Nutraceutical Targeting of Inflammation-Modulating microRNAs in Severe Forms of COVID-19: A Novel Approach to Prevent the Cytokine Storm
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has become the number one health problem worldwide. As of August 2020, it has affected more than 18 million humans and caused over 700,000 deaths worldwide. COVID-19 is an infectious disease that can lead to severe acute respiratory syndrome. Under certain circumstances, the viral infection leads to excessive and uncontrolled inflammatory response, which is associated with the massive release of inflammatory cytokines in pulmonary alveolar structures. This phenomenon has been referred to as the "cytokine storm," and it is closely linked to lung injury, acute respiratory syndrome and mortality. Unfortunately, there is currently no vaccine available to prevent the infection, and no effective treatment is available to reduce the mortality associated with the severe form of the disease. The cytokine storm associate with COVID-19 shows similarities with those observed in other pathologies such as sepsis, acute respiratory distress syndrome, acute lung injury and other viral infection including severe cases of influenza. However, the specific mechanisms that cause and modulate the cytokine storm in the different conditions remain to be determined. micro-RNAs are important regulators of gene expression, including key inflammatory cytokines involved in the massive recruitment of immune cells to the lungs such as IL1β, IL6, and TNFα. In recent years, it has been shown that nutraceutical agents can modulate the expression of miRs involved in the regulation of cytokines in various inflammatory diseases. Here we review the potential role of inflammatory-regulating-miRs in the cytokine storm associated with COVID-19, and propose that nutraceutical agents may represent a supportive therapeutic approach to modulate dysregulated miRs in this condition, providing benefits in severe respiratory diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".