ReviewCytotherapy2021
Stem cell therapy in coronavirus disease 2019: current evidence and future potential.
Review in Cytotherapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Systematic review and meta-analysis of cell therapy for COVID-19: global clinical trial landscape, published safety/efficacy outcomes, cell product manufacturing and clinical delivery.Frontiers in immunology · 2023Pooled it
- Editorial: Emerging talents in alloimmunity and transplantation: 2022.Frontiers in immunology · 2024Article
- Specific Activation of T Cells by an ACE2-Based CAR-Like Receptor upon Recognition of SARS-CoV-2 Spike Protein.International journal of molecular sciences · 2023Article
- An outlook on antigen-specific adoptive immunotherapy for viral infections with a focus on COVID-19.Cellular immunology · 2021Review
- Stem cell therapies and benefaction of somatic cell nuclear transfer cloning in COVID-19 era.Stem cell research & therapy · 2021Review
- Stem Cell Therapy Potency in Personalizing Severe COVID-19 Treatment.Stem cell reviews and reports · 2021Review
- Mesenchymal Stem Cell-Derived Exosome Therapy of Microbial Diseases: From Bench to Bed.Frontiers in microbiology · 2021Review
- Contribution of machine learning approaches in response to SARS-CoV-2 infection.Informatics in medicine unlocked · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The end of 2019 saw the beginning of the coronavirus disease 2019 (COVID-19) pandemic that soared in 2020, affecting 215 countries worldwide, with no signs of abating. In an effort to contain the spread of the disease and treat the infected, researchers are racing against several odds to find an effective solution. The unavailability of timely and affordable or definitive treatment has caused significant morbidity and mortality. Acute respiratory distress syndrome (ARDS) caused by an unregulated host inflammatory response toward the viral infection, followed by multi-organ dysfunction or failure, is one of the primary causes of death in severe cases of COVID-19 infection. Currently, empirical management of respiratory and hematological manifestations along with anti-viral agents is being used to treat the infection. The quest is on for both a vaccine and a more definitive management protocol to curtail the spread. Researchers and clinicians are also exploring the possibility of using cell therapy for severe cases of COVID-19 with ARDS. Mesenchymal stromal cells are known to have immunomodulatory properties and have previously been used to treat viral infections. This review explores the potential of mesenchymal stromal cells as cell therapy for ARDS.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.