== Principal component analysis was performed by R package factoextrathe most contributing variable to severe COVID-19 patients. who later died compared to MF-438 mild and severe cases who recovered, suggesting that this could be an important biomarker for predicting the severity of COVID-19 and post-COVID-19 syndrome. Keywords:SARS-CoV-2, biomarkers, immune response, IL-9, IFN == 1. Introduction == The clinical manifestations of COVID-19, a viral infection due to SARS-CoV-2, vary from individual to individual and even between communities with different genetic backgrounds [1]. In particular, there is a wealth of evidence suggesting that COVID-19 was less severe in Africa compared to other continents, possibly due to demography, social economy, genetics, and a trained immunity [2,3,4,5]. However, little is known about immune correlates among individuals with different clinical manifestations of COVID-19. Moreover, there is currently limited evidence on using biologics in COVID-19 to prevent severe illness or improve survival [6]. COVID-19 clinical manifestations range from fever to multiple organ failure through dry cough and pneumonia [7,8]. The cytokine storm, characterised by excessive secretion and release of high levels of cytokines by a dysfunctional immune system, has been associated with severe COVID-19 [9]. This systemic hyper-inflammation is closely associated with ARDS and/or multiple organ damage, which ultimately can lead to death [10]. Innate and adaptive immune responses KSHV ORF26 antibody to SARS-CoV-2 are heterogeneous. For instance, IL-6 and IL-8 have been associated with diagnostic and COVID-19 severity [11]), while the expression of IFN was reduced in severely ill patients [12]. On the other hand, it has been demonstrated that Th1 and Th17 cells can induce an inflammatory response in patients with COVID-19, leading to a high risk of death [13,14]. Regulatory cells were also moderately increased in severe patients, suggesting immunosuppression in COVID-19 [13]. Interestingly, individuals exposed to other MF-438 coronaviruses before the COVID-19 pandemic may have developed antibodies cross-reacting with SARS-CoV-2, protecting them from severe disease [2,12]. In animal models, Th2 cytokines (IL-13) were associated with mortality [14]. Although it is evident that immune responses play a significant role in COVID-19 clinical outcomes, the mechanisms underlying this heterogeneity are still elusive [15]. It is possible that genetic diversity can explain these variations [1], but the mechanistic characterisation of immune responses will help identify early markers of disease severity in different settings and populations. Cytokine profiles have been suggested as potential biomarkers for viral infections such as influenza or MERS [16]. Antibodies have also been associated with COVID-19 outcomes [6]. To provide timely interventions for COVID-19 and prevent death, a comprehensive understanding of the cytokine and antibody kinetics during disease progression is needed. This study aimed to assess a wide range of cytokines and T cell transcription factors to determine the most discriminating cytokine kinetics in disease severity and death outcomes in patients with COVID-19 in Rwanda. We hypothesized that severe disease could be predicted in this setting based on immune markers. == 2. Results == == 2.1. Patients Characteristics == A total of 197 patients (mild = 129, severe = 68) with COVID-19 and 20 healthy controls were included in this study. Patients baseline characteristics, treatments, and outcomes are shown inTable 1. Severe disease was characterised by fever, dyspnea, hypoxia, oxygen saturation, difficulty breathing, and multi-organ dysfunction, while mild disease was defined by fever and/or cough. Patients with severe COVID-19 were older than those with mild clinical manifestations (KruskalWallis test,p< 0.0001). == Table 1. == Patient characteristics, treatment, and outcome. n: the number of participants. * KruskalWallis for continuous variable and chi-squared for categorical variables tests MF-438 were used for statistical significance level for comparing mild and severe groups. All values are expressed in the median and interquartile range unless specified. == 2.2. Kinetic Analysis of Cytokine and Antibody Levels in the Plasma of COVID-19 Patients == We analysed the kinetic changes of inflammatory and regulatory cytokine levels, including IL-4, IL-6, IL-9, IL-10, IL-13, IL-17, TNF, IFN, TGF1, and TGF3 (active forms). The changes in total IgG and IgM were also analysed. All cytokine plasma levels were increased in infected patients compared to controls, with significant fluctuation of cytokine levels in mild and severe patients (Figure 1). For most cytokines, including IFN, TNF, IL-4, IL-6, IL-10, IL-13, and IL-17, the concentrations were higher in severe than mild cases at.