1.Differences in arousal threshold among obstructive sleep apnea patients of different genetic backgrounds and influencing factors
Rui ZHAO ; Ping YAO ; Zhiqiang ZHANG ; Zhiguo GUO ; Minqi XIE ; Hui DANG ; Yanrong JIA ; Jing CHENG ; Dongsheng LYU
Sichuan Mental Health 2026;39(3):240-245
BackgroundObstructive sleep apnea (OSA) represents a prevalent sleep disordered breathing condition characterized by complex pathophysiology. The arousal threshold (ArTH), a core non-anatomical contributor to OSA pathogenesis, is intimately tied to the disease severity and clinical phenotypes. To date, research regarding factors associated with ArTH has yielded inconsistent findings, and ArTH profiles and disparities across populations with different genetic backgrounds are not fully elucidated. ObjectiveTo explore the differences of ArTH in OSA patients with different genetic backgrounds and analyze the key factors affecting ArTH, thereby providing evidence for understanding OSA pathophysiology and formulating targeted treatment regimens. MethodsA total of 285 patients who met the diagnostic criteria for OSA, as defined by the Multidisciplinary Diagnosis and Treatment Guidelines for Adult Obstructive Sleep Apnea, were retrospectively enrolled in this study. All participants underwent overnight polysomnography (PSG) at the Sleep Medicine Center of Inner Mongolia Mental Health Center from December 2022 to May 2024. Based on the study design, the cohort was stratified into two distinct genetic background subgroups (group A and group B). Demographic and clinical characteristics, the Epworth Sleepiness Scale (ESS) score, and overnight PSG data were collected. The apnea hypopnea index (AHI), the lowest pulse oxygen saturation (LSpO2), and fraction of hypopneas (FHypopneas) were utilized as surrogate indicators to estimate ArTH in OSA patients. The influencing factors of low ArTH were tested by binary Logistic regression analysis. ResultsAmong the 285 OSA patients, there were 227 cases (79.65%) in group A and 58 cases (20.35%) in group B. Comparisons between the two genetic background subgroups revealed no statistically significant differences in the proportion of low ArTH, ESS score, PSG parameters, and the three markers for low ArTH (AHI<30 events/h, LSpO2>82.5%, FHypopneas>58.3%) (P>0.05). Binary Logistic regression analysis identified sex (OR=2.421, 95% CI: 1.070–5.478), BMI (OR=0.847, 95% CI: 0.770–0.932), N1 sleep duration (OR=0.974, 95% CI: 0.963–0.985), and hypertension (OR=0.348, 95% CI: 0.143–0.848) as independent factors associated with low ArTH in the total cohort. Stratified analysis by genetic backgrounds revealed that sex(OR=3.799, 95% CI: 1.389–10.392), BMI(OR=0.819, 95% CI:0.723–0.929), and N1 sleep duration(OR=0.973, 95% CI: 0.961–0.986) were independent factors of low ArTH in group A. In contrast, only N1 sleep duration (OR=0.951, 95% CI: 0.911–0.993) remained a significant factor in group B. ConclusionArthur may have conservative characteristics in patients with OSA with different genetic backgrounds, but the pathophysiological mechanism of OSA may have population heterogeneity. [Funded by Inner Mongolia Autonomous Region Natural Science Fundation Project (number, 2024QN08050); Intra-institutional Scientific Research Project of Inner Mongolia Mental Health Center (number, 2022QNWN0010)]
2.Model establishment for quantitative analysis of saponins of Paris polyphylla by near-infrared spectroscopy
Ping XU ; Qi MI ; Wen-xiu LUO ; You LU ; Meng-wen YU ; Xuan ZHANG ; Guo-wei ZHENG ; Chang-gui QIU ; Jia CHEN
Chinese Traditional Patent Medicine 2025;47(4):1069-1076
AIM To establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.
3.Changing antimicrobial resistance profiles of Burkholderia cepacia in hospitals across China:results from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Chunyue GE ; Yunjian HU ; Xiaoman AI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Wenhui HUANG ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(5):557-562
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.
4.Explanation and interpretation of blood transfusion provisions for children with hematological diseases in the national health standard "Guideline for pediatric transfusion".
Ming-Yi ZHAO ; Rong HUANG ; Rong GUI ; Qing-Nan HE ; Ming-Yan HEI ; Xiao-Fan ZHU ; Jun LU ; Xiao-Jun XU ; Tian-Ming YUAN ; Rong ZHANG ; Xu WANG ; Jin-Ping LIU ; Jing WANG ; Zhi-Li SHAO ; Yong-Jian GUO ; Xin-Yin WU ; Jia-Rui CHEN ; Qi-Rong CHEN ; Jia GUO ; Ming-Hua YANG
Chinese Journal of Contemporary Pediatrics 2025;27(1):18-25
To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion is one of the most commonly used supportive treatments for children with hematological diseases. This guideline provides guidance and recommendations for blood transfusions in children with aplastic anemia, thalassemia, autoimmune hemolytic anemia, glucose-6-phosphate dehydrogenase deficiency, acute leukemia, myelodysplastic syndromes, immune thrombocytopenic purpura, and thrombotic thrombocytopenic purpura. This article presents the evidence and interpretation of the blood transfusion provisions for children with hematological diseases in the "Guideline for pediatric transfusion", aiming to assist in the understanding and implementing the blood transfusion section of this guideline.
Humans
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Child
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Hematologic Diseases/therapy*
;
Blood Transfusion/standards*
;
Practice Guidelines as Topic
5.Explanation and interpretation of the compilation of blood transfusion provisions for children undergoing hematopoietic stem cell transplantation in the national health standard "Guideline for pediatric transfusion".
Rong HUANG ; Qing-Nan HE ; Ming-Yan HEI ; Xiao-Fan ZHU ; Jun LU ; Xiao-Jun XU ; Tian-Ming YUAN ; Rong ZHANG ; Xu WANG ; Jin-Ping LIU ; Jing WANG ; Zhi-Li SHAO ; Ming-Yi ZHAO ; Yong-Jian GUO ; Xin-Yin WU ; Jia-Rui CHEN ; Qi-Rong CHEN ; Jia GUO ; Rong GUI ; Ming-Hua YANG
Chinese Journal of Contemporary Pediatrics 2025;27(2):139-143
To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion for children undergoing hematopoietic stem cell transplantation is highly complex and challenging. This guideline provides recommendations on transfusion thresholds and the selection of blood components for these children. This article presents the evidence and interpretation of the transfusion provisions for children undergoing hematopoietic stem cell transplantation, with the aim of enhancing the understanding and implementation of the "Guideline for pediatric transfusion".
Humans
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Hematopoietic Stem Cell Transplantation
;
Child
;
Blood Transfusion/standards*
;
Practice Guidelines as Topic
6.Explanation and interpretation of blood transfusion provisions for critically ill and severely bleeding pediatric patients in the national health standard "Guideline for pediatric transfusion".
Rong HUANG ; Qing-Nan HE ; Ming-Yan HEI ; Ming-Hua YANG ; Xiao-Fan ZHU ; Jun LU ; Xiao-Jun XU ; Tian-Ming YUAN ; Rong ZHANG ; Xu WANG ; Jin-Ping LIU ; Jing WANG ; Zhi-Li SHAO ; Ming-Yi ZHAO ; Yong-Jian GUO ; Xin-Yin WU ; Jia-Rui CHEN ; Qi-Rong CHEN ; Jia GUO ; Rong GUI
Chinese Journal of Contemporary Pediatrics 2025;27(4):395-403
To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Critically ill children often present with anemia and have a higher demand for transfusions compared to other pediatric patients. This guideline provides guidance and recommendations for blood transfusions in cases of general critical illness, septic shock, acute brain injury, extracorporeal membrane oxygenation, non-life-threatening bleeding, and hemorrhagic shock. This article interprets the background and evidence of the blood transfusion provisions for critically ill and severely bleeding children in the "Guideline for pediatric transfusion", aiming to enhance understanding and implementation of this aspect of the guidelines. Citation:Chinese Journal of Contemporary Pediatrics, 2025, 27(4): 395-403.
Humans
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Critical Illness
;
Blood Transfusion/standards*
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Child
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Hemorrhage/therapy*
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Practice Guidelines as Topic
7.Explanation and interpretation of blood transfusion provisions for children undergoing cardiac surgery in the national health standard "Guideline for pediatric transfusion".
Rong HUANG ; Qing-Nan HE ; Ming-Yan HEI ; Ming-Hua YANG ; Xiao-Fan ZHU ; Jun LU ; Xiao-Jun XU ; Tian-Ming YUAN ; Rong ZHANG ; Xu WANG ; Jing WANG ; Zhi-Li SHAO ; Ming-Yi ZHAO ; Yong-Jian GUO ; Xin-Yin WU ; Jia-Rui CHEN ; Qi-Rong CHEN ; Jia GUO ; Rong GUI ; Jin-Ping LIU
Chinese Journal of Contemporary Pediatrics 2025;27(7):778-785
To guide clinical blood transfusion practices in pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Children undergoing cardiac surgery are at high risk of bleeding, and the causes of perioperative anemia and coagulation disorders in neonates and children are complex and varied, often necessitating the transfusion of allogeneic blood components. This guideline provides direction and recommendations for specific measures in blood management for children undergoing cardiac surgery before, during, and after surgery. This article interprets the background and evidence for the formulation of the blood transfusion provisions for children undergoing cardiac surgery, hoping to facilitate the understanding and implementation of this guideline.
Humans
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Cardiac Surgical Procedures
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Blood Transfusion/standards*
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Child
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Practice Guidelines as Topic
8.Clinical Characteristics and Prognostic Analysis of Newly Diagnosed Acute Myeloid Leukemia Patients with NRAS and KRAS Gene Mutations.
Zhang-Yu YU ; Bo CAI ; Yi WANG ; Yang-Yang LEI ; Bing-Xia LI ; Yu-Fang LI ; Yan-Ping SHI ; Jia-Xin CHEN ; Shu-Hong LIU ; Chang-Lin YU ; Mei GUO
Journal of Experimental Hematology 2025;33(3):682-690
OBJECTIVE:
To retrospectively analyze the clinical characteristics, co-mutated genes in newly diagnosed acute myeloid leukemia (AML) patients with NRAS and KRAS gene mutations, and the impact of NRAS and KRAS mutations on prognosis.
METHODS:
The clinical data and next-generation sequencing results of 80 newly diagnosed AML patients treated at our hospital from December 2018 to December 2023 were collected. The clinical characteristics, co-mutated genes of NRAS and KRAS , and the impact of NRAS and KRAS mutations on prognosis in newly diagnosed AML patients were analyzed.
RESULTS:
Among 80 newly diagnosed AML patients, NRAS mutations were detected in 20 cases(25.0%), and KRAS mutations were detected in 9 cases(11.3%). NRAS mutations predominantly occurred at codons 12 and 13 of exon 2, as well as codon 61 of exon 3, while KRAS mutations were most commonly occurred at codons 12 and 13 of exon 2, all of which were missense mutations. There were no statistically significant differences observed in terms of age, sex, white blood cell count(WBC), hemoglobin(Hb), platelet count(PLT), bone marrow blasts, first induction chemotherapy regimen, CR1/CRi1 rates, chromosome karyotype, 2022 ELN risk classification and allogeneic hematopoietic stem cell transplantation(allo-HSCT) among the NRAS mutation group, KRAS mutation group and NRAS/KRAS wild-type group (P >0.05). KRAS mutations were significantly correlated with PTPN11 mutations (r =0.344), whereas no genes significantly associated with NRAS mutations were found. Survival analysis showed that compared to the NRAS/KRAS wild-type group, patients with NRAS mutation had a relatively higher 5-year overall survival (OS) rate and relapse-free survival (RFS) rate, though the differences were not statistically significant (P =0.097, P =0.249). Compared to the NRAS/KRAS wild-type group, patients with KRAS mutation had a lower 5-year OS rate and RFS rate, with no significant differences observed (P =0.275, P =0.442). There was no significant difference in the 5-year RFS rate between the KRAS mutation group and NRAS mutation group (P =0.157), but the 5-year OS rate of patients with KRAS mutation was significantly lower than that of patients with NRAS mutation (P =0.037).
CONCLUSION
In newly diagnosed AML patients, KRAS mutation was significantly correlated with PTPN11 mutation. Compared to patients with NRAS/KRAS wild-type, those with NRAS mutation showed a more favorable prognosis, while patients with KRAS mutation showed a poorer prognosis; however, these differences did not reach statistical significance. Notably, the prognosis of AML patients with KRAS mutation was significantly inferior compared to those with NRAS mutation.
Humans
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Leukemia, Myeloid, Acute/diagnosis*
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Mutation
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Prognosis
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Proto-Oncogene Proteins p21(ras)/genetics*
;
GTP Phosphohydrolases/genetics*
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Retrospective Studies
;
Membrane Proteins/genetics*
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Female
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Male
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Middle Aged
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Adult
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Aged
9.Construction and Optimization of Alzheimer's Disease Classification Model Based on Brain Mixed Function Network Topology Parameters and Machine Learning
Xiao-yu HAN ; Xiu-zhu JIA ; Yang LI ; Meng-ying LOU ; Yong-qi NIE ; Xin-ping GUO ; Lu YU ; Zhi-yuan LI ; Lian-zheng SU
Progress in Modern Biomedicine 2025;25(11):1770-1778
Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P<0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7%,and the AUC values were up to 100%,99.5%,99.1%and 99.5%,respectively.The accuracy(98.5%),precision(98.5%),recall(98.5%),and AUC(99.1%)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0%,while Bagging has the lowest AUC value(99.1%)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13%),AUC(99.42%),recall rate(99.46%)and specificity(99.42%)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.
10.Distribution and resistance profiles of bacterial strains isolated from cerebrospinal fluid in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Juan MA ; Lixia ZHANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Han SHEN ; Wanqing ZHOU ; Wenen LIU ; Yanming LI ; Yi XIE ; Mei KANG ; Dawen GUO ; Jinying ZHAO ; Zhidong HU ; Jin LI ; Shanmei WANG ; Yafei CHU ; Yunsong YU ; Jie LIN ; Yingchun XU ; Xiaojiang ZHANG ; Jihong LI ; Bin SHAN ; Yan DU ; Ping JI ; Fengbo ZHANG ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Xiaobo MA ; Yanping ZHENG ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Hua YU ; Xiangning HUANG ; Sufang GUO ; Xuesong XU ; Chao YAN ; Fangfang HU ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Fang DONG ; Zhiyong LÜ ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Chuanqing WANG ; Pan FU ; Yunjian HU ; Xiaoman AI ; Ziyong SUN ; Zhongju CHEN ; Hong ZHANG ; Chun WANG ; Yuxing NI ; Jingyong SUN ; Kaizhen WEN ; Yirong ZHANG ; Ruyi GUO ; Yan ZHU ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Shifu WANG ; Yunsheng CHEN ; Qing MENG ; Yong ZHAO ; Ping GONG ; Ruizhong WANG ; Hua FANG ; Jilu SHEN ; Jiangshan LIU ; Hongqin GU ; Jiao FENG ; Shunhong XUE ; Bixia YU ; Wen HE ; Lin JIANG ; Longfeng LIAO ; Chunlei YUE ; Wenhui HUANG
Chinese Journal of Infection and Chemotherapy 2025;25(3):279-289
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.

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