On August 24 2024, Xiaoshan District Center for Disease Control and Prevention, Hangzhou City, received a report of two cases of varicella infection among staff in the intensive care unit (ICU) of a hospital in its jurisdiction. The center immediately organized personnel to conduct an epidemiological investigation of the cases and their close contacts. The index case was a patient admitted to the ICU who had large areas of red rash and pustules on the chest, back, and right axilla. This case was diagnosed with herpes zoster by a dermatology consultation within the hospital. The other nine secondary cases were all nursing staff in the ICU, who were clinically diagnosed with varicella between August 21 and September 1, with an attack rate of 14.06%. All secondary varicella cases had a history of contact with the herpes zoster case and no history of varicella infection. Their varicella vaccination history was unknown. Based on the results of the on-site epidemiological and sanitary investigations, it was determined that this was an outbreak of varicella in the hospital caused by a herpes zoster case. After the last case was diagnosed, no new cases were reported within the longest incubation period (21 days), and the outbreak was declared over on September 21. Close contact with the herpes zoster case was likely the main cause of the outbreak. This highlights the need for early identification and isolation of suspected herpes zoster cases in hospitals in the future, as well as enhanced protective measures to prevent nosocomial infections.

[Objective] To validate and optimize the platelet transfusion refractoriness (PTR) prediction model for patients with hematological disorders established by our center. [Methods] The data of patients with hematological diseases who received platelet transfusions from December 2021 to December 2022 were used as the training set, and data from January 2023 to December 2023 as the validation set. The validation set data was used to validate the predictive model constructed on the training set. Relevant risk factors for PTR were collected through literature review and preliminary studies。 The patients were divided into effective and ineffective groups according to the corrected count increment (CCI) of platelet counts. Predictive factors were screened using univariate and multivariate logistic regression. The calibration of the model were assessed via calibration curves, while discrimination, accuracy, sensitivity, and specificity were evaluated using receiver operating characteristic (ROC) curves Clinical utility was further analyzed with decision curve analysis (DCA). [Results] The Hosmer-Lemeshow (H-L) goodness-of-fit test for the validation set yielded S: P=0.000, indicating that the original model needs optimization. Baseline comparisons and logistic regression identified the number of red blood cell units (RBCU) and platelet units (PLT-U) transfused as key predictors for the optimized model. The H-L goodness-of-fit test S: P values for the training and validation sets were 0.930 and 0.056, respectively; the ROC areas were 0.793 5 and 0.809 4, specificities 90.95% and 84.21%, sensitivities 59.26% and 70.04%, and accuracies 78.14% and 74.10%, respectively. DCA demonstrated clinical net benefit within a prediction probability threshold range of 0.2-0.8. [Conclusion] Transfusion volumes of RBC-U and PLT-U were inversely associated with PTR in hematological patients. The resulting PTR prediction model exhibits moderate predictive efficacy and clinical benefit.

Objective:To explore the risk factors for severe acute kidney injury (AKI) in children with sepsis in the pediatric intensive care unit (PICU) and construct a prediction model to assist early clinical identification.Methods:A retrospective analysis was performed on clinical data of 987 children with sepsis admitted to the PICU of Hunan Children′s Hospital from July 1, 2018 to January 31, 2021. Children who developed severe AKI during hospitalization were included in the AKI stage 2-3 group ( n=228), and the remaining were included in the No-AKI/AKI stage 1 group ( n=759). General information and biochemical indicators were compared between the two groups. Logistic regression analysis was used to identify risk factors for severe AKI in children with sepsis, and a prediction model and nomogram were established. Results:The mortality rate in the AKI stage 2-3 group was 2.49 times that of the No-AKI/AKI stage 1 group [31.1%(71/228) vs 12.5%(95/759), P<0.05]. Compared with the No-AKI/AKI stage 1 group, the AKI stage 2-3 group had lower levels of platelet count (PLT), total protein (TP), albumin (ALB), antithrombin Ⅲ (AT3), and fibrinogen (FIB), but higher levels of lactate dehydrogenase (LDH), serum creatinine (SCr), blood urea nitrogen (BUN), magnesium ion (Mg 2+ ), activated partial thromboplastin time (APTT), fibrinogen degradation products (FDP), and D-dimer (D-D) (all P<0.05), with no significant difference in total bile acid (TBAC) ( P>0.05). Multivariate logistic regression analysis showed that decreased AT3 ( OR=0.989, 95% CI: 0.980-0.997, P=0.007), increased LDH ( OR=1.001, 95% CI: 1.000-1.001, P<0.001), increased SCr ( OR=1.051, 95% CI: 1.037-1.066, P<0.001), and increased BUN ( OR=1.099, 95% CI: 1.028-1.174, P=0.005) were risk factors for severe AKI in children with sepsis. The prediction model was Logist Pr=-3.184-0.012 X1+ 0.001 X2+ 0.050 X3+ 0.094 X4 ( X1=AT3, X2=LDH, X3=SCr, X4=BUN), with the optimal cut-off value of 0.374 (Youden index=0.560). A nomogram was constructed by binary assignment of predictive variables, with an area under the curve of 0.826 (95% CI: 0.790-0.861, P<0.001). Conclusions:The mortality rate of septic children with severe AKI in PICU is significantly increased. Decreased AT3, and increased LDH, SCr, and BUN are risk factors for severe AKI in children with sepsis. Clinicians should be alert to severe AKI when the predicted probability of the early warning model exceeds 0.374.

Objective:To explore the risk factors for severe acute kidney injury (AKI) in children with sepsis in the pediatric intensive care unit (PICU) and construct a prediction model to assist early clinical identification.Methods:A retrospective analysis was performed on clinical data of 987 children with sepsis admitted to the PICU of Hunan Children′s Hospital from July 1, 2018 to January 31, 2021. Children who developed severe AKI during hospitalization were included in the AKI stage 2-3 group ( n=228), and the remaining were included in the No-AKI/AKI stage 1 group ( n=759). General information and biochemical indicators were compared between the two groups. Logistic regression analysis was used to identify risk factors for severe AKI in children with sepsis, and a prediction model and nomogram were established. Results:The mortality rate in the AKI stage 2-3 group was 2.49 times that of the No-AKI/AKI stage 1 group [31.1%(71/228) vs 12.5%(95/759), P<0.05]. Compared with the No-AKI/AKI stage 1 group, the AKI stage 2-3 group had lower levels of platelet count (PLT), total protein (TP), albumin (ALB), antithrombin Ⅲ (AT3), and fibrinogen (FIB), but higher levels of lactate dehydrogenase (LDH), serum creatinine (SCr), blood urea nitrogen (BUN), magnesium ion (Mg 2+ ), activated partial thromboplastin time (APTT), fibrinogen degradation products (FDP), and D-dimer (D-D) (all P<0.05), with no significant difference in total bile acid (TBAC) ( P>0.05). Multivariate logistic regression analysis showed that decreased AT3 ( OR=0.989, 95% CI: 0.980-0.997, P=0.007), increased LDH ( OR=1.001, 95% CI: 1.000-1.001, P<0.001), increased SCr ( OR=1.051, 95% CI: 1.037-1.066, P<0.001), and increased BUN ( OR=1.099, 95% CI: 1.028-1.174, P=0.005) were risk factors for severe AKI in children with sepsis. The prediction model was Logist Pr=-3.184-0.012 X1+ 0.001 X2+ 0.050 X3+ 0.094 X4 ( X1=AT3, X2=LDH, X3=SCr, X4=BUN), with the optimal cut-off value of 0.374 (Youden index=0.560). A nomogram was constructed by binary assignment of predictive variables, with an area under the curve of 0.826 (95% CI: 0.790-0.861, P<0.001). Conclusions:The mortality rate of septic children with severe AKI in PICU is significantly increased. Decreased AT3, and increased LDH, SCr, and BUN are risk factors for severe AKI in children with sepsis. Clinicians should be alert to severe AKI when the predicted probability of the early warning model exceeds 0.374.

Early diagnosis is crucial for improving the prognosis of hemophagocytic lymphohistiocytosis（HLH）.However，diagnosing HLH presents numerous challenges，including lack of specific clinical manifestations，difficulty in early recognition，complex diagnostic criteria，and lengthy testing periods for some diagnostic indicators.To address these clinical challenges，researchers both in China and abroad have conducted various studies aimed at developing new biomarkers，establishing methods for the early identification of suspected cases，optimizing predictive models and diagnostic criteria for different types of HLH，and creating clear clinical diagnostic pathways.This article summarized the research progress on the early diagnosis of pediatric HLH，providing a reference for advancing early diagnosis of the disease.

Hemophagocytic syndrome，synonymous with hemophagocytic lymphohistiocytosis（HLH），represent a spectrum of hyperinflammatory response syndromes that have been the subject of intense scrutiny in terms of their etiopathogenesis and early warning strategies.Ebstein-Barr virus（EBV）-associated HLH is the most prevalent form of infection-associated secondary HLH.It is characterized by a sudden onset，rapid progression，and high mortality rate.The primary clinical features include persistent fever，often accompanied by manifestations of multi-system involvement，such as cytopenias，hepatosplenomegaly，lymphadenopathy，abnormal liver function，respiratory symptoms，neurological symptoms，and rashes.Therefore，early recognition and warning are crucial for improving the prognosis of patients with EBV-HLH.This article aimed to focus on the relationship between EBV and HLH，the diagnosis of EBV-HLH，associated warning factors，and treatment strategies.

Objective To investigate the protective effect of recombinant Schistosoma japonicum cystatin(rSj-Cys)against acute liver injury induced by lipopolysaccharide(LPS)and D-GalN in mice.Methods Adult male C57BL/6J mice with or without LPS/D-GaIN-induced acute liver injury were given intraperitoneal injections of rSj-Cys or PBS 30 min after modeling(n=18),and serum and liver tissues samples were collected from 8 mice in each group 6 h after modeling.The survival of the remaining 10 mice in each group within 24 h was observed.Serum levels of ALT,AST,TNF-α and IL-6 of the mice were measured,and liver pathologies was observed with HE staining.The hepatic expressions of macrophage marker CD68,Bax,Bcl-2 and endoplasmic reticulum stress(ERS)-related proteins were detected using immunohistochemistry or immunoblotting,and TUNEL staining was used to detect hepatocyte apoptosis.Results The survival rates of PBS-and rSj-Cys-treated mouse models of acute liver injury were 30%and 80%at 12 h and were 10%and 60%at 24 h after modeling,respectively;no death occurred in the two control groups within 24 h.The mouse models showed significantly increased serum levels of AST,ALT,IL-6 and TNF-α and serious liver pathologies with increased hepatic expressions of CD68 and Bax,lowered expression of Bcl-2,increased hepatocyte apoptosis,and up-regulated expressions of ERS-related signaling pathway proteins GRP78,CHOP and NF-κB p-p65.Treatment of the mouse models significantly lowered the levels of AST,ALT,IL-6 and TNF-α,alleviated liver pathologies,reduced hepatic expressions of CD68,Bax,GRP78,CHOP and NF-κB p-p65,and enhanced the expression of Bcl-2.In the normal control mice,rSj-Cys injection did not produce any significant changes in these parameters compared with PBS.Conclusion rSj-Cys alleviates LPS/D-GalN-induced acute liver injury in mice by suppressing ERS,attenuating inflammation and inhibiting hepatocyte apoptosis.

Phage therapy is one of the most important tools for the treatment of infections with multi-drug resistant bacteria. Such phages are usually isolated from hospital effluents, however, no systematic study on the distribution of phages in hospital effluents has been conducted so far. The aim of this study was to isolate the corresponding phages of common pathogenic bacteria isolated in the clinic as hosts, so as to assess the ecological distribution of phages in hospital wastewater and to provide a reference for the isolation and application of phages of drug-resistant bacteria in the clinic. A cross-sectional study design was used in this study. The 125 pathogenic bacteria (belonging to 16 different strains) isolated from the clinical microbiology laboratory of Qilu Hospital of Shandong University from May to June 2023 were selected as the target strains, and the phages corresponding to these strains were isolated and purified from the hospital wastewater by using the double-layer plate sandwich method. At the same time, the distribution of pathogenic bacteria in the same batch of wastewater was analyzed with the help of mNGS sequencing technology, so as to preliminarily investigate the abundance correspondence between pathogenic bacteria and phages in wastewater. The results showed that a total of 56 phage strains were isolated from 125 clinical pathogens as hosts, corresponding to six pathogens, including Acinetobacter baumannii, Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia. All six pathogenic bacteria contained strains with different degrees of drug resistance, with a higher percentage of multi-drug resistant strains in A. baumannii, Escherichia coli and P. aeruginosa. The phage acquisition rates of these six pathogens were, in descending order, Escherichia coli (80%), Stenotrophomonas maltophilia (75%), Pseudomonas aeruginosa (70%), Klebsiella pneumoniae (66.67%), Acinetobacter baumannii (36.36%) and Staphylococcus aureus (12.5%). Preliminary mNGS sequencing results showed that the pathogenic bacteria with higher abundance in the batch of effluent were Enterococcus faecalis, Klebsiella pneumoniae, Escherichia coli, Enterococcus faecalis, Acinetobacter baumannii, Klebsiella aerogenes, Klebsiella michiganensis and Pseudomonas aeruginosa. In conclusion, phages of most common clinical Gram-negative pathogens were isolated from hospital wastewater with high isolation rates; however, phages of Gram-positive pathogens were isolated at lower rates, and only phages corresponding to Staphylococcus aureus were isolated in this study. The corresponding mNGS sequencing results showed that the distribution of Gram-negative pathogens in sewage may had a positive correlation with the ecological distribution of phages.

Objective To explore the risk factors for platelet transfusion refractoriness(PTR)in patients with hemato-logical disorders,construct a prediction model and validate the model efficacy.Methods Patients with hematological disor-ders who received platelet transfusion therapy in the Chengdu Second People's Hospital from December 2021 to December 2022 were retrospectively included to judge the effectiveness of platelet transfusion and screened for risk factors by univariate and multivariate logistic regression.A prediction model for PTR was constructed using receiver operating characteristic(ROC)curve,calibration curve and decision curve(DCA)to assess the differentiation,calibration and clinical value of the model,respectively.Results A total of 334 hematological patients were included,including 168 males and 176 females,with a PTR incidence of 40.4%.Univariate and multivariate logistic regression analysis showed that platelet transfusion vol-ume,erythrocyte transfusion volume,and neutrophil ratio were risk factors for PTR(P<0.05).A prediction model for PTR in hematological patients was established based on these risk factors.The area under the model's curve was 0.8377(95%CI:0.723-0.772),the sensitivity was 58.52%,and the specificity was 89.95%.The calibration curve showed that the S∶P was 0.964,the maximum absolute difference Emax was 0.032,and the average absolute difference Eavg was 0.009.The DCA a-nalysis showed that the model had clinical application value when the risk threshold ranged from 0.2 to 0.9.Conclusion The PTR prediction model based on platelet transfusion volume,erythrocyte transfusion volume and neutrophil ratio can pro-vide a basis for effective platelet transfusion in hematological patients.

Medical simulation training,with the characteristics of safety,repeatability,authenticity,and controllability,can provide an immersive teaching experience to students by shortening their learning curve of clinical practice,improving clinical post competence and making medical practice education accessible.The technical types of medical simulation training include in basic anatomical models,single task training models,standardized patients/personnel,computer interactive training models,virtual training systems,physiological driven simulation systems,etc.It is an important component of modern medical education that can be mainly used for imparting knowledge,training skills,clinical simulation training,non operational skills,etc.