ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

Objective: To systematically analyze the confirmatory positivity of different combinations of HBsAg screening results in blood testing, providing data to support the optimization of blood donor eligibility management. Methods: A retrospective analysis was conducted on blood screening data from 174 266 voluntary blood donor samples at the Chongqing Blood Center between October 2021 and September 2022. Samples with inconsistent results between the two HBsAg enzymelinked immunosorbent assays (ELISA) and individual donor nucleic acid testing (NAT) were confirmed using an electrochemiluminescence immunoassay (ECLIA) and a neutralization test. The detection efficacy of four different HBsAg ELISA reagents was compared using the HBsAg-confirmed positive samples. Results: A total of 767(0.44%) HBV-reactive (HB-sAg and/or HBV DNA reactive) samples were detected. Among them, 344 samples with discordant serological and NAT results were collected, of which 64(18.6%) were confirmed positive by neutralization test. Additionally, 5 samples that were neutralization-negative but double-reactive for HBsAg and HBV DNA were confirmed as positive according to FDA guidance, resulting in a total of 69(20.1%) confirmed HBsAg-positive samples. There were significant differences in the neutralization test confirmation rates among different screening result categories (P<0.05): The group with dual HBsAg reagent reactivity (double reactive) & NAT-negative had the highest confirmation rate (96.9%, 31/32); the group reactive to only reagent 2 (single reactive) had a rate of 25.7% (29/113); while the confirmation rates for samples reactive to only reagent 1 and samples with isolated HBV DNA positivity were extremely low [0(0/34) and 2.4%(4/165), respectively]. The four commercial reagents showed significant differences in their ability to detect confirmed positive samples that were initially single reactive (P<0.05). Conclusion: Given the performance variations among HBsAg screening reagents, thorough performance verification is essential before implementation. When NAT is negative, dual HBsAg reactivity in screening can serve as a basis for confirming infection and directly deferring blood donors. However, confirming infection in donors with single HBsAg reactivity is more challenging, necessitating supplementary tests to rule out infection risk.

Objective:To analyze the association between healthcare workers mental health,institu-tional supplies and facilities,inter-organizational coordination during infectious disease outbreaks,and the healthcare institution resilience.Methods:An online questionnaire survey was conducted among the healthcare workforce from 146 institutions in Beijing from January 13,2023 to February 9,2023,and a total of 1 434 eligible respondents were included.The sample comprised 408 responses from tertiary hos-pitals,117 from secondary hospitals,and 909 from primary care institutions.The resilience indicator for healthcare institutions was defined as the degree to which medical services met patient demands,with in-fluencing factors including physical factors,such as material shortages and facility space adaptation or ex-pansion,organizational factors such as information sharing and patient referral,and psychological factors were evaluated using job satisfaction(extrinsic satisfaction,intrinsic satisfaction),burnout(emotional exhaustion,depersonalization,reduced personal accomplishment),and depression status.Ordered mul-ticlassification Logistic regression was used to examine the impact of various factors on the degree to which healthcare services met patient needs;additionally,demographic factors that might influence institutional resilience were controlled.Results:During the emergency response phase,93％of hospitals maintained the capacity to meet patient needs,though tertiary hospitals demonstrated significantly higher rates of service inadequacy(21.05％).Material shortages were reported across all institutions,with tertiary hos-pitals experiencing more frequent multi-item shortages.Inter-institutional collaboration patterns revealed substantial variation:87.50％of primary care facilities,42.86％of secondary hospitals,and 31.58％of tertiary hospitals.Healthcare workers across all levels reported mild depressive symptoms and moderate-to-severe burnout levels.Regression analysis showed high satisfaction(overall satisfaction β=0.04,ex-trinsic satisfaction β=0.06,and intrinsic satisfaction β=0.08),low degree of job burnout(emotional exhaustion β=-0.04,depersonalization β=-0.07 and reduced personal accomplishment β=0.01),low degree of depression(β=-0.06)were significantly associated with higher healthcare institution re-silience.In addition,material shortages were significantly associated with lower resilience,and renova-tion and expansion of treatment spaces,and information sharing,were all associated with higher resilience.Demographic factors(age,gender,marital status,educational background,etc.)had no sig-nificant impact on resilience.Conclusion:Mental health status significantly influences healthcare institu-tion resilience.As human resources constitute the core asset of healthcare institutions,strategic optimiza-tion of workforce allocation and psychological support interventions can effectively strengthen resilience.Moreover,healthcare institution resilience is positively impacted by orderly material supply chains,timely resource distribution,and adaptive reconfiguration of clinical spaces.Finally,facilitating information sharing also enhances institutional resilience.

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.

Objective:To analyze the clinical characteristics of locally advanced rectal cancer patients with pathological complete response (pCR) after neoadjuvant chemoradiotherapy combined with immunotherapy.Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 46 patients with locally advanced rectal cancer who were admitted to 6 medical centers, including Beijing Friendship Hospital of Capital Medical University et al, from June 2021 to November 2022 were collected. There were 29 males and 17 females, aged (61±4)years. Patients received neoadjuvant chemoradiotherapy combined with immune checkpoint inhibitor therapy, and under-went radical total mesorectal excision during 6-12 weeks after radiotherapy. Observation indicators: (1) comparison of clinical characteristics between pCR and non-pCR patients;(2) postoperative complications and adverse reactions of pCR and non-pCR patients. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test or Fisher exact probability. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Results:(1) Comparison of clinical characteristics between pCR and non-pCR patients. Before neoadjuvant therapy, there were 14 cases aged ≥50 years and 6 cases aged <50 years in pCR patients, versus 25 cases and 1 case in non-pCR patients, showing a significant difference between the two groups ( P<0.05). After neoadjuvant therapy, cases in clinical stage T0, T1, T2, T3, T4 were 11, 1, 5, 3, 0 for pCR patients versus 7, 4, 2, 11, 2 for non-pCR patients, cases of tumor regression grade 1, 2, 3, 4 were 11, 8, 1, 0 for pCR patients versus 7, 14, 4, 1 for non-pCR patients, cases in low-risk, medium-risk, high-risk of neoadjuvant rectal scoring and grading were 20, 0, 0 for pCR patients versus 4, 18, 4 for non-pCR patients, respectively, showing significant differences in above indicators between the two groups ( Z=-2.256, -2.104, -5.458, P<0.05). (2) Postoperative complications and adverse reactions of pCR and non-pCR patients. Postoperative complications occurred in 2 cases of pCR patients and 5 cases of non-pCR patients, postoperative adverse reactions occurred in 11 cases of pCR patients and 10 cases of non-pCR patients, showing no significant difference between the two groups ( P>0.05). Conclusion:Compared with locally advanced rectal cancer patients aged ≥50 years, those aged <50 years have significant benefits from neoadjuvant chemoradiotherapy combined with immunotherapy. Clinical T staging and magnetic resonance imaging-detected tumor regression grade after neoadjuvant therapy have predictive value for patients with pCR .

Objective:To investigate the clinical value of laparoscopic evaluation of anato-mical position of inferior mesenteric artery (IMA), inferior mesenteric vein (IMV) and left colic artery (LCA).Methods:The prospective one-arm study was conducted. The clinical data of 229 pati-ents who underwent laparoscopic left hemicolectomy for left colon or laparoscopic radical resection of rectal cancer in The Second Affiliated Hospital of Air Force Medical University from December 2022 to December 2023 were selected. The distance between the origin point of IMA and the origin point of the first branch (L1) as well as the distance from the origin point of LCA root to the junction of LCA and IMV (L2) were measured during the operation. IMA classification, the location relation-ship of LCA and IMV junction were recorded. Observation indicators: (1) situations of enrolled patients; (2) difference analysis between L1, L2 and clinical features; (3) distribution characteristics of the location relationship between LCA and IMV in different types of IMA. Mann-Whitney U test was used for comparison of measurement data with skewed distribution between groups, Kruskal-Wallis H test was used for comparison between multiple groups, and Dunn-Bonferroni test was used for pairwise comparison. Comparison of count data between groups was performed by chi-square test. Pearson or Spearman correlation analysis was conducted for correlation of continuous variables. Results:(1) Situations of enrolled patients. A total of 229 eligible patients were screened out, including 146 males and 83 females, aged 64(range, 55-71)years. The height of 229 patients was 168(range, 160-172)cm, the weight was 65.0(55.5,71.5)kg, the body surface area was (1.68±0.17)m 2, the tumor maximum diameter was 3.0(2.5,4.0)cm. The total number of lymph nodes dissected was 19(17,21), and the number of No.253 lymph node dissected was 4(3,5). The L1 was 3.50(1.20,8.00)cm, and the L2 was 2.20(0.50,7.30)cm. There were 58, 31, 32, 71, 22, 90, 26 and 212 patients with smoking, alcohol drinking, diabetes, hypertension, coronary heart disease, neoadjuvant chemo-therapy, neoadjuvant radiotherapy and preservation of the LCA, respectively. Among 229 patients, cases with BMI <18.5 kg/m 2, 18.5-23.9 kg/m 2 and >23.9 kg/m 2 were 11, 133 and 85, respectively. There were 153 cases in pathological stage Ⅰ-Ⅱ and 76 cases in stage Ⅲ. There were 168 cases of Dixon operation, 6 cases of Miles operation and 55 cases of sigmoid colon resection. There were 135 cases of IMA type 1, 44 cases of IMA type 2, 23 cases of IMA type 3, 2 cases of IMA type 4, and 25 cases of IMA type unable to judge. (2) Difference analysis between L1, L2 and clinical features.Correlation analysis showed negative correlation between the height, body surface area and L1 ( r=-0.17, -0.15, P<0.05). The L1 was 3.20(2.68,4.00)cm for male patients and 3.60(3.00,4.20)cm for female patients, respectively, showing a significant difference between the two groups ( Z=-2.37, P<0.05). The L1 of patients with IMA type 1, 2, and 3 was 3.20(2.80,4.00)cm, 3.85(3.00,4.48)cm, and 3.20(2.50,4.30)cm, respectively, showing a significant difference among them ( H=7.54, P<0.05). Further pairwise com-parison showed that there was a significant difference in L1 between patients with IMA type 2 and those with IMA type 1 ( P<0.05). The L2 of smokers and non-smokers were 2.50(1.95,3.20)cm and 2.20(1.60,2.80)cm, respectively, showing a significant difference between the two groups ( Z=-2.24, P<0.05). (3)Distribution characteristics of the location relationship between LCA and IMV in different types of IMA. There was no significant difference in LCA distribution between the anterior and posterior positions of IMV among the three IMA types (type 1, 2, 3) ( χ2=1.63, P>0.05). Conclusions:Patients with greater height have larger body surface area and shorter L1. L1 is significantly longer in female patients than in male patients. L1 is significantly longer in patients with IMA type 2 than in those with type 1. L2 is significantly longer in smokers than in non-smokers. There was no significant difference in the distribution location between LCA and IMV among patients of IMA type 1, 2 and 3.

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 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.

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.