The Advent of COVID-19; Periodontal Research Has Identified Therapeutic Targets for Severe Respiratory Disease; an Example of Parallel Biomedical Research Agendas
Bibliographic record
Abstract
The pathophysiology of SARS-CoV-2 infection is characterized by rapid virus replication and aggressive inflammatory responses that can lead to acute respiratory distress syndrome (ARDS) only a few days after the onset of symptoms. It is suspected that a dysfunctional immune response is the main cause of SARS-CoV-2 infection-induced lung destruction and mortality due to massive infiltration of hyperfunctional neutrophils in these organs. Similarly, neutrophils are recruited constantly to the oral cavity to combat microorganisms in the dental biofilm and hyperfunctional neutrophil phenotypes cause destruction of periodontal tissues when periodontitis develops. Both disease models arise because of elevated host defenses against invading organisms, while concurrently causing host damage/disease when the immune cells become hyperfunctional. This represents a clear nexus between periodontal and medical research. As researchers begin to understand the link between oral and systemic diseases and their potential synergistic impact on general health, we argue that translational research from studies in periodontology must be recognized as an important source of information that might lead to different therapeutic options which can be effective for the management of both oral and non-oral diseases. In this article we connect concepts from periodontal research on oral inflammation while exploring host modulation therapy used for periodontitis as a potential strategy for the prevention of ARDS a deadly outcome of COVID-19. We suggest that host modulation therapy, although developed initially for management of periodontitis, and which inhibits proteases, cytokines, and the oxidative stress that underlie ARDS, will provide an effective and safe treatment for COVID-19.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".