1.Equivalence of SYN008 versus omalizumab in patients with refractory chronic spontaneous urticaria: A multicenter, randomized, double-blind, parallel-group, active-controlled phase III study.
Jingyi LI ; Yunsheng LIANG ; Wenli FENG ; Liehua DENG ; Hong FANG ; Chao JI ; Youkun LIN ; Furen ZHANG ; Rushan XIA ; Chunlei ZHANG ; Shuping GUO ; Mao LIN ; Yanling LI ; Shoumin ZHANG ; Xiaojing KANG ; Liuqing CHEN ; Zhiqiang SONG ; Xu YAO ; Chengxin LI ; Xiuping HAN ; Guoxiang GUO ; Qing GUO ; Xinsuo DUAN ; Jie LI ; Juan SU ; Shanshan LI ; Qing SUN ; Juan TAO ; Yangfeng DING ; Danqi DENG ; Fuqiu LI ; Haiyun SUO ; Shunquan WU ; Jingbo QIU ; Hongmei LUO ; Linfeng LI ; Ruoyu LI
Chinese Medical Journal 2025;138(16):2040-2042
2.Establishment and verification of acute kidney injury model in sepsis patients based on albumin-corrected anion gap
Chinese Journal of Emergency Medicine 2025;34(11):1579-1585
Objective:To construct and validate a model of acute kidney injury ( AKI ) in sepsis patients based on albumin-corrected anion gap ( ACAG ).Methods:The clinical data of patients with sepsis admitted to our hospital from September 2020 to January 2025 were retrospectively analyzed. They were divided into training set and validation set according to the ratio of 7 : 3. The patients in the training set were divided into AKI group and non-AKI group according to whether AKI occurred during hospitalization. The basic information, disease treatment data and laboratory data of the patients were collected, and ACAG was calculated. Multivariate Logistic regression analysis was used to analyze the risk factors of AKI, and R language was used to construct a nomogram model to predict the risk of AKI. The receiver operating characteristic ( ROC ) curve was drawn to measure the distinguishing ability of the model, and the calibration curve was drawn to test the consistency between the predicted results of the model and the actual situation. Decision curve analysis ( DCA ) was used to judge the practicability of the model in clinical practice.Results:382 patients with sepsis were included and divided into training set ( n = 267 ) and validation set ( n = 115 ) according to the ratio of 7 : 3. Among the 382 patients, 124 cases of AKI occurred during hospitalization, accounting for 32.46%( 124 / 382 ). Among them, 85 cases of AKI occurred in the training set, accounting for 31.84%( 85 / 267 ), and 39 cases of AKI occurred in the validation set, accounting for 33.91%( 39 / 115 ). Multivariate Logistic regression analysis showed that age ( OR=2.815, 95% CI:1.316~6.022), white blood cell count ( WBC ) ( OR = 1.926, 95% CI : 1.330-2.790 ), lactic acid ( Lac ) ( OR = 2.189,95%CI : 1.300 ~ 3.687 ), serum creatinine ( Scr ) ( OR = 3.156,95% CI : 1.702 -5.852 ), blood urea nitrogen ( BUN ) ( OR = 2.951,95% CI : 1.652-5.271 ), uric acid ( OR = 3.122,95% CI: 1.588-6.139 ), C-reactive protein ( CRP ) ( OR = 2.847,95% CI: 1.384-5.856 ), ACAG ( OR = 2.953,95% CI: 1.669-5.224 ) were risk factors for AKI in sepsis patients with intensive training ( P < 0.05 ). According to the results of multivariate logistic regression analysis, a nomogram model was established. ROC analysis showed that the area under the curve ( AUC ) of the nomogram model for predicting postoperative AKI in patients with sepsis in the training set and the validation set were 0.95 ( 95% CI: 0.90-0.98 ) and 0.92 ( 95% CI: 0.89-0.97 ), respectively. The statistical values after Hosmer-Lemeshow goodness-of-fit test were 0.295 and 0.264 ( P = 0.563 and 0.488 ), respectively. The calibration curve and DCA results showed. The calibration and clinical practicability of the nomogram prediction model are good. Conclusions:Age, WBC, Lac, Scr, BUN, uric acid, CRP and ACAG are all associated with AKI in patients with sepsis. Based on these risk factors, a nomogram model was constructed to predict the risk of AKI in patients with sepsis. The model has good discrimination ability, calibration ability and clinical practical value.
3.Changing prevalence and antibiotic resistance profiles of carbapenem-resistant Enterobacterales in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Wenxiang JI ; Tong JIANG ; Jilu SHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yuanhong XU ; Ying HUANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yingchun XU ; Xiaojiang ZHANG ; 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 ; 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 ; Hong ZHANG ; Chun WANG ; Wenhui HUANG ; 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 2025;25(4):445-454
Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43%(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38%,9.73%,and 8.47%,respectively.The prevalence of CRE strains in Escherichia coli was 1.99%.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)%,followed by blood(20.88±3.40)%and urine(18.40±3.45)%.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)%.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.
4.Changing distribution and antibiotic resistance profiles of the respiratory bacterial isolates in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Ying FU ; Yunsong YU ; Jie LIN ; 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 ; 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 WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE ; Wenhui HUANG
Chinese Journal of Infection and Chemotherapy 2025;25(4):431-444
Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6%of the isolates were Gram-negative bacteria and 17.4%were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)%and(94.0±0.1)%,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P<0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9%.The prevalence of penicillin-resistant S.pneumoniae was 1.4%.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8%and 41.3%,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7%,20.8%,9.4%,29.8%,and 73.3%,respectively.The prevalence of β-lactamase was 96.1%in Moraxella catarrhalis and 60.0%in Haemophilus influenzae.The H.influenzae isolates from children(<18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P<0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.
5.Distribution and resistance profiles of bacterial strains isolated from cerebrospinal fluid in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Juan MA ; Lixia ZHANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Han SHEN ; Wanqing ZHOU ; Wenen LIU ; Yanming LI ; Yi XIE ; Mei KANG ; Dawen GUO ; Jinying ZHAO ; Zhidong HU ; Jin LI ; Shanmei WANG ; Yafei CHU ; Yunsong YU ; Jie LIN ; Yingchun XU ; Xiaojiang ZHANG ; Jihong LI ; Bin SHAN ; Yan DU ; Ping JI ; Fengbo ZHANG ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Xiaobo MA ; Yanping ZHENG ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Hua YU ; Xiangning HUANG ; Sufang GUO ; Xuesong XU ; Chao YAN ; Fangfang HU ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Fang DONG ; Zhiyong LÜ ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Chuanqing WANG ; Pan FU ; Yunjian HU ; Xiaoman AI ; Ziyong SUN ; Zhongju CHEN ; Hong ZHANG ; Chun WANG ; Yuxing NI ; Jingyong SUN ; Kaizhen WEN ; Yirong ZHANG ; Ruyi GUO ; Yan ZHU ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Shifu WANG ; Yunsheng CHEN ; Qing MENG ; Yong ZHAO ; Ping GONG ; Ruizhong WANG ; Hua FANG ; Jilu SHEN ; Jiangshan LIU ; Hongqin GU ; Jiao FENG ; Shunhong XUE ; Bixia YU ; Wen HE ; Lin JIANG ; Longfeng LIAO ; Chunlei YUE ; Wenhui HUANG
Chinese Journal of Infection and Chemotherapy 2025;25(3):279-289
Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3%)and from outpatient and emergency care patients(4.7%).Overall,19.6%of the isolates were from children and 80.4%were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0%and 32.0%,respectively.Coagulase negative Staphylococcus accounted for 73.0%of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2%in children and 45.6%in adults.The prevalence of MRCNS was 67.6%in adults and 69.5%in children.A small number of vancomycin-resistant Enterococcus faecium(2.2%)and linezolid-resistant Enterococcus faecalis(3.1%)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2%and 76.4%in children,70.5%and 63.5%in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3%and 47.7%in children,6.4%and 47.9%in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0%and 37.1%in children,81.7%and 39.9%in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.
6.Changing antibiotic resistance profiles of the bacterial strains isolated from geriatric patients in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Xiaoman AI ; Yunjian HU ; Chunyue GE ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; 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 ; 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 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 2025;25(3):290-302
Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3%of the total number of clinical isolates from all patients.Overall,21.8%of the 514 715 strains were gram-positive bacteria,and 78.2%were gram-negative bacteria.Majority(90.9%)of the strains were isolated from inpatients.About 42.9%of the strains were isolated from respiratory specimens,and 22.9%were isolated from urine.More than half(60.7%)of the strains were isolated from male patients,and 39.3%isolated from females.About 51.1%of the strains were isolated from patients aged 65-<75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8%in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2%,1.5%)and linezolid-resistant strains(3.4%,0.3%)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3%,4.0%,and 1.7%in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2%)to imipenem and meropenem were 20.9%and 22.3%,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7%-7.8%of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6%)to imipenem and meropenem were 68.4%and 70.6%respectively,while 28.5%and 24.3%of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-<75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.
7.Clinical efficacy and safety of a domestic calcipotriol/betamethasone dipropionate ointment in the treatment of stable plaque psoriasis: a multicenter, randomized, double-blind, controlled study
Lixin XIA ; Guang XIANG ; Qingchun DIAO ; Kun HUANG ; Shoumin ZHANG ; Shanshan LI ; Yumei LI ; Zhiqiang SONG ; Qing SUN ; Xiumin YANG ; Meng PAN ; Yuling SHI ; Shuping GUO ; Huiping WANG ; Tiechi LEI ; Xiaoyong ZHOU ; Songmei GENG ; Suchun HOU ; Juan SU ; Yong CUI ; Rixin CHEN ; Yanyan FENG ; Hongxia FENG ; Rushan XIA ; Zudong MENG ; Fang YIN ; Jingjing WANG ; Xinghua GAO
Chinese Journal of Dermatology 2025;58(11):1020-1026
Objective:To evaluate the clinical equivalence between a domestic calcipotriol/betamethasone dipropionate ointment and the originator product in the treatment of stable plaque psoriasis.Methods:A multicenter, randomized, double-blind, three-arm, parallel-group, active- and placebo-controlled study was conducted, and 449 patients aged 18 - 65 years with stable plaque psoriasis were enrolled from 25 hospitals (such as the First Affiliated Hospital of China Medical University). Eligible patients had a baseline physician's global assessment (PGA) score of ≥ 3 points, baseline body surface area (BSA) involvement of 5% - 30%, and a target lesion psoriasis area and severity index (TL-PASI) for plaque elevation of ≥ 3 points. Participants were randomly assigned in a 2:2:1 ratio to the test group ( n = 179), reference group ( n = 180), and placebo group ( n = 90), and applied the domestic calcipotriol/betamethasone dipropionate ointment, originator product, and ointment base respectively, once daily in the evening for 4 weeks. Efficacy and safety were assessed at weeks 1, 2, and 4. The primary efficacy endpoints were the treatment success rates and clinical success rates in each group at week 4. The per-protocol set (PPS) was used for the primary efficacy analysis, and the intention-to-treat (ITT) set for supplementary efficacy analysis. Equivalence between the test and reference preparations was tested using the Cochran-Mantel-Haenszel method adjusted for randomization strata. Superiority of the test and reference preparations over the placebo was also tested. Measurement data were compared among the 3 groups using analysis of variance or non-parametric tests, while treatment success rates, clinical success rates, and incidence rates of adverse reactions were compared using the chi-square test. Results:The ITT, PPS, and safety sets included 447, 420, and 448 patients, respectively. In the ITT set, patients were aged 43.6 ± 12.8 years, including 320 (71.6%) males and 127 (28.4%) females, and the disease duration was 11.21 ± 9.05 years; 316 (70.7%) had a PGA score of 3 points and 131 (29.3%) had a PGA score of 4 - 5 points. No significant differences in the baseline characteristics (including age, sex, disease duration and disease severity) were observed among the 3 groups (all P > 0.05). Based on the PPS analysis, the treatment success rates were 57.9% (99/171) in the test group, 50.3% (86/171) in the reference group, and 7.7% (6/78) in the placebo group, and the clinical success rates were 57.9% (99/171), 50.3% (86/171), and 10.3% (8/78), respectively; both the test and reference groups were superior to the placebo group in both treatment and clinical success rates (all P < 0.001) ; the rate differences for treatment success (90% confidence interval [ CI]: -1.3% - 16.4%) and clinical success (90% CI: -1.3% - 16.3%) between the test and reference groups were entirely within the pre-defined equivalence margin (-20% - 20%). Subgroup analyses by baseline PGA scores: for patients with a baseline PGA score of 3 points, the treatment success rates in the test, reference, and placebo groups were 60.8% (73/120), 52.1% (62/119), and 11.1% (6/54), respectively, and the corresponding clinical success rates were 61.7% (74/120), 53.8% (64/119), and 13% (7/54), respectively; the test and reference groups did not differ significantly in treatment or clinical success rates (both P > 0.05), but both showed higher success rates than the placebo group (all P < 0.001) ; the results of statistical comparisons among the 3 groups in patients with a baseline PGA score of 4 - 5 points were consistent with those observed in patients with a baseline PGA score of 3 points. The percentage reductions in PGA and TL-PASI scores from baseline to weeks 1, 2, and 4 showed significant differences among the 3 groups, which were significantly higher in the test and reference groups than in the placebo group (all P < 0.001), but did not differ between the test and reference groups (all P > 0.05). The primary adverse reactions were local skin reactions, such as pruritus, pain, and erythema. The incidence rates of adverse reactions were 8.9% (16/179) in the test group, 7.3% (13/179) in the reference group, and 7.8% (7/90) in the placebo group, with no significant difference among the 3 groups ( P > 0.05) . Conclusions:The domestic calcipotriol/betamethasone dipropionate ointment demonstrated clinical equivalence to the originator product in the treatment of stable plaque psoriasis, and the two agents exhibited comparable efficacy for patients with varying degrees of disease severity, and were comparable in the speed and degree of clinical improvement, with similar favorable safety profiles.
8.Transcutaneous bilirubin curves in healthy neonates based on multicenter remote monitoring data
Bi ZE ; Xiaoyue DONG ; Jin WANG ; Chuan NIE ; Jiajun ZHU ; Fang GUO ; Falin XU ; Chunhui YANG ; Bizhen SHI ; Zhankui LI ; Xinhua ZHANG ; Jing LI ; Bin YI ; Xiuying TIAN ; Lejia ZHANG ; Jun TANG ; Xinlin HOU ; Jiahua XU ; Guoying HUANG ; Shuping HAN ; Wenhao ZHOU
Chinese Journal of Pediatrics 2025;63(12):1318-1324
Objective:To establish 30-day of age transcutaneous bilirubin (TcB) reference curves for healthy neonates, and to investigate regional variations in bilirubin dynamics.Methods:A multicenter retrospective cohort study was conducted. A total of 220 950 healthy neonates born at a gestational age of 35-<42 weeks, with a birth weight ≥2 000 g, who did not receive phototherapy within 60 h after birth were recruited. All of them underwent remote TcB monitoring using the Bilibaby remote jaundice monitoring system between August 1 st, 2020 and December 31 st, 2024 in 426 hospitals. TcB data were collected within the period from birth to 30-day of age. The P40, P75, and P95 of TcB values were calculated, and dynamic TcB curves for 30-day of age were constructed. Patterns of bilirubin change, rates of change, and transition outcomes were described. Regional comparisons between South and North were conducted using linear mixed-effects models for TcB trajectories and Pearson′s chi-square test for outcome differences. Results:A total of 220 950 neonates were included, of whom 101 711 (46.03%) were female. Gestational age at birth was (38.75±1.12) weeks, and birth weight was (3 272±417) g. TcB levels increased rapidly within 3-day of age, peaked at 4-6-day of age, with peak values at P40, P75, and P95 of 200.6, 239.7 and 275.4 μmol/L (11.8, 14.1 and 16.2 mg/dl), respectively. TcB levels gradually declined thereafter and stabilized after 13-day of age, with values at P40, P75, and P95 fluctuating between 147.9-159.8, 190.4-200.6, and 231.2-239.7 μmol/L (8.7-9.4, 11.2-11.8, 13.6-14.1 mg/dl), respectively. Notably, among neonates categorized as low-or low-intermediate-risk within 3-day of age, 6 700 (12.76%) progressed to intermediate-high or high risk between 4 and 30 days of age. Before 13-day of age, TcB levels in the southern regions were consistently higher than those in the northern regions ( P=0.039); from 14 to 30 days of age, the overall TcB levels had no statistically difference, but the temporal changes in TcB still showed regional differences (degrees of freedom=3, all interaction P<0.05). Among neonates classified as low-or low-intermediate risk within 3-day of age, 25 326 were from southern regions, of whom 4 254 (16.80%) progressed to intermediate-high or high risk between 4 and 30 days of age. In northern regions, 27 193 neonates were classified as low-or low-intermediate risk within 3-day of age, among whom 2 446 (8.99%) progressed to intermediate-high or high risk. The risk progression between the 2 regions had statistically difference ( χ2=716.49, P<0.001). Conclusions:A TcB percentile curve for neonates within 30-day of age was established, revealing that both the overall TcB level and its temporal trend were higher in southern than in northern newborns. These findings provide baseline data to support continuous management of neonatal jaundice.
9.Changing resistance profiles of Haemophilus influenzae and Moraxella catarrhalis isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Hui FAN ; Chunhong SHAO ; Jia WANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Yunsheng CHEN ; Qing MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Wenqi SONG ; Kaizhen WEN ; Yirong ZHANG ; Chuanqing WANG ; Pan FU ; Chao ZHUO ; Danhong SU ; Jiangwei KE ; Shuping ZHOU ; Hua ZHANG ; Fangfang HU ; Mei KANG ; Chao HE ; Hua YU ; Xiangning HUANG ; Yingchun XU ; Xiaojiang ZHANG ; Wenen LIU ; Yanming LI ; Lei ZHU ; Jinhua MENG ; Shifu WANG ; Bin SHAN ; Yan DU ; Wei JIA ; Gang LI ; Jiao FENG ; Ping GONG ; Miao SONG ; Lianhua WEI ; Xin WANG ; Ruizhong WANG ; Hua FANG ; Sufang GUO ; Yanyan WANG ; Dawen GUO ; Jinying ZHAO ; Lixia ZHANG ; Juan MA ; Han SHEN ; Wanqing ZHOU ; Ruyi GUO ; Yan ZHU ; Jinsong WU ; Yuemei LU ; Yuxing NI ; Jingrong SUN ; Xiaobo MA ; Yanqing ZHENG ; Yunsong YU ; Jie LIN ; Ziyong SUN ; Zhongju CHEN ; Zhidong HU ; Jin LI ; Fengbo ZHANG ; Ping JI ; Yunjian HU ; Xiaoman AI ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Xuesong XU ; Chao YAN ; Yi LI ; Shanmei WANG ; Hongqin GU ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Jihong LI ; Bixia YU ; Cunshan KOU ; Jilu SHEN ; Wenhui HUANG ; Xiuli YANG ; Likang ZHU ; Lin JIANG ; Wen HE ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(1):30-38
Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91%of the total clinical isolates and 4.07%of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89%were isolated from children and 33.11%were isolated from adults.More than 90%of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79%in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06%were isolated from children and 19.94%isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0%.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.
10.Changing distribution and antimicrobial resistance profiles of clinical isolates in children:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Qing MENG ; Lintao ZHOU ; Yunsheng CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Chuanqing WANG ; Aimin WANG ; Lei ZHU ; Jinhua MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Zhiyong LÜ ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Fangfang HU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Wei JIA ; Gang LI ; Kaizhen WEN ; Yirong ZHANG ; Yan JIN ; Chunhong SHAO ; Yong ZHAO ; Ping GONG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Sufang GUO ; Jiao FENG ; Ziyong SUN ; Zhongju CHEN ; Wen'en LIU ; Yanming LI ; Xiaobo MA ; Yanping ZHENG ; Dawen GUO ; Jinying ZHAO ; Ruizhong WANG ; Hua FANG ; Lixia ZHANG ; Juan MA ; Jihong LI ; Zhidong HU ; Jin LI ; Yuxing NI ; Jingyong SUN ; Ruyi GUO ; Yan ZHU ; Yi XIE ; Mei KANG ; Yuanhong XU ; Ying HUANG ; Shanmei WANG ; Yafei CHU ; Hua YU ; Xiangning HUANG ; Lianhua WEI ; Fengmei ZOU ; Han SHEN ; Wanqing ZHOU ; Yunzhuo CHU ; Sufei TIAN ; Shunhong XUE ; Hongqin GU ; Xuesong XU ; Chao YAN ; Bixia YU ; Jinju DUAN ; Jianbang KANG ; Jiangshan LIU ; Xuefei HU ; Yunsong YU ; Jie LIN ; Yunjian HU ; Xiaoman AI ; Chunlei YUE ; Jinsong WU ; Yuemei LU
Chinese Journal of Infection and Chemotherapy 2025;25(1):48-58
Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1%of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9%)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5%)was lower than that in inpatient children(31.5%).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1%)than in inpatient children(59.4%).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14%,11.7%,47.8%in outpatients,but 24.2%,20.6%,and 52.8%in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.

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