ObjectiveTo elucidate the underlying mechanism through which Zhuluan decoction suppresses excessive autophagy in human ovarian granulosa cells （KGN） and ameliorates premature ovarian insufficiency （POI） via the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR） signaling pathway. MethodsThe optimal concentration of cyclophosphamide for inducing a POI model in KGN cells was identified via the cell counting kit-8 （CCK-8） assay. Subsequently， the impacts of varying concentrations of Zhuluan decoction-containing serum on the viability of the KGN cell model were assessed. After the optimal drug concentration was determined， KGN cells were categorized into the following groups： blank control （20% blank serum）， model （20% blank serum + 5 μmol·L^-1 cyclophosphamide）， Zhuluan decoction-containing serum （20% Zhuluan decoction-containing serum + 5 μmol·L^-1 cyclophosphamide）， autophagy inhibitor （20% blank serum + 5 μmol·L^-1 cyclophosphamide + 20 μmol·L^-1 chloroquine phosphate）， autophagy inhibitor + Zhuluan decoction-containing serum （20% Zhuluan decoction-containing serum + 5 μmol·L^-1 cyclophosphamide + 20 μmol·L^-1 chloroquine phosphate）， and estradiol valerate （20% estradiol valerate-containing serum + 5 μmol·L^-1 cyclophosphamide）. Following 48 hours of incubation， flow cytometry was utilized to measure the apoptosis rate of KGN cells in each group. Western blotting was employed to quantify the protein levels of PI3K， phosphorylated （p）-Akt， Akt， p-mTOR， and mTOR， along with the expression levels of autophagy-related proteins such as Beclin1， autophagy-related 5 homolog （ATG5）， and microtubule-associated protein 1 light chain 3 （LC3）， in each group. Additionally， monodansylcadaverine （MDC） staining was performed to evaluate the extent of autophagy in each group. ResultsIncubation of KGN cells with 5 μmol·L^-1 cyclophosphamide for 48 h successfully established a POI model， marked by a significant inhibition of KGN cell proliferation. Notably， the inhibitory effect of cyclophosphamide on KGN cell proliferation exhibited a positive correlation with its concentration. Zhuluan decoction-containing serum at 20% and 30% promoted cell proliferation and mitigated the inhibitory effect of cyclophosphamide on KGN cell proliferation， with comparable therapeutic efficacy observed at both concentrations. Compared with the blank control group， the model group displayed an elevated apoptosis rate （P<0.01）， reduced protein levels of PI3K， p-Akt， and p-mTOR （P<0.01）， increased protein levels of Beclin1， LC3， and ATG5 （P<0.01）， no significant alterations in the protein levels of Akt and mTOR， and an enhanced MDC autophagy fluorescence intensity （P<0.01）. In comparison to that the model group， the apoptosis rates in the blank control group， model group， Zhuluan decoction-containing serum group， autophagy inhibitor group， autophagy inhibitor + Zhuluan decoction-containing serum group， and estradiol valerate group all reduced （P<0.05， P<0.01）， with the most pronounced reduction observed in the autophagy inhibitor + Zhuluan decoction-containing serum group. The protein levels of PI3K， p-Akt， and p-mTOR were higher in other groups than in the model group （P<0.05， P<0.01）， being the highest in the autophagy inhibitor + Zhuluan decoctio-containing serum group （P<0.01）. The protein levels of Beclin1 and ATG5 were lower in other groups than in the model group （P<0.05， P<0.01）. The expression level of LC3 declined in the Zhuluan decoction-containing serum group and the estradiol valerate group （P<0.05， P<0.01）， while it decreased without statistical significance in the autophagy inhibitor group and the autophagy inhibitor + Zhuluan decoction-containing serum group. ConclusionZhuluan decoction may activate the PI3K/Akt/mTOR pathway to inhibit excessive autophagy and counteract the detrimental effects of cyclophosphamide on the KGN cell model， thus managing POI.

ObjectiveTo elucidate the underlying mechanism through which Zhuluan decoction suppresses excessive autophagy in human ovarian granulosa cells （KGN） and ameliorates premature ovarian insufficiency （POI） via the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR） signaling pathway. MethodsThe optimal concentration of cyclophosphamide for inducing a POI model in KGN cells was identified via the cell counting kit-8 （CCK-8） assay. Subsequently， the impacts of varying concentrations of Zhuluan decoction-containing serum on the viability of the KGN cell model were assessed. After the optimal drug concentration was determined， KGN cells were categorized into the following groups： blank control （20% blank serum）， model （20% blank serum + 5 μmol·L^-1 cyclophosphamide）， Zhuluan decoction-containing serum （20% Zhuluan decoction-containing serum + 5 μmol·L^-1 cyclophosphamide）， autophagy inhibitor （20% blank serum + 5 μmol·L^-1 cyclophosphamide + 20 μmol·L^-1 chloroquine phosphate）， autophagy inhibitor + Zhuluan decoction-containing serum （20% Zhuluan decoction-containing serum + 5 μmol·L^-1 cyclophosphamide + 20 μmol·L^-1 chloroquine phosphate）， and estradiol valerate （20% estradiol valerate-containing serum + 5 μmol·L^-1 cyclophosphamide）. Following 48 hours of incubation， flow cytometry was utilized to measure the apoptosis rate of KGN cells in each group. Western blotting was employed to quantify the protein levels of PI3K， phosphorylated （p）-Akt， Akt， p-mTOR， and mTOR， along with the expression levels of autophagy-related proteins such as Beclin1， autophagy-related 5 homolog （ATG5）， and microtubule-associated protein 1 light chain 3 （LC3）， in each group. Additionally， monodansylcadaverine （MDC） staining was performed to evaluate the extent of autophagy in each group. ResultsIncubation of KGN cells with 5 μmol·L^-1 cyclophosphamide for 48 h successfully established a POI model， marked by a significant inhibition of KGN cell proliferation. Notably， the inhibitory effect of cyclophosphamide on KGN cell proliferation exhibited a positive correlation with its concentration. Zhuluan decoction-containing serum at 20% and 30% promoted cell proliferation and mitigated the inhibitory effect of cyclophosphamide on KGN cell proliferation， with comparable therapeutic efficacy observed at both concentrations. Compared with the blank control group， the model group displayed an elevated apoptosis rate （P<0.01）， reduced protein levels of PI3K， p-Akt， and p-mTOR （P<0.01）， increased protein levels of Beclin1， LC3， and ATG5 （P<0.01）， no significant alterations in the protein levels of Akt and mTOR， and an enhanced MDC autophagy fluorescence intensity （P<0.01）. In comparison to that the model group， the apoptosis rates in the blank control group， model group， Zhuluan decoction-containing serum group， autophagy inhibitor group， autophagy inhibitor + Zhuluan decoction-containing serum group， and estradiol valerate group all reduced （P<0.05， P<0.01）， with the most pronounced reduction observed in the autophagy inhibitor + Zhuluan decoction-containing serum group. The protein levels of PI3K， p-Akt， and p-mTOR were higher in other groups than in the model group （P<0.05， P<0.01）， being the highest in the autophagy inhibitor + Zhuluan decoctio-containing serum group （P<0.01）. The protein levels of Beclin1 and ATG5 were lower in other groups than in the model group （P<0.05， P<0.01）. The expression level of LC3 declined in the Zhuluan decoction-containing serum group and the estradiol valerate group （P<0.05， P<0.01）， while it decreased without statistical significance in the autophagy inhibitor group and the autophagy inhibitor + Zhuluan decoction-containing serum group. ConclusionZhuluan decoction may activate the PI3K/Akt/mTOR pathway to inhibit excessive autophagy and counteract the detrimental effects of cyclophosphamide on the KGN cell model， thus managing POI.

Background As common public facilities essential to daily life, hair and beauty salons frequently contain various airborne toxic and hazardous pollutants potentially leading to adverse health effects for salon practitioners. Objective To characterize the indoor air pollution profiles of common contaminants in hair and beauty salons in Shanghai and to evaluate the associated health risks for practitioners, in order to provide a scientific basis for strengthening the public health management in Shanghai and protecting the health of practitioners. Methods The air quality monitoring data of hair and beauty salons in Shanghai from 2016 to 2024 were obtained from the “Health Hazard Factors Monitoring Program for Public Places” of the National Institute of Environmental Health, Chinese Center for Disease Control and Prevention. Monitoring indicators included particulate matter ≤10 μm in aerodynamic diameter (PM₁₀), particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5), formaldehyde, ammonia, benzene, toluene, and xylene. Indicator compliance rates were calculated across various years in accordance with GB 9666-1996 Hygienic standard for barber shop and beauty shop and GB 37488-2019 Hygiene indicators and limit for public places; specifically, PM_2.5 was assessed against the limits stipulated in GB/T 18883-2022 Standards for indoor air quality. A questionnaire survey was conducted among salon practitioners to collect weekly working days and daily working hours. The non-carcinogenic risks associated with inhalation exposure to formaldehyde, ammonia, benzene, toluene, and xylene as well as the carcinogenic risks posed by formaldehyde and benzene were evaluated following WS/T 777-2021 Technical guide for environmental health risk assessment of chemical exposure and the U.S. Environmental Protection Agency inhalation risk model. Results The overall compliance rates of PM₁₀, formaldehyde, ammonia, benzene, and toluene in the air of hair and beauty salons in Shanghai from 2016 to 2024 were 92.13%, 96.59%, 96.15%, 94.93%, and 94.97%, respectively; the overall compliance rate of xylene was a little lower (85.92%), and the overall compliance rate of PM_2.5 was 57.18%. The P₅₀ concentrations of PM₁₀, PM_2.5, formaldehyde, ammonia, benzene, toluene, and xylene did not exceed the corresponding limits. The P₅₀ of non-carcinogenic risk indicator (hazard quotient, HQ) for formaldehyde, ammonia, benzene, toluene, and xylene were <1. The probabilities of non-carcinogenic risk HQ >1 for formaldehyde and xylene were 41.4% and 10.9%, respectively, which were higher than that of other pollutants. The P₅₀ of carcinogenic risk (CR) for formaldehyde and benzene were between 1.0×10⁻⁶ and 1.0×10⁻⁴, while the probabilities of CR >1.0×10⁻⁴ were 16.9% and 14.0%, respectively. Conclusion The overall compliance rate of common pollutant concentrations in the air of hair and beauty salons in Shanghai is high, and the hygienic condition meets the requirements of national standards. The non-carcinogenic health risks posed by formaldehyde and xylene to employees (with formaldehyde being more prominent), as well as the carcinogenic risks associated with formaldehyde and benzene, deserve heightened attention in future health supervision.

Objective To investigate the causal relationship between metabolic syndrome and breast cancer by using a bidirectional two-sample Mendelian randomization (MR) approach. Methods Genome-wide association study (GWAS) summary statistics for metabolic syndrome and breast cancer were acquired from the Integrative Epidemiology Unit GWAS database and the GWAS Catalog, with populations encompassing the United States and East Asia. A bidirectional causal design was employed: a forward analysis with metabolic syndrome as the exposure and breast cancer as the outcome, followed by a reverse analysis wherein their roles were interchanged. The inverse-variance weighting (IVW) method was primarily used for effect estimation, supplemented by MR-Egger regression, the weighted median method, the simple mode method, and the weighted mode method. Instrument variable strength was screened using the F-statistic (F>10). Robustness of the results was assessed through heterogeneity tests, horizontal pleiotropy tests, forest plots, and leave-one-out sensitivity analyses. Results The IVW analysis indicated no significant causal relationship between metabolic syndrome and breast cancer (OR=1.00, 95%CI: 0.97-1.03), P>0.05). Sensitivity analyses yielded consistent results, suggesting the good robustness of the study findings. Conclusion This study found no evidence to support a causal relationship, either positive or negative, between metabolic syndrome and breast cancer.

Atherosclerosis (AS), the primary pathological contributor to cardiovascular diseases (CVDs), has increasingly affected younger populations due to modern dietary habits and sedentary lifestyles. Current diagnostic modalities, including ultrasound, MRI, and CT, primarily identify advanced lesions and inadequately evaluate plaque vulnerability, thereby hindering early detection. Conventional treatments, which involve long-term medications associated with side effects such as hepatic injury and surgical interventions that carry risks of restenosis and hemorrhage, underscore the urgent need for non-invasive, cost-effective early diagnostic methods and targeted therapies. Gut microbiota metabolites are pivotal in AS pathogenesis, with trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs) serving as functionally opposing biomarkers. TMAO is produced when gut bacteria, specifically Firmicutes and Proteobacteria, metabolize dietary choline and carnitine into trimethylamine (TMA), which the liver subsequently converts to TMAO via flavin-containing monooxygenase 3 (FMO3); TMAO is then excreted in urine. Variability in TMAO levels is influenced by marine food consumption and FMO3 modulation, which can be affected by genetics, age, and diet. Mechanistically, TMAO exacerbates AS by disrupting cholesterol metabolism, inducing endothelial dysfunction through the elevation of reactive oxygen species (ROS) and pro-inflammatory cytokines such as IL-6, and reducing nitric oxide levels. Additionally, TMAO activates NF-κB and NLRP3 pathways while enhancing platelet reactivity. Clinically, elevated TMAO levels correlate with early AS and serve as predictors of mortality in patients with stable coronary artery disease (CAD) and acute coronary syndrome (ACS), as well as major adverse cardiovascular events (MACE) in stroke patients. Conversely, SCFAs—namely acetate, propionate, and butyrate—are produced by gut bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii through the fermentation of dietary fiber. These metabolites exert anti-AS effects: acetate aids in maintaining metabolic homeostasis; propionate protects endothelial function and reduces plaque area; and butyrate fortifies intestinal barriers while suppressing inflammation. Furthermore, SCFAs cross-regulate bile acid metabolism, thereby influencing TMAO levels, and antagonize the pro-inflammatory and lipid-disrupting effects of TMAO. The use of TMAO and SCFAs as standalone biomarkers is constrained by limitations. TMAO lacks specificity, while SCFA levels fluctuate based on gut microbiota and dietary intake. Traditional AS risk assessment tools, which include clinical indicators, imaging techniques, and single biomarkers such as CRP, LDL-C, and ASCVD scores, overlook gut metabolism and demonstrate inadequate performance in younger populations. This review advocates for an “antagonistic-complementary” combined strategy: utilizing acetate and TMAO for early AS, propionate and TMAO for progressive AS, and butyrate and TMAO for advanced AS, addressing endothelial dysfunction, lipid deposition, and plaque stability/thrombosis risk, respectively. For clinical application, standardization of detection methods is crucial; liquid chromatography-mass spectrometry (LC-MS) is the gold standard, necessitating a unified sample pretreatment protocol, such as extraction with 1% formic acid in methanol. Additionally, dried blood spots (DBS) facilitate non-invasive testing, provided that dietary controls are implemented prior to detection, including a 12-hour fast and avoidance of high-choline and high-fiber foods. Existing challenges encompass the absence of standardized systems, limited large-scale validation, and ambiguous interactions with conditions such as hypertension. The authors’ team has previously established connections between gut metabolites and AS, including the reduction of TMAO as a preventive measure for AS, thereby reinforcing this proposed strategy. Future research should prioritize standardization, the development of machine learning-optimized models, validation of interventions, and the exploration of multi-omics-based “gut microbiota-metabolite-vascular” networks. In conclusion, the combined detection of TMAO and SCFAs offers a novel framework for AS risk assessment, facilitating early diagnosis and targeted interventions while enhancing the integration of gut metabolism into cardiovascular disease management.

Atherosclerosis (AS), the primary pathological contributor to cardiovascular diseases (CVDs), has increasingly affected younger populations due to modern dietary habits and sedentary lifestyles. Current diagnostic modalities, including ultrasound, MRI, and CT, primarily identify advanced lesions and inadequately evaluate plaque vulnerability, thereby hindering early detection. Conventional treatments, which involve long-term medications associated with side effects such as hepatic injury and surgical interventions that carry risks of restenosis and hemorrhage, underscore the urgent need for non-invasive, cost-effective early diagnostic methods and targeted therapies. Gut microbiota metabolites are pivotal in AS pathogenesis, with trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs) serving as functionally opposing biomarkers. TMAO is produced when gut bacteria, specifically Firmicutes and Proteobacteria, metabolize dietary choline and carnitine into trimethylamine (TMA), which the liver subsequently converts to TMAO via flavin-containing monooxygenase 3 (FMO3); TMAO is then excreted in urine. Variability in TMAO levels is influenced by marine food consumption and FMO3 modulation, which can be affected by genetics, age, and diet. Mechanistically, TMAO exacerbates AS by disrupting cholesterol metabolism, inducing endothelial dysfunction through the elevation of reactive oxygen species (ROS) and pro-inflammatory cytokines such as IL-6, and reducing nitric oxide levels. Additionally, TMAO activates NF-κB and NLRP3 pathways while enhancing platelet reactivity. Clinically, elevated TMAO levels correlate with early AS and serve as predictors of mortality in patients with stable coronary artery disease (CAD) and acute coronary syndrome (ACS), as well as major adverse cardiovascular events (MACE) in stroke patients. Conversely, SCFAs—namely acetate, propionate, and butyrate—are produced by gut bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii through the fermentation of dietary fiber. These metabolites exert anti-AS effects: acetate aids in maintaining metabolic homeostasis; propionate protects endothelial function and reduces plaque area; and butyrate fortifies intestinal barriers while suppressing inflammation. Furthermore, SCFAs cross-regulate bile acid metabolism, thereby influencing TMAO levels, and antagonize the pro-inflammatory and lipid-disrupting effects of TMAO. The use of TMAO and SCFAs as standalone biomarkers is constrained by limitations. TMAO lacks specificity, while SCFA levels fluctuate based on gut microbiota and dietary intake. Traditional AS risk assessment tools, which include clinical indicators, imaging techniques, and single biomarkers such as CRP, LDL-C, and ASCVD scores, overlook gut metabolism and demonstrate inadequate performance in younger populations. This review advocates for an “antagonistic-complementary” combined strategy: utilizing acetate and TMAO for early AS, propionate and TMAO for progressive AS, and butyrate and TMAO for advanced AS, addressing endothelial dysfunction, lipid deposition, and plaque stability/thrombosis risk, respectively. For clinical application, standardization of detection methods is crucial; liquid chromatography-mass spectrometry (LC-MS) is the gold standard, necessitating a unified sample pretreatment protocol, such as extraction with 1% formic acid in methanol. Additionally, dried blood spots (DBS) facilitate non-invasive testing, provided that dietary controls are implemented prior to detection, including a 12-hour fast and avoidance of high-choline and high-fiber foods. Existing challenges encompass the absence of standardized systems, limited large-scale validation, and ambiguous interactions with conditions such as hypertension. The authors’ team has previously established connections between gut metabolites and AS, including the reduction of TMAO as a preventive measure for AS, thereby reinforcing this proposed strategy. Future research should prioritize standardization, the development of machine learning-optimized models, validation of interventions, and the exploration of multi-omics-based “gut microbiota-metabolite-vascular” networks. In conclusion, the combined detection of TMAO and SCFAs offers a novel framework for AS risk assessment, facilitating early diagnosis and targeted interventions while enhancing the integration of gut metabolism into cardiovascular disease management.

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: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE To construct three-class （insufficient， normal， excessive） and two-class （insufficient， normal） models for predicting plasma concentration of valproic acid （VPA）， and compare the performance of these two models， with the aim of providing a reference for formulating clinical medication strategies. METHODS The clinical data of 480 patients who received VPA treatment and underwent blood concentration test at the Xi’an International Medical Center Hospital were collected from November 2022 to September 2024 （a total of 695 sets of data）. In this study， predictive models were constructed for target variables of three-class and two-class models. Feature ranking and selection were carried out using XGBoost scores. Twelve different machine learning algorithms were used for training and validation， and the performance of the models was evaluated using three indexes: accuracy， F1 score， and the area under the working characteristic curve of the subject （AUC）. RESULTS XGBoost feature importance scores revealed that in the three-class model， the importance ranking of kidney disease and electrolyte disorders was higher. However， in the two-class model， the importance ranking of these features significantly decreased， suggesting a close association with the excessive blood concentration of VPA. In the three-class model， Random Forest method performed best， with F1 score of 0.704 0 and AUC of 0.519 3 on the test set； while in the two-class model， CatBoost method performed optimally， with F1 score of 0.785 7 and AUC of 0.819 5 on the test set. CONCLUSIONS The constructed three-class model has the ability to predict excessive VPA blood concentration， but its prediction and model generalization abilities are poor； the constructed two-class model can only perform classification prediction for insufficient and normal blood concentration cases， but its model performance is stronger.

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.