Back

Evaluation of lymphocyte subtypes in COVID-19 patients

Rezaei, M.; Marjani, M.; Tabarsi, P.; Moniri, A.; purabdollah, M.; Abtahian, Z.; Kazempour Dizaji, M.; Dalil Roofchayee, N.; Dezfuli, N. K.; Mansouri, D.; Hossein-Khannazer, N.; Varahram, M.; Mortaz, E.; Velayati, A. A.

2021-07-16 infectious diseases
10.1101/2021.07.12.21260382 medRxiv
Show abstract

BackgroundAlthough the many aspects of COVID-19 have not been yet recognized, it seems that the dysregulation of the immune system has a very important role in the progression of the disease. In this study the lymphocyte subsets were evaluated in COVID-19 patients with different severity. MethodsIn this prospective study, the levels of peripheral lymphocyte subsets (CD3+, CD4+, CD8+ T cells; CD19+ and CD20+ B cells; CD16+/CD56+ NK cells, and CD4+/CD25+/FOXP3+ regulatory T cells) were measured in 67 confirmed patients with COVID-19 on the first day of admission. ResultsThe mean age of cases was 51.3 {+/-} 14.8 years. Thirty-two patients (47.8%) were classified as severe cases and 11 (16.4%) patients were categorized as critical. The frequency of blood lymphocytes, CD3+ cells, CD25+FOXP3+ T cells; and absolute count of CD3+ T cells, CD25+FOXP3+ T cells, CD4+ T cells, CD8+ T cells, CD16+56+ lymphocytes were lower in more severe cases in comparison to milder cases. Percentages of lymphocytes, T cells, and NK cells were significantly lower inthe patients who died (p= 0.002 and P= 0.042, p=0.006, respectively). ConclusionFindings of this cohort study suggests that the frequency of CD4+, CD8+, CD25+FOXP3+ T cells, and NK cells were difference in the severe COVID-19 patients. Moreover, lower frequency of, T cells, and NK cells are predictors of mortality of these patients.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.