Lithium salts as a treatment for COVID-19: Pre-clinical outcomes
Bibliographic record
Abstract
INTRODUCTION: Identifying effective drugs for Coronavirus disease 2019 (COVID-19) is urgently needed. An efficient approach is to evaluate whether existing approved drugs have anti-SARS-CoV-2 effects. The antiviral properties of lithium salts have been studied for many years. Their anti-inflammatory and immune-potentiating effects result from the inhibition of glycogen synthase kinase-3. AIMS: To obtain pre-clinical evidence on the safety and therapeutic effects of lithium salts in the treatment of COVID-19. RESULTS: value of 4 mmol/L. Lithium-treated wells showed a significantly higher percentage of monolayer conservation than viral control, particularly at concentrations higher than 6 mmol/L, verified through microscopic observation, the neutral red assay, and the determination of N protein in the supernatants of treated wells. Hamsters treated with lithium showed less intense disease with fewer signs. No lithium-related mortality or overt signs of toxicity were observed during the experiment. A trend of decreasing viral load in nasopharyngeal swabs and lungs was observed in treated hamsters compared to controls. CONCLUSIONS: These results provide pre-clinical evidence of the antiviral and immunotherapeutic effects of lithium against SARS-CoV-2, which supports an advance to clinical trials on COVID-19's patients.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".