Exosomes of mesenchymal stem cells as nano-cargos for anti-SARS-CoV-2 asRNAs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 . 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it