1.Stress distribution on the maxilla when wearing the Twin-block appliance for Class Ⅱ malocclusion
Shuai LI ; Hua LIU ; Yonghui SHANG ; Yicong LIU ; Qihang ZHAO ; Wen LIU
Chinese Journal of Tissue Engineering Research 2025;29(5):881-887
BACKGROUND:The Twin-block orthodontic appliance is commonly used for the correction of Class Ⅱ malocclusion.Its mechanism of action in stimulating mandibular growth has been confirmed in many studies,but its impact on maxillary growth is not very clear. OBJECTIVE:By establishing a finite element model to analyze the stress distribution of the maxillary complex,surrounding bone sutures,and maxillary dentition in patients with Class Ⅱ malocclusion wearing Twin-block orthodontic appliances. METHODS:One patient with Class Ⅱ malocclusion who underwent orthodontic treatment at Qingdao Hospital/Qingdao Municipal Hospital of Shandong Rehabilitation University was selected.The bite force data of the patient when wearing the Twin-block orthodontic appliance was measured,and CBCT data were collected.A finite element model was established,including the maxillary complex,peripheral sutures,Twin-block orthodontic appliance,and maxillary dentition.ABAQUS software was used to simulate the stress distribution in the maxilla and maxillary dentition when the patient was wearing the Twin-block appliance. RESULTS AND CONCLUSION:The equivalent stress on the maxillary anterior teeth was significantly smaller than that on the posterior teeth,and the maximum equivalent stress on both sides of the teeth were 4.797 5 Mpa and 8.716 1 Mpa,respectively,which were located at the first premolar.The maximum displacements were presented at the maxillary incisors on both sides of the teeth,which were 0.080 5 mm and 0.081 0 mm,respectively.The maximum equivalent stress on the bone suture was 1.284 Mpa,which was mainly concentrated in the pterygopalatine suture and the frontal-maxillary suture on both sides,and there was almost no difference in the force of the rest of bone sutures;the maximum displacement of the bone suture was 0.07 mm,with the pterygopalatine suture having the largest displacement,followed by the frontal-maxillary suture.The maximal equivalent stress on the maxillary complex was 27.18 Mpa,which was mainly concentrated on both sides of the anterior pyriform foramen of the maxilla,around the nasofrontal suture and around the pterygopalatine suture at the posterior part of the jaws.The maximal displacement of the maxilla was 0.07 mm,which was mainly concentrated on the maxillary alveolar bone.All these findings show that the occlusal force acts on the maxillary complex through the Twin-block appliance,resulting in clockwise rotation of the maxilla and steepening of the dentition plane.Measures should be taken to compensate for this tendency,for example,by considering maxillary molar elongation and intrusion in the process of occlusion,which are not only able to flatten the occlusal plane,but facilitate the mandibular protraction,thereby further improving Class Ⅱ malocclusion orthodontic treatment.
2.Targeting PPARα for The Treatment of Cardiovascular Diseases
Tong-Tong ZHANG ; Hao-Zhuo ZHANG ; Li HE ; Jia-Wei LIU ; Jia-Zhen WU ; Wen-Hua SU ; Ju-Hua DAN
Progress in Biochemistry and Biophysics 2025;52(9):2295-2313
Cardiovascular disease (CVD) remains one of the leading causes of mortality among adults globally, with continuously rising morbidity and mortality rates. Metabolic disorders are closely linked to various cardiovascular diseases and play a critical role in their pathogenesis and progression, involving multifaceted mechanisms such as altered substrate utilization, mitochondrial structural and functional dysfunction, and impaired ATP synthesis and transport. In recent years, the potential role of peroxisome proliferator-activated receptors (PPARs) in cardiovascular diseases has garnered significant attention, particularly peroxisome proliferator-activated receptor alpha (PPARα), which is recognized as a highly promising therapeutic target for CVD. PPARα regulates cardiovascular physiological and pathological processes through fatty acid metabolism. As a ligand-activated receptor within the nuclear hormone receptor family, PPARα is highly expressed in multiple organs, including skeletal muscle, liver, intestine, kidney, and heart, where it governs the metabolism of diverse substrates. Functioning as a key transcription factor in maintaining metabolic homeostasis and catalyzing or regulating biochemical reactions, PPARα exerts its cardioprotective effects through multiple pathways: modulating lipid metabolism, participating in cardiac energy metabolism, enhancing insulin sensitivity, suppressing inflammatory responses, improving vascular endothelial function, and inhibiting smooth muscle cell proliferation and migration. These mechanisms collectively reduce the risk of cardiovascular disease development. Thus, PPARα plays a pivotal role in various pathological processes via mechanisms such as lipid metabolism regulation, anti-inflammatory actions, and anti-apoptotic effects. PPARα is activated by binding to natural or synthetic lipophilic ligands, including endogenous fatty acids and their derivatives (e.g., linoleic acid, oleic acid, and arachidonic acid) as well as synthetic peroxisome proliferators. Upon ligand binding, PPARα activates the nuclear receptor retinoid X receptor (RXR), forming a PPARα-RXR heterodimer. This heterodimer, in conjunction with coactivators, undergoes further activation and subsequently binds to peroxisome proliferator response elements (PPREs), thereby regulating the transcription of target genes critical for lipid and glucose homeostasis. Key genes include fatty acid translocase (FAT/CD36), diacylglycerol acyltransferase (DGAT), carnitine palmitoyltransferase I (CPT1), and glucose transporter (GLUT), which are primarily involved in fatty acid uptake, storage, oxidation, and glucose utilization processes. Advancing research on PPARα as a therapeutic target for cardiovascular diseases has underscored its growing clinical significance. Currently, PPARα activators/agonists, such as fibrates (e.g., fenofibrate and bezafibrate) and thiazolidinediones, have been extensively studied in clinical trials for CVD prevention. Traditional PPARα agonists, including fenofibrate and bezafibrate, are widely used in clinical practice to treat hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) levels. These fibrates enhance fatty acid metabolism in the liver and skeletal muscle by activating PPARα, and their cardioprotective effects have been validated in numerous clinical studies. Recent research highlights that fibrates improve insulin resistance, regulate lipid metabolism, correct energy metabolism imbalances, and inhibit the proliferation and migration of vascular smooth muscle and endothelial cells, thereby ameliorating pathological remodeling of the cardiovascular system and reducing blood pressure. Given the substantial attention to PPARα-targeted interventions in both basic research and clinical applications, activating PPARα may serve as a key therapeutic strategy for managing cardiovascular conditions such as myocardial hypertrophy, atherosclerosis, ischemic cardiomyopathy, myocardial infarction, diabetic cardiomyopathy, and heart failure. This review comprehensively examines the regulatory roles of PPARα in cardiovascular diseases and evaluates its clinical application value, aiming to provide a theoretical foundation for further development and utilization of PPARα-related therapies in CVD treatment.
3.Early research of applying contrast-enhanced ultrasound radiomics model to forecast pathological grades in bladder urothelial carcinoma
Wen LI ; Hua HONG ; Qian LIU ; Yang LIU ; Danyan LIANG ; Senlin BAO ; Heyang LIU
Chinese Journal of Ultrasonography 2025;34(11):999-1006
Objective:To investigate the predictive value of a machine learning model combining contrast-enhanced ultrasound(CEUS)parameters,radiomics features of ultrasound images,and clinical data for pathological grading in bladder urothelial carcinoma(BUC).Methods:A retrospective analysis was conducted on 174 BUC patients from Inner Mongolia Autonomous Region People 's Hospital and the First Affiliated Hospital of Baotou Medical College from December 2017 to March 2024. One hundred and thirteen BUC patients from the former hospital were randomly divided into training group and internal test group in a ratio of 7 to 3,while 61 BUC patients from the latter hospital served as an external test group. The patients were stratified into low-grade bladder urothelial carcinoma(LGBUC)and high-grade bladder urothelial carcinoma(HGBUC)groups based on pathology. Two-dimensional grayscale ultrasound images were subjected to super-resolution(SR)reconstruction,followed by extraction and screening of radiomics features in comparison with CEUS video sequences. Selected features were input into a support vector machine(SVM)to build the radiomics model. CEUS parameters,conventional ultrasound metrics and clinical data with statistical significance between LGBUC and HGBUC groups were input into SVM to construct the clinical model. The radiomics and clinical model outputs were fused via multivariate Logistic regression to form a combined model. Model performances were evaluated using ROC curves,calibration curves,and clinical decision curves. Results:Seven radiomics features from SR images were used to build the radiomics model,while CEUS parameters(peak intensity and time-to-peak half),age,tumor-wall interface and tumor-wall angle formed the clinical model. The combined model integrated these outputs. All 3 models exhibited respective strengths,the combined model showed superior robustness. The AUCs of the combined model in the training,internal test and external test groups were 0.92,0.84 and 0.82,respectively.Conclusions:The combined model combining CEUS parameters,ultrasound radiomics features,and clinical data accurately predicts BUC pathological grade,providing a potential tool for clinical diagnosis and treatment.
4.Chemical constituents from Commelina communis
Hong-ting YI ; Ding-mei LIANG ; Bin LEI ; Hong-ling ZENG ; Zhong-wen CHEN ; Hua LIU ; Feng LIU
Chinese Traditional Patent Medicine 2025;47(3):827-833
AIM To study the chemical constituents from Commelina communis L.METHODS The 95%ethanol extract from C.Communis was isolated and purified by activated charcoal,silica gel,Sephadex LH-20,and HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Seventeen compounds were isolated and identified as p-hydroxyl ethyl cinnamate(1),p-hydroxybenzaldehyde(2),vanillin(3),4-hydroxy-2,3-dimethyl-2-nonen-4-olide(4),hemeratrol A(5),chakyunglupulin B(6),chakyunglupulin A(7),2-(2-hydroxyethyl)-3-methylfumaric acid(8),N-cis-feruloyl tyramine(9),N-trans-coumaroyltyramine(10),5,6,7,3',4',5'-hexamethoxyflavone(11),N-trans-sinapoyltyramine(12),dihydro-feruloyltyramine(13),N-trans-feruloyltyramine(14),2-phenylethanol-β-D-glucoside(15),quercetin-3-O-β-D-glucoside(16),and isorhamnetin-3-O-β-D-glucopyranoside(17).CONCLUSION Compounds 4-8,10 and 11 are isolated from Commelina genus for the first time,and 1,9,12-15 are first isolated from this plant.
5.Diagnostic value and influencing factors of endoscopic ultrasonography for rectal neuroendocrine neoplasms
Xiaotong WANG ; Xiaowei WANG ; Wenjun ZHAO ; Zeyuan DIAO ; Wen SONG ; Yao LIU ; Zhenzhen SUI ; Ya LIU ; Hua LIU
Chinese Journal of Digestive Endoscopy 2025;42(6):474-479
Objective:To investigate the diagnostic value and influencing factors of endoscopic ultrasonography (EUS) for detecting rectal neuroendocrine neoplasms (R-NENs).Methods:A retrospective case-control study was performed on data of patients with suspected R-NENs by white light endoscopy who underwent endoscopic diagnosis and treatment or surgical operation and obtained pathological diagnosis at the Affiliated Hospital of Qingdao University from March 2016 to June 2023. Clinical data, EUS characteristics and pathological results were statistically analyzed, and the diagnostic accuracy of EUS for R-NENs were obtained by comparing the EUS results with the pathological results. Influencing factors affecting accuracy were analyzed by using the binary logistic regression model.Results:A total of 317 patients were included. The sensitivity, the specificity, the positive predictive value and the negative predictive value of EUS in diagnosing R-NENs were 98.03% (249/254), 34.92% (22/63), 85.86% (249/290) and 81.48% (22/27) respectively. The accuracy was 85.49% (271/317) and the Jorden index was 0.33. Tumor size ≤5 mm ( P=0.002, OR=2.892, 95% CI: 1.464-5.713), absence of surface vascular dilation ( P=0.019, OR=2.613, 95% CI: 1.170-5.837), normal tumor coloration ( P=0.001, OR=3.460, 95% CI: 1.645-7.279) and erythematous surface appearance ( P=0.048, OR=7.242, 95% CI: 1.015-51.680) were independent risk factors affecting the accuracy of R-NENs diagnosis by EUS. Depth assessment accuracy of EUS was 76.77% (195/254), with echo heterogeneity ( P<0.001, OR=4.008, 95% CI: 1.980-8.113) and surface depression ( P=0.035, OR=2.664, 95% CI: 1.073-6.615) emerging as significant factors affecting invasion depth evaluation. Conclusion:EUS demonstrates substantial clinical utility for R-NENs assessment, with diagnostic performance being significantly associated with tumor morphology and sonographic features. Macroscopic characteristics including tumor size, vascular patterns, and chromatic features influence diagnostic accuracy, while echo-textural heterogeneity and surface depression affect invasion depth precision. These findings underscore the clinical relevance of comprehensive EUS evaluation in R-NENs management.
6.Investigation on ventilator use and management in secondary and tertiary medical institutions in Sichuan Province
Zheng-hua LIANG ; Si-mei WANG ; Qiu-yu LIU ; Jin-long XU ; Ze-fang LIU ; Dan WEN
Chinese Medical Equipment Journal 2025;46(3):75-80
Objective To investigate the current situation of the ventilator use and management in secondary and tertiary medical institutions in Sichuan Province to provide references for management and training of ventilator users.Methods A questionnaire survey involving in 235 nurses from 13 secondary and tertiary medical institutions in Sichuan Province was conducted in terms of general information of the respondents,pipeline connection and parameter setup of ventilators,cleaning,sterilization and maintenance of ventilators,identification and treatment of ventilator alarms and hospital-acquired infection prevention and control.Results The results showed 20.4%of the respondents were nurses from critical care departments,and 79.6%of the respondents underwent the training for using the ventilator in their department;91.9%of the respondents could conduct ventilator self-testing by connecting the simulated lungs after the ventilator was switched on;66.4%of the respondents indicated that the initial parameters of the ventilator were set by the doctor;60.9%of the respondents proved that the performance monitoring and routine maintenance of the ventilator were carried out by the nurse;63.4%of the respondents showed disposable lines were commonly used in the departments.There were 89.8%of the respondents said the daily sterilization and management of the ventilator were performed by the nurse;46.0%of the respondents expressed the external surface of the ventilator was disinfected mainly with gamma disinfectant wipes;44.7%of the respondents indicated the external surface of the ventilator was sterilized every day;48.5%of the respondents said the internal airway of the ventilator was disinfected;57.0%of the respondents proved the disinfection was conducted after the expiratory flow sensors were used by any patient.There were 74.5%of the respondents that had paid attention to the ventilator waveform,and by the ventilator waveform only 21.3%were correct in determining whether there was secretion or fluid in the circuit and 22.1%in clarifying whether there was air leakage in the circuit.There were 59.1%of the respondents indicated that the closed suction tube was replaced once every 24 h;71.1%of the respondents could perform airbag pressure monitoring by the special pressure gauge.Conclusion Most of the nurses from secondary and tertiary medical institutions in Sichuan Province can use ventilators correctly,who have problems in disinfection specifications,infection prevention and control or recognition of ventilation waveform.It's suggested the training be strengthened to enhance the clinical nursing staffs in ventilator management.[Chinese Medical Equipment Journal,2025,46(3):75-80]
7.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.
8.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.
9.Surveillance of antimicrobial resistance in clinical isolates of Escherichia coli:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shanmei WANG ; Bing MA ; Yi LI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Aimin WANG ; 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 ; 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 2025;25(1):39-47
Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)%.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0%vs 29.5%).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7%vs 7.8%,0.8%vs 0.1%,both P<0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)%with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9%and 1.8%,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1%).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10%.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P<0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.
10.Identification of GSK3 family and regulatory effects of brassinolide on growth and development of Nardostachys jatamansi.
Yu-Yan LEI ; Zheng MA ; Jing WEI ; Wen-Bing LI ; Ying LI ; Zheng-Ming YANG ; Shao-Shan ZHANG ; Jing-Qiu FENG ; Hua-Chun SHENG ; Yuan LIU
China Journal of Chinese Materia Medica 2025;50(2):395-403
This study identified 8 members including NjBIN2 of the GSK3 family in Nardostachys jatamansi by bioinformatics analysis. Moreover, the phylogenetic tree revealed that the GKS3 family members of N. jatamansi had a close relationship with those of Arabidopsis. RT-qPCR results showed that NjBIN2 presented a tissue-specific expression pattern with the highest expression in roots, suggesting that NjBIN2 played a role in root growth and development. In addition, the application of epibrassinolide or the brassinosteroid(BR) synthesis inhibitor(brassinazole) altered the expression pattern of NjBIN2 and influenced the photomorphogenesis(cotyledon opening) and root development of N. jatamansi, which provided direct evidence about the functions of NjBIN2. In conclusion, this study highlights the roles of BIN2 in regulating the growth and development of N. jatamansi by analyzing the expression pattern and biological function of NjBIN2. It not only enriches the understanding about the regulatory mechanism of the growth and development of N. jatamansi but also provides a theoretical basis and potential gene targets for molecular breeding of N. jatamansi with improved quality in the future.
Brassinosteroids/metabolism*
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Steroids, Heterocyclic/metabolism*
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Gene Expression Regulation, Plant/drug effects*
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Plant Proteins/metabolism*
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Phylogeny
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Nardostachys/metabolism*
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Plant Growth Regulators/pharmacology*
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Plant Roots/drug effects*

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