Enhanced fatty acid oxidation through metformin and baicalin as therapy for COVID-19 and associated inflammatory states in lung and kidney
Why this work is in the frame
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Bibliographic record
Abstract
Progressive respiratory failure is the primary cause of death in the coronavirus disease 2019 (COVID-19) pandemic. It is the final outcome of the acute respiratory distress syndrome (ARDS), characterized by an initial exacerbated inflammatory response, metabolic derangement and ultimate tissue scarring. A positive balance of cellular energy may result crucial for the recovery of clinical COVID-19. Hence, we asked if two key pathways involved in cellular energy generation, AMP-activated protein kinase (AMPK)/acetyl-CoA carboxylase (ACC) signaling and fatty acid oxidation (FAO) could be beneficial. We tested the drugs metformin (AMPK activator) and baicalin (CPT1A activator) in different experimental models mimicking COVID-19 associated inflammation in lung and kidney. We also studied two different cohorts of COVID-19 patients that had been previously treated with metformin. These drugs ameliorated lung damage in an ARDS animal model, while activation of AMPK/ACC signaling increased mitochondrial function and decreased TGF-β-induced fibrosis, apoptosis and inflammation markers in lung epithelial cells. Similar results were observed with two indole derivatives, IND6 and IND8 with AMPK activating capacity. Consistently, a reduced time of hospitalization and need of intensive care was observed in COVID-19 patients previously exposed to metformin. Baicalin also mitigated the activation of pro-inflammatory bone marrow-derived macrophages (BMDMs) and reduced kidney fibrosis in two animal models of kidney injury, another key target of COVID-19. In human epithelial lung and kidney cells, both drugs improved mitochondrial function and prevented TGF-β-induced renal epithelial cell dedifferentiation. Our results support that favoring cellular energy production through enhanced FAO may prove useful in the prevention of COVID-19-induced lung and renal damage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it