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.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is a zoonotic infectious disease with high mortality, and hemophagocytic lymphohistiocytosis (HLH) is one of the rare fatal complications of SFTS. Early prediction of the occurrence of HLH and the identification of prognostic factors in SFTS patients with HLH are crucial for effective clinical management. Methods: Univariate and multivariate logistic regression were used to analyze the demographic characteristics, clinical manifestations at admission and laboratory parameters of 272 SFTS patients. The ROC curve was used to calculate the optimal critical value of each index based on the survival outcomes of patients, and the kinetic characteristics of laboratory markers predicting the prognosis of patients with HLH were analyzed. Results: Decreased platelet count, reduced ALT/AST ratio, elevated LDH, and increased DD were identified as independent risk factors for HLH in SFTS patients. Age, fibrinogen (FIB), and procalcitonin (PCT) were independent risk factors for mortality in SFTS patients with HLH (P < 0.05). The combination of these three factors can effectively predict patient prognosis (AUC = 0.903). Patients aged ≥64 years, with FIB ≤ 2.23 g/L, and PCT ≥ 0.9 ng/ml exhibited higher mortality rates. The dynamic characteristics of PCT and FIB levels significantly differed between the survival and death groups in SFTS patients with HLH. Conclusion: Early laboratory indicators can timely identify HLH complications in SFTS patients. Close monitoring of elderly patients and regular assessment of PCT and FIB levels can effectively reduce mortality.

Coagulation dysfunction plays a critical role in the pathogenesis and prognosis of secondary haemophagocytic lymphohistiocytosis (sHLH) in children. This study aims to systematically analyze the coagulation profiles in paediatric sHLH patients, evaluate their prognostic value and provide an effective basis for reducing mortality in children with HLH. A total of 209 paediatric patients with sHLH were enrolled in this study. Coagulation parameters at admission were collected and compared across groups stratified by aetiology, prognosis and presence of disseminated intravascular coagulation (DIC). The dynamic evolution of coagulation parameters was analyzed using LOWESS curve fitting. LASSO regression was applied to screen for potential risk factors for DIC in sHLH patients, followed by univariate and multivariate logistic regression to identify independent risk factors. Similarly, Kaplan-Meier survival analysis along with univariate and multivariate logistic regression models were used to determine independent risk factors associated with prognosis in sHLH patients. Paediatric patients with secondary haemophagocytic lymphohistiocytosis (sHLH) presented with significant coagulation abnormalities upon hospital admission, as evidenced by markedly elevated prothrombin time (PT), international normalized ratio (INR), activated partial thromboplastin time (APTT), thrombin time (TT) and D-dimer (DD) levels (all p < 0.01). Those with infection-associated HLH demonstrated significantly prolonged PT (p = 0.009), APTT (p < 0.001) and TT (p = 0.0028), along with significantly lower fibrinogen (FIB) levels (p < 0.001), compared to patients with autoimmune-associated HLH. Compared to survivors, deceased HLH patients had significantly higher PT and INR (p < 0.01), as well as significantly elevated DD (p = 0.014). Significant differences were observed in coagulation parameters - PT, INR, APTT, TT, DD, FIB, thrombin-antithrombin complex (TAT) and tissue-type plasminogen activator-inhibitor complex (t-PAIC) - between HLH patients with and without disseminated intravascular coagulation (DIC) (all p < 0.05), and the dynamic changes in these parameters (particularly PT, FIB and DD) also differed notably between the two groups. Neurological involvement, hyper-ferritinaemia and elevated INR were identified as independent risk factors for DIC, while neurological involvement and the presence of DIC itself were independent predictors of mortality in paediatric patients. Coagulation dysfunction serves as a core pathological driver in paediatric sHLH, being especially severe in infection-associated cases. Dynamic monitoring of key coagulation parameters and ferritin levels is crucial for early risk warning and timely intervention. Targeted management of coagulation abnormalities, together with proactive prevention and control of neurological complications, may improve outcomes in paediatric patients.

Age has been found to be the single most significant factor in COVID-19 severity and outcome. However, the age-related severity factors of COVID-19 have not been definitively established. In this study, we detected SARS-CoV-2-specific antibody responses and infectious disease-related blood indicators in 2360 sera from 783 COVID-19 patients, with an age range of 1–92 years. In addition, we recorded the individual information and clinical symptoms of the patients. We found that the IgG responses for S1, N, and ORF3a and the IgM for NSP7 were associated with severe COVID-19 at different ages. The IgM responses for the S-protein peptides S1-113 (aa 673–684) and S2-97 (aa 1262–1273) were associated with severe COVID-19 in patients aged <60. Furthermore, we found that the IgM for S1-113 and NSP7 may play a protective role in patients aged 80, respectively. Regarding clinical parameters, we analyzed the diagnostic ability of five clinical parameters for severe COVID-19 in six age groups and identified three-target panel, glucose, IL-6, myoglobin, IL-6, and NT proBNP as the appropriate diagnostic markers for severe COVID-19 in patients aged 80, respectively. The age-associated severity factors revealed here will facilitate our understanding of COVID-19 immunity and diagnosis, and eventually provide meaningful information for combating the pandemic.

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 (COVID-ONE humoral immune). COVID-ONE humoral immunity is based on a dataset that contains the IgG/IgM responses to 21 of 28 known SARS-CoV-2 proteins and 197 spike protein peptides against 2,360 COVID-19 samples collected from 783 patients. In addition, 96 clinical parameters for the 2,360 samples and information for the 783 patients are integrated into the database. Furthermore, COVID-ONE humoral immune 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-humoral immune is freely available at www.COVID-ONE.cn .

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 (COVID-ONE humoral immune). COVID-ONE humoral immunity is based on a dataset that contains the IgG/IgM responses to 21 of 28 known SARS-CoV-2 proteins and 197 spike protein peptides against 2,360 COVID-19 samples collected from 783 patients. In addition, 96 clinical parameters for the 2,360 samples and information for the 783 patients are integrated into the database. Furthermore, COVID-ONE humoral immune 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-humoral immune is freely available at www.COVID-ONE.cn .