loses 21 components， while 7 new components are added. Differences are observed in the water content， total ash， ethanol-soluble extract， and the contents of genipin gentiobioside， geniposide， crocin Ⅰ， and crocin Ⅱ of ginger-processed G. jasminoides from different producing areas. Notably， samples from Fujian exhibit high contents of genipin gentiobioside and ethanol-soluble extract， while samples from Jiangxi have a high content of crocin Ⅰ.

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.

ObjectiveHepatocellular carcinoma (HCC) represents 90% of all primary liver cancers. The main risk factors associated with HCC include viral hepatitis (B and/or C), alcohol abuse, and metabolic dysfunction-associated steatotic liver disease (MASLD), which progressively advance to liver fibrosis, cirrhosis, and ultimately evolve into HCC. Surgical resection represents the most effective treatment for HCC, while recent advances in immunotherapy, including immune checkpoint inhibitors and adoptive cell therapies, have provided improved treatment prospects for patients with unresectable HCC. However, the complex metabolic heterogeneity of HCC limits the therapeutic efficacy. Metabolic intermediates acyl-CoA not only provide energy and substrates for numerous biochemical reactions but also serve as donors for protein lysine acylation, a major class of post-translational modification (PTM). Therefore, a deeper understanding of the molecular mechanisms underlying protein lysine acylation and hepatocarcinogenesis is urgently needed. MethodsThe levels of protein lysine acylation and silence information regulator 5 (SIRT5) expression levels in clinical HCC samples were analyzed by Western blot. Quantitative malonylome and succinylome of HCC samples were analyzed by antibody-based affinity enrichment coupled with tandem mass spectrometry. The proliferation of HCC cells was analyzed with Cell Counting Kit-8 (CCK-8) assays, the apoptosis was quantified by Annexin V-FITC/propidium iodide (PI) staining coupled with flow cytometry, and the ability of cells to migrate was assayed by Transwell assays. The enzymatic activity of glutathione S-transferase Mu 1 (GSTM1) was quantified. Transgenic mice with hepatic overexpression of SIRT5 were constructed using CRISPR-Cas9, and primary hepatocarcinogenesis was induced by administration of diethylnitrosamine. ResultsWestern blot analysis indicated that the expression level of SIRT5 was elevated in clinical samples from HCC patients, and the levels of lysine malonylation, glutarylation, and succinylation were significantly reduced in HCC tissues. Knockout of SIRT5 in MHCC-97H and MHCC-97L hepatoma cells suppressed cell proliferation, and increased the percentage of apoptotic cells significantly. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially malonylome and succinylome of HCC samples revealed significant enrichment in two major classes of biological processes: core energy metabolism (e.g., glycolysis/gluconeogenesis, tricarboxylic acid metabolic process, fatty acid beta oxidation) and detoxification and oxidative stress response (e.g., response to toxic substance, chemical carcinogenesis, reactive oxygen species (ROS)). SIRT5 removes malonylation from lysine residues in GSTM1 and restores its detoxification activity, which is crucial for the survival of hepatocytes under stressed conditions. More importantly, in vivo experiment indicated that hepatic-specific overexpression of SIRT5 in mice accelerated diethylnitrosamine-induced liver fibrosis and hepatocarcinogenesis, indicating the critical role of SIRT5 in HCC progression. ConclusionThis study highlights the previously unrecognized SIRT5-GSTM1 axis as a key regulator in hepatocarcinogenesis, and suggests a potential target for the treatment of patients with HCC.

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.

The machine learning is used increasingly and widely in acupuncture prescription optimization, intelligent treatment and precision medicine, and has obtained a certain achievement. But, there are still some problems remained to be solved such as the poor interpretability of the model, the inconsistency of data quality of acupuncture research, and the clinical application of constructed models. Researches in future should focus on the acquisition of high-quality clinical and experimental data sets, take various machine learning algorithms as the basis, and construct professional models to solve various problems, so as to drive the high-quality development of acupuncture research.
Acupuncture Therapy/trends* ; Machine Learning ; Humans ; Algorithms

The circadian clock plays a critical role in regulating various physiological processes, including gene expression, metabolic regulation, immune response, and the sleep-wake cycle in living organisms. Post-translational modifications (PTMs) are crucial regulatory mechanisms to maintain the precise oscillation of the circadian clock. By modulating the stability, activity, cell localization and protein-protein interactions of core clock proteins, PTMs enable these proteins to respond dynamically to environmental and intracellular changes, thereby sustaining the periodic oscillations of the circadian clock. Different types of PTMs exert their effects through distincting molecular mechanisms, collectively ensuring the proper function of the circadian system. This review systematically summarized several major types of PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation and oxidative modification, and overviewed their roles in regulating the core clock proteins and the associated pathways, with the goals of providing a theoretical foundation for the deeper understanding of clock mechanisms and the treatment of diseases associated with circadian disruption.
Protein Processing, Post-Translational/physiology* ; Circadian Rhythm/physiology* ; Humans ; Animals ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Phosphorylation ; Acetylation ; Ubiquitination ; Sumoylation

The phase changes and quantity-quality transfer of 17 batches of Ostreae Concha(Ostrea rivularis) during the raw material-calcined decoction pieces-standard decoction process were analyzed. The content of calcium carbonate(CaCO_3), the main component, was determined by chemical titration, and the extract yield and transfer rate were calculated. The CaCO_3 content in the raw material, calcined decoction pieces, and standard decoction was 94.39%-98.80%, 95.03%-99.22%, and 84.58%-90.47%, respectively. The process of raw material to calcined decoction pieces showed the yield range of 96.85% to 98.55% and the CaCO_3 transfer rate range of 96.92% to 99.27%. The process of calcined decoction pieces to standard decoction showed the extract yield range of 2.86% to 5.48% and the CaCO_3 transfer rate range of 2.59% to 5.13%. The results of X-ray fluorescence(XRF) assay showed that the raw material, calcined decoction pieces, and standard decoction mainly contained Ca, Na, Mg, Si, Br, Cl, Al, Fe, Cr, Mn, and K. The chemometric results showed an increase in the relative content of Cr, Fe, and Si from raw material to calcined decoction pieces and an increase in the relative content of Mg, Al, Br, K, Cl, and Na from calcined decoction pieces to standard decoction. X-ray diffraction(XRD) was employed to establish XRD characteristic patterns of the raw material, calcined decoction pieces, and standard decoction. The XRD results showed that the main phase of all three was calcite, and no transformation of crystalline form or generation of new phase was observed. Fourier transform infrared spectroscopy(FTIR) was employed to establish the FTIR characteristic spectra of the raw material, calcined decoction pieces, and standard decoction. The FTIR results showed that the raw material had internal vibrations of O-H, C-H, C=O, C-O, and CO■ groups. Due to the loss of organic matter components after calcination, no information about the vibrations of C-H, C=O, and C-O groups was observed in the spectra of calcined decoction pieces and standard decoction. In summary, this study elucidated the quantity-quality transfer and phase changes in the raw material-calcined decoction pieces-standard decoction process by determining the CaCO_3 content, calculating the extract yield and transfer rate, and comparing the element changes, FTIR characteristic spectra, and XRD characteristic pattern. The results were reasonable and reliable, laying a foundation for the subsequent process research and quality control of the formula granules of calcined Ostreae Concha(O. rivularis Gould), and providing ideas and methods for the quality control of the whole process of raw material-decoction pieces-standard decoction-formula granules of Ostreae Concha and other testacean traditional Chinese medicine.
Drugs, Chinese Herbal/isolation & purification* ; Calcium Carbonate/analysis* ; Quality Control

This study explores the role and underlying molecular mechanisms of fucoidan sulfate(FPS) in regulating heme oxygenase-1(Hmox1)-mediated ferroptosis to ameliorate myocardial injury in diabetic cardiomyopathy(DCM) through in vivo and in vitro experiments and network pharmacology analysis. In vivo, a DCM rat model was established using a combination of "high-fat diet feeding + two low-dose streptozotocin(STZ) intraperitoneal injections". The rats were randomly divided into four groups: normal, model, FPS, and dapagliflozin(Dapa) groups. In vitro, a cellular model was created by inducing rat cardiomyocytes(H9c2 cells) with high glucose(HG), using zinc protoporphyrin(ZnPP), an Hmox1 inhibitor, as the positive control. An automatic biochemical analyzer was used to measure blood glucose(BG), serum aspartate aminotransferase(AST), serum lactate dehydrogenase(LDH), and serum creatine kinase-MB(CK-MB) levels. Echocardiography was used to assess rat cardiac function, including ejection fraction(EF) and fractional shortening(FS). Pathological staining was performed to observe myocardial morphology and fibrotic characteristics. DCFH-DA fluorescence probe was used to detect reactive oxygen species(ROS) levels in myocardial tissue. Specific assay kits were used to measure serum brain natriuretic peptide(BNP), myocardial Fe~(2+), and malondialdehyde(MDA) levels. Western blot(WB) was used to detect the expression levels of myosin heavy chain 7B(MYH7B), natriuretic peptide A(NPPA), collagens type Ⅰ(Col-Ⅰ), α-smooth muscle actin(α-SMA), ferritin heavy chain 1(FTH1), solute carrier family 7 member 11(SLC7A11), glutathione peroxidase 4(GPX4), 4-hydroxy-2-nonenal(4-HNE), and Hmox1. Immunohistochemistry(IHC) was used to examine Hmox1 protein expression patterns. FerroOrange and Highly Sensitive DCFH-DA fluorescence probes were used to detect intracellular Fe~(2+) and ROS levels. Transmission electron microscopy was used to observe changes in mitochondrial morphology. In network pharmacology, FPS targets were identified through the PubChem database and PharmMapper platform. DCM-related targets were integrated from OMIM, GeneCards, and DisGeNET databases, while ferroptosis-related targets were obtained from the FerrDb database. A protein-protein interaction(PPI) network was constructed for the intersection of these targets using STRING 11.0, and core targets were screened with Cytoscape 3.9.0. Molecular docking analysis was conducted using AutoDock and PyMOL 2.5. In vivo results showed that FPS significantly reduced AST, LDH, CK-MB, and BNP levels in DCM model rats, improved cardiac function, decreased the expression of myocardial injury proteins(MYH7B, NPPA, Col-Ⅰ, and α-SMA), alleviated myocardial hypertrophy and fibrosis, and reduced Fe~(2+), ROS, and MDA levels in myocardial tissue. Furthermore, FPS regulated the expression of ferroptosis-related markers(Hmox1, FTH1, SLC7A11, GPX4, and 4-HNE) to varying degrees. Network pharmacology results revealed 313 potential targets for FPS, 1 125 targets for DCM, and 14 common targets among FPS, DCM, and FerrDb. Hmox1 was identified as a key target, with FPS showing high docking activity with Hmox1. In vitro results demonstrated that FPS restored the expression levels of ferroptosis-related proteins, reduced intracellular Fe~(2+) and ROS levels, and alleviated mitochondrial structural damage in cardiomyocytes. In conclusion, FPS improves myocardial injury in DCM, with its underlying mechanism potentially involving the regulation of Hmox1 to inhibit ferroptosis. This study provides pharmacological evidence supporting the therapeutic potential of FPS for DCM-induced myocardial injury.
Animals ; Ferroptosis/drug effects* ; Rats ; Diabetic Cardiomyopathies/physiopathology* ; Male ; Rats, Sprague-Dawley ; Polysaccharides/pharmacology* ; Heme Oxygenase-1/genetics* ; Myocytes, Cardiac/metabolism* ; Myocardium/pathology* ; Humans ; Cell Line ; Heme Oxygenase (Decyclizing)