The Role of Exosomes in the Treatment, Prevention, Diagnosis, and Pathogenesis of COVID-19
Bibliographic record
Abstract
The novel coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), continues to be a major health concern. In search for novel treatment strategies against COVID-19, exosomes have attracted the attention of scientists and pharmaceutical companies worldwide. Exosomes are small extracellular vesicles, secreted by all types of cells, and considered as key mediators of intercellular communication and stem-cell paracrine signaling. Herein, we reviewed the most recent literature about the role of exosomes as potential agents for treatment, prevention, diagnosis, and pathogenesis of COVID-19. Several studies and ongoing clinical trials have been investigating the anti-inflammatory, immunomodulatory, and reparative effects of exosomes derived from mesenchymal stem/stromal cells for COVID-19-related acute lung injury. Other studies reported that exosomes play a key role in convalescent plasma therapy for COVID-19, and that they could be of use for the treatment of COVID-19 Kawasaki's-like multisystem inflammatory syndrome and as drug delivery nanocarriers for antiviral therapy. Harnessing some advantageous aspects of exosome biology, such as their endogenous origin, capability of crossing biological barriers, high stability in circulation, and low toxicity and immunogenicity, several companies have been testing exosome-based vaccines against SARS-CoV-2. As they carry cargos that mimic the status of parent cells, exosomes can be isolated from a variety of sources, including plasma, and employed as biomarkers of COVID-19. Lastly, there is growing evidence supporting the role of exosomes in COVID-19 infection, spread, reactivation, and reinfection. The lessons learned using exosomes for COVID-19 will help determine their efficacy and applicability in other clinical conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".