Explanatory predictive model for COVID-19 severity risk employing machine learning, shapley addition, and LIME.

The rapid spread of SARS-CoV-2 threatens global public health and impedes the operation of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 infection and examine its risk factors. To produce more effective treatment options and vaccines, it is still necessary to investigate biomarkers and immune responses in order to gain a deeper understanding of disease pathophysiology. This study aims to determine how cytokines influence the severity of SARS-CoV-2 infection. We measured the plasma levels of 48 cytokines in the blood of 87 participants in the COVID-19 study. Several Classifiers were trained and evaluated using Machine Learning and Deep Learning to complete missing data, generate synthetic data, and fill in any gaps. To examine the relationship between cytokine storm and COVID-19 severity in patients, the Shapley additive explanation (SHAP) and the LIME (Local Interpretable Model-agnostic Explanations) model were applied. Individuals with severe SARS-CoV-2 infection had elevated plasma levels of VEGF-A, MIP-1b, and IL-17. RANTES and TNF were associated with healthy individuals, whereas IL-27, IL-9, IL-12p40, and MCP-3 were associated with non-Severity. These findings suggest that these cytokines may promote the development of novel preventive and therapeutic pathways for disease management. In this study, the use of artificial intelligence is intended to support clinical diagnoses of patients to determine how each cytokine may be responsible for the severity of COVID-19, which could lead to the identification of several cytokines that could aid in treatment decision-making and vaccine development.

Polymorphism of human leukocyte antigen-A, -B, and -DRB1 in a Moroccan population from Casablanca: study of the allelic and the haplotypic frequencies.

We have studied the distribution of HLA-A, -B and DRB1 alleles and haplotypes by sequence specific oligonucleotide amplification in a sample of 125 unrelated healthy Moroccan individuals from Casablanca in Morocco. The city of Casablanca is known of its big ethnic diversity, especially Arabs and Berbers. The most frequent alleles found were: HLA-A*02 (18.4%), -A*01 (11.2%), -A*03 (10.8%), -B*51 (8.06%),-B*44 (7.66%), -B*08 (6.85%), -DRB1*04 (15.98%), DRB1*03 and DRB1*07 (13.92%) and -DRB1*01 (10%). High frequency for five two-locus haplotypes was observed for A*03-B*51 (5%), A*02-DRB1*03 (5.5%), A*02-DRB1*04 and A*01-DRB1*04 (5%) and B*35-DRB1*04 (4%). No predominant haplotype was observed for HLA A-B-DRB1. Our results confirm and extend the current knowledge about genetic pattern of the Moroccan of Casablanca. This study will serve as a reference for further anthropological studies, as well as studies of HLA and disease associations in the Moroccan population.

HLA-A, -B, -DRB1 alleles and haplotypes frequencies in Moroccan patients with leukemia.

Previous studies have demonstrated some significant differences in HLA allele frequencies in leukemic patients and normal subjects. In Moroccan leukemic patients, the frequency of HLA alleles has not already been determined. We have analyzed HLA class I and class II alleles and haplotypes in 62 Moroccan leukemic patients and 98 unrelated normal subjects using PCR-SSO method. Significant positive association with the disease, in patients compared to controls, was found for three alleles: HLA-B*44 (12.7% vs 6.6%; p = 0.02), HLA-DRB1*13 (11.8% vs 9.79%; p = 0.04) and HLA-DRB*01 (4.5% vs 10.7%; p = 0.05). Regarding haplotypes analysis, no significant association was found between patients and control groups. It is suggested that HLA-B*44 and HLA-DRB1*13 alleles may play a presumptive predisposing factor while the HLA-DRB*01 allele could be a protective genetic factor against leukemia.