Objective:This study aimed to examine the association between malnutrition diagnosed by the Global Leadership Initiative on Malnutrition(GLIM)criteria and clinical outcomes in surgical patients,as well as to assess its prognostic impact on postoperative adverse clinical outcomes.Methods:Electronic databases,including PubMed,Embase,Web of Science,CINAHL,Scopus,The Cochrane Library,Clinical Trials,CNKI,Wanfang Data Knowledge Service Platform,and the Chinese Biomedical Literature Database,were systematically searched.Relevant cohort studies utilizing GLIM criteria to preoperatively diagnose malnutrition in surgical inpatients were included.The exposed group comprised surgical patients diagnosed with preoperative malnutrition using GLIM criteria,while the control group consisted of surgically treated patients without malnutrition as per GLIM criteria.Literature quality was evaluated using the Newcastle-Ottawa Scale(NOS),and meta-analysis was performed using Review Manager 5.4 software.Results:Fourteen literatures were included,with a total sample size of 10,045 patients.Meta-analysis revealed that the malnourished group had a higher incidence of postoperative complications compared to the non-malnourished group[risk ratio(RR)=1.81,95%CI:1.66～1.98),P<0.00001].Additionally,the incidence of severe complications was significantly higher in GLIM-diagnosed malnourished patients.The malnourished group exhibited poorer overall survival[hazard ratio(HR)=1.90,95%CI:1.55～2.34,P<0.00001]and disease-free survival[HR=2.25,95%CI:1.02～4.93,P=0.04]compared to the non-malnourished group.Conclusion:GLIM-diagnosed malnutrition is significantly associated with adverse clinical outcomes in surgical patients,increasing postoperative complication rates and reducing overall and disease-free survival.The GLIM criteria demonstrate value in predicting adverse clinical outcomes in this population.Further high-quality studies are warranted to validate these findings.

Objective:To construct a nanoparticle vaccine displaying the prefusion F (preF) protein of respiratory syncytial virus (RSV) using the I53-50 protein nanoparticle platform, and to systematically evaluate its immunogenicity and protective efficacy.Methods:The RSV preF trimer antigen was genetically fused to I53-50A and assembled in vitro with I53-50B to form preF-I53-50 nanoparticles, theoretically displaying 20 preF antigens per particle. The structure and purity were characterized by size-exclusion chromatography, SDS-PAGE, and negative-stain electron microscopy. BALB/c mice were intramuscularly immunized with varying doses (1 μg or 5 μg) of preF antigen or an equimolar amount of preF-I53-50 nanoparticles. Humoral immunity, B-cell responses, and protective efficacy were assessed following intranasal viral challenge.Results:The preF-I53-50 nanoparticles self-assembled into spherical structures (50-60 nm in diameter) with uniformly arrayed antigens. The nanoparticle vaccine enhanced RSV-specific IgG1 and IgG2a antibody responses, promoting a Th1-biased immune profile. At equimolar preF doses, the neutralizing antibody titers induced by 1 μg and 5 μg nanoparticle formulations were 2.8-fold and 2.3-fold higher, respectively, than those elicited by preF alone ( P<0.05). Notably, even the low-dose nanoparticle group outperformed the high-dose preF group (1.6-fold increase). Viral challenge experiments demonstrated that preF-I53-50 effectively suppressed pulmonary viral replication, mitigated pathological damage, and induced stronger germinal center and memory B-cell responses, suggesting enhanced B-cell affinity maturation and long-term immune memory. Conclusion:The preF-I53-50 vaccine improves the immunogenicity and protective efficacy of RSV preF through multivalent antigen display.

AIM To analyze the dynamic change patterns of aflatoxin B1,aflatoxin B2,aflatoxin G1,aflatoxin G2,T-2 toxin and deoxynivalenol contents during the fermentation of Massa Medicata Fermentata.METHODS The analysis was performed on a 40 ℃ thermostatic Waters ACQUITY UPLC HSS T3 column(100 mm×2.1 mm,1.8 μm),with the mobile phase comprising of 0.01％formic acid-[acetonitrile-methanol(1∶1)]flowing at 0.3 mL/min,and electron spray ionization source was adopted in positive ion scanning with multiple reaction monitoring mode.RESULTS Six mycotoxins showed good linear relationships within their own ranges(R2＞0.998 0),whose average recoveries were 76.1％-119.3％with the RSDs of 0.49％-9.27％,and except for deoxynivalenol,their contents demonstrated the trends of growing out of nothing and gradually increasing.CONCLUSION The risk of mycotoxin infection exists in the fermentation of Massa Medicata Fermentata.This simple,efficient,rapid and sensitive method can provide a reference for whole-process monitoring the fermentation process for Massa Medicata Fermentata.

Aim To investigate the protective effect of Shengmai Yin on myocardial ischemia/reperfusion in-jury(MI/RI)in vitro and in vivo and to unravel the underlying mechanism.Methods SD rats were divid-ed into the sham group,model group,and Shengmai Yin group(SM).Rat MI/RI model was established.Cardiac function,infarct area,pathological changes,cardiomyocyte apoptosis,macrophage infiltration,and serum cTnT and CK-MB levels were measured.The mRNA and protein expressions of Calpain-1 and Cal-pain-2 were assessed.The hypoxia/reoxygenation(H/R)model was constructed in H9c2 cells.The active ingredients of Shengmai Yin were screened using net-work pharmacology and verified by CCK-8.In the car-diomyocytes H/R model,Fluo-4 AM staining was used to detect the changes of Ca2+levels.Results Com-pared with model group,LVEF and LVFS of Shengmai Yin-treated rats increased,myocardial infarction area was reduced,while myocardial tissue injury was allevi-ated.Myocardial apoptosis rate and the number of macrophages were reduced.Similarly,cTnT and CK-MB levels decreased.In addition,the expression lev-els of Calpain-1 and Calpain-2 mRNA and protein de-creased in the SM treatment group.Under the H/R model,all the active ingredients of Shengmai decoction had protective effects on cardiomyocytes,and the treat-ment could reduce the level of Ca2+in cardiomyocytes.Conclusions Shengmai Yin has protective effects on MI/RI in rats.This effect may be related to the de-crease in Ca2+levels,as well as Calpain-1 and Calap-in-2 mRNA and protein expression.

Objective To investigate the expression of arginase Ⅱ(ArgⅡ)in kidney tissue of rats with diabetic nephropathy(DN)and its significance in the development of DN.Methods A total of 10 male SD rats were randomly divided into the control group and the model group,with 5 rats in each group.An rat model of DN was developed by feeding with high-sugar and high-fat diet combined with intra-peritoneal injection of low-dose streptozotocin(45 mg/kg),and they were sacrificed after 11 weeks of continued feeding.The body weight,and biochemical indexes of blood and urine of rats were determined.The right kidney was weighed and histopathological examination was performed.The pathological changes of kidney tissues and protein expression of ArgⅡ and CD68+were observed,and the immunofluores-cence double staining was used to observe the distribution and expression of ArgⅡand a marker of renal macrophage activation CD68+;the protein expression of ArgⅡ,NF-κB,TNF-α and IL-6 in kidney tissues was determined by Western blot.Results Compared with the control group,the ratio of kidney weight to body weight,24-hour urine volume,24-hour urine protein,fasting blood glucose,urea nitrogen and insulin level in the model group were significantly increased(P<0.05).The renal histopathology showed that the mesangial cells of the renal glomerular were necrotic with vascular dilatation,and the renal tubular epithelial cells were steatosis and congestion.Compared with the control group,the protein expression of ArgⅡ,CD68+,NF-κB,TNF-α and IL-6 in the kidney tissues of the model group were significantly increased(P<0.05).Immunofluorescence double staining demonstrated the co-expression of ArgⅡ and CD68+in renal tissue,and the fluorescence intensities of both ArgⅡ and CD68+in the model group were significantly stronger than those in the control group(P<0.01).Conclusion The expression of ArgⅡ is increased in DN,which may be participated in the occurrence of inflammatory lesions in DN.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

The incidence of pediatric inflammatory bowel disease(IBD)is rising,with an especially high proportion of early-onset cases in Asia.Conventional treatments such as glucocorticoids and immunosuppressants have limited efficacy and notable adverse effects,whereas biologic therapies substantially improve remission rates and quality of life.Therapeutic drug monitoring(TDM),by assessing trough concentrations and anti-drug antibodies,enables individualized dose optimization,reduces immunogenicity,and prolongs treatment persistence.However,challenges remain,including insufficient standardization and the lack of pediatric-specific concentration thresholds.This review summarizes recent advances in biologics and TDM in pediatric IBD to inform precision treatment.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective:To explore the incidence status,influencing factors and short-term prognosis characteristics of early onset coronary heart disease in women in Wansheng District of Chongqing,and to provide scientific basis for formulating regional prevention and treatment strategies.Methods:This study was a single-center retrospective study,100 coronary heart disease in women from January 2022 to December 2023 at Chongqing Wansheng Economic and Technological Development Zone People's Hospital were prospective selected,and they were divided into early onset group of 40 cases(≤ 65 years old)and late onset group of 60 cases(＞65 years old)based on their age of onset.Another 60 healthy women who underwent physical examinations during the same period to exclude coronary heart disease were selected as the control group.Univariate factor and multiple factor logistic regression analysis were used to identify independent risk factors for early onset coronary heart disease in women.Draw receiver operating characteristic(ROC)curve for the subjects,the efficacy of risk factors in predicting early onset coronary heart disease based on the area under the curve(AUC)of ROC curve were evaluated.Patients were followed up for 1 year to observe the occurrence of major adverse cardiovascular events(MACE).Result:Among 100 fcoronary heart disease in women,the early onset group accounted for 40.00％(40/100).Univariate analysis showed that age,hyperlipidemia history,smoking history,hypertension history,family history,diabetes history,total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C)were related to the early onset coronary heart disease.Multivariate analysis showed that,hyperlipidemia history(OR=4.124,95％CI:2.343-7.217),smoking history(OR=3.564),hypertension(OR=3.253),family history(OR=2.981),diabetes history(OR=2.873)were independent risk factors.ROC curve analysis results showed that joint evaluation had the best predictive value,with AUC of 0.829,which was higher than the AUC of individual evaluation for each factor.The incidence of MACE in the early onset group(45.00％)was significantly higher than that in the late onset group(P＜0.05).Conclusion:Early onset coronary heart disease in women in Wansheng District of Chongqing is related to the hyperlipidemia history,smoking,hypertension history,family history and diabetes history.The incidence of MACE in early-onset patients followed up for 1 year is higher than that in late-onset patients.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.