In December 2019, cases of severe coronavirus 2019 (COVID-19) infection rapidly progressed to acute respiratory failure. This study aims to assess the association between the neutrophil-to-lymphocyte ratio (NLR) and the incidence of severe COVID-19 infection. A retrospective cohort study was conducted on 210 patients with COVID-19 infection who were admitted to the Central Hospital of Wuhan from 27 January 2020 to 9 March 2020. Peripheral blood samples were collected and examined for lymphocyte subsets by flow cytometry. Associations between tertiles of NLR and the incidence of severe illness were analysed by logistic regression. Of the 210 patients with COVID-19, 87 were diagnosed as severe cases. The mean NLR of the severe group was higher than that of the mild group (6.6 vs. 3.3, P < 0.001). The highest tertile of NLR (5.1–19.7) exhibited a 5.9-fold (95% CI 1.3–28.5) increased incidence of severity relative to that of the lowest tertile (0.6–2.5) after adjustments for age, diabetes, hypertension and other confounders. The number of T cells significantly decreased in the severe group (0.5 vs. 0.9, P < 0.001). COVID-19 might mainly act on lymphocytes, particularly T lymphocytes. NLR was identified as an early risk factor for severe COVID-19 illness. Patients with increased NLR should be admitted to an isolation ward with respiratory monitoring and supportive care.

Background: The global mortality rate for coronavirus disease 2019 (COVID-19) is 3.68%, but the mortality rate for critically ill patients is as high as 50%. Therefore, the exploration of prognostic predictors for patients with COVID-19 is vital for prompt clinical intervention. Our study aims to explore the predictive value of hematological parameters in the prognosis of patients with severe COVID-19.Methods: Ninety-eight patients who were diagnosed with COVID-19 at Jingzhou Central Hospital and Central Hospital of Wuhan, Hubei Province, were included in this study. Results:The median age of the patients was 59 years; the median age of patients with a good prognosis was 56 [28-79] years, and the median age of patients with a poor outcome was 67 years.The patients in the poor outcome group were older than the patients in the good outcome group (P<0.05).The comparison of hematological parameters showed that lymphocyte count (Lym#), red blood cells (RBCs), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) were significantly lower in the poor outcome group than in the good outcome group (P<0.05). Further, the red cell volume distribution width-CV (RDW-CV) and red cell volume distribution width-SD (RDW-SD) were significantly higher in the poor outcome group than in the good outcome group (P<0.0001). Receiver operating characteristic (ROC) curves showed RDW-SD, with an area under the ROC curve (AUC) of 0.870 [95% confidence interval (CI) 0.796-0.943], was the most significant single parameter for predicting the prognosis of severe patients. When the cut-off value was 42.15, the sensitivity and specificity of RDW-SD for predicting the prognosis of severe patients were 73.1% and 80.2%, respectively. Reticulocyte (RET) channel results showed the RET level was significantly higher in critical patients than in moderate patients and severe patients (P<0.05), which may be one cause of the elevated RDW in patients with a poor outcome. Conclusions:In this study, the hematological parameters of COVID-19 patients were statistically analyzed. RDW was found to be a prognostic predictor for patients with severe COVID-19, and the increase in RET may contribute to elevated RDW.

Introduction: To retrospectively analyze epidemiological, clinical and hematological characteristics of COVID-19 patients. Methods: The demographic, symptoms, and physiological parameters of 88 patients were collected and analyzed. The performance of complete blood count (CBC) indexes for monitoring and predicting the severity of COVID-19 in patients was evaluated by analyzing and comparing CBC results among different COVID-19 patient groups. Results: White blood cells (WBCs), the neutrophil percentage (Neu%), absolute neutrophil count (Neu#), and neutrophil-to-lymphocyte ratio (NLR) were significantly higher in the critical group than in the other three groups (P < .05), while the lymphocyte percentage (Lym%), monocyte percentage (Mon%), lymphocyte count (Lym#), and lymphocyte-to-monocyte ratio (LMR) were significantly lower in the critical group than in the other three groups (P < .05). WBCs, the Neu%, Neu#, NLR, and neutrophil-to-monocyte ratio (NMR) were significantly higher in the severe group than in the mild and moderate groups (P < .05), while the Lym% was significantly lower in the severe group than in the mild and moderate groups (P < .05). The Mon%, Lym#, and LMR were significantly lower in the severe group than in the moderate group (P < .05). Using receiver operating characteristic (ROC) curve analysis to differentiate severe and nonsevere patients, the areas under the curve (AUCs) for the NLR, Neu%, and Lym% were 0.733, 0.732, and 0.730, respectively. When differentiating critical patients from noncritical patients, the AUCs for the NLR, Neu%, and Lym% were 0.832, 0.831, and 0.831. Conclusions: The NLR is valuable for differentiating and predicting patients who will become critical within 4 weeks after the onset of COVID-19.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for ~80% of all lung cancer cases. The aim of the present study was to identify key genes and pathways in NSCLC, in order to improve understanding of the mechanism of lung cancer. The GSE33532 gene expression dataset, containing 20 normal and 80 NSCLC samples, was used. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to obtain the enrichment data of differently expressed genes (DEGs). Disease modules within NSCLC were constructed by Cytoscape, using protein-protein interaction (PPI) from the Search Tool for the Retrieval of Interacting Genes database. In addition, the Kaplan Meier plotter KMplot was used to assess the top hub genes in the PPI network. As a result, 1,795 genes were identified in NSCLC; 729 were upregulated and 1,066 were downregulated. The results of the GO analysis indicated that the upregulated DEGs were significantly enriched in ‘biological processes’ (BP), including ‘cell cycle and nuclear division’; the downregulated DEGs were also significantly enriched in BP, including ‘response to wounding’, ‘anatomical structure morphogenesis’ and ‘response to stimulus’. Upregulated DEGs were also enriched in ‘cell cycle’, ‘DNA replication’ and the ‘tumor protein 53 signaling pathway’, while the downregulated DEGs were also enriched in ‘complement and coagulation cascades’, ‘malaria’ and ‘cell adhesion molecules’. The top 9 hub genes were cyclin-dependent kinase 9 (CDK1), polo-like kinase 1, aurora kinase B, cell division cycle 20, baculoviral initiator of apoptosis repeat containing 5, mitotic checkpoint serine/threonine kinase B, proliferating cell nuclear antigen (PCNA), centromere protein A and MAD2 mitotic arrest deficient-like 1, and the KMplot results revealed that the high expression levels of these genes resulted in significantly low survival rates, compared with low expression samples (P<0.05), with the exception of PCNA and CDK1. In the pathway crosstalk analysis, 26 nodes and 41 interactions were divided into two groups: One module of the two groups primarily included ‘metabolism of amino acid’ and the other primarily contained ‘tumor necrosis signaling’ pathways. In conclusion, the present study assisted in improving the understanding of the molecular mechanisms underlying NSCLC development, and the results may help the understanding of the biological mechanism of NSCLC.

This study aimed to investigate the status and risk factors of post-traumatic stress disorder (PTSD) in patients with acute myocardial infarction (AMI) after emergency percutaneous coronary intervention (PCI) in acute and convalescence phases. A longitudinal study design was used. Two questionnaire surveys were conducted in the acute stage of hospitalization, and 3 months after onset in patients. Logistic regression was used to analyze the risk factors for PTSD in AMI patients. The incidence of PTSD was 33.1 and 20.4% in acute and convalescent patients, respectively. The risk factors related to PTSD were door-to-balloon time (DTB) (≥92.6 min), left ventricular ejection fraction (LVEF) (&lt;50%), smoking, anxiety, and depression. AMI patients after PCI had PTSD in the acute and convalescent stage. The findings indicate that tailored measures should be developed and carried out to prevent PTSD and improve the mental health of patients with AMI after undergoing PCI.