Fighting the storm: novel anti- TNFα and anti-IL-6 C. sativa lines to tame cytokine storm in COVID-19
Bibliographic record
Abstract
Abstract The main aspects of severe COVID-19 disease pathogenesis include the increasing hyper-induction of proinflammatory cytokines, also known as ‘cytokine storm’, that precedes acute respiratory distress syndrome (ARDS) and often leads to death. COVID-19 patients often suffer from lung fibrosis, a serious and untreatable condition. There remains no effective treatment for these complications. Out of the cytokines, TNFα and IL-6 play crucial roles in cytokine storm pathogenesis and are likely responsible for the escalation in disease severity. These cytokines also partake in the molecular pathogenesis of fibrosis. Therefore, new approaches are urgently needed that can efficiently and swiftly block TNFα, IL-6, and the inflammatory cytokine cascade in order to curb inflammation and prevent fibrosis, and lead to disease remission.Cannabis sativa has been proposed to modulate gene expression and inflammation and is under investigation for several potential therapeutic applications against autoinflammatory diseases and cancer. Here, we hypothesized that the extracts of our novel C. sativa lines may be used to modulate the expression of pro-inflammatory cytokines and pathways involved in inflammation and fibrosis. To analyze the anti-inflammatory effects of novel C. sativa lines, we used a well-established full thickness human 3D skin artificial EpiDermFTTM tissue model, whereby tissues were exposed to UV to induce inflammation and then treated with extracts of seven new cannabis lines.We noted that out of seven studied extracts of novel C. sativa lines, three (#4, #8 and #14) were the most effective, causing profound and concerted down-regulation of TNFα, IL-6, CCL2, and other cytokines and pathways related to inflammation and fibrosis. Most importantly, one of the tested extracts had no effects at all, and one exerted effects that may be deleterious, signifying that cannabis is not generic and cultivar selection must be based on thorough pre-clinical studies.The observed pronounced inhibition of TNFα and IL-6 is the most important finding, as these molecules are currently considered to be the main actionable targets in COVID-19 cytokine storm and ARDS pathogenesis. Many currently trialed agents, such as anti-TNFα and anti-IL-6 biologics are expensive and cause an arrays of side effects. On the other hand, anti-TNFα and anti-IL-6 cannabis extracts that are generally regarded as safe (GRAS) modalities can be a useful addition to the current anti-inflammatory regimens to treat COVID-19, as well as various rheumatological diseases and conditions, and ‘inflammaging’ - the inflammatory underpinning of aging and frailty.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".