Cannabinoids and the Endocannabinoid System in Early SARS-CoV-2 Infection and Long COVID-19—A Scoping Review
Bibliographic record
Abstract
Coronavirus disease-19 (COVID-19) is a highly contagious illness caused by the SARS-CoV-2 virus. The clinical presentation of COVID-19 is variable, often including symptoms such as fever, cough, headache, fatigue, and an altered sense of smell and taste. Recently, post-acute "long" COVID-19 has emerged as a concern, with symptoms persisting beyond the acute infection. Vaccinations remain one of the most effective preventative methods against severe COVID-19 outcomes and the development of long-term COVID-19. However, individuals with underlying health conditions may not mount an adequate protective response to COVID-19 vaccines, increasing the likelihood of severe symptoms, hospitalization, and the development of long-term COVID-19 in high-risk populations. This review explores the potential therapeutic role of cannabinoids in limiting the susceptibility and severity of infection, both pre- and post-SARS-CoV-19 infection. Early in the SARS-CoV-19 infection, cannabinoids have been shown to prevent viral entry, mitigate oxidative stress, and alleviate the associated cytokine storm. Post-SARS-CoV-2 infection, cannabinoids have shown promise in treating symptoms associated with post-acute long COVID-19, including depression, anxiety, post-traumatic stress injury, insomnia, pain, and decreased appetite. While current research primarily focuses on potential treatments for the acute phase of COVID-19, there is a gap in research addressing therapeutics for the early and post-infectious phases. This review highlights the potential for future research to bridge this gap by investigating cannabinoids and the endocannabinoid system as a potential treatment strategy for both early and post-SARS-CoV-19 infection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".