Objective To construct an index system for assessment of schistosomiasis transmission risk following natural disasters such as rainstorms, floods, earthquakes, mudslides, and landslides, so as to provide insights into rapid identification of schistosomiasis transmission risk post-disasters and formulation of targeted schistosomiasis control strategies. Methods An initial framework for the index system for assessment of schistosomiasis transmission risk following natural disasters was drafted through literature review, brainstorming, and focus group discussions. Two rounds of expert correspondence consultations were conducted using the Delphi method to refine and finalize the system, and the degrees of expert activeness, authority and endorse ment, and consensus were evaluated. In addition, the weights of each index were calculated using the analytic hierarchy process. Results A total of 18 experts participated in the consultation. The expert positive coefficients were 100.00% and 94.44% for two rounds of consultations, with authority coefficients of 0.92 and 0.94, respectively. The coefficients of coordination on the index importance, rationality and operability were 0.209, 0.185, 0.222 and 0.407, 0.214, 0.257 for two rounds of consultations, respectively, and all consistency tests were statistically significant (χ² = 246.771 to 505.278, all P values < 0.001). Following two rounds of expert consultations, an index system consisting of 6 first-level indicators, 15 second-level indicators, and 49 third-level indicators was ultimately constructed. In terms of first-level indicators, “disaster situation”, “previous epidemics”, “healthcare guarantee”, “response capacity” and “emergency recovery” had the highest weights, each at 18.18%. Regarding second-level indicators, “Schistosoma japonicum infections in animals”, “S. japonicum infections in snails” and “medical treatment” had the highest weights, each at 7.35%. In terms of third-level indicators, ten items had the highest weights, including “identification of schistosomiasis cases”, “detection of S. japonicum infections in wild feces”, “detection of S. japonicum infections in snails”, “reserves of schistosomiasis diagnostic/testing reagents and consumables”, “reserves of chemotherapy agents for human and animal schistosomiasis”, “reserves of cercariacides”, “periodical surveillance on schistosomiasis”, “identification of schistosomiasis transmission risk and timely response”, “normal provision of diagnosis and treatment services” and “post-disaster schistosomiasis surveillance”, each at 2.40%. Conclusion A scientific, systematic, and practical index system has been constructed for assessment of schistosomiasis transmission risk following natural disasters, which may provide insights into rapid post-disaster identification of schistosomiasis transmission risk, formulation of targeted schistosomiasis control strategies and optimization of resource allocation.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

ObjectiveTo explore the mechanism of action of Paeoniae Radix Rubra and Aconiti Lateralis Radix Praeparata （CSFZ） in the treatment of acute-on-chronic liver failure （ACLF） through network pharmacology， molecular docking， and animal experiments. MethodsNetwork pharmacology was used to identify potential targets and related signaling pathways for the treatment of ACLF with CSFZ. Molecular docking was used to examine the binding activity of the core components with corresponding key targets. An ACLF rat model was established by subcutaneous and tail vein injections of bovine serum albumin combined with lipopolysaccharide （LPS） + D-galactosamine （D-GalN） intraperitoneal injection. A normal control group （NC）， a model group， a CSFZ group （CSFZ， 5.85 g·kg^-1）， and a hepatocyte growth-promoting granule group （HGFG， 4.05 g·kg^-1） were set up in this study. Pathological changes in rat liver tissue were observed using hematoxylin and eosin （HE） and Masson staining. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression levels of interleukin-6 （IL-6）， B-cell lymphoma-2 （Bcl-2）， Caspase-3， and albumin （ALB）. Real-time quantitative polymerase chain reaction （Real-time PCR） and Western blot were used to measure the mRNA and protein expression levels of phosphoinositide 3-kinase （PI3K）， protein kinase B （Akt）， phosphorylated PI3K （p-PI3K）， and phosphorylated Akt （p-Akt）. ResultsNetwork pharmacology screening identified 49 active ingredients of CSFZ， 103 action targets， and 3 317 targets related to ACLF. Among these， 74 targets overlapped with CSFZ drug targets. Key nodes in the protein-protein interaction （PPI） network included Akt1， tumor necrosis factor （TNF）， IL-6， Bcl-2， and Caspase-3. Gene Ontology （GO） functional analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis identified multiple signaling pathways， with the PI3K/Akt signaling pathway being the most frequent. Molecular docking showed that the core components of the drug exhibited good binding activity with the corresponding key targets. Animal experiments confirmed that CSFZ significantly improved liver tissue pathological damage in ACLF rats， reduced the release of inflammatory factors and liver cell apoptosis， and upregulated the expression levels of the PI3K/Akt signaling pathway. ConclusionThrough network pharmacology， molecular docking， and in vivo experiments， this study confirms the effect of CSFZ in reducing liver cell inflammatory damage and inhibiting liver cell apoptosis. The specific mechanism may be related to its involvement in regulating the PI3K/Akt signaling pathway.

ObjectiveTo study the effect and mechanism of Shentong Zhuyutang in treating intervertebral disc degeneration （IDD） in rats. MethodsIn the cell experiment， male rats were administrated with normal saline or low-， medium-， and high-dose （3.38， 6.75，13.5 g·kg^-1， respectively） Shentong Zhuyutang by gavage， respectively， and serum samples were collected after 7 days of continuous administration. Another 10 male rats were selected for the isolation of nucleus pulposus cells. The cell model of IDD was established by treatment with interleukin （IL）-1β. The modeled cells were then treated with Shentong Zhuyutang-containing serum and the ferroptosis inhibitor ferrostatin-1 （Fer-1）， respectively， to investigate the effects of Shentong Zhuyutang-containing serum on the proliferation and ferroptosis of nucleus pulposus cells. To study the role of silent information regulator 1 （SIRT1）/nuclear factor erythroid 2-related factor 2 （Nrf2） in the regulation of ferroptosis in nucleus pulposus cells by Shentong Zhuyutang-containing serum， this study treated the cells with the SIRT1 inhibitor Ex 527 and the Nrf2 inhibitor ML385， respectively， in addition to the treatment with IL-1β and high-dose Shentong Zhuyutang-containing serum. The cell-counting kit-8 （CCK-8） assay and EdU staining were employed to measure the cell viability and proliferation， respectively. The Fe²⁺， glutathione （GSH）， and malondiadehyde （MDA） levels were measured by colorimetric assay. Western blot was employed to determine the protein levels of glutathione peroxidase 4 （GPX4）， acyl-CoA synthetase long-chain family 4 （ACSL4）， Collagen Ⅱ， Aggrecan， SIRT1， and Nrf2. Immunofluorescence was used detect SIRT1 expression. In the animal experiment， male rats were treated with anulus puncture for the modeling of IDD. Rats were randomly assigned into sham operation， model， Shentong Zhuyutang-containing serum （13.5 g·kg^-1）， and positive control （nimesulide dispersible tablets， 0.18 mg·kg^-1） groups. Rats in the drug intervention groups were administrated with corresponding agents at 1 mL·kg^-1， and those in the sham operation and model groups were administrated with equal volumes of normal saline， once daily for 28 consecutive days. At the end of the last administration， the histopathological changes in the intervertebral discs of rats were observed by hematoxylin-eosin staining and scored by the Masuda method. Western blot was employed to determine the protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ in the nucleus pulposus tissue. ResultsCompared with the control group， the IL-1β group of nucleus pulposus cells showed elevated levels of Fe²⁺， MDA， and ACSL4 （P<0.05）， decreased cell viability， lowered GSH level， and down-regulated protein levels of GPX4， Collagen Ⅱ， and Aggrecan （P<0.05）. Shentong Zhuyutang-containing serum and Fer-1 reversed the effects of IL-1β on the viability and ferroptosis of nucleus pulposus cells and up-regulated the protein levels of Collagen Ⅱ and Aggrecan in nucleus pulposus cells （P<0.05）. Compared with the control group， the IL-1β group showcased down-regulated expression of Sirt1 and Nrf2 in nucleus pulposus cells （P<0.05）. Compared with the IL-1β group， the high-dose Shentong Zhuyutang-containing serum+IL-1β group showed up-regulated expression of SIRT1 and Nrf2 in nucleus pulposus cells （P<0.05）. Compared with the high-dose Shentong Zhuyutang-containing serum+IL-1β group， the ML385 group showed down-regulated protein levels of Nrf2 and GPX4， lowered GSH level， and elevated Fe²⁺ and MDA levels （P<0.05）. In addition， the Ex 527 group showed down-regulated protein levels of SIRT1， Nrf2， and GPX4 （P<0.05）. The results of the animal experiment showed that compared with the sham operation group， the model group had severe degeneration of the intervertebral disc tissue with increased pathological score， up-regulated protein level of ACSL4 （P<0.05）， and down-regulated protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ （P<0.05）. Compared with the model group， the Shentong Zhuyutang group showed alleviated IDD with declined pathological score， down-regulated protein level of ACSL4 （P<0.05）， and up-regulated protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ （P<0.05）. ConclusionShentong Zhuyutang may activate the SIRT1/Nrf2 signaling pathway to inhibit the ferroptosis of nucleus pulposus cells， thereby delaying the process of IDD in rats.

Objective: To analyze the monitoring results of key occupational hazard factors in workplaces across different industries in Aba Tibetan and Qiang Autonomous Prefecture (Aba Prefecture), Sichuan Province, so as to provide the basis for optimizing occupational disease prevention and control measures. Methods: Data of key occupational hazard factors in workplaces in Aba Prefecture from 2022 to 2024 were collected through the Workplace Occupational Hazard Monitoring System. Descriptive analyses were conducted on the over-standard rates of occupational hazard factors, completion rates of occupational health examinations, and participation rates in occupational health training. Results: From 2022 to 2024, 165 enterprise-times in Aba Prefecture were monitored, with 112 enterprise-times exceeding the standards, the over-standard rate of enterprises was 67.88%. A total of 1 005 worksite positions were monitored, with 302 exceeding the standards, the over-standard rate of worksite positions was 30.05%. The over-standard rates of enterprises and worksite positions showed upward trends from 2022 to 2024 (both P<0.05). The over-standard rates of enterprises in the ferrous metal mining and dressing industry and non-ferrous metal mining and dressing industry were both 100%. The top three over-standard rates of worksite positions were in the non-metallic mining and dressing industry, ferrous metal mining and dressing industry, and non-ferrous metal mining and dressing industry, at 62.50%, 56.00%, and 53.13%, respectively. Worksite positions exposed to silica dust, noise, and coal dust had relatively high over-standard rates, at 17.00%, 10.04%, and 7.27%, respectively. The actual numbers of physical examinations for personnel at worksite positions with dust, chemical factors, and physical factors were 9 398, 2 469, and 10 928 person-times, respectively, with completion rates of 95.78%, 81.03%, and 100.00%, respectively. The actual number of re-examinations were 428, 129, and 1 121 person-times, respectively, with re-examination rates of 64.26%, 27.27%, and 81.53%, respectively. Among the monitored enterprises, 102 enterprise-times participated in training for persons in charge, with a participation rate of 61.82%; 108 enterprise-times participated in training for occupational health management personnel, with a participation rate of 65.45%; and 10 489 person-times of workers exposed to hazards participated in occupational health training for workers, with a participation rate of 86.24%. Conclusions The over-standard rate of key occupational hazards factors in workplaces in Aba Prefecture was relatively high. The completion rate of occupational health examinations was good, but the re-examination rate was low. It is recommended to focus on strengthening the prevention and control of occupational hazard factors in the mining and dressing industry and promoting the effective implementation of occupational health examinations and occupational health training.

Objective:To discuss the effect of erythritol on glucose and lipid metabolism in the body,and to clarify the mechanism of erythritol affecting liver metabolism based on metabonomics.Methods:The male ICR mice were randomly divided into normal group,sucrose group(2％sucrose),low dose of erythritol(1％erythritol)group,medium dose of erythritol(2％erythritol)group,and high dose of erythritol(4％erythritol)group,with 10 mice in each group.The corresponding concentrations of sucrose and erythritol solutions were prepared and placed in water bottles,and the mice were allowed to drink and eat freely for 12 consecutive weeks;the body mass,food intakes,and water intakes of the mice in various groups were measured.Commercial kits were used to detect the serum triglyceride(TG),total cholesterol(TC),and blood glucose levels of the mice in various groups;the liver indexes of the mice were calculated.Ultra performance liquid chromatography-orbitrap exactive mass spectrometry(UPLC-OE-MS)non-targeted metabonomics was used to detect the liver metabolites of the mice normal group and high dose of erythritol group;bioinformatics analysis was used to screen the differential liver metabolites between the two groups with variable importance in projection(VIP)＞1 and adjusted P＜0.05;Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis were performed to investigate the functional roles of the differential liver metabolites.Results:Compared with normal group,there were no significant differences in the body mass,food intake,liver index,and blood lipid levels of the mice in various groups(P＞0.05);compared with normal group,the blood glucose level of the mice in high dose of erythritol group was significantly increased(P＜0.01).The metabonomics analysis of the liver tissues of the mice in two groups identified 1 144 metabolites,mainly including lipids and lipid-like molecules(17.39％),organic acids and derivatives(10.87％),organic heterocyclic compounds(5.80％),and organic oxygen compounds(5.07％).Compared with normal group,there were 138 differential liver metabolites in the mice in high dose of erythritol group,among which 112 metabolites were up-regulated and 26 metabolites were down-regulated.The KEGG signal pathway enrichment analysis results showed that the differential metabolites were mainly enriched in metabolism,steroid hormone biosynthesis,cortisol synthesis and metabolism,and Cushing's syndrome pathways;the further topological analysis of the metabolic pathways results showed that the differential metabolites were mainly involved in sphingolipid metabolism,tricarboxylic acid cycle,riboflavin metabolism,steroid hormone biosynthesis,and purine metabolism signal pathways.Conclusion:Long-term intake of high dose of erythritol can increase the blood glucose level in the mice,and the mechanism may be that it affects the tricarboxylic acid cycle by interfering with riboflavin metabolism and interferes with sphingolipid metabolism,leading to impairment of the blood glucose control system.

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.