Objective To explore the influencing factors of the severe hemorrhagic fever with renal syn-drome(HFRS)and establish a relevant prediction model,so as to provide scientific basis for effective treat-ment and improving clinical management.Methods Patients diagnosed with HFRS who were hospitalized in the hospital from January 2018 to December 2023 were included as the study objects,and were divided into a mild group(79 cases)and a severe group(34 cases)based on clinical severity.Multivariate Logistic regression analysis was used to explore the significant influencing factors of clinical severity of HFRS,and a prediction model of HFRS severity was established based on the multivariate Logistic regression analysis results.The prediction value of the prediction model was analyzed by receiver operating characteristic curve and the area under the curve(AUC).Results Age(OR=1.069,95％CI:1.025-1.115,P=0.002),urinary protein(OR=6.584,95％CI:1.438-30.141,P=0.015),lactate dehydrogenase(OR=1.002,95％CI:1.001-1.004,P=0.002)and serum creatinine(OR=1.006,95％CI:1.003-1.009,P＜0.001)were independent factors influencing the severity of HFRS.Based on the results of multivariate Logistic regression analysis,a predictive model(P)was established:P=1/[1+e^-(-9.458+age×0.067+urinary protein×1.885+lac-tate dehydrogenase×0.002+serum creatinine×0.006)].In addition,the AUC of the prediction model was 0.873(95％CI:0.810-0.936,P＜0.001).Conclusion The prediction efficacy of the prediction model of se-vere HFRS is better than age,urinary protein,lactate dehydrogenase and serum creatinine.The prediction model is simple and practical,which is convenient for clinicians to predict the severe HFRS,and take effective treatment and intervention in time,so as to improve the clinical prognosis of patients.

Objective:To construct a mortality prediction model for severe fever with thrombocytopenia syndrome (SFTS) and to evaluate its prediction ability.Methods:Clinical data of 120 hospitalized patients with SFTS at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from January 2017 to June 2023 were retrospective analyzed. Based on clinical prognosis, patients were divided into survival group ( n=89) and fatal group ( n=31). The risk factors of SFTS mortality were analyzed with multivariate Logistic regression, based on which a mortality risk prediction model was constructed. The predictive value of the model was examined with receiver operator characteristic (ROC) curve. SPSS 23.0 software was used to process and analyze the data. Results:Logistic regression analysis indicated that skin petechiae ( OR=5.171, 95% CI 1.617-16.530, P=0.006), mental disturbance ( OR=5.481, 95% CI 1.540-19.512, P=0.009), increased serum lactate dehydrogenase level ( OR=1.002, 95% CI 1.001-1.004, P<0.001), and increased serum creatinine level ( OR=1.018, 95% CI: 1.007-1.029, P=0.002) were independent risk factors for SFTS mortality. A mortality risk prediction model was established based on the regression coefficient of risk factors: Logit( P)=-6.623+ skin petechiae×1.643+ mental disturbance × 1.701+ lactate dehydrogenase level (U/L)×0.002+ creatinine level (μmol/L)×0.018. The area under ROC curve (AUC) of the prediction model was 0.91 (95% CI 0.86-0.96, P<0.001), and its predictive ability was higher than that of skin petechiae ( Z=3.788, P<0.001), mind change ( Z=5.728, P<0.001), lactate dehydrogenase ( Z=2.309, P=0.021), and creatinine ( Z=2.064, P=0.039). Conclusion:The mortality prediction model constructed based on skin petechiae, mental disturbance, lactate dehydrogenase, and creatinine has good predictive value for the prognosis of SFTS patients.

Coronavirus disease 2019(COVID-19),which is caused by SARS-CoV-2,varies with regard to symptoms and mortality rates among populations.Humoral immunity plays critical roles in SARS-CoV-2 infection and recovery from COVID-19.However,differences in immune responses and clinical features among COVID-19 patients remain largely unknown.Here,we report a database for COVID-19-specific IgG/IgM immune responses and clinical parameters(named COVID-ONE-hi).COVID-ONE-hi is based on the data that contain the IgG/IgM responses to 24 full-length/truncated proteins corresponding to 20 of 28 known SARS-CoV-2 proteins and 199 spike protein peptides against 2360 serum samples collected from 783 COVID-19 patients.In addition,96 clinical parameters for the 2360 serum samples and basic information for the 783 patients are integrated into the database.Furthermore,COVID-ONE-hi provides a dashboard for defining samples and a one-click analysis pipeline for a single group or paired groups.A set of samples of interest is easily defined by adjusting the scale bars of a variety of parameters.After the"START"button is clicked,one can readily obtain a comprehensive analysis report for further interpretation.COVID-ONE-hi is freely available at www.COVID-ONE.cn.

Objective To evaluate mini-dose pre-injection test in the use of contrast-enhanced magnetic resonance angiography (CEMRA), and to inspect the possibility of contrast medium peak-time prediction by age, body weight and heart rate.Methods The data from mini-dose pre-injection test of contrast medium before vertebral artery CEMRA were retrospectively reviewed in 55 patients. The linear correlation and regression of the data including age, body weight, heart rate, and the reaching-time, peak-value-time, duration and peak-value-signal of contrast medium was performed by using SPSS software.Results The age (n=55, =62 years old, M=59 years old), body weight (n=55, = 63 kg), heart rate (n=40, =73 beats per minute), peak-value-time (n=55,=17.5 seconds), peak signal intensity (n=55,=472), and duration of contrast (n=49,=10.35 seconds)were analyzed. No statistically significant correlation existed between peak-value-time of contrast medium and the age (r=0.231, t=1.728, P=0.090), body weight (r=0.118, t=0.865, P=0.392), and heart rate (r= -0.046, t=-0.284, P=0.776). The peak-value-time correlated negatively with peak signal intensity (r=-0.322, t=-2.56, P=0.016)and positively with duration of contrast (r=0.658, t=5.99, P=0.000). The peak signal intensity was negatively correlated with body weight(r=-0.356, t=-2.77, P=0.008). The linear regression analysis show b=-0.284, t=-2.285, P=0.026 between peak-value-signal and peak-value-time, b=-0.322, t=2.590, P=0.012 between peak-value-signal and body weight.Conclusion Mini-dose pre-injection test was more helpful to adjust the rate of contrast medium injection and determine the time delay during scanning. But the prediction of contrast peak-time based on age, body weight and heart rate was unreliable.