A Novel Method for the Production of an Autologous Anti-Inflammatory and Anti-Catabolic Product (Cytorich) from Human Blood: A Prospective Treatment for the COVID-19-Induced Cytokine Storm
Bibliographic record
Abstract
BACKGROUND Autologous blood-derived products can target specific inflammatory molecular pathways and have potentially beneficial therapeutic effects on inflammatory pathologies. The purpose of this study was to assess in vitro the anti-inflammatory and anti-catabolic potential of an autologous blood product as a possible treatment for COVID-19-induced cytokine storm. MATERIAL AND METHODS Blood samples from healthy donors and donors who had recovered from COVID-19 were incubated using different techniques and analyzed for the presence of anti-inflammatory, anti-catabolic, regenerative, pro-inflammatory, and procatabolic molecules. RESULTS The highest concentrations of therapeutic molecules for targeting inflammatory pathways were found in the blood that had been incubated for 24 h at 37°C, whereas a significant increase was observed after 6 h of incubation in blood from COVID-19-recovered donors. Beneficially, the 6-h incubation process did not downregulate anti-COVID-19 immunoglobulin G concentrations. Unfortunately, increases in matrix metalloproteinase 9, tumor necrosis factor alpha, and interleukin-1 were detected in the product after incubation; however, these increases could be blocked by adding citric acid, with no effect on the concentration of the target therapeutic molecules. Our data allow for safer and more effective future treatments. CONCLUSIONS An autologous blood-derived product containing anti-inflammatory and anti-catabolic molecules, which we term Cytorich, has a promising therapeutic role in the treatment of a virus-induced cytokine storm, including that associated with COVID-19.
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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) | 0.001 | 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".