Exosomes of mesenchymal stem cells as nano-cargos for anti-SARS-CoV-2 asRNAs
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 2019 and rapidly spread worldwide.Since then, scientists have searched to find an effective treatment for coronavirus disease 2019 (COVID-19).In this regard, several antiviral drugs are currently undergoing clinical trial studies to evaluate their safety and efficacy in the treatment of COVID-19.Some of these drugs have been designed based on this fact that SARS-CoV-2 is a positive-sense single-stranded RNA virus and previous studies showed the efficacy of anti-RNA virus, single strand RNA inhibiting antisense RNAs (asRNAs), for silencing virus replication, in vitro.Exosomes can be suggested as a promising candidate to transfer the anti-SARS-CoV-2 asRNAs to human respiratory epithelium.Exosomes are secreted by mesenchymal stem cells (MSCs) and can be loaded by asRNAs of an anti-RNA virus.MSCs-secreted exosomes as a nano-cargo of asRNAs of anti-SARS-CoV-2 have other therapeutic potentials such as immunomodulatory effects of their cytokine contents, affinity to respiratory epithelial attachment, anti-fibrotic activity in lung, non-toxicity for normal cells, and not triggering an immune response.Moreover, inhalation of anti-SARS-CoV-2 asRNAs may stop SARS-CoV-2 replication.Producing specific anti-SARS-CoV-2 asRNAs by targeting the genome of virus and their delivery by MSCs exosomes are suggested and discussed.This approach will potentially shed light on gene therapy of the other human lung diseases via inhalational delivery using exosomes in future.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".