1.National bloodstream infection bacterial resistance surveillance report (2022) : Gram-negative bacteria
Zhiying LIU ; Yunbo CHEN ; Jinru JI ; Chaoqun YING ; Qing YANG ; Haishen KONG ; Haifeng MAO ; Hui DING ; Pengpeng TIAN ; Jiangqin SONG ; Yongyun LIU ; Jiliang WANG ; Yan JIN ; Yuanyuan DAI ; Yizheng ZHOU ; Yan GENG ; Fenghong CHEN ; Lu WANG ; Yanyan LI ; Dan LIU ; Peng ZHANG ; Junmin CAO ; Xiaoyan LI ; Dijing SONG ; Xinhua QIANG ; Yanhong LI ; Qiuying ZHANG ; Guolin LIAO ; Ying HUANG ; Baohua ZHANG ; Liang GUO ; Aiyun LI ; Haiquan KANG ; Donghong HUANG ; Sijin MAN ; Zhuo LI ; Youdong YIN ; Kunpeng LIANG ; Haixin DONG ; Donghua LIU ; Hongyun XU ; Yinqiao DONG ; Rong XU ; Lin ZHENG ; Shuyan HU ; Jian LI ; Qiang LIU ; Liang LUAN ; Jilu SHEN ; Lixia ZHANG ; Bo QUAN ; Xiaoping YAN ; Xiaoyan QI ; Dengyan QIAO ; Weiping LIU ; Xiusan XIA ; Ling MENG ; Jinhua LIANG ; Ping SHEN ; Yonghong XIAO
Chinese Journal of Clinical Infectious Diseases 2024;17(1):42-57
Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of national bloodstream infection Bacterial Resistant Investigation Collaborative System(BRICS)were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute(CLSI). WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:During the study period,9 035 strains of Gram-negative bacteria were collected from 51 hospitals,of which 7 895(87.4%)were Enterobacteriaceae and 1 140(12.6%)were non-fermenting bacteria. The top 5 bacterial species were Escherichia coli( n=4 510,49.9%), Klebsiella pneumoniae( n=2 340,25.9%), Pseudomonas aeruginosa( n=534,5.9%), Acinetobacter baumannii complex( n=405,4.5%)and Enterobacter cloacae( n=327,3.6%). The ESBLs-producing rates in Escherichia coli, Klebsiella pneumoniae and Proteus spp. were 47.1%(2 095/4 452),21.0%(427/2 033)and 41.1%(58/141),respectively. The prevalence of carbapenem-resistant Escherichia coli(CREC)and carbapenem-resistant Klebsiella pneumoniae(CRKP)were 1.3%(58/4 510)and 13.1%(307/2 340);62.1%(36/58)and 9.8%(30/307)of CREC and CRKP were resistant to ceftazidime/avibactam combination,respectively. The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)complex was 59.5%(241/405),while less than 5% of Acinetobacter baumannii complex was resistant to tigecycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa(CRPA)was 18.4%(98/534). There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of main Gram-negative bacteria resistance among different regions,with statistically significant differences in the prevalence of CRKP and CRPA( χ2=20.489 and 20.252, P<0.001). The prevalence of CREC,CRKP,CRPA,CRAB,ESBLs-producing Escherichia coli and Klebsiella pneumoniae were higher in provinicial hospitals than those in municipal hospitals( χ2=11.953,81.183,10.404,5.915,12.415 and 6.459, P<0.01 or <0.05),while the prevalence of CRPA was higher in economically developed regions(per capita GDP ≥ 92 059 Yuan)than that in economically less-developed regions(per capita GDP <92 059 Yuan)( χ2=6.240, P=0.012). Conclusions:The proportion of Gram-negative bacteria in bloodstream infections shows an increasing trend,and Escherichia coli is ranked in the top,while the trend of CRKP decreases continuously with time. Decreasing trends are noted in ESBLs-producing Escherichia coli and Klebsiella pneumoniae. Low prevalence of carbapenem resistance in Escherichia coli and high prevalence in CRAB complex have been observed. The composition ratio and antibacterial spectrum of bloodstream infections in different regions of China are slightly different,and the proportion of main drug resistant bacteria in provincial hospitals is higher than those in municipal hospitals.
2.Variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip
Xu-Dong LUO ; Xin-Rong LI ; Cheng-Yi LI ; Peng QI ; Ting-Ting LIANG ; Shu-Bin LIU ; Zheng-Ze QIANG ; Jun-Gang HE ; Xu LI ; Xiao-Cheng WEI ; Xiao-Li FENG ; Ming-Wei WANG
Chinese Traditional Patent Medicine 2024;46(3):747-754
AIM To investigate the variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip.METHODS UPLC-MS/MS was adopted in the content determination of formononetin,ononin,calycosin,calycosin-7-glucoside,medicarpin,genistein,luteolin,liquiritigenin,isoliquiritigenin,vanillic acid,ferulic acid,γ-aminobutyric acid,adenosine and betaine,after which cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition to explore differential components.RESULTS After rubbing strip,formononetin,calycosin,liquiritigenin and γ-aminobutynic acid demonstrated increased contents,along with decreased contents of ononin,calycosin-7-glucoside and vanillic acid.The samples with and without rubbing strip were clustered into two types,calycosin-7-glucoside,formononetin,γ-aminobutynic acid,vanillic acid,calycosin-7-glucoside and formononetin were differential components.CONCLUSION This experiment clarifies the differences of chemical constituents in Hedysari Radix before and after rubbing strip,which can provide a reference for the research on rubbing strip mechanism of other medicinal materials.
3.Dahuang Huanglian Xiexintang and Its Modified Prescription Improve Type 2 Diabetes Mellitus: A Review
Dong AN ; Yanhui ZHAI ; Yankui GAO ; Rong LIU ; Qi ZHOU ; Xiangdong ZHU ; Yonglin LIANG
Chinese Journal of Experimental Traditional Medical Formulae 2024;30(24):141-151
Type 2 diabetes mellitus (T2DM) is based on insulin resistance (IR) and insulin secretion deficiency, with the specific mechanisms still unclear. Current research involves mechanisms such as glycolipid toxicity, inflammatory response, oxidative stress damage, and mitochondrial dysfunction. Modern traditional Chinese medicine (TCM) scholars have named it "blood glucose collateral disease" based on the clinical characteristics and natural progression of T2DM. This condition is primarily manifested as abnormal blood sugar levels in the early stages, and as the disease progresses, it gradually causes widespread damage to the body's veins and collaterals, ultimately leading to lesions in vessels and collaterals. Among these, "spleen heat" (obesity type) is the most common clinical type of T2DM. The concept of "internal heat-induced elimination" runs through both the onset and complications of T2DM, with internal heat being a key factor in its pathogenesis. The clinical application of Dahuang Huanglian Xiexintang and its modifications has achieved significant therapeutic effects. This paper reviews the origins and treatment characteristics of Dahuang Huanglian Xiexintang, along with clinical application research and experimental studies related to T2DM treatment, involving mechanisms for regulating glucose and lipid metabolism disorders, improving IR, modulating inflammatory responses, combating oxidative stress damage, regulating autophagy-related signaling pathways, modulating intestinal flora, inhibiting pyroptosis, and alleviating endoplasmic reticulum stress, with the purpose to provide direction for further research on the prevention and treatment of T2DM and its related complications, to offer reference for developing Dahuang Huanglian Xiexintang as a rapid hypoglycemic Chinese patent medicine for obese T2DM, and to better guide the clinical promotion of this drug.
4.Discussion on WU Wei's Thoughts for the Treatment of Atrial Fibrillation Based on the Theory of Stasis-Toxin Causing Palpitation
Hui-Qi ZHAI ; Yi-Hua LI ; Liang KANG ; Run-Jia YU ; Rong LI ; Hui WU ; Xiao-Xiong ZHOU ; Zhi-Yi DU ; Qing-Min CHU ; Wei WU
Journal of Guangzhou University of Traditional Chinese Medicine 2024;41(5):1316-1322
For the treatment of atrial fibrillation,Professor WU Wei innovatively put forward the theory of heart-blood-vessels trinity and the theory of stasis-toxin causing palpitation.It is believed that atrial fibrillation is caused by stasis and toxin,and affects the heart,blood and vessels.The core pathogenesis of atrial fibrillation is due to qi stagnation,blood stasis and toxin.The treatment for atrial fibrillation should be closely based on the pathogenesis,the therapeutic principles of treating from the perspective of stasis and together by removing toxin gradually is advocated.And the therapy of regulating qi,activating blood and removing stasis is also the way to remove toxin.The medication is based on the modified Taoren Honghua Decoction,which is mainly composed of Persicae Semen,Carthami Flos,Chuanxiong Rhizoma,Corydalis Rhizoma,Rehmanniae Radix,Paeoniae Radix Rubra,Salviae Miltiorrhizae Radix et Rhizoma,Jujubae Fructus,Puerariae Lobatae Radix,Nardostachyos Radix et Rhizoma,Ostreae Concha,Poria,and Polygonati Odorati Rhizoma.According to the characteristics of Lingnan climate and atrial fibrillation mostly being easy to affect the emotions,the pungent drugs in the prescription are usually removed,and the specific herbal pair of Puerariae Lobatae Radix-Nardostachyos Radix et Rhizoma is added to remove toxin according to the differentiation of disease.Moreover,for the treatment of atrial fibrillation,Professor WU Wei also adopts traditional Chinese medicine(TCM)external treatment such as foot bath,acupuncture and moxibustion,and physical-breathing exercise as well as health-care methods for comprehensive regulation,relieving the toxin and restoring the original qi.During the treatment atrial fibrillation,Professor WU Wei follows the principle of precise intervention and comprehensive regulation with Chinese medicine,so as to achieve the purpose of eliminating symptoms,restoring sinus rhythm and improving physical constitution.The thoughts of Professor WU Wei for the syndrome differentiation and treatment of atrial fibrillation will provide reference for the treatment of atrial fibrillation with TCM.
5.Role of Guiqi Yiyuan ointment combined with cisplatin in the treatment of Lewis lung cancer based on PI3K/Akt/mTOR signal pathway
Chao YUAN ; Si-Qi KONG ; Jian-Qing LIANG ; Yi ZHANG ; Rong HU ; Yue ZHANG ; Yu LIU ; Jin-Tian LI
The Chinese Journal of Clinical Pharmacology 2024;40(10):1424-1428
Objective To observe the inhibitory effect of Guiqi Yiyuan ointment on tumor growth in mice with Lewis lung cancer,and to explore the molecular mechanism of Guiqi Yiyuan ointment combined with cisplatin through phosphoinositide 3-kinase/protein kinase B/mammalian rapamycin target protein(PI3K/Akt/mTOR)signal pathway.Methods Sixty C57BL/6 mice were randomly divided into 6 groups with 10 mice in each group.Except for the blank group(0.9%NaCl),Lewis lung cancer-bearing mice were randomly divided into model group(0.9%NaCl),control group(0.9%NaCl,cisplatin 5 mg·kg-1)and low,medium,high dose experimental groups(Guiqi Yiyuan ointment 1.6,3.3,6.6 g·kg-1,cisplatin 5 mg·kg-1).Flow cytometry was used to detect bone marrow-derived suppressor cells(MDSCs);the expression of related proteins in tumor tissues was detected by Western blot.Results The tumor inhibition rates in control group and low,medium,high dose experimental groups were(39.87±4.45)%,(45.74±14.97)%,(57.78±4.70)%and(69.82±11.05)%.The proportion of MDSCs in bone marrow of in blank group,model group,control group and low,medium,high dose experimental groups were(36.13±1.08)%,(68.63±2.94)%,(58.93±2.02)%,(58.00±1.50)%,(50.93±5.06)%and(43.07±2.41)%.The protein expressions of p-PI3K/PI3K in model group,control group and low,medium and high experimental groups were 0.97±0.03,0.77±0.02,0.72±0.01,0.68±0.03 and 0.53±0.02;PTEN were 0.21±0.07,0.65±0.07,0.74±0.06,0.99±0.13,1.11±0.13;p-Akt/Akt were 1.01±0.02,0.82±0.02,0.77±0.00,0.72±0.03 and 0.52±0.04;p-mTOR/mTOR were 1.01±0.01,0.76±0.05,0.69±0.07,0.59±0.06 and 0.47±0.06.There were significant differences between low,medium,high experimental groups and control group(all P<0.05).Conclusion Guiqi Yiyuan ointment combined with cisplatin can significantly improve the quality of life and inhibit tumor growth in mice.The mechanism may be the inhibition of PI3K/Akt/mTOR signal pathway and the enhancement of tumor cell apoptosis and autophagy.
6.Effects of Platycodon grandiflorum Bai powder in the treatment non-small cell lung cancer rats
Chao YUAN ; Jin-Tian LI ; Jian-Qing LIANG ; Yi ZHANG ; Si-Qi KONG ; Rong HU ; Yue ZHANG ; Yu LIU
The Chinese Journal of Clinical Pharmacology 2024;40(11):1608-1612
Objective To observe the effects of traditional Chinese medicine compound Platycodon grandiflorum Bai powder on the growth of subcutaneously implanted tumor and the expression of B-cell lymphoma-2(Bcl-2),Bcl-2 associated X protein(Bax),cysteinyl aspartate specific proteinase(caspase)-3 and caspase-9 in subcutaneously implanted tumor of Lewis lung cancer mice.Methods The model of transplanted tumor of Lewis lung cancer in mice was established.Seventy SPF male C57BL/6 mice were randomly divided into blank group,model group,low dose experimental group,medium dose experimental group,high dose experimental group,control group and combined group.Blank group and model group were given 0.9%NaCl 0.2 mL by gavage;control group was given 0.9%NaCl by gavage and 25 mg·kg-1cisplatin intraperitoneally;high,medium,low dose experimental groups were given 193,96,48 mg·kg-1·d-1 Platycodon grandiflorum Bai powder 0.2 mL by gavage,respectively;combined group was given 96 mg·kg-1·d-1 Platycodon grandiflorum Bai powder 0.2 mL by gavage,and 25 mg·kg-1 cisplatin intraperitoneally,once every other day.The myelogenous suppressor cells(MDSCs)of mouse bone marrow were detected by flow cytometry,and the expressions of Bel-2,Bax,caspase-3 and caspase-9 in tumor cells were detected by immunofluorescence.Results The percentage of MDSCs in bone marrow of mice in blank group,model group,low dose experimental,medium,high dose experimental group,control group and combination group were(32.50±2.76)%,(63.13±3.14)%,(48.43±2.23)%,(42.53±1.28)%,(32.93±3.56)%,(51.30±4.25)%and(19.90±6.21)%,respectively.The fluorescence intensities of Bax in model group,low dose experimental group,medium dose experimental group,high dose experimental group,control group and combination group were 10.42±0.68,12.40±1.23,15.14±0.65,22.95±1.76,27.18±1.62 and 31.61±1.28;Bel-2 were 36.85±0.80,33.92±4.20,28.88±1.01,20.04±2.21,15.69±2.36 and 6.05±0.73;caspase-3 were 5.28±0.44,7.63±0.55,9.66±0.85,14.73±1.18,17.95±1.29 and 22.92±1.95;caspase-9 were 9.48±0.90,11.57±0.72,13.45±0.93,15.73±1.44,19.20±0.96 and 23.21±1.51.There were significant differences between medium,high dose experimental groups and model group(all P<0.05),and there were significant differences between combined group and control group(all P<0.05).Conclusion Platycodon grandiflorum Bai powder can up-regulating the expression of Bax,caspase-3 and caspase-9,down-regulating the expression of Bel-2,inhibiting MDSCs,promoting tumor cell apoptosis and inhibiting tumor growth.
7.Machine learning-based quantitative prediction of drug drug interaction using drug label information
Lu-Hua LIANG ; Yu-Xi XU ; Bei QI ; Lu-Yao WANG ; Chang LI ; Rong-Wu XIANG
The Chinese Journal of Clinical Pharmacology 2024;40(16):2396-2400
Objective To construct machine learning models that can be used to predict AUC fold change(FC)using a database of existing pharmacokinetic(PK)and drug-drug interaction(DDI)information,which can be used to explore the possibility of predicting existing drug interactions and to provide certain rational recommendations for clinical drug use.Methods PK data of DDIs and AUC fold change data were extracted from FDA-approved drug labels.Peptide and pharmacodynamic(PD)information related to drug interactions were retrieved through DrugBank,and PPDT identification of relevant peptide IDs was performed using Protein Resource(UniProt),and a matrix normalization code was used to generate multidimensional vector data that were easy to analysis.The effect of PPDT on the AUC,and the resulting multiplicity change was used as the dependent variable for machine learning model construction.The model with the smallest root mean square error(RMES)value was used for model construction to train a bagged decision tree(Bagged)prediction model.The models were tested using the trained models for some of the drug tests.The models were evaluated by reviewing the available literature findings on detection of drug interaction pairs and analyzing and comparing the predicted values.Results A total of 16 pairs of model drug pairs were tested for the effects of 16 drugs on tacrolimus,and it was found that the accuracy of the prediction of the presence or absence of drug interactions was 81.25%;the prediction results were classified according to the FDA standard classification of the strong and weak for the strength of drug interactions,and the results showed that the prediction of the strength of drug interactions,with a large deviation from the larger prediction was less.Conclusion The evaluation of the model to predict the presence or absence of drug interactions was general;however,after classifying the strengths and weaknesses of drug interactions,the prediction of drug interactions was better,and the prediction results indicated that the model prediction performance has a certain reference value for potential DDI assessment before clinical trials.
8.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.
9.National bloodstream infection bacterial resistance surveillance report(2022): Gram-positive bacteria
Chaoqun YING ; Yunbo CHEN ; Jinru JI ; Zhiying LIU ; Qing YANG ; Haishen KONG ; Haifeng MAO ; Hui DING ; Pengpeng TIAN ; Jiangqin SONG ; Yongyun LIU ; Jiliang WANG ; Yan JIN ; Yuanyuan DAI ; Yizheng ZHOU ; Yan GENG ; Fenghong CHEN ; Lu WANG ; Yanyan LI ; Dan LIU ; Peng ZHANG ; Junmin CAO ; Xiaoyan LI ; Dijing SONG ; Xinhua QIANG ; Yanhong LI ; Qiuying ZHANG ; Guolin LIAO ; Ying HUANG ; Baohua ZHANG ; Liang GUO ; Aiyun LI ; Haiquan KANG ; Donghong HUANG ; Sijin MAN ; Zhuo LI ; Youdong YIN ; Kunpeng LIANG ; Haixin DONG ; Donghua LIU ; Hongyun XU ; Yinqiao DONG ; Rong XU ; Lin ZHENG ; Shuyan HU ; Jian LI ; Qiang LIU ; Liang LUAN ; Jilu SHEN ; Lixia ZHANG ; Bo QUAN ; Xiaoping YAN ; Xiaoyan QI ; Dengyan QIAO ; Weiping LIU ; Xiusan XIA ; Ling MENG ; Jinhua LIANG ; Ping SHEN ; Yonghong XIAO
Chinese Journal of Clinical Infectious Diseases 2024;17(2):99-112
Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-positive bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-positive bacteria from blood cultures in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System(BRICS)were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute(CLSI). WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:A total of 3 163 strains of Gram-positive pathogens were collected from 51 member units,and the top five bacteria were Staphylococcus aureus( n=1 147,36.3%),coagulase-negative Staphylococci( n=928,29.3%), Enterococcus faecalis( n=369,11.7%), Enterococcus faecium( n=296,9.4%)and alpha-hemolyticus Streptococci( n=192,6.1%). The detection rates of methicillin-resistant Staphylococcus aureus(MRSA)and methicillin-resistant coagulase-negative Staphylococci(MRCNS)were 26.4%(303/1 147)and 66.7%(619/928),respectively. No glycopeptide and daptomycin-resistant Staphylococci were detected. The sensitivity rates of Staphylococcus aureus to cefpirome,rifampin,compound sulfamethoxazole,linezolid,minocycline and tigecycline were all >95.0%. Enterococcus faecium was more prevalent than Enterococcus faecalis. The resistance rates of Enterococcus faecium to vancomycin and teicoplanin were both 0.5%(2/369),and no vancomycin-resistant Enterococcus faecium was detected. The detection rate of MRSA in southern China was significantly lower than that in other regions( χ2=14.578, P=0.002),while the detection rate of MRCNS in northern China was significantly higher than that in other regions( χ2=15.195, P=0.002). The detection rates of MRSA and MRCNS in provincial hospitals were higher than those in municipal hospitals( χ2=13.519 and 12.136, P<0.001). The detection rates of MRSA and MRCNS in economically more advanced regions(per capita GDP≥92 059 Yuan in 2022)were higher than those in economically less advanced regions(per capita GDP<92 059 Yuan)( χ2=9.969 and 7.606, P=0.002和0.006). Conclusions:Among the Gram-positive pathogens causing bloodstream infections in China, Staphylococci is the most common while the MRSA incidence decreases continuously with time;the detection rate of Enterococcus faecium exceeds that of Enterococcus faecalis. The overall prevalence of vancomycin-resistant Enterococci is still at a low level. The composition ratio of Gram-positive pathogens and resistant profiles varies slightly across regions of China,with the prevalence of MRSA and MRCNS being more pronounced in provincial hospitals and areas with a per capita GDP≥92 059 yuan.
10.Diagnostic value of hematological parameters for prostate cancer in patients with gray-zone prostate-specific antigen levels
Peng GE ; Yu-Xin ZHENG ; Zi-Rong YAN ; Liang LI ; Wang LI ; Jun-Qi WANG
National Journal of Andrology 2024;30(8):701-708
Objective:To evaluate the diagnostic value of hematological parameters for PCa with prostate-specific antigen(PSA)of 4-10 μg/L and construct a risk-stratification model with these parameters.Methods:We retrospectively analyzed the da-ta on the males undergoing the initial prostatic biopsy in the Affiliated Hospital of Xuzhou Medical University with PSA of 4-10 μg/L from March 2010 to April 2021.According to the results of biopsy,we classified the patients into a PCa and a non-PCa group,and compared the hematological parameters between the two groups.We performed univariate and multivariate logistic regression analyses,identified the independent risk factors for PCa,constructed a risk-stratification model for the prediction of PCa and evaluated its effi-ciency.Results:A total of 415 cases were included in this study,107(25.8%)in the PCa and 308(74.2%)in the non-PCa group.Compared with the non-PCa males,the PCa patients showed a significantly older age,higher ratios of neutrophil to lymphocyte and platelet to lymphocyte,systemic immune-inflammation index(SII),red blood cell distribution width and cystatin C(CysC)level(all P<0.05),but lower red blood cell count and hemoglobin and free/total PSA(f/tPSA)levels(all P<0.05).Multivariate logis-tic regression analysis indicated that age,f/tPSA,SII and CysC were independent risk factors for the prediction of PCa(all P<0.05).Five prediction models were constructed based on the above risk factors,and the area under the ROC curve(AUC)of the four-parameter(age+f/tPSA+SII+CysC)model was 0.745(95%CI:0.694-0.796),significantly higher than those of the other mod-els(P<0.05).A risk-stratification model(low-,intermediate-,and high-risk)was also constructed based on the total nomogram scores,which showed a comparable performance to that of the Prostate Imaging Reporting and Data System(PI-RADS)for the predic-tion of PCa(AUC:0.727[95%CI:0.650-0.804]vs 0.734[95%CI:0.658-0.811]).However,the prediction rate by the risk-stratification model was evidently higher in the low-risk males than in those with low PI-RADS scores(1-2)(39.4%vs 22.2%).Conclusion:SII and CysC are independent risk factors for the prediction of PCa in patients with gray-zone PSA levels.The risk-stratification model based on age,SII,CysC and f/tPSA is comparable to PI-RADS in the diagnostic efficiency of PCa,with an even higher prediction rate in low-risk patients than in those with low PI-RADS scores,and contributive to precision screening and reduction of excessive biopsies in the diagnosis of PCa with gray-zone PSA.

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