Co-Administration of Menstrual Blood-Derived Stem Cells and Remdesivir for the Treatment of Severe Coronavirus Disease 2019 (COVID-19) Induced Pneumonia: A Research Protocol
Bibliographic record
Abstract
Introduction: Remdesivir (Veklury), a viral ribonucleic acid (RNA)-dependent RNA polymerase inhibitor designed by Gilead Sciences, has shown reductions in recovery time for coronavirus disease 2019 (COVID-19) patients, although its efficacy remains controversial. It has been proposed that combining remdesivir with immunomodulators may improve clinical efficacy. Mesenchymal stem cells (MSCs) exert immunomodulatory properties, which resolve COVID-19-induced pneumonia in early-phase trials. Menstrual blood-derived stem cells (MenSCs) present a novel MSC source, superior in availability, proliferative ability, and ethicality than traditional stem cell sources. This study aims to investigate the efficacy of remdesivir-MenSC combination therapy in resolving severe COVID-19-induced pneumonia. Methods: A randomized, double-blind, controlled study will be performed to assess two primary endpoints: time of recovery, defined as no longer requiring ongoing medical care, and normalization of the immune system, defined as the change in the concentration of key cytokines from baseline. Safety will also be measured as the frequency of treatment-related adverse events (AE). The study will aim to recruit 400 eligible subjects, aged 18 to 75, hospitalized with severe COVID-19, and they will be assigned to either receive intravenous (IV) infusions of MenSCs and remdesivir, or receive only remdesivir. A stratified log-rank test will be conducted to compare the time of recovery between study arms, with stratification by disease severity (baseline ordinal score). Two-way repeated measures ANOVA will be used to compare cytokine levels over time in the treatment group compared to the control group. Discussion: We expect remdesivir-MenSC combination therapy to surpass remdesivir in clinical efficacy and safety profile by improving clinical status, lowering duration of hospitalization, reducing mortality, and lowering the incidence of treatment-related AEs. Conclusion: Investigating this promising approach is an essential step in determining the feasibility of stem cell-based treatments in improving current COVID-19 therapeutics and patient outcomes. In particular, evaluating the clinical potential of MenSCs may provide insight into future therapeutic research as the literature has shown that MenSCs are superior to traditional MSC sources.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".