Using an untargeted metabolomics approach to analyze serum metabolites in COVID-19 patients with nucleic acid turning negative.

Background: The coronavirus disease of 2019 (COVID-19) is a severe public health issue that has infected millions of people. The effective prevention and control of COVID-19 has resulted in a considerable increase in the number of cured cases. However, little research has been done on a complete metabonomic examination of metabolic alterations in COVID-19 patients following treatment. The current project pursues rigorously to characterize the variation of serum metabolites between healthy controls and COVID-19 patients with nucleic acid turning negative via untargeted metabolomics. Methods: The metabolic difference between 20 COVID-19 patients (CT ≥ 35) and 20 healthy controls were investigated utilizing untargeted metabolomics analysis employing High-resolution UHPLC-MS/MS. COVID-19 patients' fundamental clinical indicators, as well as health controls, were also collected. Results: Out of the 714 metabolites identified, 203 still significantly differed between COVID-19 patients and healthy controls, including multiple amino acids, fatty acids, and glycerophospholipids. The clinical indexes including monocytes, lymphocytes, albumin concentration, total bilirubin and direct bilirubin have also differed between our two groups of participators. Conclusion: Our results clearly showed that in COVID-19 patients with nucleic acid turning negative, their metabolism was still dysregulated in amino acid metabolism and lipid metabolism, which could be the mechanism of long-COVID and calls for specific post-treatment care to help COVID-19 patients recover.

Analysis of blood samples from 42 patients with varying degrees of infection during the epidemic of COVID-19: a retrospective study.

BACKGROUND: Blood samples from 42 patients with coronavirus disease 2019 (COVID-19) with varying degrees of infection were examined to further explore the relationship between clinical features, immune factors and COVID-19, as well as the diagnostic and predictive values of clinical features and immune factors in severe disease progression. METHODS: This study included 42 nucleic acid-positive COVID-19 patients admitted to the First Hospital of Jiaxing from January 26, 2020 to February 21, 2020, who were divided into mild-moderate group and severe group based on respiratory rate, resting oxygen saturation and alveolar oxygen partial pressure/O2 inhalation. On February 21, 2020, clinical data including sex, age, body mass index (BMI), past medical history, clinical symptoms, hematology indexes [white blood cell (WBC); neutrophil (NEUT); lymphocyte (LYM); C-reactive protein (CRP)] were collected. The chi-square test was used to compare the clinical data differences between the two groups, so as to perform comparative analysis in the context of serious disease development. RESULTS: There were 8 cases of severe disease, and 34 cases of mild and moderate symptoms. Comparative analysis showed that patients with advanced age (≥60 years, OR =5.800, P=0.0286), history of hypertension (OR =5.800, P=0.0286) and pulmonary lobe lesions (≥4, OR =6.273, P=0.0270) were more likely to develop serious diseases. In addition, according to clinical symptoms, chest pain was more prominent in patients with severe disease. Laboratory tests showed that levels of WBC (severe 4.96±1.76 vs. mild-moderate 5.45±2.01, P=0.5300), NEUT (severe 3.56±1.44 vs. mild-moderate 3.94±1.87, P=0.5945) and LYM (severe 0.91±0.25 vs. mild-moderate 1.11±0.51, P=0.2903) were normal or decreased, but CRP level (severe 31.03±9.38 vs. mild-moderate 12.53±15.73, P=0.0029) was obviously increased, especially in patients with severe disease, with statistically significant difference between groups. CONCLUSIONS: Patients with hypertension and advanced age are more likely to develop deteriorate with COVID-19, and the number of lung lobes with lesions and chest pain may indicate disease progression. Notably, CRP level is significantly elevated in severe disease and it may be closely related to COVID-19 progression.

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers.

In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19.

Efficacy and safety of glucocorticoids in treatment of COVID-19: a retrospective study.

BACKGROUND: Novel coronavirus pneumonia is a novel kind of highly contagious disease without any specific drugs. Considering the successful experience of antiviral therapy combined with glucocorticoids (GCs) in severe acute respiratory syndrome, this study was designed to evaluate the clinical efficacy of GCs in treating patients with coronavirus disease 2019 (COVID-19). METHODS: A cohort of 42 patients with COVID-19 admitted to The First Hospital of Jiaxing from January 4, 2020, to February 16, 2020, were included and grouped into a test group (n=20) and control group (n=22) based on their therapeutic regimens. There were no significant differences in baseline characteristics between patients in the two groups. Conventional treatment (antiviral therapy) was given to patients in both groups, while an additional hormone drug (GCs) was used in patients in the test group. Indices including body temperature, blood routine indices [white blood cell (WBC), lymphocyte, monocyte, and C-reactive protein (CRP)], blood biochemical indices [alanine aminotransferase (ALT) and aspartate aminotransferase (AST)], and complications were recorded during the treatment. Time to achieve negative virus nucleic acid (nCoV-RNA) testing, and hospital stays were also observed and compared between the two groups. RESULTS: All included patients completed the trial. After treatment, superior therapeutic efficacy was achieved in patients in the test group, with body temperature dropping more significantly with a much shorter recovery time compared to the control group (P=0.0412). Simultaneously, the percentage of patients with abnormal blood routine indices (WBC), monocyte, and (CRP) in the test group was reduced more sharply, while no noticeable difference was observed in the number of patients who developed abnormal blood biochemical indices during treatment between the two groups. Additionally, a shorter duration of hospital stays was found in the test group relative to the control group (14.84±8.76 vs. 18.25±7.42 days, P>0.05). Patients who received GCs had a shorter recovery time for body temperature and inflammation. CONCLUSIONS: Hormonotherapy with GCs can accelerate the recovery time for body temperature as well as inflammation in patients with COVID-19. It deserves promotion and application in the clinical treatment of coronavirus disease as a form of adjuvant medicine. The ongoing focus of research is on long-term adverse events in GCs.