Objective: To evaluate the efficacy and safety of ruxolitinib combined with low-dose hormone for patients with acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods: Thirty patients with aGVHD after allo-HSCT admitted to the Department of Hematology of the General Hospital of Western Theater Command from November 2021 to November 2024 were retrospectively analyzed. All patients were treated with low-dose hormone (methylprednisolone 0.3-1 mg kg -d ) combined with ruxolitinib 5-10 mg d . The efficacy and adverse reactions were observed during the follow-up period to analyze the survival outcomes of the patients. Results: A total of 30 patients with aGVHD after allo-HSCT were included in this study, consisting of 15 (50%) males and 15 (50%) females with a median age of 34 year-old (ranging from 14 to 62). Classification by disease type: there were 18 cases of acute myeloid leukemia, 4 cases of acute lymphoblastic leukemia, 4 cases of aplastic anemia, and 4 cases of myelodysplastic syndrome. Classification by aGVHD severity: there were 27 cases (90%) of Ⅱ-Ⅳ degree aGVHD and 11 cases (36.7%) of Ⅲ-Ⅳ degree aGVHD. Ruxolitinib in combination with low-dose glucocorticoid treatment yield responses in 28 (93.3%) patients, of which 27 (90%) achieved complete remission (CR), while 1 (3.3%) showed partial remission (PR). One patient (3.3%) had no response (NR), and 1 patient (3.3%) exhibited progressed disease (PD). Overall survival (OS) at 1 year of transplantation was 73.9% (95%CI 49.5% to 87.7%), progression-free survival (PFS) was 93.3% (95%CI 75.9% to 98.3%), non-relapse mortality (NRM) was 20.6% (95%CI 7.9% to 47.4%), and median survival time was 27.6 months. Conclusion: Ruxolitinib combined with low-dose hormones is safe and effective in the treatment of aGVHD after allo-HSCT.

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 Metabolomics combined with machine learning algorithms was used to systematically study the preoperative serum metabolites of patients with early postoperative cognitive dysfunction(POCD)after heart valve replacement,so as to screen biomarkers that may predict early POCD after heart valve replacement and explore the corresponding metabolic regulatory mechanisms.Methods A total of 60 patients underwent heart valve replacement under extracorporeal circulation were selected and divided into early-POCD group(group P)and non-POCD group(group N)according to whether POCD occurred or not.Metabolomic analysis was performed on preoperative serum samples of patients in group P and group N to screen the differential metabolites and metabolic pathways.The biomarkers related to early POCD were identified by random forest algorithm.Results A total of 532 differential metabolites were detected by metabonomics analysis,and 5 biomarkers were screened by random forest algorithm,namely quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol.Among them,the main metabolic pathways were phenylalanine metabolism,primary bile acid biosynthesis,ascorbic acid and aldonate metabolism,pentose and glucuronate interconversion,tryptophan metabolism,drug metabolism-cytochrome P450,porphyrin and chlorophyll metabolism.Conclusion Many metabolic pathways in patients with early POCD after heart valve replacement under extracorporeal circulation have changed before operation,which may lead to the occurrence of early POCD.Quinoline,3'-sialyllactose,sphingomyelin(d18∶1/20∶0),lysophosphatidylcholine[P-18∶1(9Z)]and 25-hydroxycholesterol may be biomarkers for predicting early POCD.

Objective To explore the association between walking exercise guided by 6 minute walking test(6MWT)and the incidences of 3-year major adverse cardiovascular event(MACE)in elderly patients with acute coronary syndrome(ACS)after percutaneous coronary intervention(PCI).Methods A total of 628 elderly ACS patients who undergoing PCI and obtaining success-ful coronary revascularization in our department from November 2018 to April 2019 were enrolled,and divided into 6MWT group(n=147)and control group(n=481)based on participa-ting in walking exercise guided by 6MWT or not.All of them were followed up for 3 years.The incidences of MACE[including coronary target vascular restenosis,acute myocardial infarction,heart failure,ischemic or hemorrhagic stroke]and all-cause death were observed.Univariate and multivariate Cox proportional analyses and Kaplan-Meier survival curve analysis were employed for data statistical analyses.Results At the end of follow-up,the incidences of target vascular restenosis(6.9％vs 2.0％,P=0.028),heart failure(3.7％vs 0％,P=0.036),stroke(3.7％vs 0％,P=0.036),and total MACE incidence(15.0％vs 4.1％,P=0.000)were statistically higher in the control group than the 6MWT group.Kaplan-Meier survival curve analysis showed that the cumulative incidence of MACE was significantly lower in the 6MWT group than the control group(Plog rank=0.001).Multivariate Cox regression analysis showed that not participating in walking exercise guided by 6MWT was an independent risk factor for occurrence of 3-year MACE(HR=3.102,95％CI:1.327-7.250,P=0.009).Conclusion Walking exercise guided by 6MWT reduces the incidence of 3-year MACE and improves the long-term prognosis of elderly ACS patients after PCI.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

Objective To elucidate the mechanisms of trauma-induced coagulopathy(TIC),clarify the specific pathogenic factors and pathophysiological processes,and discover the effective diagnostic indicators and therapeutic targets.Methods Transcriptomic data of traumatic hemorrhagic shock patients were obtained from the Gene Expression Omnibus(GEO)to identify differentially expressed genes(DEGs).Coagulation-related genes(CRGs)from the Kyoto Encyclopedia of Genes and Genomes(KEGG)were intersected with DEGs.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO)and random forest(RF),were applied to identify key genes.The CIBERSORT algorithm was used to analyze the correlation between key genes and immune cell infiltration.Through consensus clustering,subtype analysis was conducted on trauma patients to compare the infiltration of immune cells.A rat model of traumatic hemorrhagic shock was established to validate coagulation function and the expression of key genes.Results The dataset included samples from 17 healthy controls and 478 patients with traumatic hemorrhagic shock.A total of 6 315 DEGs were identified under the screening criterion of corrected P<0.05.Gene set enrichment analysis(GSEA)showed that the up-regulated DEGs were significantly enriched in the glucose metabolism pathway,while the down-regulated DEGs were enriched in the immune reaction-related pathways.Through cross-analysis of DEGs and CRGs,a total of 65 differentially expressed coagulation-related genes(DE-CRGs)were screened out.GO functional enrichment showed that these genes were mainly located in secreting granular membranes and platelet α-granules,and were involved in physiological processes such as blood coagulation,regulation of body fluid levels,and wound healing.KEGG pathway analysis revealed that these genes were significantly enriched in pathways such as platelet activation,complement and coagulation cascade reactions,Rap1 signaling pathway,and human cytomegalovirus infection.Six key DE-CRGs were identified through machine learning.Receiver operating characteristic(ROC)curve analysis indicated that these genes had good diagnostic efficacy.CIBERSORT analysis revealed a significant correlation between key genes and immune cell infiltration.Patients were classified into two subtypes based on the six key genes:subtype A was rich in CD8+T cells and activated NK cells,presented an immune-active state;subtype B was mainly composed of monocytes and resting NK cells,with insufficient activation of immune pathways.Animal experiments on rats showed that hemorrhagic shock can lead to coagulation dysfunction.The results of qRT-PCR further confirmed that the expression trend of key genes was consistent with the results of bioinformatics analysis.Conclusion In this study,through transcriptomics and machine learning methods,six key genes closely related to TIC were systematically screened out,namely GNA13,PIK3R3,ITGAM,MAPK14,PPP1CC and LYN,and their close connections with coagulation function and immune infiltration were revealed.Animal experiments have further verified the value of these genes as potential diagnostic and therapeutic targets.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective To observe the difference of bone micro-structure in different regions of proximal femur,micro-CT scanning was performed on 30 proximal femur specimens to explain the mechanism of proximal femur fracture and to provide anatomical basis for prosthesis design.Methods Totally 30 intact proximal femur specimens were obtained from 60-80 year-old cadavers.Micro-CT scanning was used to measure the trabecular thickness(Tb.Th),trabecular number(Tb.N),trabecular space(Tb.Sp),connectivity(Conn)and bone mineral density(BMD)and other parameters in 7 regions of proximal femur,including proximal pressure trabecular(PPT),distal pressure trabecular(DPT),femoral head-neck junction(FHNJ),head and neck of femoral neck(HNFN),the base of femoral neck(BPFN),intertrochanteric line(IL)and greater trochanter(GT).Results The bone mineral density of IL and GT were higher than those of BPFN,FHNJ,DPT and PPT.The trabecular thickness of GT was the largest,followed by IL,BPFN and HNFN,and the smallest was FHNJ,DPT and PPT.The trabecular space of IL was larger than that of GT,and the data of both were larger than those of other parts,among which DPT and PPT were the smallest.The trabecular number of IL and GT were the smallest,BPFN,HNFN and FHNJ were larger,and DPT was the largest.The volume fraction of IL was the smallest,BPFN and HNFN were larger,DPT and PPT were the largest.Conclusion The bone density,trabecular thickness,bone volume,and total volume of GT and IL in the proximal femur of elderly patients are all relatively large,so the reason for the high incidence of fractures is not due to weak internal bone microstructure;The bone density,trabecular thickness,and trabecular gap at the proximal and distal ends of the vertical trabecular bone are relatively small.If it is necessary to perform core decompression for prosthesis filling at this location,the design should be conducive to the mechanical conduction of the prosthesis and the regeneration of surrounding bone tissue.