ReviewMolecular metabolism2015
Dissecting diabetes/metabolic disease mechanisms using pluripotent stem cells and genome editing tools.
Review in Molecular metabolism, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 26 citations in OpenAlex.
- A critical review on therapeutic approaches of CRISPR-Cas9 in diabetes mellitus.Naunyn-Schmiedeberg's archives of pharmacology · 2023Review
- Multidisciplinary Effort to Drive Precision-Medicine for the Future.Frontiers in digital health · 2022Article
- Maturity Onset Diabetes of the Young-New Approaches for Disease Modelling.International journal of molecular sciences · 2021Review
- Modeling Type 1 Diabetes Using Pluripotent Stem Cell Technology.Frontiers in endocrinology · 2021Review
- HNF4A Haploinsufficiency in MODY1 Abrogates Liver and Pancreas Differentiation from Patient-Derived Induced Pluripotent Stem Cells.iScience · 2019Article
- Modelling the endocrine pancreas in health and disease.Nature reviews. Endocrinology · 2019Review
- Metformin from mother to unborn child - Are there unwarranted effects?EBioMedicine · 2018Review
- Induced Pluripotent Stem Cell-Derived Endothelial Cells in Insulin Resistance and Metabolic Syndrome.Arteriosclerosis, thrombosis, and vascular biology · 2017Review
- Is Transforming Stem Cells to Pancreatic Beta Cells Still the Holy Grail for Type 2 Diabetes?Current diabetes reports · 2016Review
- Knowledge Gaps in Rodent Pancreas Biology: Taking Human Pluripotent Stem Cell-Derived Pancreatic Beta Cells into Our Own Hands.Frontiers in endocrinology · 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
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundDiabetes and metabolic syndromes are chronic, devastating diseases with increasing prevalence. Human pluripotent stem cells are gaining popularity in their usage for human in vitro disease modeling. With recent rapid advances in genome editing tools, these cells can now be genetically manipulated with relative ease to study how genes and gene variants contribute to diabetes and metabolic syndromes. SCOPE OF REVIEW: We highlight the diabetes and metabolic genes and gene variants, which could potentially be studied, using two powerful technologies - human pluripotent stem cells (hPSCs) and genome editing tools - to aid the elucidation of yet elusive mechanisms underlying these complex diseases. MAJOR
conclusionshPSCs and the advancing genome editing tools appear to be a timely and potent combination for probing molecular mechanism(s) underlying diseases such as diabetes and metabolic syndromes. The knowledge gained from these hiPSC-based disease modeling studies can potentially be translated into the clinics by guiding clinicians on the appropriate type of medication to use for each condition based on the mechanism of action of the disease.
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.