Targeting MMP-Regulation of Inflammation to Increase Metabolic Tolerance to COVID-19 Pathologies: A Hypothesis
Bibliographic record
Abstract
Many individuals infected with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) develop no or only mild symptoms, but some can go on onto develop a spectrum of pathologies including pneumonia, acute respiratory distress syndrome, respiratory failure, systemic inflammation, and multiorgan failure. Many pathogens, viral and non-viral, can elicit these pathologies, which justifies reconsidering whether the target of therapeutic approaches to fight pathogen infections should be (a) the pathogen itself, (b) the pathologies elicited by the pathogen interaction with the human host, or (c) a combination of both. While little is known about the immunopathology of SARS-CoV-2, it is well-established that the above-mentioned pathologies are associated with hyper-inflammation, tissue damage, and the perturbation of target organ metabolism. Mounting evidence has shown that these processes are regulated by endoproteinases (particularly, matrix metalloproteinases (MMPs)). Here, we review what is known about the roles played by MMPs in the development of COVID-19 and postulate a mechanism by which MMPs could influence energy metabolism in target organs, such as the lung. Finally, we discuss the suitability of MMPs as therapeutic targets to increase the metabolic tolerance of the host to damage inflicted by the pathogen infection, with a focus on SARS-CoV-2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".