Is Inhaled Furosemide A Potential Therapeutic For COVID-19?
Bibliographic record
Abstract
The potentially lethal COVID-19 infection caused by the novel Severe Acute Respiratory Disease Coronavirus-2 (SARS-CoV-2) has evolved into a global crisis. There are two major processes that lead to the morbidity and mortality of this disease: initially the viral infection, followed by a host inflammatory response that frequently results in excessive secretion of inflammatory cytokines (e.g. IL-6, IL-8, IL-10, TNFα), developing into a self-targeting toxic “cytokine storm” in which the lungs fill with inflammatory secretions causing critical pulmonary tissue damage. Even though the search for a vaccine and anti-viral agents has already been initiated, the de novo development of a safe, COVID-19 specific solution may take years; regrettably, the need for a therapeutic that is available immediately is growing daily. Therefore, repurposing an already approved drug offers a promising approach to address this urgent need. A truly effective therapeutic, however, should be available not only for the single individual in a developed country, but also for the many people in developing countries. As presented in this review, inhaled furosemide, a small molecule capable of inhibiting IL-6, IL-8 and TNFα within the lung, may be an agent capable of treating the COVID-19 cytokine storm in both resource-rich and developing countries. Furosemide is a “repurpose-able” small molecule therapeutic, that is safe, easily synthesized, handled and stored, and is available in reasonable quantities worldwide.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".