OBJECTIVE To screen characteristic peaks of hypervirulent Klebsiella pneumoniae(hvKP)using ma-trix-assisted laser desorption ionization time-of-flight mass spectrometry(MALDI-TOF MS)combined with EX-Smartspec software and establish a rapid detection model for hvKP.METHODS Based on identification criteria of any positive peg-344,iroB,iucA,rmpA,prmpA2 genes or siderophore production＞30 μg/ml,89 hvKP and 72 classical Klebsiella pneumoniae(cKP)strains were initially collected and validated for virulence via Galleria mellonella assays.A diagnostic model distinguishing hvKP from cKP was constructed using EX-Smartspec soft-ware and a convolutional neural network algorithm,integrating characteristic peaks and cluster analysis to provide a rapid and accurate clinical diagnostic tool.RESULTS MALDI-TOF MS analysis identified a characteristic hvKP peak at(3 835±100)ppm.Receiver operating characteristic(ROC)curve analysis revealed optimal performance in distinguishing hvKP with an area under the curve(AUC)=0.741.When AUC ≥0.089,the model demonstra-ted high sensitivity(86.41％),specificity(69.90％),accuracy(78.16％),positive predictive value(74.17％),and negative predictive value(83.72％)in differentiating hvKP from cKP.Cluster analysis further validated the model's classification accuracy.Additionally,the typing classification model exhibited high accuracy(approxi-mately 0.95 and 0.90 in training and validation phases,respectively)and low loss values(-0.18 and 0.30).Val-idation of 6 randomly selected hvKP and 5 cKP strains showed a 100.00％pass rate.CONCLUSION The estab-lished diagnostic model for hvKP and cKP provides a rapid and accurate clinical tool for timely treatment of hvKP-related infections.

OBJECTIVE To screen characteristic peaks of hypervirulent Klebsiella pneumoniae(hvKP)using ma-trix-assisted laser desorption ionization time-of-flight mass spectrometry(MALDI-TOF MS)combined with EX-Smartspec software and establish a rapid detection model for hvKP.METHODS Based on identification criteria of any positive peg-344,iroB,iucA,rmpA,prmpA2 genes or siderophore production＞30 μg/ml,89 hvKP and 72 classical Klebsiella pneumoniae(cKP)strains were initially collected and validated for virulence via Galleria mellonella assays.A diagnostic model distinguishing hvKP from cKP was constructed using EX-Smartspec soft-ware and a convolutional neural network algorithm,integrating characteristic peaks and cluster analysis to provide a rapid and accurate clinical diagnostic tool.RESULTS MALDI-TOF MS analysis identified a characteristic hvKP peak at(3 835±100)ppm.Receiver operating characteristic(ROC)curve analysis revealed optimal performance in distinguishing hvKP with an area under the curve(AUC)=0.741.When AUC ≥0.089,the model demonstra-ted high sensitivity(86.41％),specificity(69.90％),accuracy(78.16％),positive predictive value(74.17％),and negative predictive value(83.72％)in differentiating hvKP from cKP.Cluster analysis further validated the model's classification accuracy.Additionally,the typing classification model exhibited high accuracy(approxi-mately 0.95 and 0.90 in training and validation phases,respectively)and low loss values(-0.18 and 0.30).Val-idation of 6 randomly selected hvKP and 5 cKP strains showed a 100.00％pass rate.CONCLUSION The estab-lished diagnostic model for hvKP and cKP provides a rapid and accurate clinical tool for timely treatment of hvKP-related infections.

Objective To analyze the detection rate of carbapenem-resistant Klebsiella pneumoniae(CRKP)in Guangdong Provincial Second Hospital of Traditional Chinese Medicine(the hospital)and analyze the main drug resistance genes and virulence genes of CRKP,so as to understand the molecular epidemiologi-cal mechanism of its infection strains.Methods The detection rate of CRKP infection in the hospital from 2020 to 2023 was retrospectively analyzed,and a total of 84 strains of CRKP were collected from July to De-cember 2022 in the hospital.The clinical data of the strains were collected,and the corresponding drug resist-ance genes and virulence genes were amplified by PCR.The modified carbapenem inactivation method(mCIM)was uesd to detect carbapenemase.Results The detection rates of CRKP in the Guangdong Provincial Second Hospital of Traditional Chinese Medicine from 2020 to 2023 were relatively high,and were higher than 46.00％.84 non-repeated CRKP strains were collected from July to December of 2022,and most samples were from respiratory tract,accounting for 55.95％.The patients were mainly from acupuncture and rehabilitation departments,accounting for 34.52％.Drug sensitivity tests showed that CRKP was highly resistant to various cephalosporin and extended spectrum beta lactam drugs,and only showed high sensitivity to tigecycline and polymyxin.The positive rate of mCIM test was 84.52％(71/84),and the other 15.48％results were neutral,which failed to determine whether they produced carbapenemase.A total of 73 strains were detected with car-bapenemase gene,accounting for 86.90％,involving 4 genotypes.The detection rates of blaKPC,blaNDM,blaIMP,and blaOXA-48 were 83.33％,2.38％,1.19％,and 1.19％,respectively.One of them carried both blaKPC and blaNDM genes,and multiple β-lactamases were detected.The detection rates of blaSHV,bla-TEM,blaCTX-M-9,and blaCTX-M-1 were 96.43％,78.57％,64.29％,and 2.38％,respectively.The detection rates of five virulence genes,blaiucA,blarmpA2,blairoB,blapeg-334,and blarmpA,were 42.86％,41.67％,27.38％,3.57％,and 2.38％,respectively.The proportion of strains carrying three or more virulence genes was 17.85％(15/84).Conclusion The detection rate of CRKP in the hospital is relatively high,and the drug resistance situation is serious,with Klebsiella pneumoniae carbapenemase(KPC)as the main drug resistance gene.A high proportion of Carbapenem-resistant hypervirulent Klebsiella pneumoniae(CR-hvKp)strains is found,and the infection prevention and control situation is not optimistic.It is necessary to further strengthen the hospital infection control measures and standardize the scientific and reasonable drug use.

Objective: To investigate the utilization of antibacterial drugs in the old community-acquired pneumonia (CAP) with chronic obstructive pulmonary disease(COPD) in our hospital.To analyze the rational application of antibacterial drugs, thus to provide a powerful reference for clinical diagnosis and treatment. Methods: From June 2011 to June 2013, 100 patients with COPD and CAP were selected in Xiaolan People's Hospital.The average age was (76.95±6.57) years old, including 62 males and 38 females.The utilization of antibacterial drugs was investigated by retrospective study in the patients. Results: In the course of treatment, the rate of using the antibacterial drugs in 100 patients was 100%, concerning 13 varieties of 5 major categories, including β-lactam (including β-lactam/β-lactamase inhibitors), quinolones, carbapenems, macrolides, aminoglycosides.The largest frequency was piperacillin/sulbactam, up to 92.Ceftezole, cefotian and azithromycin were less than 0.9 for DUI in DDDS ordering 10 drugs, it showed that the frequency was insufficient or the dosage was too small.For example, insufficient frequency of cephalosporin once a day and small dosage of azithromycin 0.25g once a day.Combination with two kinds of antimicrobial drugs was common, it was relatively rational between the combination of drugs, usually cephalosporins+ quinolones, β-lactam/β-lactamase inhibitors+ quinolones, β-lactam/β-lactamase inhibitors+ macrolides. Conclusion The etiology of 100 elderly patients with COPD and CAP in our hospital is mainly Gram-negative bacteria.The anti-infection treatment is mainly cefdiazine and piperacillin/sulbactam, and the combined drug was mainly quinolones.The drug regimen and treatment course are reasonable, there is a high prognosis in the patients.

Objectives To investigate gestational multiple metabolic abnormalities aggregation and diagnostic criteria for gestational metabolic syndrome(GMS),and to analyze the risk factors of GMS.Methods A cohort study recruiting 309 pregnant women with preeclampsia,627 pregnant women with gestational diabetes mellitus(GDM)and 1245 normal pregnant women was performed from January 2008 to December 2011 in Guangdong Women and Children's Hospital.Information regarding age,gestational weeks,basic blood pressure,admission blood pressure,height and body mass index(BMI)before pregnancy was recorded.Biochemical indicators including fasting plasma glucose(FPG),fasting insulin (FINS),total cholesterol(TC),triglyceride(TG),high density lipoprotein(HDL-C),low density lipoprotein(LDL-C),free fatty acids(FFA)were tested.GMS was diagnosed with three or all of the following conditions:(1)overweight and/or obesity before pregnancy(BMI ≥ 25 kg/m2);(2)hypertension with blood pressure ≥ 140/90 mm Hg(1 mm Hg =0.133 kPa);(3)hyperglycemia:diagnosed as GDM;(4)dyslipidemia with TG≥3.23 mmol/L The incidence of GMS of the three groups were calculated and the risk factors were analyzed.Results(1)The age,gestational weeks,basic blood pressure,admission blood pressure,BMI before pregnancy of women with preeclampsia and women with GDM were significantly different compared to normal women,respectively(P ＜ 0.01).(2)Biochemical indicators of women with preeclampsia were as following:FPG(4.6 ± 1.0)mmol/L,FINS(10.1 ± 5.6)mU/L,TC(6.3 ±1.6)mmol/L,TG(3.9 ± 1.8)mmol/L,HDL-C(1.4 ±0.4)mmol/L,LDL-C(3.0 ± 1.0)mmol/L,FFA (0.8 ±0.4)mmol/L.And those in women with GDM were:FPG(4.7 ± 0.9)mmoL/L,FINS(10.2 ± 5.8)mU/L,TC(5.7 ± 1.3)mmol/L,TG(3.2 ± 1.1)mmol/L,HDL-C(1.4 ± 0.4)mmol/L,LDL-C (2.7 ± 0.9)mmol/L,FFA(0.6 ± 0.3)mmol/L In normal pregnant women they were:FPG(4.3 ±0.5)mmol/L,FINS(9.0±4.4)mU/L,TC(5.7 ±1.1)mmol/L,TG(2.8 ±1.1)mmol/L,HDL-C (1.5 ± 0.4)mmol/L,LDL-C(2.9 ± 0.8)mmol/L,FFA(0.6 ± 0.2)mmol/L Statistic differences were found in preeclampsia and GDM women compared to normal women respectively(P ＜ 0.01).(3)The prevalence of GMS in preeclampsia group and in GDM group was 26.2％(81/309)and 13.6％(85/627),statistically different from that of the control group(0)(P ＜0.01).(4)Compared to normal women,women with preeclampsia had higher risk of developing GMS(OR =1.62,95 ％ CI 1.31-2.00,P ＜ 0.01).The risk factors were BMI(OR =1.29,95％ CI 1.13-1.47)and TG(OR =2.49,95％ CI 1.87-3.31).Also,women with GDM had higher risk of developing GMS than normal women(OR =1.27,95％ CI 1.09-1.49,P ＜ 0.01),and the risk factors were BMI(OR =1.13,95 ％ CI 1.04-1.23)and TG(OR =1.16,95 ％ CI 1.02-1.33).TG was the independent risk factor in both preeclampsia women and GDM women(P ＜ 0.01,P ＜ 0.05).HDL-C seemed to have less importance in identifying GMS(P ＞ 0.05).Conclusions According to the GMS diagnostic criteria used in this study,some preeclampsia patients and some GDM women had aggregation of multiple metabolic abnormalities including pre-pregnancy overweight/obesity,hyperglycemia,high blood pressure and dyslipidemia.TG was the independent risk factor for GMS.HDL-C seemed to have less importance in identifying GMS.