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.

Objective:To explore the effectiveness of the"full-chain"integrated medical and elderly care service model in addressing key issues in medical-nursing services such as weak medical support capacity and insufficient provision of community-and home-based medical-nursing services.Methods:The development pathway for the"full-chain"integrated medical-elderly care service standardization system,encompassing core components such as operational mechanisms,smart platforms,policy documents,and quality control systems was systematically outlined.Effectiveness based on dimensions including service coverage,quality improvement,talent development,and social benefits was evaluated.With standardization as the core driver,the'1234567'management model was innovatively implemented.Results:The model leveraged the downward allocation of high-quality resources from tertiary general hospitals to strengthen subdistrict community health service centers.By collaborating with subdistrict elderly-care service centers,it established"subdistrict medical-elderly care and wellness service centers".These centers enhanced the capabilities of"community-embedded elderly-care complexes",including community daytime care centers,established two-way referral channels between medical and elderly care services,aligned with healthcare demands to provide elderly individuals with reliable medical support.It reduced the burden on families and society,stimulated market vitality,boosted domestic demand,promoted the development of integrated medical-elderly care and wellness initiatives,thereby advancing the silver economy.With provincial government endorsement,the model had been applied to 203 communities across 37 counties by the end of 2024.Conclusion:The established"full-chain"integrated medical-elderly care service model facilitates regional high-quality development in integrated care by consolidating healthcare group resources and seamlessly connecting the service chain across hospitals,nursing homes,community institutions,and home-based settings,thereby creating a practical paradigm for comprehensive elderly care service delivery.

Objective:To investigate the effects and molecular mechanisms of Wilms tumor 1-associated proteins(WTAP)on the cell biological properties of esophageal squamous cell carcinoma(ESCC)cells.Methods:31 pairs of ESCC tissues and their paired paracancerous tissues that were surgically resected at the Second Clinical Medical College of Chuanbei Medical College between September 2019 and April 2021 were collected.The esophageal cancer cells KYSE30,KYSE410,KYSE150,KYSE510,TE-1,and normal human esophageal epithelial cells HET-1A were routinely cultured.Transfection reagents were used to transfect si-NC,si-WTAP#1 and si-WTAP#2 nucleic acids into KYSE150 and KYSE510 cells.The cells were divided into si-NC,si-WTAP#1 and si-WTAP#2 groups.The expressions of WTAP and MAP3K9 mRNA were detected in the cells of each group by qPCR assay.CCK-8 assay,clone formation assay,and scratch healing assay,Transwell assay were employed to detect the effects of knockdown of WTAP expression on ESCC cell proliferation,migration,invasion and apoptosis.WB assay was used to detect the expressions of WTAP,MAP3K9,EMT and MAPK pathway-related proteins in ESCC cells of each group knocked down of WTAP;immunohistochemistry to detect the expression of WTAP proteins in ESCC tissues,immunoprecipitation of methylated RNA(MeRIP)-qPCR assay to detect the level of MAP3K9 m6A in ESCC cells,actinomycin D assay to detect the stability of mRNA of MAP3K9,and database data to analyze the expression,target genes,functional enrichment,and interacting RNA of WTAP.Results:WTAP was highly expressed in ESCC tissues and cells(P<0.05 or P<0.01 or P<0.001)and correlated with the degree of differentiation(P<0.01);the expression of WTAP mRNA and its protein were successfully knocked down in KYSE150 and KYSE510 cells(P<0.01 or P<0.001);the knockdown of WTAP significantly inhibited the proliferation,migration and invasion of KYSE150 and KYSE510 cells(P<0.05 or P<0.01 or P<0.001),and promoted the apoptosis of KYSE150 and KYSE510 cells(P<0.05 or P<0.01).Knockdown of WTAP resulted in a significant decrease in the m6A level of MAP3K9(P<0.05),and its mRNA expression level and mRNA stability were both significantly reduced(P<0.05).Database data analysis showed that WTAP target genes clustered in the MAPK signaling pathway;the expression levels of MAP3K9,p-ERK,N-cadherin,and MMP9 were significantly reduced(P<0.05 or P<0.01),and the expression level of E-cadherin was significantly elevated(P<0.05 or P<0.01)in the KYSE150 and KYSE510 cells after knockdown of WTAP.Conclusions:WTAP is highly expressed in ESCC tissues and cells and correlates with their differentiation.It promotes the stability of MAP3K9 mRNA through m6A modification,activates the MAPK pathway and thus promotes the malignant biological behaviors of ESCC cells.

Objective: To investigate the attention cognitive function and symptom correlations of school aged children with attention deficit hyperactivity disorder (ADHD)using event related potential (ERP) technology, so as to provide references for the early diagnosis of children with ADHD. Methods: A total of 52 school aged children diagnosed with ADHD at the outpatient department of the Third Affiliated Hospital of Zhengzhou University from September 2022 to September 2024 and 50 age /sex matched healthy controls were selected. The ERP experiment adopted the auditory Oddball task to conduct comparative analyses of the amplitude and latency of the mismatch negative(MMN) at the Fz, Cz, and Pz points of the scalp electrode and the P3a component respectively. The symptom assessment scales adopted the Swanson,Nolan,and Pelham-Ⅳ Rating Scale (SNAP-Ⅳ) and the Parent Symptom Questionnaire (PSQ), which were filled out by the parents. Spearman correlation analysis was used to analyze the correlation between ERP components and symptoms in schoolaged children with ADHD. Results: The latency of MMN components in the healthy control group on the Fz lead was (188.30±2.06)ms, and the amplitude was (-15.54±1.35)μV; the latency of the P3a component on the Pz lead was (312.82±7.80)ms, and the amplitude was (3.80±0.18)μV. The latency of MMN components in the ADHD group on the Fz lead was (188.94±1.39)ms, and the amplitude was (-14.78±1.40)μV; the latency of the P3a component on the Pz lead was (317.21±5.65)ms, and the amplitude was (3.70±0.13)μV. Compared with normal children, the MMN of children with ADHD had smaller amplitudes in the Fz and Cz leads, and the P3a had greater latency and smaller amplitudes in the Cz and Pz leads ( t =2.79,2.20；-2.04，-3.25；2.35,3.21， P <0.05). Correlation analysis showed that the latency of MMN in children with ADHD was positively correlated with the inattention score in the SNAP-Ⅳ( r =0.22), and the amplitude of MMN was negatively correlated with the inattention score in the SNAP-Ⅳ and the learning problem score in PSQ ( r = -0.26 , -0.34)( P <0.05). The latency of P3a was positively correlated with the scores of inattention in the SNAP-Ⅳ and the score of learning problems in the PSQ ( r =0.26 ,0.24); the amplitude of P3a was negatively correlated with the scores of attention deficit and hyperactivity/impulsivity in the SNAP-Ⅳ and the scores of learning problems and impulsivity/hyperactivity in the PSQ( r = -0.26 , -0.22, -0.25,-0.32)( P <0.05). Conclusions School aged ADHD children exhibit abnormal MMN/P3a components, indicating attention related cognitive dysfunction. Symptoms such as inattention, learning problems and hyperactivity/impulsivity in children with ADHD are related to abnormal components of MMN and P3a.

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.

OBJECTIVE To investigate the current status of monitoring indicators related to the"Perioperative In-fection Control"in medical institutions above the secondary level in Guizhou Province,and to delve into the imple-mentation of key measures for infection prevention and control during the perioperative period for patients under-going surgical operations.METHODS Based on the"Implementation Plan for the'Perioperative Infection Control'Initiative in Guizhou Province",a"Case Investigation Form on Key Measures for Infection Prevention and Control During the Perioperative Period for Patients Undergoing Surgical Operation"was developed.A total of 138 medi-cal institutions participated in the case investigation,and a total of 2 128 cases were investigated.RESULTS The overall monitoring indicators for the"Perioperative Infection Control"initiative in the 138 medical institutions a-bove the secondary level in Guizhou Province were at a relatively low level.The skin cleansing compliance rate was 80.32％,the hair removal compliance rate was 16.43％,the prophylactic antibacterial drug administration rate within 0.5-1 hour before surgery was 55.94％and the antibacterial drug discontinuation rate within 24 hours after prophylactic medication for type Ⅰ incision surgeries was 56.48％.The hair removal compliance rate was higher in tertiary medical institutions(19.21％)than in secondary medical institutions(14.34％)(P=0.039).The distri-bution of the four monitoring indicators varied in clinical departments and surgery types,with statistically signifi-cant differences(P＜0.05).The preferred method for surgical site skin cleansing in medical institutions across the province was local wiping,primarily with clean water(57.21％).The primary method for hair removal was razors(68.82％),and hair removal on the day of surgery was most common(61.75％).CONCLUSIONS Conduc-ting a survey on the current status of"Perioperative Infection Control"initiative monitoring indicators in medi-cal institutions in Guizhou Province helps to understand the implementation of key measures for perioperative in-fection prevention and control and set intervention targets,thus providing references for establishing a dynam-ic monitoring indicator change mechanism.

Objective:To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods:Data were extracted from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 900 diabetic patients were identified, and after excluding those with missing data or invalid questionnaires, 769 patients were included in the analysis. Patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients, and a nomogram model was developed. The predictive performance of the model was assessed by the area under the receiver operating characteristic curve ( AUC). Model calibration and accuracy were evaluated using bootstrap resampling, calibration plots, and the Hosmer-Lemeshow test. The clinical utility was further assessed by decision curve analysis (DCA) and clinical impact curves (CIC) . Results:Among the 769 patients, 366 (47.59%) had depression. Logistic regression analysis showed that place of residence, pain, difficulty in toileting, difficulty in bathing, sleep duration, physical exercise, life satisfaction, and children's satisfaction were independent predictors of depression in diabetic patients. A nomogram was constructed based on these variables, yielding an AUC of 0.775. At the optimal cutoff value of 0.557, the model demonstrated a sensitivity of 59.1% and a specificity of 84.8%, indicating good discriminative ability. The Hosmer-Lemeshow test showed (χ 2=15.821, P=0.105), suggesting good agreement between predicted and observed outcomes. In the validation set, the AUC was 0.778, with Hosmer-Lemeshow (χ 2=8.557, P=0.575). DCA and CIC indicated favorable clinical applicability of the model. Conclusions:The depression risk prediction model constructed in this study demonstrated good predictive performance. It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.

This study aims to understand the epidemic distribution characteristics,antimicrobial resistance,virulence genes,and biofilm adhesion ability of Enterococcus in yaks on the Tibetan plateau.Three hundred and forty-six fresh yak fecal samples and 311 milk samples were collected from four provinces on the Tibetan plateau(Xizang,Sichuan,Gansu,Qinghai),totaling 657 sam-ples.Bacterial isolation and identification were conducted,followed by 16S rDNA gene detection and the construction of a systematic evolutionary tree.The isolated strains were tested for antimi-crobial resistance and virulence genes using PCR,and sensitivity tests were performed using 18 types of antibiotics.The biofilm adhesion ability of the isolated bacteria was determined using an improved semi-quantitative crystal violet staining method.The results showed that the total isola-tion rate of Enterococcus was 32.27％,with Sichuan having the highest at 60.23％,followed by Gansu,Qinghai,and Tibet autonomous region at 42.70％,23.47％,and 18.31％respectively.In terms of sample types,the isolation rate in fecal samples was 36.71％,and in milk samples,it was 27.33％.Through PCR amplification,bands of approximately 1 400 bp were obtained,and 5 strains were selected for evolutionary analysis,forming a separate cluster.Among the 212 isolated strains,a high resistance to clindamycin,quinupristin-dalfopristin,linezolid,levofloxacin,and erythromycin was observed,with various resistance phenomena,accounting for 60.85％.Only 5 out of 12 resist-ant genes were detected,namely erm(B),tet(L),tet(O),tet(M),and ant(6)-Ia.All 13 virulence genes were detected in Enterococcus,with detection rates in the range of 5.19％to 95.76％,where cpd was 95.75％,gelE was 91.98％,efaA was 86.79％,asal was 86.32％,and the rest ranged from 5.19％to 55.66％.The fsr virulence gene was not detected in Enterococcus from milk sources.Among the isolated strains,3.30％showed medium adhesive ability,48.58％showed weak adhesive ability,and 48.11％showed no adhesive ability.The above research revealed the preva-lence of yak derived Enterococcus,the carrying status of resistance and virulence genes,and the correlation between biofilm phenotypes,laying the foundation for mastering research data on yak-derived Enterococcus in the Tibetan plateau.

Objective:To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods:Data were extracted from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 900 diabetic patients were identified, and after excluding those with missing data or invalid questionnaires, 769 patients were included in the analysis. Patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients, and a nomogram model was developed. The predictive performance of the model was assessed by the area under the receiver operating characteristic curve ( AUC). Model calibration and accuracy were evaluated using bootstrap resampling, calibration plots, and the Hosmer-Lemeshow test. The clinical utility was further assessed by decision curve analysis (DCA) and clinical impact curves (CIC) . Results:Among the 769 patients, 366 (47.59%) had depression. Logistic regression analysis showed that place of residence, pain, difficulty in toileting, difficulty in bathing, sleep duration, physical exercise, life satisfaction, and children's satisfaction were independent predictors of depression in diabetic patients. A nomogram was constructed based on these variables, yielding an AUC of 0.775. At the optimal cutoff value of 0.557, the model demonstrated a sensitivity of 59.1% and a specificity of 84.8%, indicating good discriminative ability. The Hosmer-Lemeshow test showed (χ 2=15.821, P=0.105), suggesting good agreement between predicted and observed outcomes. In the validation set, the AUC was 0.778, with Hosmer-Lemeshow (χ 2=8.557, P=0.575). DCA and CIC indicated favorable clinical applicability of the model. Conclusions:The depression risk prediction model constructed in this study demonstrated good predictive performance. It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.

Objective:To investigate the developmental trajectories of post-stroke fatigue in patients with cerebral infarction and analyse its influencing factors, thereby providing a theoretical basis for clinical intervention measures.Methods:A cross-sectional survey employed convenience sampling to recruit cerebral infarction patients admitted to Shaoxing Seventh People's Hospital from January to December 2023. Data collection occurred at four time points: 1 day prior to discharge (T1), 1 month post-discharge (T2), three months post-discharge (T3), and six months post-discharge (T4). Data collection comprised general information questionnaires, the Fatigue Severity Scale, the Family Care Index Scale, the Post-Traumatic Growth Inventory, and the Disease Uncertainty Scale. Latent class growth models were employed to identify latent trajectories of post-stroke fatigue, with multifactorial logistic regression analysing influencing factors.Results:Data were collected from 260 stroke patients with complete records, comprising 136 males and 124 females aged 40 - 80 years. The incidence of post-stroke fatigue at time points T1 to T4 was 46.92% (122/260), 46.15% (120/260), 45.00% (117/260), and 43.46% (113/260), respectively. The Scores of post-stroke fatigue at time points T1-T4 were (4.56 ± 1.52), (4.25 ± 1.52), (4.18 ± 1.58), (4.09 ± 1.66) respectively, showing an overall slight downward trend in fatigue scores across time points. Results from latent class growth modelling indicated that post-stroke fatigue among cerebral infarction patients could be categorised into three groups: low fatigue (38 cases, C1 group, 14.62%), moderate fatigue decline (136 cases, C2 group, 52.31%), and persistent high fatigue (86 cases, C3 group, 33.08%). Univariate analysis revealed statistically significant differences among the three groups in age, educational attainment, carotid atherosclerosis, smoking status, family functioning, post-traumatic growth, and disease uncertainty ( χ2/ H/ F values ranging from 11.22 to 18.52, all P<0.05). Logistic regression analysis indicated that, with C3 group as reference, age ( OR = 0.478, 95% CI 0.255- 0.894), educational attainment ( OR = 3.147, 95% CI 1.558 - 6.359), carotid atherosclerosis ( OR = 0.260, 95% CI 0.094 - 0.722), smoking ( OR = 0.216, 95% CI 0.072 - 0.650), family functioning ( OR = 0.284, 95% CI 0.133 - 0.607), post-traumatic growth score ( OR = 1.115, 95% CI 1.059- 1.174), and disease uncertainty score ( OR = 0.908, 95% CI 0.873 - 0.946) were all factors influencing C1 group (all P<0.05); post-traumatic growth score ( OR = 1.039, 95% CI 1.010 - 1.068) and disease uncertainty score ( OR = 0.965, 95% CI 0.946 - 0.985) were factors influencing C2 group (both P<0.05). The regression model likelihood ratio ( χ2 = 113.73, P<0.001). Conclusions:Post-stroke fatigue in cerebral infarction patients exhibits three distinct developmental trajectories, reflecting population heterogeneity. Clinical practice should prioritise elderly patients, those with lower educational attainment, carotid atherosclerosis, and smoking habits. Targeted interventions to enhance family functioning, improve post-traumatic growth levels, and reduce disease uncertainty may lower the incidence of post-stroke fatigue.