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.

OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

OBJECTIVE To analyze the prototype components absorbed into blood and brain of Lithocarpus litseifolius leaves， so as to provide a reference for clarifying the pharmacological material basis of its prevention and treatment of central nervous system dis eases. METHODS The ethanol extract of L. litseifolius leaves， as well as the gastric lavage fluid and perfusion solution were prepared. Using rats as subjects， plasma samples of intestinal wall metabolism， intestinal flora metabolism and hepatic metabolism were prepared via in situ intestinal perfusion and closed intestinal loop method； while comprehensive metabolic plasma samples， brain tissue samples， and cerebrospinal fluid samples were collected after intragastric administration. UPLC-HRMS technology was utilized to analyze and identify chemical components and prototype components absorbed into blood and brain of L. litseifolius leaves. RESULTS A total of 66 chemical constituents were identified in L. litseifolius leaves， primarily consisting of flavonoids， organic acids， and others. A total of 16， 13， 11， and 5 prototype components were identified in intestinal wall metabolism， intestinal flora metabolism， hepatic metabolism， and comprehensive metabolic plasma samples， respectively. Additionally， 4 prototype components were detected in brain tissue and 9 in cerebrospinal fluid. Phloridzin， trilobatin， phloretin-2- O -malonyl hexoside， and phloretin were identified as common components across all sample types. CONCLUSIONS Prototype components absorbed into blood and brain of L. litseifolius leaves， such as phloridzin， trilobatin， phloretin， and other components may serve as the pharmacological material basis for their therapeutic effects on central nervous system diseases.

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.

Objective To analyze the infection status and epidemic trends of soil-borne nematode infections in Yubei, Chongqing City, in 2010,2021, and 2022. Methods The local populations from four survey sites of four towns in 2010 and five sites of five towns in 2021 and 2022 were surveyed regarding their basic information using a unified form. Fecal samples of the participants were collected and tested for soil-borne nematode infections using the modified Kato-Katz thick smear method. Results In 2010, 2 049 participants were surveyed, followed by 1 000 participants in 2021 and 2022. The overall prevalence of parasitic infections declined significantly from 3.86% to 0.20%. In 2010, soil-transmitted nematode included hookworms(3.81%) and roundworms(0.29%). In 2021, the infection rates of roundworms and hookworms were 1.70% and 0.10% respectively. Notably, only Ascaris was identified in 2022(0.20%). The≥60 age group consistently exhibited the highest infection rates across all surveys, followed by the 40-59 age group. The infection rates of males in the three surveys were 3.31%,1.92%, and 0.20% respectively, and those of females were 4.37%, 1.46%, and 0.20% respectively. There was no statistically significant difference in the infection rates between males and females. Educational attainment was inversely associated with infection; in 2010, the highest prevalence was observed among those with primary education or below, whereas in 2021, illiterate or semi-literate individuals showed the highest susceptibility. The occupational distribution of infections in 2010 indicated that retirees (8.33%), farmers(4.86%), and homemakers or unemployed individuals(3.45%)were the most affected. However, in 2021 and 2022, farmers emerged as the predominant occupational group with soil-transmitted nematode infections. Conclusions The infection rate of soil-borne nematodes showed a decreasing trend in Yubei, and the infection species changed from hookworms in 2010 to Ascaris in 2022. Farmers, the elderly, and people with low education levels should continue to be the focus of preventive and control efforts.

Objective: To assess the current status of bacterial detection capabilities in various blood collection and supply and related laboratories across China, and to identify key factors affecting the consistency of test results. Methods: A pooled analysis was conducted on data from 21 external quality assessment (EQA) rounds for blood bacterial detection carried out by the China International Transfusion Infection Prevention and Control (CITIC) project from 2019 to 2025. The study covered three types of laboratories-blood centers, central blood stations, and commercial laboratories—across 23 provincial-level administrative regions in China. Statistics were compiled on the number of participating laboratories, and the concordance rates were calculated for different institution types, different automated blood culture systems, different bacterial concentrations, and different strains. Non-standard reporting issues were also reviewed, and statistical methods were applied for analysis. Results: Over the seven-year period, the number of participating laboratories increased from 161 to 236, with an average annual growth rate of 6.58%, and the annual concordance rates ranged from 82.61% to 94.92%. The overall laboratory concordance rates for the three types of participating laboratories were all>89.0% (P>0.05), while commercial laboratories showed a significant year-by-year increase (70.97%-100%, P<0.05). The concordance rates of automated blood culture systems from eight manufacturers showed no significant differences (97.98%-100%, P>0.05), indicating stable performance across all devices. The overall sample concordance rate was 98.88%, with negative samples showing a lower rate than positive samples (97.97% vs 99.14%); positive sample rates increased with higher concentrations; and low-concentration Gram-negative bacterial samples had a lower concordance rate than Gram-positive ones (93.15% vs 100%). A total of 50 instances of non-standard result reporting were identified, with incorrect time units accounting for the highest proportion (52.0%). Conclusion The participation scale and overall testing proficiency of blood collection and supply and related laboratories nationwide have improved year by year, and all types of automated blood culture devices can meet the requirements for blood bacterial screening. Detection of negative and low-concentration positive samples remains a weak link in current bacterial EQA, and the standardization of laboratory data reporting requires further strengthening. Continuous: external quality assessment, targeted training, and standardized reporting procedures can effectively enhance the homogenization level of blood bacterial testing across the country and ensure the safety of clinical transfusion.

Aim To evaluate the role of the glucose transporter protein 1(GLUT1)-dependent glycolytic in the attenuation of oxygen-glucose deprivation-reoxygen-ation(OGD/R)injury in HK-2 cells by dexmedetomi-dine(Dex).Methods C57/BL6 mice were random-ly divided into three groups(n=6),namely,sham operation group(Sham group),renal ischemia reper-fusion group(I/R group)and Dex group(I/R+Dex group).Serum creatinine(Cr)and urea nitrogen(BUN)were measured,while the levels of key glyco-lytic enzymes HK2,PFKFB3 and GLUT1 were meas-ured.HK-2 cells were cultured and randomised into seven groups(n=6),which was treated with OGD/R,overexpression or interference with GLUT1,Dex and glycolysis inhibitor 2-DG.CCK-8 and LDH activi-ty were used to detect cellular damage.Glycolysis lev-els were detected by lactate and ECAR.The inflamma-tory level was reflected by qRT-PCR for IL-6 and TNF-α.qRT-PCR and Western blot were performed to de-tect the levels of GLUT1,HK2,and PFKFB3.Results Dex significantly ameliorated kidney injury and HK-2 cell injury(P＜0.05).Dex inhibited the OGD/R-induced rise in lactate and extracellular acidification rate(ECAR),as evidenced by suppression of the ex-pression of GLUT1,HK2 and PFKFB3(P＜0.05).In vitro experiments showed that GLUT1 knockdown sig-nificantly improved OGD/R-induced cellular damage.Lactate,ECAR,glycolysis-related mRNAs and pro-teins were inhibited by GLUT1 knockdown(P＜0.05).Significantly,there were no significant differ-ences in above indexes after Dex treatment based on GLUT1 knockdown.Overexpression of GLUT1 abroga-ted the protective effects of Dex,while reversing the inhibitory effects of Dex on the expression of GLUT1,HK2,and PFKFB3(P＜0.05).Conclusions Dexmedetomidine attenuates OGD/R induced injury in HK-2 cells by inhibiting GLUT1-dependent glycolysis.

Objective To establish a high-throughput non-targeted screening and prioritization method for emerging pollutants(EPs)in sewage using direct injection high-resolution mass spectrometry(HRMS).Methods The sewage samples were filtered by membrane filter and directly subjected to the liquid chromatography-time-of-flight mass spectrometer based on a method modified from our previous study.A C18 chromatographic column was applied for a gradient elution separation,and accurate mass and mass spectral fragment information were obtained through the MS full scan mode and MS/MS DIA data collection mode.After peak detection and alignment,the features from the raw data through open source software MZmine 3,and then high-throughput screening strategies such as MassBank and PubChem databases were used for compound annotation.Finally,the candidate features were confirmed with chemical standards by compared their retention time and mass spectrum fragmentation ion peaks.Results 13 EPs were identified,including 7 industrial chemicals,4 pharmaceuticals,1 pesticide and 1 metabolite.High detection rates were observed for metformin(86.2％),2-hydroxybenzothiazole(79.3％),1,2-benzisothiazole-3-one(72.4％),and 1,2-benzisothiazole-3-one(72.4％).The quantitative concentration range of EPs was 1.37～19.05 ng/mL,with the high concentrations observed for melamine(19.05 ng/mL)and furosemide(18.49 ng/mL).Ecological risk assessment identified 1,2-benzisothiazol-3-one,4-aminoacetophenone,creatinine,2-hydroxybenzothiazole,and furosemide as key pollutants.Conclusion This direct injection coupled with HRMS workflow enables efficient non-targeted screening and prioritization of emerging EPs in sewage samples,highlighting five ecotoxicologically critical EPs.The methodology enhances environmental monitoring capabilities and provide critical technical support for interdisciplinary research such as environmental forensics and health risk assessment.

This study investigated the infection status,drug resistance,and molecular characteristics of Shiga toxin-producing Escherichia coli(STEC)in domestic goats in Chengkou county,Chongqing.In August 2023,283 fecal samples were collected from households in Chengkou county.After enrichment with EC broth and inoculation onto selective media,samples that tested positive for stx1/stx2 were selected for further isolation.The positive strains were investigated with antimicrobial susceptibility testing and whole genome sequencing.According to the whole genomic sequences,the stx subtypes,serotypes,multi-locus sequence types,virulence genes,drug resistance genes,and phylogenetic relationships of the STEC strains were analyzed.Forty-six strains of STEC were isolated from 283 goat fecal samples,thus resulting in a detection rate of 16.25%.The 46 STEC strains were categorized into 12 O∶H serotypes,among which O76∶H19 and O8∶H7 predominated,each represented by 9 strains.Five STEC strains were identified as serotype O157∶H7.The 46 STEC strains were categorized into 11 sequence types(STs),among which ST675 and ST196 predominated,each represented by nine strains,accounting for a 19.57%proportion.The strains were categorized into 7 stx subtypes,among which stx1c(26/46,56.52%),followed by stx2k(9/46,19.57%)predominated.All nine Stx2k-STEC strains were identified as serotype O8∶H7 and sequence type ST196.In antimicrobial susceptibility testing,2 STEC strains were resistant to ampicillin,one strain was resistant to ampicillin/sulbactam,one strain was resistant to cefazolin,and one strain was resistant to cefoxitin.Nine Stx2k-STEC strains were found to carry the beta-lactam resistance gene blaEC-18.Antimicrobial sensitivity tests revealed that the nine Stx2k-STEC strains were sensitive to all 15 tested antibiotics.Moreover,phylogenetic analysis indicated that the 9 Stx2k-STEC strains were remarkably similar but showed high genetic diversity with respect to that of the Stx2k-STEC strains isolated from other regions in China.Goatsare an important animal reservoir for STEC in theChengkou district of Chongqing,and novel sequence type Stx2k-STEC strains distinct from those found in other regions of China were identified in this region.