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.

Objective: To establish a progressive research strategy for “colonic components analysis - efficacy verification and mechanism exploration - gut microbiota”, screen pharmacodynamic substances, and investigate their mechanism via gut microbiota. Methods: The pharmacodynamics of Gegen Qinlian decoction (GQD) were assessed using a mouse model of dextran sulfate sodium-induced ulcerative colitis (UC). Ultra-performance liquid chromatography-quadrupole-orbitrap mass spectrometer was used to identify the prototype and metabolic components of GQD in the colon during UC. To analyze the structure and function of characteristic genera of GQD and its active components, 16S rRNA sequencing was performed. Results: We identified 67 prototypic and 14 metabolic components of GQD in the UC colon. The primary prototype components are flavonoids and alkaloids, including puerarin (PUE), baicalin (BAI), and berberine (BER). The metabolism was predominantly sulfonation. Efficacy verification showed that the main active components, puerarin, baicalin, and berberine, had good therapeutic effects on UC. The results of 16S rRNA gene sequencing showed that GQD improved UC by regulating the structure and function of the gut microbiota. The abundance of gut microbiota involved in the metabolism of the prototype components was influenced by the corresponding components. The function prediction results showed that PUE was the most comparable to GQD, with 24 consistent pathways. BAI and BER showed comparable gut microbiota regulation pathways. Characteristic pathways of BER include glucometabolic processes. Conclusion This study focused on the key issues in the gut microbiota pathway and developed a progressive research strategy to understand the transformation mechanisms of colonic components. This research systematically analyzed the active components and metabolic transformation of GQD in the colon during the pathological state of UC, as well as changes in the structure and function of the gut microbiota, clarified the mechanism of GQD and its active components in improving UC via the gut microbiota pathway.

Objective To develop a machine learning model integrating preoperative chest CT radiomic features with clinical data for predicting 5-year postoperative recurrence risk in stage Ⅰ non-small cell lung cancer(NSCLC)patients undergoing surgical resection.Methods A total of 217 patients with pathologically confirmed stage Ⅰ NSCLC(selected from 778 initially screened cases based on our inclusion and exclusion criteria)treated in Army Medical Center of PLA between January 2014 and December 2019 were retrospectively enrolled,including 53 recurrence cases and 164 non-recurrence cases within 5-year follow-up.They were randomly divided into a training set(n=173)and a validation set(n=44)in a ratio of 8:2.Radiomic models were established based on extracted features from tumor-dominant regions of interest(ROI)on CT images,while clinical models were developed using demographic characteristics and preoperative laboratory examinations.A combined model was further constructed by integrating both feature sets,and model performance was compared to identify the optimal predictive model.Results This study screened the features from non-contrast CT images and ultimately selected 7 radiomic features for constructing radiomic model.Among 6 machine learning algorithms,the adaptive boosting(Adaboost)model demonstrated the best overall predictive performance,with an area under the curve(AUC)of 0.866(95%CI:0.808～0.923;accuracy:0.832,specificity:0.884)in the training set and of 0.806(95%CI:0.630～0.983;accuracy:0.795,specificity:0.971)in the validation set.Univariate and multivariate logistic regression analyses identified 4 clinical features for clinical model construction.The clinical model achieved an AUC value of 0.874(95%CI:0.821～0.928;accuracy:0.827,specificity:0.891)in the training set and 0.813(95%CI:0.677～0.948;accuracy:0.636,specificity:0.600)in the validation set.By integrating the 7 radiomic features and 4 clinical features using a feature-level fusion strategy,the combined model exhibited further improved predictive performance,with an AUC value of 0.953(95%CI:0.924～0.983;accuracy:0.884,specificity:0.860)and 0.852(95%CI:0.729～0.976;accuracy:0.682,specificity:0.629),respectively in the training set and the validation set.Conclusion The combined model integrating preoperative CT radiomic features with clinical risk factors may provide an evidence-based framework for evaluating 5-year postoperative recurrence risk in stage Ⅰ NSCLC patients.

Objective To compare the value of ultrashort echo time(UTE)and T1W sequences for detecting bone erosions of gouty arthritis.Methods Forty-four gouty patients were prospectively enrolled,including 32 cases with affected feet and 12 cases with affected knee.MR UTE and T1W sequence scanning of the affected area were performed,and subjectively scoring of imaging quality of 2 kinds of MRI were evaluated,respectively.Then total number and total score of bone erosions of each case were calculated according to all affected bones.Taken DECT as reference standard,the efficacy of UTE and T1WI for detecting bone erosions was assessed through comparing with DECT using Kappa coefficient.Results The imaging quality score of T1WI was lower than that of DECT(all P＜0.05),while no significant difference was found between UTE and DECT(all P＞0.05).There was high agreement between UTE and DECT for detecting bone erosions(κ=0.949),while the agreement between T1WI and DECT ranged from good to high(κ=0.718 to 0.805).The total number and total score of bone erosions based on T1WI were significantly lower than those based on DECT(all P＜0.05),while no significant difference was found between UTE and DECT(all P＞0.05).Conclusion UTE was better than T1WI for detecting bone erosions of gouty arthritis.

Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.

Objective To explore the safety and effects of alpha-lipoic acid (ALA) in patients with ischemic heart failure (IHF). Methods A randomized, double-blind, placebo-controlled trial was designed (ClinicalTrial.gov registration number NCT03491969). From January 2019 to January 2023, 300 patients with IHF were enrolled in four medical centers in China, and were randomly assigned at a 1∶1 ratio to receive ALA (600 mg daily) or placebo on top of standard care for 24 months. The primary outcome was the composite outcome of hospitalization for heart failure (HF) or all-cause mortality events. The second outcome included non-fatal myocardial infarction (MI), non-fatal stroke, changes of left ventricular ejection fraction (LVEF) and 6-minute walking distance (6MWD) from baseline to 24 months after randomization. Results Finally, 138 patients of the ALA group and 139 patients of the placebo group attained the primary outcome. Hospitalization for HF or all-cause mortality events occurred in 32 patients (23.2%) of the ALA group and in 40 patients (28.8%) of the placebo group (HR＝0.753, 95%CI 0.473-1.198, P＝0.231; Figure 1A-1C). The absolute risk reduction (ARR) was 5.6%, the relative risk reduction (RRR) associated with ALA therapy was approximately 19.4% compared to placebo, corresponding to a number needed to treat (NNT) of 18 patients to prevent one event. In the secondary outcome analysis, the composite outcome of the major adverse cardiovascular events (MACE) including the hospitalization for HF, all-cause mortality events, non-fatal MI or non-fatal stroke occurred in 35 patients (25.4%) in the ALA group and 47 patients (33.8%) in the placebo group (HR＝0.685, 95%CI 0.442-1.062, P＝0.091; Figure 1D). Moreover, greater improvement in LVEF (β＝3.20, 95%CI 1.14-5.23, P＝0.002) and 6MWD (β＝31.7, 95%CI 8.3-54.7, P＝0.008) from baseline to 24 months after randomization were observed in the ALA group as compared to the placebo group. There were no differences in adverse events between the study groups. Conclusions These results show potential long-term beneficial effects of adding ALA to IHF patients. ALA could significantly improve LVEF and 6MWD compared to the placebo group in IHF patients.

Objective To explore the differences in the radiological features,clinical,and laboratory indicators of frequent versus infrequent gout flares in the feet and ankles using dual-energy computed tomography(DECT).Methods A retrospective selection was made on 385 gout patients,who were divided into the frequent flare group(≥2 gout attacks per year,219 cases)and the infre-quent flare group(<2 gout attacks per year,166 cases).Clinical data,laboratory indicators,and DECT imaging findings were col-lected for statistical analysis.Binary logistic regression was used to analyze the independent risk factors for frequent gout flares and receiver operating characteristic(ROC)curve was plotted.Results Statistically significant differences were found between the fre-quent flare group and the infrequent flare group in terms of disease duration,body mass index(BMI),blood pressure,triglyceride(TG),serum uric acid(SUA),monosodium urate(MSU)crystal deposition,total volume of MSU crystals,maximum diameter of individ-ual tophi,number of affected joints,bone erosion,maximum depth of bone erosion,soft tissue swelling,bone proliferation and sclero-sis,and joint space narrowing(P<0.05).SUA levels,MSU crystal deposition,total volume of MSU crystals,and maximum depth of bone erosion were identified as independent risk factors for frequent gout(P<0.05).Both the combination of four factors model and the maximum depth of bone erosion model had better diagnostic efficacy.Conclusion Gout patients with high SUA levels,MSU crystal deposition,larger total volume of MSU crystals,and greater maximum depth of bone erosion are more likely to experience frequent gout attacks.Patients with bone erosion depth>3.200 mm are more likely identified early as having frequent gout.

Objective·To explore the diagnostic,therapeutic,and prognostic value of metagenomics next-generation sequencing(mNGS)in patients with pneumonia-induced sepsis.Methods·This study consisted of a multicenter,prospective,non-randomized controlled trial and a diagnostic test.Patients with pneumonia-induced sepsis who were hospitalized in four hospitals across China were enrolled between March 2020 and October 2021.All patients met the Sepsis-3 criteria issued by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine,as well as the clinical diagnostic standard of pneumonia.Enrolled patients were assigned based on their preference to either the conventional test-only group[receiving only conventional test(CMT)]or the combined mNGS test group(receiving CMT and mNGS concurrently).The primary outcome was the 7-day all-cause mortality rate,and secondary outcomes included the changes in SOFA and APACHE Ⅱ scores from baseline to day 7,28-day all-cause mortality rate,the composite endpoint of mechanical ventilation or death within 28 d,28 d ventilation-free days,28 d hospital-free days,and the average daily hospitalization cost.Propensity score matching was used to balance covariates between the two groups.Kaplan-Meier curves were plotted and Cox proportional hazards models were built to compare the risk of death between the two groups.Pathogen detection results from infection site samples in the combined mNGS test group were used for the diagnostic test.The clinically-adjudicated causative pathogens was used as the reference standard.The results of traditional pathogen detection and mNGS detection were compared respectively with the reference standard.The positive percent agreement,negative percent agreement,positive predictive value,and negative predictive value between the two methods and the reference standard were calculated.McNemar's χ2 test was used to evaluate the causative pathogen detection capabilities of the two methods.Results·A total of 533 patients were enrolled,of whom 311 opted for additional mNGS testing,while 222 received only conventional pathogenetic testing.In the non-randomized controlled trial,after propensity score matching to balance covariates,the 7-day all-cause mortality was lower in the combined mNGS test group compared to the conventional test-only group[4.8%vs 8.6%,HR 0.37(95%CI 0.15?0.91),P=0.031].Additionally,the 28-day ventilation-free days were increased in the combined mNGS test group(19.9 d vs 18.4 d,P=0.041).No significant difference was observed between the two groups in terms of 28-day all-cause mortality or the average daily hospitalization costs.In the diagnostic test,compared to the reference standard,the positive percent agreement of mNGS with the clinical composite judgment for causative pathogens was higher than that of CMT[91.9%(95%CI 87.7%?95.0%)vs 56.1%(95%CI 49.7%?62.4%),P<0.001].Conversely,the negative percent agreement of mNGS was lower than that of CMT[29.2%(95%CI 18.6%?41.8%)vs 69.2%95%CI 56.6%?80.1%),P<0.001].The negative predictive value of nNGS was higher than that of CMT[48.7%(95%CI 32.4%?65.2%)vs 29.4%(95%CI 22.3%?37.3%),P=0.001].Conclusion·In patients with pneumonia-induced sepsis,mNGS of infection site samples demonstrated a higher detection rate of causative pathogen compared to CMT.Furthermore,the combination of mNGS with CMT may help reduce the 7-day all-cause mortality,suggesting that mNGS has clinical value and potential for application in the management of sepsis caused by pulmonary infections.

Objective To establish an initial diagnostic method for β-thalassemia (BT) using a double antibody sandwich ELISA approach. Methods The hybridoma producing monoclonal antibodies against anti-HbF were screened using a trailing count method. The mAbs were evaluated through ELISA,modified immunocytochem-istry,and Western blot analysis. A double antibody-sandwich ELISA assay was established by labeling the pairs of mAbs with ALP using the glutaral method,and this detection system was used to analyze 40 serum samples. Results The results demonstrate the successful generation of nine hybridoma cell lines capable of secreting highly active anti-HbF monoclonal antibodies (mAbs). Specifically,four mAbs (3F7,4G1,6C1,and 9H7) exhibited exclusive reactivity towards HbF without any cross-reactivity with hemoglobin variants (HbA and HbA2). These four HbF-specific mAbs displayed exceptional specificity and sensitivity,with a maximum titer of 1:256000 and the highest affinity constant (Ka) recorded at 2.36×108 L/mol. Among these mAbs,optimal antibody pairing was achieved using capture antibody 3F7 in conjunction with ALP-4G1 for the development of a sandwich ELISA detec-tion method. By employing this approach,fetal and healthy human blood samples were successfully quantified for HbF levels with an impressive detection sensitivity reaching up to 80％. Conclusion This sandwich ELISA dem-onstrated precise quantification of HbF levels,making it suiTab.for both research and diagnostic purposes in the field of β-thalassemia.