1.Tumor Therapy: Targeted Substances Metabolism Reprogramming Induces Tumor Ferroptosis
Jin-Ping ZHANG ; Yue-Qing WANG ; Mo WANG ; Xin-Yue WANG ; Xiao-Qin MOU ; Xi ZHENG ; Chuang CHENG ; Jing HE ; Li-Li ZOU ; Xiao-Wen LIU
Progress in Biochemistry and Biophysics 2024;51(7):1540-1550
There are huge differences between tumor cells and normal cells in material metabolism, and tumor cells mainly show increased anabolism, decreased catabolism, and imbalance in substance metabolism. These differences provide the necessary material basis for the growth and reproduction of tumor cells, and also provide important targets for the treatment of tumors. Ferroptosis is an iron-dependent form of cell death characterized by an imbalance of iron-dependent lipid peroxidation and lipid membrane antioxidant systems in cells, resulting in excessive accumulation of lipid peroxide, causing damage to lipid membrane structure and loss of function, and ultimately cell death. The regulation of ferroptosis involves a variety of metabolic pathways, including glucose metabolism, lipid metabolism, amino acid metabolism, nucleotide metabolism and iron metabolism. In order for tumor cells to grow rapidly, their metabolic needs are more vigorous than those of normal cells. Tumor cells are metabolically reprogrammed to meet their rapidly proliferating material and energy needs. Metabolic reprogramming is mainly manifested in glycolysis and enhancement of pentose phosphate pathway, enhanced glutamine metabolism, increased nucleic acid synthesis, and iron metabolism tends to retain more intracellular iron. Metabolic reprogramming is accompanied by the production of reactive oxygen species and the activation of the antioxidant system. The state of high oxidative stress makes tumor cells more susceptible to redox imbalances, causing intracellular lipid peroxidation, which ultimately leads to ferroptosis. Therefore, in-depth study of the molecular mechanism and metabolic basis of ferroptosis is conducive to the development of new therapies to induce ferroptosis in cancer treatment. Ferroptosis, as a regulated form of cell death, can induce ferroptosis in tumor cells by pharmacologically or genetically targeting the metabolism of substances in tumor cells, which has great potential value in tumor treatment. This article summarizes the effects of cellular metabolism on ferroptosis in order to find new targets for tumor treatment and provide new ideas for clinical treatment.
2.Selection and analysis of calculation formulas for resting energy expenditure in patients with severe burns based on different metabolic stages
Wen ZOU ; Chunmao HAN ; Ronghua JIN ; Tao SHEN
Chinese Journal of Burns 2024;40(7):634-642
Objective:To explore the changes in resting energy expenditure (REE) values in patients with severe burns under different metabolic stages and the selection of the optimal calculation formula.Methods:This study was a retrospective and observational study. From April 2020 to December 2023, 40 patients (32 males and 8 females, aged (54±17) years) with severe burns meeting inclusion criteria were treated in the Second Affiliated Hospital of Zhejiang University School of Medicine. After admission, the patients were given routine clinical treatments such as sedation and analgesia, debridement, and skin grafting. At 3, 5, 7, 9, 11, 14 days after injury and every 7 days thereafter, the REE values (i.e., REE measured values) were measured by indirect calorimetry in patients with severe burns who met the measurement conditions till the patients recovered or died. On the day the patient's REE was measured, Milner, Hangang, the Third Military Medical University, Carlson, and Peng Xi team's linear formula were used respectively to calculate the REE value (i.e., REE formula values). The post-injury time to measure REE in patients was calculated, and the clinical characteristics of patients in acute inhibition, hypermetabolic, metabolic balance, and metabolic remodeling phases were compared. The REE measured values and the difference between the REE formula values and the REE measured values of patients under the 4 different metabolic phases were calculated.Compared with the REE measured values, the 10% accuracy rate and 20% accuracy rate were calculated to evaluate the accuracy of the REE formula values. The absolute percentage error (APE) of the REE formula values were calculated to evaluate the deviation. The metabolic formula (i.e., the optimal calculation formula) that was closest to the measured REE values was screened out, and further exploration was conducted to identify the key factors that affected the accuracy of the optimal calculation formula under different metabolic phases.Results:The post-injury time to measure REE in patients with severe burns was (40±19) days. Comparisons showed that under the 4 different metabolic phases, patients in the metabolic remodeling phase had the highest age, height, weight, body mass index, total body surface area. Age in the metabolic remodeling phase was significantly higher than that in the acute inhibition and hypermetabolic phases (with t values of -3.02 and -4.20, respectively, with all P values <0.05), weight was significantly higher than that in the hypermetabolic and metabolic balance phases (with t values of -1.97 and -2.61, respectively, with all P values <0.05), body mass index was significantly higher than that in the hypermetabolic phase ( t=-2.90, P<0.05), and total body surface area was significantly larger than that in the hypermetabolic and metabolic balance phases (with t values of -2.02 and -2.27, respectively, with all P values <0.05). There was no significant change in patients' REE measured values under the 4 different metabolic stages ( P>0.05). Except for the Peng Xi team's linear formula ( P>0.05), the difference between REE measured values and REE formula values calculated by using Milner, Hangang, the Third Military Medical University, and Carlson formulas respectively was statistically significant under different metabolic stages (with H values of 14.50, 27.15, and 37.26, respectively, F=11.80, P<0.05). Comprehensive analysis of 10% accuracy, 20% accuracy, and APE showed that in the acute inhibition phase, the REE formula values calculated by Peng Xi team's linear formula was closest to REE measured values, and the APE of the REE formula values calculated by Peng Xi team's linear formula was significantly lower than those calculated by Milner formula, Hangang formula, the Third Military Medical University formula, and Carlson formula (with t values of 9.00, -2.10, 5.95, and 6.68, respectively, with all P values <0.05). In the hypermetabolic phase, the REE formula values calculated by Hangang formula were closest to REE measured values, with significantly lower APE of the REE formula values calculated by Hangang formula than those calculated by using Milner formula, the Third Military Medical University formula, Carlson formula, and Peng Xi team's linear formula (with t values of 10.20, 10.33, 10.65, and 5.87, respectively, with all P values <0.05). In the metabolic balance phase, the REE formula values calculated by Hangang formula were again closest to REE measured values, with significantly lower APE of the REE formula values calculated by Hangang formula than those calculated by Milner formula, the Third Military Medical University formula, and Carlson formula (with t values of 7.11, 8.52, and 8.60, respectively, with all P values <0.05). In the metabolic remodeling phase, the REE formula values calculated by the Third Military Medical University were closest to REE measured values, with significantly lower APE of the REE formula values calculated by the Third Military Medical University formula than those calculated by Milner formula, Hangang formula, and Carlson formula (with t values of 5.12, 2.45, and 6.26, respectively, with all P values <0.05). No significant key factors affected the accuracy of the Peng Xi team's linear formula in the acute inhibition phase ( P>0.05). In the hypermetabolic phase, total burn area was a key factor affecting the accuracy of Hangang formula (with odds ratio of 1.00, with 95% confidence interval of 1.00-1.10, P<0.05). In the metabolic balance phase, post-injury days was a key factor affecting the accuracy of Hangang formula (with odds ratio of 1.30, with 95% confidence interval of 1.10-1.40, P<0.05). In the metabolic remodeling phase, no significant key factors affected the accuracy of the Third Military Medical University formula ( P>0.05). Conclusions:When calculating REE values in patients with severe burns, it is recommended to use the Peng Xi team's linear formula during the acute inhibition phase, the Hangang formula during the hypermetabolic and metabolic balance phases, and the Third Military Medical University formula during the metabolic remodeling phase. Additionally, it is crucial to ensure the accuracy of key factors affecting the optimal calculation formula in the hypermetabolic and metabolic balance phases.
3.Effects of Needle Retention Time for Scalp Acupuncture on Motor Dysfunction and Serum C-Reactive Protein,Blood Glucose and Blood Lipid of Post-Stroke Patients
Qi ZHONG ; Hai-Yan CAI ; Bing-Xu JIN ; Zhan-Xin HUO ; Hui-Yi LUO ; Qiu-Xia ZHONG ; Hao-Xun GUO ; Jia-Wen CHEN ; Shu-Hui ZOU
Journal of Guangzhou University of Traditional Chinese Medicine 2024;41(6):1510-1516
Objective To observe the improvement of motor dysfunction and serum levels of C-reactive protein(CRP),blood glucose and blood lipid in post-stroke patients treated with scalp acupuncture at different needle retention time.Methods A total of 120 patients with motor dysfunction after stroke were randomly divided into control group,observation group 1 and observation group 2,with 40 cases in each group.The patients in the 3 groups were treated with scalp acupuncture,body acupuncture and routine rehabilitation exercise,once a day and 6 times a week,lasting for 2 weeks.The control group was given scalp acupuncture with retaining of needles for 30 minutes,the observation group 1 was given scalp acupuncture with retaining of needles for one hour,and the observation group 2 was given scalp acupuncture with retaining of needles for 2 hours.Before and after treatment,the 3 groups were observed in the changes of the scale scores of National Institutes of Health Stroke Scale(NIHSS),Fugl-Meyer Assessment(FM A),Berg Balance Scale(BBS)and modified Barthel Index(MBI),and the levels of laboratory indicators of peripheral blood CRP,fasting plasma glucose(FPG),total cholesterol(TC),triglyceride(TG),high-density lipoprotein cholesterol(HDL-C)and low-density lipoprotein cholesterol(LDL-C).After treatment,the clinical safety of the three groups was evaluated.Results(1)After treatment,the scale scores of NIHSS in the three groups were lower(P<0.01)and the scale scores FMA,BBS and MBI were higher than those before treatment(P<0.05 or P<0.01).The comparison of post-treatment scale scores showed that the differences among the three groups were statistically significant(P<0.01).The intergroup comparison showed that the decrease of NIHSS score and the increase of FMA,BBS and MBI scores in the observation group 2 were significantly superior to those in the control group and the observation group 1(P<0.01);the improvement of FMA score in the observation group 1 was significantly superior to that in the control group(P<0.01),while the improvement of NIHSS,BBS and MBI scores tended to be superior to that in the control group without statistically significant differences(P>0.05).The results indicated that the curative effect of scalp acupuncture plus exercise regimen was positively correlated with the duration of needle retention for scalp acupuncture.(2)After treatment,the laboratory indicator levels of CRP and FPG in the peripheral blood of the three groups,the levels of TG and LDL-C in the two observation groups and the level of HDL-C in the observation group 2 were improved compared with those before treatment(P<0.05 or P<0.01).Statistically significant differences were presented in the post-treatment levels of CRP and TG in peripheral blood among the three groups(P<0.05 or P<0.01).The intergroup comparison showed that the improvement of CRP and TG levels in the observation group 2 was significantly superior to that in the control group,and the improvement of CRP level in the observation group 2 was significantly superior to that in the observation group 1,the differences being statistically significant(P<0.05 or P<0.01).The TC level in the three groups after treatment did not differ from that before treatment,and there was no significant difference in TC level after treatment among the three groups either(P>0.05).(3)During the treatment,no adverse reactions such as fainting,needle breaking and hematoma occurred in the three groups,the vital signs of the patients were stable,and there were no obvious abnormal changes in pulse,blood pressure and respiratory rate.Conclusion Scalp acupuncture can effectively improve the motor function of post-stroke patients in a pasitive time-effect relationship with the needle retention,and better the curative effect can be achieved by retaining of the needle for 2 h.
4.Changing distribution and resistance profiles of common pathogens isolated from urine in the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yanming LI ; Mingxiang ZOU ; Wen'en LIU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; 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 ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; 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 ; 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 2024;24(3):287-299
Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8%(62 219/261 893),and gram-negative bacteria 76.2%(199 674/261 893).The most common species were E.coli(46.7%),E.faecium(10.4%),K.pneumoniae(9.8%),E.faecalis(8.7%),P.mirabilis(3.5%),P.aeruginosa(3.4%),SS.agalactiae(2.6%),and E.cloacae(2.1%).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2%,52.8%and 37.0%,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7%,18.5%,16.4%,and 40.3%,respectively.Lower than 10%of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90%of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was<2%.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.
5.Changing resistance profiles of Enterococcus in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Na CHEN ; Ping JI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; 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 ; Yunjian HU ; Xiaoman AI ; 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 ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):300-308
Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7%)and Enterococcus faecalis(41.5%).The strains were mainly isolated from urinary tract specimens(46.9%±2.6%),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6%).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1%and 1.3%,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7%in 2015 to 3.4%in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3%.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.
6.Changing resistance profiles of Enterobacter isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shaozhen YAN ; Ziyong SUN ; Zhongju CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Fengbo ZHANG ; Ping JI ; Zhidong HU ; Jin LI ; Sufang GUO ; Han SHEN ; Wanqing ZHOU ; Yingchun XU ; Xiaojiang ZHANG ; Xuesong XU ; Chao YAN ; Chuanqing WANG ; Pan FU ; Wei JIA ; Gang LI ; Yuanhong XU ; Ying HUANG ; Dawen GUO ; Jinying ZHAO ; Wen'en LIU ; Yanming LI ; Hua YU ; Xiangning HUANG ; Bin SHAN ; Yan DU ; Shanmei WANG ; Yafei CHU ; Yuxing NI ; Jingyong SUN ; Yunsong YU ; Jie LIN ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Yan JIN ; Chunhong SHAO ; Jihong LI ; Lixia ZHANG ; Juan MA ; Yunzhuo CHU ; Sufei TIAN ; Jinju DUAN ; Jianbang KANG ; Ruizhong WANG ; Hua FANG ; Fangfang HU ; Yunjian HU ; Xiaoman AI ; Fang DONG ; Zhiyong LÜ ; Hong ZHANG ; Chun WANG ; Yong ZHAO ; Ping GONG ; Lei ZHU ; Jinhua MENG ; Xiaobo MA ; Yanping ZHENG ; Jinsong WU ; Yuemei LU ; Ruyi GUO ; Yan ZHU ; Kaizhen WEN ; Yirong ZHANG ; Chunlei YUE ; Jiangshan LIU ; Wenhui HUANG ; Shunhong XUE ; Xuefei HU ; Hongqin GU ; Jiao FENG ; Shuping ZHOU ; Yan ZHOU ; Yunsheng CHEN ; Qing MENG ; Bixia YU ; Jilu SHEN ; Rui DOU ; Shifu WANG ; Wen HE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(3):309-317
Objective To examine the changing antimicrobial resistance profile of Enterobacter spp.isolates in 53 hospitals across China from 2015 t0 2021.Methods The clinical isolates of Enterobacter spp.were collected from 53 hospitals across China during 2015-2021 and tested for antimicrobial susceptibility using Kirby-Bauer method or automated testing systems according to the CHINET unified protocol.The results were interpreted according to the breakpoints issued by the Clinical & Laboratory Standards Institute(CLSI)in 2021(M100 31st edition)and analyzed with WHONET 5.6 software.Results A total of 37 966 Enterobacter strains were isolated from 2015 to 2021.The proportion of Enterobacter isolates among all clinical isolates showed a fluctuating trend over the 7-year period,overall 2.5%in all clinical isolates amd 5.7%in Enterobacterale strains.The most frequently isolated Enterobacter species was Enterobacter cloacae,accounting for 93.7%(35 571/37 966).The strains were mainly isolated from respiratory specimens(44.4±4.6)%,followed by secretions/pus(16.4±2.3)%and urine(16.0±0.9)%.The strains from respiratory samples decreased slightly,while those from sterile body fluids increased over the 7-year period.The Enterobacter strains were mainly isolated from inpatients(92.9%),and only(7.1±0.8)%of the strains were isolated from outpatients and emergency patients.The patients in surgical wards contributed the highest number of isolates(24.4±2.9)%compared to the inpatients in any other departement.Overall,≤ 7.9%of the E.cloacae strains were resistant to amikacin,tigecycline,polymyxin B,imipenem or meropenem,while ≤5.6%of the Enterobacter asburiae strains were resistant to these antimicrobial agents.E.asburiae showed higher resistance rate to polymyxin B than E.cloacae(19.7%vs 3.9%).Overall,≤8.1%of the Enterobacter gergoviae strains were resistant to tigecycline,amikacin,meropenem,or imipenem,while 10.5%of these strains were resistant to polycolistin B.The overall prevalence of carbapenem-resistant Enterobacter was 10.0%over the 7-year period,but showing an upward trend.The resistance profiles of Enterobacter isolates varied with the department from which they were isolated and whether the patient is an adult or a child.The prevalence of carbapenem-resistant E.cloacae was the highest in the E.cloacae isolates from ICU patients.Conclusions The results of the CHINET Antimicrobial Resistance Surveillance Program indicate that the proportion of Enterobacter strains in all clinical isolates fluctuates slightly over the 7-year period from 2015 to 2021.The Enterobacter strains showed increasing resistance to multiple antimicrobial drugs,especially carbapenems over the 7-year period.
7.Changing resistance profiles of Proteus,Morganella and Providencia in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yunmin XU ; Xiaoxue DONG ; Bin SHAN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Ping JI ; Fengbo ZHANG ; 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 ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; 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 ; Hongyan ZHENG ; 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 WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(4):410-417
Objective To understand the changing distribution and antimicrobial resistance profiles of Proteus,Morganella and Providencia in hospitals across China from January 1,2015 to December 31,2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods Antimicrobial susceptibility testing was carried out following the unified CHINET protocol.The results were interpreted in accordance with the breakpoints in the 2021 Clinical & Laboratory Standards Institute(CLSI)M100(31 st Edition).Results A total of 32 433 Enterobacterales strains were isolated during the 7-year period,including 24 160 strains of Proteus,6 704 strains of Morganella,and 1 569 strains of Providencia.The overall number of these Enterobacterales isolates increased significantly over the 7-year period.The top 3 specimen source of these strains were urine,lower respiratory tract specimens,and wound secretions.Proteus,Morganella,and Providencia isolates showed lower resistance rates to amikacin,meropenem,cefoxitin,cefepime,cefoperazone-sulbactam,and piperacillin-tazobactam.For most of the antibiotics tested,less than 10%of the Proteus and Morganella strains were resistant,while less than 20%of the Providencia strains were resistant.The prevalence of carbapenem-resistant Enterobacterales(CRE)was 1.4%in Proteus isolates,1.9%in Morganella isolates,and 15.6%in Providencia isolates.Conclusions The overall number of clinical isolates of Proteus,Morganella and Providencia increased significantly in the 7-year period from 2015 to 2021.The prevalence of CRE strains also increased.More attention should be paid to antimicrobial resistance surveillance and rational antibiotic use so as to prevent the emergence and increase of antimicrobial resistance.
8.Changing distribution and resistance profiles of Klebsiella strains in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Chuyue ZHUO ; Yingyi GUO ; Chao ZHUO ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; 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 ; Yunjian HU ; Xiaoman AI ; 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 WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(4):418-426
Objective To understand the changing distribution and antimicrobial resistance profiles of Klebsiella strains in 52 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Antimicrobial susceptibility testing was carried out according to the unified CHINET protocol.The susceptibility results were interpreted according to the breakpoints in the Clinical & Laboratory Standards Institute(CLSI)M100 document.Results A total of 241,549 nonduplicate Klebsiella strains were isolated from 2015 to 2021,including Klebsiella pneumoniae(88.0%),Klebsiella aerogenes(5.8%),Klebsiella oxytoca(5.7%),and other Klebsiella species(0.6%).Klebsiella strains were mainly isolated from respiratory tract(48.49±5.32)%.Internal medicine(22.79±3.28)%,surgery(17.98±3.10)%,and ICU(14.03±1.39)%were the top 3 departments where Klebsiella strains were most frequently isolated.K.pneumoniae isolates showed higher resistance rate to most antimicrobial agents compared to other Klebsiella species.Klebsiella isolates maintained low resistance rates to tigecycline and polymyxin B.ESBLs-producing K.pneumoniae and K.oxytoca strains showed higher resistance rates to all the antimicrobial agents tested compared to the corresponding ESBLs-nonproducing strains.The K.pneumoniae and carbapenem-resistant K.pneumoniae(CRKP)strains isolated from ICU patients demonstrated higher resistance rates to majority of the antimicrobial agents tested than the strains isolated from non-ICU patients.The CRKP strains isolated from adult patients had higher resistance rates to most of the antimicrobial agents tested than the corresponding CRKP strains isolated from paediatric patients.Conclusions The prevalence of carbapenem-resistant strains in Klebsiella isolates increased greatly from 2015 to 2021.However,the Klebsiella isolates remained highly susceptible to tigecycline and polymyxin B.Antimicrobial resistance surveillance should still be strengthened for Klebsiella strains.
9.Changing resistance profiles of Staphylococcus isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yuling XIAO ; Mei KANG ; Yi XIE ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Ping JI ; Fengbo ZHANG ; 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 ; Yunjian HU ; Xiaoman AI ; 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 WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(5):570-580
Objective To investigate the changing distribution and antibiotic resistance profiles of clinical isolates of Staphylococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Staphylococcus according to the unified protocol of CHINET(China Antimicrobial Surveillance Network)using disk diffusion method and commercial automated systems.The CHINET antimicrobial resistance surveillance data from 2015 to 2021 were interpreted according to the 2021 CLSI breakpoints and analyzed using WHONET 5.6.Results During the period from 2015 to 2021,a total of 204,771 nonduplicate strains of Staphylococcus were isolated,including 136,731(66.8%)strains of Staphylococcus aureus and 68,040(33.2%)strains of coagulase-negative Staphylococcus(CNS).The proportions of S.aureus isolates and CNS isolates did not show significant change.S.aureus strains were mainly isolated from respiratory specimens(38.9±5.1)%,wound,pus and secretions(33.6±4.2)%,and blood(11.9±1.5)%.The CNS strains were predominantly isolated from blood(73.6±4.2)%,cerebrospinal fluid(12.1±2.5)%,and pleural effusion and ascites(8.4±2.1)%.S.aureus strains were mainly isolated from the patients in ICU(17.0±7.3)%,outpatient and emergency(11.6±1.7)%,and department of surgery(11.2±0.9)%,whereas CNS strains were primarily isolated from the patients in ICU(32.2±9.7)%,outpatient and emergency(12.8±4.7)%,and department of internal medicine(11.2±1.9)%.The prevalence of methicillin-resistant strains was 32.9%in S.aureus(MRSA)and 74.1%in CNS(MRCNS).Over the 7-year period,the prevalence of MRSA decreased from 42.1%to 29.2%,and the prevalence of MRCNS decreased from 82.1%to 68.2%.MRSA showed higher resistance rates to all the antimicrobial agents tested except trimethoprim-sulfamethoxazole than methicillin-susceptible S.aureus(MSSA).Over the 7-year period,MRSA strains showed decreasing resistance rates to gentamicin,rifampicin,and levofloxacin,MRCNS showed decreasing resistance rates to gentamicin,erythromycin,rifampicin,and trimethoprim-sulfamethoxazole,but increasing resistance rate to levofloxacin.No vancomycin-resistant strains were detected.The prevalence of linezolid-resistant MRCNS increased from 0.2%to 2.3%over the 7-year period.Conclusions Staphylococcus remains the major pathogen among gram-positive bacteria.MRSA and MRCNS were still the principal antibiotic-resistant gram-positive bacteria.No S.aureus isolates were found resistant to vancomycin or linezolid,but linezolid-resistant strains have been detected in MRCNS isolates,which is an issue of concern.
10.Efficacy and safety of mitoxantrone hydrochloride liposome injection in treatment of peripheral T-cell lymphomas: a multicenter, non-interventional, ambispective cohort, real-world study (MOMENT)
Huiqiang HUANG ; Zhiming LI ; Lihong LIU ; Liang HUANG ; Jie JIN ; Hongyan TONG ; Hui ZHOU ; Zengjun LI ; Zhenqian HUANG ; Wenbin QIAN ; Kaiyang DING ; Quande LIN ; Ming HOU ; Yunhong HUANG ; Jingbo WANG ; Pengcheng HE ; Xiuhua SUN ; Xiaobo WANG ; Zunmin ZHU ; Yao LIU ; Jinhai REN ; Huijing WU ; Liling ZHANG ; Hao ZHANG ; Liangquan GENG ; Jian GE ; Ou BAI ; Liping SU ; Guangxun GAO ; Xin LI ; Yanli YANG ; Yijian CHEN ; Aichun LIU ; Xin WANG ; Yi WANG ; Liqun ZOU ; Xiaobing HUANG ; Dongping HUANG ; Shujuan WEN ; Donglu ZHAO ; Jun MA
Journal of Leukemia & Lymphoma 2023;32(8):457-464
Objective:To evaluate the efficacy and safety of mitoxantrone hydrochloride liposome injection in the treatment of peripheral T-cell lymphoma (PTCL) in a real-world setting.Methods:This was a real-world ambispective cohort study (MOMENT study) (Chinese clinical trial registry number: ChiCTR2200062067). Clinical data were collected from 198 patients who received mitoxantrone hydrochloride liposome injection as monotherapy or combination therapy at 37 hospitals from January 2022 to January 2023, including 166 patients in the retrospective cohort and 32 patients in the prospective cohort; 10 patients in the treatment-na?ve group and 188 patients in the relapsed/refractory group. Clinical characteristics, efficacy and adverse events were summarized, and the overall survival (OS) and progression-free survival (PFS) were analyzed.Results:All 198 patients were treated with mitoxantrone hydrochloride liposome injection for a median of 3 cycles (range 1-7 cycles); 28 cases were treated with mitoxantrone hydrochloride liposome injection as monotherapy, and 170 cases were treated with the combination regimen. Among 188 relapsed/refractory patients, 45 cases (23.9%) were in complete remission (CR), 82 cases (43.6%) were in partial remission (PR), and 28 cases (14.9%) were in disease stabilization (SD), and 33 cases (17.6%) were in disease progression (PD), with an objective remission rate (ORR) of 67.6% (127/188). Among 10 treatment-na?ve patients, 4 cases (40.0%) were in CR, 5 cases (50.0%) were in PR, and 1 case (10.0%) was in PD, with an ORR of 90.0% (9/10). The median follow-up time was 2.9 months (95% CI 2.4-3.7 months), and the median PFS and OS of patients in relapsed/refractory and treatment-na?ve groups were not reached. In relapsed/refractory patients, the difference in ORR between patients with different number of treatment lines of mitoxantrone hydrochloride liposome injection [ORR of the second-line, the third-line and ≥the forth-line treatment was 74.4% (67/90), 73.9% (34/46) and 50.0% (26/52)] was statistically significant ( P = 0.008). Of the 198 PTCL patients, 182 cases (91.9%) experienced at least 1 time of treatment-related adverse events, and the incidence rate of ≥grade 3 adverse events was 66.7% (132/198), which was mainly characterized by hematologic adverse events. The ≥ grade 3 hematologic adverse events mainly included decreased lymphocyte count, decreased neutrophil count, decreased white blood cell count, and anemia; non-hematologic adverse events were mostly grade 1-2, mainly including pigmentation disorders and upper respiratory tract infection. Conclusions:The use of mitoxantrone hydrochloride liposome injection-containing regimen in the treatment of PTCL has definite efficacy and is well tolerated, and it is a new therapeutic option for PTCL patients.

Result Analysis
Print
Save
E-mail