Single-factor and multifactor analysis of immune function and nucleic acid negative time in patients with COVID-19

Abstract

To analyse the differential indicators of COVID-19 in severe and mild cases and to study the factors affecting the immune function of patients and the time required for oropharyngeal swabs to become negative. Age, albumin (ALB) levels, prealbumin (PAB) levels, high-sensitivity C-reactive protein (hs-CRP) levels, platelet counts, lymphocyte counts, neutrophil counts, CD3+, CD4+, CD8+ T cell counts and the time for oropharyngeal swabs to become negative were collected from 37 patients with COVID-19; the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were calculated as indicators of inflammation. An independent-sample t test was used to analyse differences between the severe and mild groups, and factors affecting the CD3+, CD4+ and CD8+ T cell counts and the time for the nucleic acid tests of oropharyngeal to convert to negative were identified by single-factor and multifactor analyses. Lymphocyte, ALB, PAB, CD3+, CD4+ and CD8+ T cell levels in the severe group were lower than those in the mild group, the P values were 0.048, 0.004, 0.033, 0.033, 0.015 and 0.013, respectively. The neutrophil count and PLR were higher in the severe group compared with that in the patients of mild group; the P values were all 0.000. Single-factor analysis showed that age, ALB level, PAB level, hs-CRP level, platelet count, the NLR, the PLR and the time to a negative nucleic acid test were the main factors influencing CD3+ T cells; the P values were 0.001, 0.031, 0.001, 0.010, 0.005, 0.002, 0.000 and 0.048, respectively. Age, ALB level, PAB level, hs-CRP level, platelet count, the NLR, the PLR and time to a negative nucleic acid test were the main factors influencing CD8+ T cells; the P values were 0.000, 0.012, 0.000, 0.005, 0.002, 0.004, 0.005 and 0.003, respectively. Age, PAB level, hs-CRP level, platelet count, the NLR and the PLR were the main factors influencing CD4+ T cells; the P values were 0.001, 0.006, 0.030, 0.041, 0.005 and 0.001, respectively. Age, ALB level, PAB level, hs-CRP level, platelet count, the NLR, CD3+ T cell count and CD8+ T cell count were the main factors influencing the time to a negative nucleic acid test in oropharyngeal swabs, and the P values were 0.032, 0.043, 0.013, 0.016, 0.042, 0.049, 0.048 and 0.003, respectively. Multivariate analysis showed that the PLR and platelet count were the main factors influencing CD3+ T cells. The P values were all 0.000. The PLR and platelet count were the main factors influencing CD4+ T cells. The P values were 0.000 and 0.001, respectively. The PLR and platelet count were also the main factors influencing CD8+ T cells. The P values were 0.004 and 0.001. CD8+ T cells affected the time to a negative nucleic acid test in oropharyngeal swab samples, and the P value was 0.002. There were differences in the PLR, PAB level, ALB level and T cells between the severe and mild groups. The platelet count and PLR were the main factors influencing the immune function of patients with COVID-19, and CD8+ T cells influenced the negative conversion time of the nucleic acid test in oropharyngeal swabs.

Serum Prealbumin Concentrations, COVID-19 Severity, and Mortality: A Systematic Review and Meta-Analysis

“…Overall, they included 4,616 COVID-19 patients, 3,502 (48% males, mean age 52 years) with low severity or who survived and 1,114 (61% males, mean age 64 years) with high severity or who died. Three studies were prospective ( 24 , 30 , 36 ),14 were retrospective ( 22 , 23 , 25 – 29 , 31 , 33 , 34 , 37 – 40 ), whereas the remaining two did not explicitly state the study design ( 32 , 35 ). Disease severity, based on current clinical guidelines, was assessed in 11 studies ( 22 , 23 , 25 , 28 , 30 , 33 , 35 , 37 – 40 ), prolonged viral clearance in two ( 26 , 36 ), transfer to ICU in one ( 29 ), survival in three ( 27 , 31 , 32 ), presence of acute respiratory distress syndrome in one ( 34 ), and multiple end points in one ( 24 ).…”

[Retracted] Regulation of Quality of Life and Immune Function in Patients with Thyroid Cancer Treated by Deep Learning Technology

“…Finally, a new feature extraction method of thyroid ultrasound image combining texture, shape, and attenuation feature information is proposed, which has higher classification accuracy [ 8 ]. Based on the processing of large sample thyroid ultrasound images, Fu et al [ 9 ] proposed a predictive association rule classification algorithm to deal with the mining of high-dimensional database. With the further development of association rules, other algorithms have emerged as auxiliary optimization methods.…”

“…A selective weighted dynamic integration method is proposed. The parallel integration of multiple classifiers increases the stability of the classification model and proves a better classification algorithm than BP neural network [ 9 ]. Cheng et al [ 10 ] combined the model of low-level features automatically extracted from images and high-level knowledge search from experts.…”