Adamantanes for the treatment of neurodegenerative diseases in the presence of SARS-CoV-2
Bibliographic record
Abstract
Advent of the acute respiratory coronavirus SARS-CoV-2 has resulted in the search for novel antiviral agents and in the repurposing of existing agents with demonstrated efficacy against other known coronaviruses in the search for an agent with antiviral activity for use during the COVID-19 pandemic. Adamantanes including amantadine, rimantadine, and memantine have well-established benefit in the treatment of neurodegenerative diseases including Parkinson’s disease (PD), Alzheimer’s disease (AD) and fatigue related to Multiple sclerosis (MS) all of which are known comorbidities related to COVID-19 Moreover, results of basic pharmacological studies both in vitro and in vivo reveal that amantadine has the potential to inhibit SARS-CoV-2 via down-regulation of host-cell proteases resulting in impaired viral genome release into the host cell and via amantadine’s property as an NMDA receptor antagonist resulting in the prevention of the acute lung injury and respiratory distress that is characteristic of COVID-19. Cases suggestive of COVID-19 prophylaxis have been reported in patients with PD or MS or severe cognitive impairment treated in all cases for several months with an adamantane [amantadine or memantine] who were subsequently infected with SARS-CoV-2 confirmed by RT-PCR, and, in all cases, no signs of infectious disease were encountered. Amantadine is effective for the treatment of fatigue in MS and for the neurological complications of Traumatic Brain Injury (TBI).
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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 | 0.000 |
| 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 | 0.001 |
| 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".