OBJECTIVE To analyze the influencing factors for achieving target plasma drug concentration （trough） （abbreviated as “PDC”） of vancomycin in patients from high-altitude regions and establish a predictive model for PDC using single- center data， providing references for rational clinical drug use. METHODS Inpatients with vancomycin （1 g， q12 h） administered intravenously in our hospital from January 2021 to June 2024 were retrospectively included. Demographic data， liver and kidney function and hematological indexes were collected. Spearman correlation analysis was used to evaluate the correlation between vancomycin PDC and each detection index. Univariate analysis was used to evaluate the differences of each index in patients with different PDC， and the effects of different gender， body mass index， age and underlying diseases （hypertension/diabetes） on vancomycin PDC. Based on the results of correlation analysis and univariate analysis， multiple linear stepwise regression analysis was used to obtain the independent predictors of vancomycin PDC and construct the prediction model. RESULTS A total of 141 patients were included， with an overall attainment rate of 46.81% for the target PDC of vancomycin. Correlation analysis showed that the vancomycin PDC was positively correlated with age， blood urea nitrogen， uric acid （UA）， serum creatinine （CRE） and β2- microglobulin （β2-MG）， and negatively correlated with height， weight， creatinine clearance rate （CCR）， glomerular filtration rate （GFR）， alanine transaminase （ALT）， hemoglobin （HGB）， white blood cell count and neutrophils （P＜0.05）. There were significant differences in age， CRE and other 14 indexes among different PDC groups （P＜0.05 or P＜0.01）. Age and underlying diseases had significant effects on vancomycin PDC （P＜0.05 or P＜0.01）. CCR， direct bilirubin （DBil）， β2-MG， UA， HGB and height （standardized coefficients were －0.371， 0.367， 0.169， 0.232， －0.140， －0.132； P＜0.05） were independent predictors of vancomycin PDC. The F value of the regression equation was 34.858 （P＜0.05）， the R2 was 0.610， and the adjusted R2 was 0.592. CONCLUSIONS The vancomycin PDC of patients in high-altitude regions is affected by multiple factors such as renal function， liver function and hematological indexes. CCR， HGB and height could be used to predict vancomycin PDC negatively， while DBil， β2-MG and UA could be used to predict vancomycin PDC positively. The variables of the established prediction model could explain 59.2% of the variation of vancomycin PDC.

Meropenem （MEM） is one of the important drugs for the treatment of severe infections， but the standard dose is often difficult to achieve an effective therapeutic concentration target. This article reviews the related studies on the population pharmacokinetics of MEM in patients with severe infection. It is found that the apparent volume of distribution （Vd） and clearance rate are the most important factors affecting the dose adjustment， and the factors affecting Vd include serum albumin， age， overall weight， shock status， and chest/abdomen/cerebrospinal fluid drainage. The main factors affecting the clearance rate were renal function， renal replacement therapy treatment mode and combination therapy. For adult patients with severe infections in China， MEM is recommended to be administered in an individualized manner based on glomerular filtration rate， with a dosage range of 500 to 1 500 mg given every 4 to 6 hours， and prolonged infusion is preferred. When the minimum inhibitory concentration （MIC） of the pathogenic bacteria reaches 64 mg/L， therapeutic drug monitoring is required. For therapeutic efficacy， it is essential to ensure that the trough concentration remains above the MIC； to prevent drug resistance， it should be maintained above 4×MIC. Regarding safety， it is recommended that the upper limit of the trough concentration be 32 mg/L， and blood sampling for monitoring can be conducted as early as after 1 to 2 doses of administration.

This study investigated the effect of Wuling San on transforming growth factor-β1(TGF-β1)-induced fibrosis, inflammation, and oxidative stress in human renal tubular epithelial cells(HK-2) and its mechanism of antioxidant stress injury. HK-2 cells were cultured in vitro and divided into a control group, a TGF-β1 model group, and three treatment groups receiving Wuling San-containing serum at low(2.5%), medium(5.0%), and high(10.0%) doses. TGF-β1 was used to establish the model in all groups except the control group. CCK-8 was used to analyze the effect of different concentrations of Wuling San on the activity of HK-2 cells with or without TGF-β1 stimulation. The expression of key fibrosis molecules, including actin alpha 2(Acta2), collagen type Ⅰ alpha 1 chain(Col1α1), collagen type Ⅲ alpha 1 chain(Col3α1), TIMP metallopeptidase inhibitor 1(Timp1), and fibronectin 1(Fn1), was detected using qPCR. The expression levels of inflammatory cytokines, including tumor necrosis factor-α(TNF-α), interleukin-1β(IL-1β), interleukin-6(IL-6), interleukin-8(IL-8), and interleukin-4(IL-4), were measured using ELISA kits. Glutathione peroxidase(GSH-Px), malondialdehyde(MDA), catalase(CAT), and superoxide dismutase(SOD) biochemical kits were used to analyze the effect of Wuling San on TGF-β1-induced oxidative stress injury in HK-2 cells, and the expression of nuclear factor E2-related factor 2(Nrf2), heme oxygenase 1(HO-1), and NAD(P)H quinone oxidoreductase 1(NQO1) was analyzed by qPCR and immunofluorescence. The CCK-8 results indicated that the optimal administration concentrations of Wuling San were 2.5%, 5.0%, and 10.0%. Compared with the control group, the TGF-β1 model group showed significantly increased levels of key fibrosis molecules(Acta2, Col1α1, Col3α1, Timp1, and Fn1) and inflammatory cytokines(TNF-α, IL-1β, IL-6, IL-8, and IL-4). In contrast, the Wuling San administration groups were able to dose-dependently inhibit the expression levels of key fibrosis molecules and inflammatory cytokines compared with the TGF-β1 model group. Wuling San significantly increased the activities of GSH-Px, CAT, and SOD enzymes in TGF-β1-stimulated HK-2 cells and significantly inhibited the level of MDA. Furthermore, compared with the control group, the TGF-β1 model group exhibited a significant reduction in the expression of Nrf2, HO-1, and NQO1 genes and proteins. After Wuling San intervention, the expression of Nrf2, HO-1, and NQO1 genes and proteins was significantly increased. Correlation analysis showed that antioxidant stress enzymes(GSH-Px, CAT, and SOD) and Nrf2 signaling were significantly negatively correlated with key fibrosis molecules and inflammatory cytokines in the TGF-β1-stimulated HK-2 cell model. In conclusion, Wuling San can inhibit TGF-β1-induced fibrosis in HK-2 cells by activating the Nrf2 signaling pathway, improving oxidative stress injury, and reducing inflammation.
Humans ; Oxidative Stress/drug effects* ; Transforming Growth Factor beta1/metabolism* ; Fibrosis/genetics* ; Cell Line ; Drugs, Chinese Herbal/pharmacology* ; Epithelial Cells/immunology* ; Inflammation/metabolism*

Objective:To explore the impact of the hospital health literacy environment on patients′ postoperative pain self-management behaviors, aiming to provide insights for hospitals to implement the Comprehensive Pain Management Pilot Work Program in hospitals and to promote self-health management among patients with other diseases or symptoms. Methods:From November to December 2023, a convenience sampling method was used to select postoperative patients from three grade A tertiary general hospitals in Zhejiang Province for an on-site questionnaire survey. The Chinese version of brief health literacy screen (BHLS), short-form health literacy environment scale (SF-HLES) and postoperative pain self-management behavior questionnaire (PPSMB) were used as survey tools to investigate the health literacy level of patients, the health literacy environment of the hospital, and the postoperative pain management behaviors of patients. Two-way ANOVA was used to compare the impact of different dimensions of the hospital health literacy environment on postoperative pain management behaviors among patients with different levels of health literacy. Multiple linear regression analysis was used to explore the relationship between the hospital health literacy environment, individual health literacy, and patients′ postoperative pain self-management behaviors, and to discuss the impact of individual health literacy on patients′ postoperative pain self-management behaviors under different hospital health literacy environments.Results:341 valid questionnaires were collected. The average score of the hospitals′ SF-HLES was (73.62±19.54) points. The average score of the patients′ BHLS was (9.65±2.88) points. The average score of the patients′ PPSMB was (25.99±6.35) points. Two-way ANOVA results showed that the interaction between individual health literacy and the clinical dimension ( F=5.463, P=0.020) and structural dimension ( F=6.470, P=0.011) of the hospital health literacy environment had a statistically significant impact on patients′ postoperative pain self-management behaviors, while the interaction with the interpersonal dimension ( F=0, P=0.984) had no statistically significant impact on pain self-management behaviors. Simple effect analysis indicated that only in the high health literacy environment of the clinical and structural dimensions did the difference in pain self-management behaviors between patients with good health literacy and those with limited health literacy had statistical significance ( P<0.001). Multiple linear regression analysis results showed that for each 1-point increase in the patients′ BHLS score, their PPSMB score increased by 3.74 points ( β1=0.832, P<0.001); for each 1-point increase in the hospital′s SF-HLES score, the patients′ PPSMB score could increase by 0.198 points ( β2=0.610, P<0.001). In a low health literacy environment, individual health literacy did not affect pain self-management behaviors ( P>0.05); however, in a high health literacy environment, for each 1-point increase in the patients′ BHLS score, their PPSMB score correspondingly increased by 4.037 points ( β4=0.317, P<0.001). Conclusions:The positive impact of individual health literacy on pain self-management is contingent upon a high-quality hospital health literacy environment. This suggests that optimizing the hospital health literacy environment is a necessary precondition for implementing the relevant content of the Comprehensive Pain Management Pilot Work Program and can provide a reference for promote self-health management among patients with pain and other diseases or symptoms.

Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8％(62 219/261 893),and gram-negative bacteria 76.2％(199 674/261 893).The most common species were E.coli(46.7％),E.faecium(10.4％),K.pneumoniae(9.8％),E.faecalis(8.7％),P.mirabilis(3.5％),P.aeruginosa(3.4％),SS.agalactiae(2.6％),and E.cloacae(2.1％).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2％,52.8％and 37.0％,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7％,18.5％,16.4％,and 40.3％,respectively.Lower than 10％of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90％of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was＜2％.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.

Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7％)and Enterococcus faecalis(41.5％).The strains were mainly isolated from urinary tract specimens(46.9％±2.6％),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6％).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1％and 1.3％,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7％in 2015 to 3.4％in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3％.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.

Objective To examine the changing antimicrobial resistance profile of Enterobacter spp.isolates in 53 hospitals across China from 2015 t0 2021.Methods The clinical isolates of Enterobacter spp.were collected from 53 hospitals across China during 2015-2021 and tested for antimicrobial susceptibility using Kirby-Bauer method or automated testing systems according to the CHINET unified protocol.The results were interpreted according to the breakpoints issued by the Clinical & Laboratory Standards Institute(CLSI)in 2021(M100 31st edition)and analyzed with WHONET 5.6 software.Results A total of 37 966 Enterobacter strains were isolated from 2015 to 2021.The proportion of Enterobacter isolates among all clinical isolates showed a fluctuating trend over the 7-year period,overall 2.5％in all clinical isolates amd 5.7％in Enterobacterale strains.The most frequently isolated Enterobacter species was Enterobacter cloacae,accounting for 93.7％(35 571/37 966).The strains were mainly isolated from respiratory specimens(44.4±4.6)％,followed by secretions/pus(16.4±2.3)％and urine(16.0±0.9)％.The strains from respiratory samples decreased slightly,while those from sterile body fluids increased over the 7-year period.The Enterobacter strains were mainly isolated from inpatients(92.9％),and only(7.1±0.8)％of the strains were isolated from outpatients and emergency patients.The patients in surgical wards contributed the highest number of isolates(24.4±2.9)％compared to the inpatients in any other departement.Overall,≤ 7.9％of the E.cloacae strains were resistant to amikacin,tigecycline,polymyxin B,imipenem or meropenem,while ≤5.6％of the Enterobacter asburiae strains were resistant to these antimicrobial agents.E.asburiae showed higher resistance rate to polymyxin B than E.cloacae(19.7％vs 3.9％).Overall,≤8.1％of the Enterobacter gergoviae strains were resistant to tigecycline,amikacin,meropenem,or imipenem,while 10.5％of these strains were resistant to polycolistin B.The overall prevalence of carbapenem-resistant Enterobacter was 10.0％over the 7-year period,but showing an upward trend.The resistance profiles of Enterobacter isolates varied with the department from which they were isolated and whether the patient is an adult or a child.The prevalence of carbapenem-resistant E.cloacae was the highest in the E.cloacae isolates from ICU patients.Conclusions The results of the CHINET Antimicrobial Resistance Surveillance Program indicate that the proportion of Enterobacter strains in all clinical isolates fluctuates slightly over the 7-year period from 2015 to 2021.The Enterobacter strains showed increasing resistance to multiple antimicrobial drugs,especially carbapenems over the 7-year period.

Objective To understand the changing distribution and antimicrobial resistance profiles of Proteus,Morganella and Providencia in hospitals across China from January 1,2015 to December 31,2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods Antimicrobial susceptibility testing was carried out following the unified CHINET protocol.The results were interpreted in accordance with the breakpoints in the 2021 Clinical & Laboratory Standards Institute(CLSI)M100(31 st Edition).Results A total of 32 433 Enterobacterales strains were isolated during the 7-year period,including 24 160 strains of Proteus,6 704 strains of Morganella,and 1 569 strains of Providencia.The overall number of these Enterobacterales isolates increased significantly over the 7-year period.The top 3 specimen source of these strains were urine,lower respiratory tract specimens,and wound secretions.Proteus,Morganella,and Providencia isolates showed lower resistance rates to amikacin,meropenem,cefoxitin,cefepime,cefoperazone-sulbactam,and piperacillin-tazobactam.For most of the antibiotics tested,less than 10％of the Proteus and Morganella strains were resistant,while less than 20％of the Providencia strains were resistant.The prevalence of carbapenem-resistant Enterobacterales(CRE)was 1.4％in Proteus isolates,1.9％in Morganella isolates,and 15.6％in Providencia isolates.Conclusions The overall number of clinical isolates of Proteus,Morganella and Providencia increased significantly in the 7-year period from 2015 to 2021.The prevalence of CRE strains also increased.More attention should be paid to antimicrobial resistance surveillance and rational antibiotic use so as to prevent the emergence and increase of antimicrobial resistance.