ReviewViruses2015
Modeling Viral Infectious Diseases and Development of Antiviral Therapies Using Human Induced Pluripotent Stem Cell-Derived Systems.
Review in Viruses, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 31 citations in OpenAlex.
- Induced Pluripotent Stem Cells in Birds: Opportunities and Challenges for Science and Agriculture.Veterinary sciences · 2024Review
- An equine iPSC-based phenotypic screening platform identifies pro- and anti-viral molecules against West Nile virus.Veterinary research · 2024Article
- ReprogrammedMicroorganisms · 2021Article
- The Use of Stem Cell-Derived Organoids in Disease Modeling: An Update.International journal of molecular sciences · 2021Review
- hiPSCs for predictive modelling of neurodegenerative diseases: dreaming the possible.Nature reviews. Neurology · 2021Review
- SARS-CoV-2 Infection and Disease Modelling Using Stem Cell Technology and Organoids.International journal of molecular sciences · 2021Review
- Stem Cell Therapy Potency in Personalizing Severe COVID-19 Treatment.Stem cell reviews and reports · 2021Review
- Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing.Stem cells translational medicine · 2021Article
- Modelling Neurotropic Flavivirus Infection in Human Induced Pluripotent Stem Cell-Derived Systems.International journal of molecular sciences · 2019Article
- In vitro and ex vivo systems at the forefront of infection modeling and drug discovery.Biomaterials · 2019Review
- Modeling Host-Virus Interactions in Viral Infectious Diseases Using Stem-Cell-Derived Systems and CRISPR/Cas9 Technology.Viruses · 2019Review
- Induced Pluripotency and Gene Editing in Disease Modelling: Perspectives and Challenges.International journal of molecular sciences · 2015Review
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 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent biotechnology breakthrough of cell reprogramming and generation of induced pluripotent stem cells (iPSCs), which has revolutionized the approaches to study the mechanisms of human diseases and to test new drugs, can be exploited to generate patient-specific models for the investigation of host-pathogen interactions and to develop new antimicrobial and antiviral therapies. Applications of iPSC technology to the study of viral infections in humans have included in vitro modeling of viral infections of neural, liver, and cardiac cells; modeling of human genetic susceptibility to severe viral infectious diseases, such as encephalitis and severe influenza; genetic engineering and genome editing of patient-specific iPSC-derived cells to confer antiviral resistance.
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.