Objectives: In December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19. Methods: In this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy. Results: The data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954–10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097–0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111–0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746–0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients. Conclusions: We designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management.

SUMMARY: Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.

Background: Self-care is crucial for maintaining the health and quality of life of individuals undergoing physical examinations, especially those with abnormal test results because they are at a higher risk of experiencing worse outcomes. However, there is currently a lack of comprehensive literature on the impact of sociodemographic and clinical factors on self-care practices related to serum potassium concentration among individuals undergoing physical examinations. Therefore, this study aimed to explore the sociodemographic and clinical factors influencing serum potassium concentration. Methods: Data from 43,151 individuals who underwent physical examinations were retrospectively collected in January, April, July, and October of 2019–2021. The serum potassium concentrations of these individuals were compared based on sex, age, and residential area. Additionally, the whole cohort and a subset of 6698 individuals with available occupational information were included to analyze the sociodemographic factors associated with serum potassium concentration using logistic regression analysis. Furthermore, a propensity score matching approach was employed to match 642 individuals with abnormal serum potassium concentrations to 642 with normal serum potassium concentrations. Pearson’s correlation analysis was subsequently used to investigate the clinical factors contributing to abnormalities in serum potassium concentration. Results: High temperatures; older age; male sex; living in the southern part of the city; and chemical, communication system, and transportation occupations were associated with a higher likelihood of experiencing abnormal serum potassium concentrations. Individuals with abnormal serum potassium concentrations had a higher prevalence of underlying diseases. Compared with the hypokalemia group, the hyperkalemia group exhibited a higher incidence of diabetes and cardiovascular disease. In the hyperkalemia group, serum potassium concentration positively correlated with serum creatinine concentration and negatively correlated with the estimated glomerular filtration rate (eGFR). In contrast, in the hypokalemia group, serum potassium concentration negatively correlated with creatinine concentrations, blood glucose concentration, and systolic and diastolic blood pressure, and positively correlated with eGFR. Conclusions: Sociodemographic and clinical factors can affect blood potassium concentration. During daily self-care, it is essential for individuals with abnormal potassium concentrations to avoid exposure to relevant sociodemographic risk factors and seek medical attention as soon as possible to screen for diseases, such as hypertension, cardiovascular disease, and diabetes.

Purpose: We evaluated the differences between patients with SARS-CoV-2 Omicron variant infections and Fever outpatients, so that prevention and control measures can be taken in time. Patients and Methods: This study retrospectively analyzed 65 patients with SARS-CoV-2 Omicron variant. Sixty-nine age- and sex-matched Fever outpatients were enrolled during the same period of time. We also reanalyzed data from 81 SARS-CoV-2 Wild-Type-infected patients. We compared the clinical characteristics and initial indexes of routine tests among the 3 groups. Results: A total of 93.8% of the patients with Omicron infections had clinical symptoms, and the major symptoms were cough, fever and pharyngalgia. Pharyngalgia was a specific manifestation in Omicron group compared to Wild-Type group. The white blood cell of the Omicron group was lower than that of the Fever group [5.0 (3.6– 6.1) vs 10.1 (7.6– 12.9) × 10⁹/L, P < 0.001]. The neutrophil count in Omicron group was lower than that in Fever and Wild-Type group [2.6 (1.8– 3.9) vs 8.1 (5.9– 10.9), P < 0.001; 2.6 (1.8– 3.9) vs 3.4 (2.5– 4.7) × 10⁹/L, P < 0.001]. The white blood cell and neutrophil counts were lower in Omicron group than in the Fever group. The top 5 major symptoms were fever, cough, pharyngalgia, headache and expectoration. Conclusion: There are differences between the patients with Omicron infections and Fever outpatients, both in clinical manifestations and initial routine hematology indicators. We hope to provide some clues for early identification combined with a history of living in the epidemic area.

The prevalence of hyperuricemia (HUA) has been increasing among adolescents, yet the relationship between body composition and HUA remains underexplored. T