Immune Response Dynamics and Biomarkers in COVID-19 Patients.

BACKGROUND: The immune response dynamics in COVID-19 patients remain a subject of intense investigation due to their implications for disease severity and treatment outcomes. We examined changes in leukocyte levels, eosinophil activity, and cytokine profiles in patients hospitalized with COVID-19. METHODS: Serum samples were collected within the first 10 days of hospitalization/confirmed infection and analyzed for eosinophil granule proteins (EGP) and cytokines. Information from medical records including comorbidities, clinical symptoms, medications, and complete blood counts were collected at the time of admission, during hospitalization and at follow up approximately 3 months later. RESULTS: Serum levels of eotaxin, type 1 and type 2 cytokines, and alarmin cytokines were elevated in COVID-19 patients, highlighting the heightened immune response (p < 0.05). However, COVID-19 patients exhibited lower levels of eosinophils and eosinophil degranulation products compared to hospitalized controls (p < 0.05). Leukocyte counts increased consistently from admission to follow-up, indicative of recovery. CONCLUSION: Attenuated eosinophil activity alongside elevated chemokine and cytokine levels during active infection, highlights the complex interplay of immune mediators in the pathogenesis COVID-19 and underscores the need for further investigation into immune biomarkers and treatment strategies.

Machine learning enabled detection of COVID-19 pneumonia using exhaled breath analysis: a proof-of-concept study.

Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%-81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%-74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.

Gene Polymorphisms of Epithelial Cell-Derived Alarmins and Their Effects on Protein Levels and Disease Severity in Patients with COVID-19.

BACKGROUND: The immune response in COVID-19 is characterized by the release of alarmin cytokines, which play crucial roles in immune activation and inflammation. The interplay between these cytokines and genetic variations may influence disease severity and outcomes, while sex differences might further contribute to variations in the immune response. METHODS: We measured the levels of alarmin cytokines in a cohort of COVID-19 and non-COVID-19 patients using a sensitive Meso Scale Discovery system. Additionally, we conducted an SNP analysis to identify genetic variations within the IL-33 and TSLP genes. The association between these genetic variations, cytokine production, and COVID-19 severity was examined. RESULTS: Our findings revealed elevated levels of IL-33 and IL-25 in COVID-19-positive patients compared to COVID-19-negative patients (p < 0.05), indicating their potential as therapeutic targets for disease modulation. Moreover, a minor allele within the IL-33 gene (rs3939286) was found to be associated with a protective effect against severe COVID-19 (p < 0.05), and minor alleles of the TSLP gene (rs2289276 and rs13806933) were found to significantly reduce TSLP protein levels in serum (p < 0.05). Sex-specific effects of TSLP and IL-33 SNPs were observed, suggesting a potential influence of sex hormones and genetic variations on the regulation of cytokine production. CONCLUSION: The present study highlights the importance of alarmin cytokines and genetic variations in COVID-19 severity, providing valuable insights into personalized treatment approaches. Our results suggest that targeting alarmin cytokines may offer potential therapeutic benefits in managing COVID-19. Furthermore, the sex-specific effects of genetic variations emphasize the need to consider individual genetic profiles and sex differences when designing targeted interventions.