Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine.

Coronavirus disease-2019 (COVID-19) has made the world more cautious about widespread viruses, and a tragic pandemic that was caused by a novel coronavirus has harmed human beings in recent years. The new coronavirus pneumonia outbreak is spreading rapidly worldwide. We collect arterial blood samples from 51 patients with a COVID-19 diagnosis. Blood gas analysis is performed using a Siemens RAPID Point 500 blood gas analyzer. To accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity, a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in this paper. This method uses specular reflection learning to improve the original HHO algorithm and is referred to as HHOSRL. The experimental results show that the selected indicators, such as age, partial pressure of oxygen, oxygen saturation, sodium ion concentration, and lactic acid, are essential for the early accurate assessment of COVID-19 severity by the proposed feature selection method. The simulation results show that the established methodlogy can achieve promising performance. We believe that our proposed model provides an effective strategy for accurate early assessment of COVID-19 and distinguishing disease severity. The codes of HHO will be updated in https://aliasgharheidari.com/HHO.html.

COVID-19 pneumonia: CD8+ T and NK cells are decreased in number but compensatory increased in cytotoxic potential.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is posing a huge threat to human health worldwide. We aim to investigate the immune status of CD8+ T and NK cells in COVID-19 patients. METHODS: The count and immune status of lymphocytes were detected by flow cytometry in 32 COVID-19 patients and 18 healthy individuals. RESULTS: As the disease progression in COVID-19 patients, CD8+ T and NK cells were significantly decreased in absolute number but highly activated. After patients' condition improved, the count and immune status of CD8+ T and NK cells restored to some extent. GrA+CD8+ T and perforin+ NK cells had good sensitivity and specificity for assisting diagnosis of COVID-19. CONCLUSIONS: As the disease progression, the declined lymphocytes in COVID-19 patients might lead to compensatory activation of CD8+ T and NK cells. GrA+CD8+ T and perforin+ NK cells might be used as meaningful indicators for assisting diagnosis of COVID-19.

Continuous tracking of COVID-19 patients' immune status.

BACKGROUND: COVID-19 is threating human health worldwide. We aim to investigate the dynamic changes of immune status in COVID-19 patients with clinical evolution. METHODS: Sixty-one COVID-19 patients (42 mild cases and 19 severe cases, 51 cases without secondary infection as non-infection group and 10 cases with secondary bacterial/fungal infection as infection group) and 52 healthy controls (HCs) were enrolled from our hospital. Leucocyte classification, lymphocyte subsets and cytokines were detected by full-automatic blood cell analyzer and flow cytometer, respectively. RESULTS: Upon admission, eosinophils and lymphocyte subsets decreased significantly, while neutrophils, monocytes, basophils, IL-2, IL-6, IL-10 and IFN-γ increased significantly in COVID-19 patients compared to HCs. CD3+ T and DN (CD3+CD4-CD8-) cells appeared sustained decline, leucocytes, neutrophils and IL-10 showed sustained increase in severe group compared to mild group. Compared with the non-infection group, we observed a depletion of eosinophils, CD3+ T and CD4+ T cells, but leucocytes, neutrophils, IL-6 and IL-10 on the contrary in the infection group. Besides, in severe group of COVID-19 patients, DN cells were negatively correlated with IL-10, and DP (CD3+CD4+CD8+) cells were negatively correlated with IL-6. Lymphocytes, eosinophils, CD3+ T cells, CD4+ T cells, IL-6 and IL-10 all had great diagnostic efficacy (AUC, 0.905-0.975) for COVID-19. The laboratory indicators of COVID-19 patients with improved condition also showed a recovery trend with time. CONCLUSIONS: The immune status of COVID-19 patients is different in each stage, and dynamic monitoring of related indicators can help predict the disease and may avoid cytokine storms.