1.Expert consensus on the clinical strategies for orthodontic treatment with clear aligners.
Yan WANG ; Hu LONG ; Zhihe ZHAO ; Ding BAI ; Xianglong HAN ; Jun WANG ; Bing FANG ; Zuolin JIN ; Hong HE ; Yuxin BAI ; Weiran LI ; Min HU ; Yanheng ZHOU ; Hong AI ; Yuehua LIU ; Yang CAO ; Jun LIN ; Huang LI ; Jie GUO ; Wenli LAI
International Journal of Oral Science 2025;17(1):19-19
Clear aligner treatment is a novel technique in current orthodontic practice. Distinct from traditional fixed orthodontic appliances, clear aligners have different material features and biomechanical characteristics and treatment efficiencies, presenting new clinical challenges. Therefore, a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique. This expert consensus discusses case selection and grading of treatment difficulty, principle of clear aligner therapy, clinical procedures and potential complications, which are crucial to the clinical success of clear aligner treatment.
Humans
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Consensus
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Orthodontic Appliance Design
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Orthodontic Appliances, Removable
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Tooth Movement Techniques/methods*
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Malocclusion/therapy*
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Orthodontics, Corrective/instrumentation*
2.National clinical three-tiered surveillance and stratified precision detection report on respiratory infectious pathogens in 2024
Jingwen AI ; Jikui DENG ; Min DONG ; Xiaohong GAO ; Jiawei GENG ; Xiaoli HU ; Zhu JIN ; Hongyan LIU ; Yongzhong LI ; Xi LIU ; Yuanwang QIU ; Lihong QU ; Binhuang SUN ; Wei SONG ; Hongyu WANG ; Junping WANG ; Sen WANG ; Xiaoming XIONG ; Daokun YANG ; Liaoyun ZHANG ; Yanliang ZHANG ; Xianghong ZHOU ; Wenhong ZHANG
Chinese Journal of Infectious Diseases 2025;43(2):79-89
Objective:To analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.
3.National clinical three-tiered surveillance and stratified precision detection report on respiratory infectious pathogens in 2024
Jingwen AI ; Jikui DENG ; Min DONG ; Xiaohong GAO ; Jiawei GENG ; Xiaoli HU ; Zhu JIN ; Hongyan LIU ; Yongzhong LI ; Xi LIU ; Yuanwang QIU ; Lihong QU ; Binhuang SUN ; Wei SONG ; Hongyu WANG ; Junping WANG ; Sen WANG ; Xiaoming XIONG ; Daokun YANG ; Liaoyun ZHANG ; Yanliang ZHANG ; Xianghong ZHOU ; Wenhong ZHANG
Chinese Journal of Infectious Diseases 2025;43(2):79-89
Objective:To analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.
4.Distribution and antimicrobial resistance profiles of clinical isolates from blood samples:results from China Antimicrobial Surveillance Network (CHINET) from 2015 to 2021
Min ZHONG ; Xiangning HUANG ; Hua YU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Shanmei WANG ; Yafei CHU ; Wenen LIU ; Yanming LI ; Dawen GUO ; Jinying ZHAO ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Ziyong SUN ; Zhongju CHEN ; Yunsong YU ; Jie LIN ; Jihong LI ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; Ping JI ; Fang DONG ; Zhiyong LÜ ; Han SHEN ; Wanqing ZHOU ; Sufang GUO ; Zhidong HU ; Jin LI ; Chuanqing WANG ; Pan FU ; Hong ZHANG ; Chun WANG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Lixia ZHANG ; Juan MA ; Yuxing NI ; Jingyong SUN ; Jinju DUAN ; Jianbang KANG ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Xuesong XU ; Chao YAN ; Yunjian HU ; Xiaoman AI ; Jinsong WU ; Yuemei LU ; Fangfang HU ; Lianhua WEI ; Fengmei ZOU ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Xiaobo MA ; Yanping ZHENG ; Kaizhen WEN ; Yirong ZHANG ; Yunsheng CHEN ; Qing MENG ; Xuefei HU ; Ruizhong WANG ; Hua FANG ; Ruyi GUO ; Yan ZHU ; Jilu SHEN ; Wenhui HUANG ; Bixia YU ; Jiao FENG ; Yong ZHAO ; Ping GONG ; Shunhong XUE ; Hongqin GU ; Wen HE ; Jiangshan LIU ; Chunlei YUE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(6):664-677
Objective To investigate the distribution and antimicrobial resistance of bacterial isolates from blood samples in the hospitals participating in China Antimicrobial Surveillance Network (CHINET) from 2015 to 2021.Methods Bacterial strains isolated from blood samples were collected from 52 medical centers participating in CHINET from 2015 to 2021 for analysis of bacetrial distribution and antimicrobial resistance.Results A total of 153591 isolates were collected,48.8% of which were gram-positive bacteria and 51.2% were gram-negative bacteria.The top five bacterial strains were coagulase negative Staphylococcus (28.2%),Escherichia coli (20.7%),Klebsiella (13.7%),Enterococcus (7.2%),and Staphylococcus aureus (6.6%).Compard to female patients,male patients showed lower proportion of E.coli and higher proportions of other bacterial species in all the bacterial isolaets from blood samples.The proportions of Streptococcus pneumoniae and Salmonella in all the bacterial isolaets from blood samples were higher in children compared to adults.Enterobacterales species showed various resistance rates to antimicrobial agents.Overall,≥58.0%,≥36.8% and ≥56.8% of E.coli strains were resistant to cefotaxime,gentamicin and levofloxacin respectively over the 7-year period.However,less than 2.5% of the E.coli strains were resistant to carbapenems.K.pneumoniae showed higher resistance rates to imipenem and meropenem than other Enterobacterales species.During the 7-year period,the prevalence of imipenem-resistant and meropenem-resistant K.pneumoniae increased from 21.4% and 19.9% in 2015 to 25.7% and 26.6% in 2021,respectively.However,carbapenems still maintained good antibacterial activity against other Enterobacterales,associaetd with lower resistance rates.In the 7-year period,Acinetobacter baumannii showed a dwonward trend in the resistance rates to imipenem and meropenem,but remained 72.9% and 73.2% respectively in 2021.The prevalence of imipenem-resistant and meropenem-resistant P.aeruginosa decreased from 26.7% and 22.9% in 2015 to 18.5% and 14.7% in 2021,respectively.The prevalence of PRSP was 1.5% in the isolaets from adults and and 0.8% in the isolates from children.Less than 3.0% of the Enterococcus faecium and Enterococcus faecalis strains were resistant to vancomycin,teicolanin,or linezolid.The prevalence of methicillin-resistant S.aureus (MRSA) and coagulase negative Staphylococcus (MRCNS) was 32.1% and 81.0%,respectively.The prevalence of MRSA was relatively stable,28.5% in 2015 and 28.0% in 2021.Conclusions Coagulase negative Staphylococcus,E.coli and K.pneumoniae were the main bacterial species isolated from blood samples in the hospitals participaing in the CHINET from 2015 to 2021.Significant sex and age differences were found in the distribution of bcterial isolates from blood samples.The overall resistance rates of the top bacetrial strains from blood samples to antimicrobial agents showed a downward trend.Ongoing surveillance of antimicrobial resistance for the isolates from blood samples is still essential for prescribing rational antimicrobial therapies and curbing bacterial resistance.
5.Antimicrobial resistance profile of clinical isolates in hospitals across China:report from the CHINET Antimicrobial Resistance Surveillance Program,2023
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; 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 ; Wei LI ; 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 ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Hua FANG ; Penghui ZHANG ; Bixia YU ; Ping GONG ; Haixia SHI ; Kaizhen WEN ; Yirong ZHANG ; Xiuli YANG ; Yiqin ZHAO ; Longfeng LIAO ; Jinhua WU ; Hongqin GU ; Lin JIANG ; Meifang HU ; Wen HE ; Jiao FENG ; Lingling YOU ; Dongmei WANG ; Dong'e WANG ; Yanyan LIU ; Yong AN ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Jianping WANG ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Cunshan KOU ; Shunhong XUE ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Xiaoyan ZENG ; Wen LI ; Yan GENG ; Zeshi LIU
Chinese Journal of Infection and Chemotherapy 2024;24(6):627-637
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in healthcare facilities in major regions of China in 2023.Methods Clinical isolates collected from 73 hospitals across China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2023 Clinical & Laboratory Standards Institute (CLSI) breakpoints.Results A total of 445199 clinical isolates were collected in 2023,of which 29.0% were gram-positive and 71.0% were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species (excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi) (MRSA,MRSE and MRCNS) was 29.6%,81.9% and 78.5%,respectively.Methicillin-resistant strains showed significantly higher resistance rates to most antimicrobial agents than methicillin-susceptible strains (MSSA,MSSE and MSCNS).Overall,92.9% of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 91.4% of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis had significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 93.1% in the isolates from children and and 95.9% in the isolates from adults.The resistance rate to carbapenems was lower than 15.0% for most Enterobacterales species except for Klebsiella,22.5% and 23.6% of which were resistant to imipenem and meropenem,respectively .Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.6% to 10.0%.The resistance rate to imipenem and meropenem was 21.9% and 17.4% for Pseudomonas aeruginosa,respectively,and 67.5% and 68.1% for Acinetobacter baumannii,respectively.Conclusions Increasing resistance to the commonly used antimicrobial agents is still observed in clinical bacterial isolates.However,the prevalence of important crabapenem-resistant organisms such as crabapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a slightly decreasing trend.This finding suggests that strengthening bacterial resistance surveillance and multidisciplinary linkage are important for preventing the occurrence and development of bacterial resistance.
6.Surveillance of bacterial resistance in tertiary hospitals across China:results of CHINET Antimicrobial Resistance Surveillance Program in 2022
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; 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 ; Wei LI ; 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 ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Yanyan LIU ; Yong AN
Chinese Journal of Infection and Chemotherapy 2024;24(3):277-286
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5%were gram-positive and 70.5%were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3%,76.7%and 77.9%,respectively.Overall,94.0%of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8%of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2%in the isolates from children and 95.7%in the isolates from adults.The resistance rate to carbapenems was lower than 13.1%in most Enterobacterales species except for Klebsiella,21.7%-23.1%of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1%to 13.3%.The prevalence of meropenem-resistant strains decreased from 23.5%in 2019 to 18.0%in 2022 in Pseudomonas aeruginosa,and decreased from 79.0%in 2019 to 72.5%in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.
7.Distribution and antimicrobial resistance profiles of clinical isolates from blood samples:results from China Antimicrobial Surveillance Network (CHINET) from 2015 to 2021
Min ZHONG ; Xiangning HUANG ; Hua YU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Shanmei WANG ; Yafei CHU ; Wenen LIU ; Yanming LI ; Dawen GUO ; Jinying ZHAO ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Ziyong SUN ; Zhongju CHEN ; Yunsong YU ; Jie LIN ; Jihong LI ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; Ping JI ; Fang DONG ; Zhiyong LÜ ; Han SHEN ; Wanqing ZHOU ; Sufang GUO ; Zhidong HU ; Jin LI ; Chuanqing WANG ; Pan FU ; Hong ZHANG ; Chun WANG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Lixia ZHANG ; Juan MA ; Yuxing NI ; Jingyong SUN ; Jinju DUAN ; Jianbang KANG ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Xuesong XU ; Chao YAN ; Yunjian HU ; Xiaoman AI ; Jinsong WU ; Yuemei LU ; Fangfang HU ; Lianhua WEI ; Fengmei ZOU ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Xiaobo MA ; Yanping ZHENG ; Kaizhen WEN ; Yirong ZHANG ; Yunsheng CHEN ; Qing MENG ; Xuefei HU ; Ruizhong WANG ; Hua FANG ; Ruyi GUO ; Yan ZHU ; Jilu SHEN ; Wenhui HUANG ; Bixia YU ; Jiao FENG ; Yong ZHAO ; Ping GONG ; Shunhong XUE ; Hongqin GU ; Wen HE ; Jiangshan LIU ; Chunlei YUE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(6):664-677
Objective To investigate the distribution and antimicrobial resistance of bacterial isolates from blood samples in the hospitals participating in China Antimicrobial Surveillance Network (CHINET) from 2015 to 2021.Methods Bacterial strains isolated from blood samples were collected from 52 medical centers participating in CHINET from 2015 to 2021 for analysis of bacetrial distribution and antimicrobial resistance.Results A total of 153591 isolates were collected,48.8% of which were gram-positive bacteria and 51.2% were gram-negative bacteria.The top five bacterial strains were coagulase negative Staphylococcus (28.2%),Escherichia coli (20.7%),Klebsiella (13.7%),Enterococcus (7.2%),and Staphylococcus aureus (6.6%).Compard to female patients,male patients showed lower proportion of E.coli and higher proportions of other bacterial species in all the bacterial isolaets from blood samples.The proportions of Streptococcus pneumoniae and Salmonella in all the bacterial isolaets from blood samples were higher in children compared to adults.Enterobacterales species showed various resistance rates to antimicrobial agents.Overall,≥58.0%,≥36.8% and ≥56.8% of E.coli strains were resistant to cefotaxime,gentamicin and levofloxacin respectively over the 7-year period.However,less than 2.5% of the E.coli strains were resistant to carbapenems.K.pneumoniae showed higher resistance rates to imipenem and meropenem than other Enterobacterales species.During the 7-year period,the prevalence of imipenem-resistant and meropenem-resistant K.pneumoniae increased from 21.4% and 19.9% in 2015 to 25.7% and 26.6% in 2021,respectively.However,carbapenems still maintained good antibacterial activity against other Enterobacterales,associaetd with lower resistance rates.In the 7-year period,Acinetobacter baumannii showed a dwonward trend in the resistance rates to imipenem and meropenem,but remained 72.9% and 73.2% respectively in 2021.The prevalence of imipenem-resistant and meropenem-resistant P.aeruginosa decreased from 26.7% and 22.9% in 2015 to 18.5% and 14.7% in 2021,respectively.The prevalence of PRSP was 1.5% in the isolaets from adults and and 0.8% in the isolates from children.Less than 3.0% of the Enterococcus faecium and Enterococcus faecalis strains were resistant to vancomycin,teicolanin,or linezolid.The prevalence of methicillin-resistant S.aureus (MRSA) and coagulase negative Staphylococcus (MRCNS) was 32.1% and 81.0%,respectively.The prevalence of MRSA was relatively stable,28.5% in 2015 and 28.0% in 2021.Conclusions Coagulase negative Staphylococcus,E.coli and K.pneumoniae were the main bacterial species isolated from blood samples in the hospitals participaing in the CHINET from 2015 to 2021.Significant sex and age differences were found in the distribution of bcterial isolates from blood samples.The overall resistance rates of the top bacetrial strains from blood samples to antimicrobial agents showed a downward trend.Ongoing surveillance of antimicrobial resistance for the isolates from blood samples is still essential for prescribing rational antimicrobial therapies and curbing bacterial resistance.
8.Antimicrobial resistance profile of clinical isolates in hospitals across China:report from the CHINET Antimicrobial Resistance Surveillance Program,2023
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; 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 ; Wei LI ; 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 ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Hua FANG ; Penghui ZHANG ; Bixia YU ; Ping GONG ; Haixia SHI ; Kaizhen WEN ; Yirong ZHANG ; Xiuli YANG ; Yiqin ZHAO ; Longfeng LIAO ; Jinhua WU ; Hongqin GU ; Lin JIANG ; Meifang HU ; Wen HE ; Jiao FENG ; Lingling YOU ; Dongmei WANG ; Dong'e WANG ; Yanyan LIU ; Yong AN ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Jianping WANG ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Cunshan KOU ; Shunhong XUE ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Xiaoyan ZENG ; Wen LI ; Yan GENG ; Zeshi LIU
Chinese Journal of Infection and Chemotherapy 2024;24(6):627-637
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in healthcare facilities in major regions of China in 2023.Methods Clinical isolates collected from 73 hospitals across China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2023 Clinical & Laboratory Standards Institute (CLSI) breakpoints.Results A total of 445199 clinical isolates were collected in 2023,of which 29.0% were gram-positive and 71.0% were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species (excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi) (MRSA,MRSE and MRCNS) was 29.6%,81.9% and 78.5%,respectively.Methicillin-resistant strains showed significantly higher resistance rates to most antimicrobial agents than methicillin-susceptible strains (MSSA,MSSE and MSCNS).Overall,92.9% of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 91.4% of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis had significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 93.1% in the isolates from children and and 95.9% in the isolates from adults.The resistance rate to carbapenems was lower than 15.0% for most Enterobacterales species except for Klebsiella,22.5% and 23.6% of which were resistant to imipenem and meropenem,respectively .Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.6% to 10.0%.The resistance rate to imipenem and meropenem was 21.9% and 17.4% for Pseudomonas aeruginosa,respectively,and 67.5% and 68.1% for Acinetobacter baumannii,respectively.Conclusions Increasing resistance to the commonly used antimicrobial agents is still observed in clinical bacterial isolates.However,the prevalence of important crabapenem-resistant organisms such as crabapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a slightly decreasing trend.This finding suggests that strengthening bacterial resistance surveillance and multidisciplinary linkage are important for preventing the occurrence and development of bacterial resistance.
9.Treatment of patent ductus arteriosus in very preterm infants in China.
Ai Min QIAN ; Rui CHENG ; Xin Yue GU ; Rong YIN ; Rui Miao BAI ; Juan DU ; Meng Ya SUN ; Ping CHENG ; K L E E shoo K LEE ; Li Zhong DU ; Yun CAO ; Wen Hao ZHOU ; You Yan ZHAO ; Si Yan JIANG
Chinese Journal of Pediatrics 2023;61(10):896-901
Objective: To describe the current status and trends in the treatment of patent ductus arteriosus (PDA) among very preterm infants (VPI) admitted to the neonatal intensive care units (NICU) of the Chinese Neonatal Network (CHNN) from 2019 to 2021, and to compare the differences in PDA treatment among these units. Methods: This was a cross-sectional study based on the CHNN VPI cohort, all of 22 525 VPI (gestational age<32 weeks) admitted to 79 tertiary NICU within 3 days of age from 2019 to 2021 were included. The overall PDA treatment rates were calculated, as well as the rates of infants with different gestational ages (≤26, 27-28, 29-31 weeks), and pharmacological and surgical treatments were described. PDA was defined as those diagnosed by echocardiography during hospitalization. The PDA treatment rate was defined as the number of VPI who had received medication treatment and (or) surgical ligation of PDA divided by the number of all VPI. Logistic regression was used to investigate the changes in PDA treatment rates over the 3 years and the differences between gestational age groups. A multivariate Logistic regression model was constructed to compute the standardized ratio (SR) of PDA treatment across different units, to compare the rates after adjusting for population characteristics. Results: A total of 22 525 VPI were included in the study, with a gestational age of 30.0 (28.6, 31.0) weeks and birth weight of 1 310 (1 100, 1 540) g; 56.0% (12 615) of them were male. PDA was diagnosed by echocardiography in 49.7% (11 186/22 525) of all VPI, and the overall PDA treatment rate was 16.8% (3 795/22 525). Of 3 762 VPI who received medication treatment, the main first-line medication used was ibuprofen (93.4% (3 515/3 762)) and the postnatal day of first medication treatment was 6 (4, 10) days of age; 59.3% (2 231/3 762) of the VPI had been weaned from invasive respiratory support during the first medication treatment, and 82.2% (3 092/3 762) of the infants received only one course of medication treatment. A total of 143 VPI underwent surgery, which was conducted on 32 (22, 46) days of age. Over the 3 years from 2019 to 2021, there was no significant change in the PDA treatment rate in these VPI (P=0.650). The PDA treatment rate decreased with increasing gestational age (P<0.001). The PDA treatment rates for VPI with gestational age ≤26, 27-28, and 29-31 weeks were 39.6% (688/1 737), 25.9% (1 319/5 098), and 11.4% (1 788/15 690), respectively. There were 61 units having a total number of VPI≥100 cases, and their rates of PDA treatment were 0 (0/116)-47.4% (376/793). After adjusting for population characteristics, the range of standardized ratios for PDA treatment in the 61 units was 0 (95%CI 0-0.3) to 3.4 (95%CI 3.1-3.8). Conclusions: From 2019 to 2021, compared to the peers in developed countries, VPI in CHNN NICU had a different PDA treatment rate; specifically, the VPI with small birth gestational age had a lower treatment rate, while the VPI with large birth gestational age had a higher rate. There are significant differences in PDA treatment rates among different units.
Infant
;
Infant, Newborn
;
Male
;
Humans
;
Female
;
Ductus Arteriosus, Patent/drug therapy*
;
Infant, Premature
;
Cross-Sectional Studies
;
Ibuprofen/therapeutic use*
;
Infant, Very Low Birth Weight
;
Persistent Fetal Circulation Syndrome
;
Infant, Premature, Diseases/therapy*
10.In silico screening method for non‑responders to cardiac resynchronization therapy in patients with heart failure: a pilot study
Minki HWANG ; Jae‑Sun UHM ; Min Cheol PARK ; Eun Bo SHIM ; Chan Joo LEE ; Jaewon OH ; Hee Tae YU ; Tae‑Hoon KIM ; Boyoung JOUNG ; Hui‑Nam PAK ; Seok‑Min KANG ; Moon‑Hyoung LEE
International Journal of Arrhythmia 2022;23(1):2-
Background:
Cardiac resynchronization therapy (CRT) is an effective treatment option for patients with heart failure (HF) and left ventricular (LV) dyssynchrony. However, the problem of some patients not responding to CRT remains unresolved. This study aimed to propose a novel in silico method for CRT simulation.
Methods:
Three-dimensional heart geometry was constructed from computed tomography images. The finite ele‑ ment method was used to elucidate the electric wave propagation in the heart. The electric excitation and mechani‑ cal contraction were coupled with vascular hemodynamics by the lumped parameter model. The model parameters for three-dimensional (3D) heart and vascular mechanics were estimated by matching computed variables with measured physiological parameters. CRT effects were simulated in a patient with HF and left bundle branch block (LBBB). LV end-diastolic (LVEDV) and end-systolic volumes (LVESV), LV ejection fraction (LVEF), and CRT responsiveness measured from the in silico simulation model were compared with those from clinical observation. A CRT responder was defined as absolute increase in LVEF ≥ 5% or relative increase in LVEF ≥ 15%.
Results:
A 68-year-old female with nonischemic HF and LBBB was retrospectively included. The in silico CRT simu‑ lation modeling revealed that changes in LVEDV, LVESV, and LVEF by CRT were from 174 to 173 mL, 116 to 104 mL, and 33 to 40%, respectively. Absolute and relative ΔLVEF were 7% and 18%, respectively, signifying a CRT responder.In clinical observation, echocardiography showed that changes in LVEDV, LVESV, and LVEF by CRT were from 162 to 119 mL, 114 to 69 mL, and 29 to 42%, respectively. Absolute and relative ΔLVESV were 13% and 31%, respectively, also signifying a CRT responder. CRT responsiveness from the in silico CRT simulation model was concordant with that in the clinical observation.
Conclusion
This in silico CRT simulation method is a feasible technique to screen for CRT non-responders in patients with HF and LBBB.

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