Article in Biology open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 32% of its field
1 · What the graph read from 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.
2 · The registry
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.
3 · Its place in the literature
Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
13 authors at 4 institutions in 3 countries.
Ming-Hsuan WenWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.ORCID 0000-0003-3850-1225
Hector Barbosa TrianaWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.
Richard ButlerWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.
Hsiang-Wei HuDepartment of Artificial Intelligence in Healthcare, International Academia of Biomedical Innovation Technology, Taipei 10488, Taiwan.
Yang-Hong DaiDepartment of Artificial Intelligence in Healthcare, International Academia of Biomedical Innovation Technology, Taipei 10488, Taiwan.ORCID 0000-0001-8054-0944
Nicola LawrenceWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.ORCID 0000-0001-9773-2360
Jun-Jie HongScientific Research Services, Phalanx Biotech Group, Hsinchu 30077, Taiwan.
Nigel GarrettWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.
Rue Jones-GreenWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.
Emma L RawlinsWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.ORCID 0000-0001-7426-3792
Ziqi DongWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.
Magdalena J KoziolWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.ORCID 0000-0002-4123-4343
J B GurdonWellcome Trust/Cancer Research UK Gurdon Institute, Henry Wellcome Building of Cancer and Developmental Biology University of Cambridge, Cambridge CB2 1QN, UK.ORCID 0000-0002-5621-3799
University of Cambridge · GBChinese Academy of Medical Sciences & Peking Union Medical College · CNIndustrial Technology Research Institute · TWTri-Service General Hospital · TW
Funding
Cancer Research UK (C6946/A14492Cancer Research UK C6946/A14492Medical Research Council MR/P009581/1Wellcome TrustWellcome Trust 101050/Z/13/ZWellcome Trust 101800 X5ID:35597
6 · The paper itself
Abstract
The ultimate aim of nuclear reprogramming is to provide stem cells or differentiated cells from unrelated cell types as a cell source for regenerative medicine. A popular route towards this is transcription factor induction, and an alternative way is an original procedure of transplanting a single somatic cell nucleus to an unfertilized egg. A third route is to transplant hundreds of cell nuclei into the germinal vesicle (GV) of a non-dividing Amphibian meiotic oocyte, which leads to the activation of silent genes in 24 h and robustly induces a totipotency-like state in almost all transplanted cells. We apply this third route for potential therapeutic use and describe a procedure by which the differentiated states of cells can be reversed so that totipotency and pluripotency gene expression are regained. Differentiated cells are exposed to GV extracts and are reprogrammed to form embryoid bodies, which shows the maintenance of stemness and could be induced to follow new directions of differentiation. We conclude that much of the reprogramming effect of eggs is already present in meiotic oocytes and does not require cell division or selection of dividing cells. Reprogrammed cells by oocytes could serve as replacements for defective adult cells in humans.
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.
Deterministic nuclear reprogramming of mammalian nuclei to a totipotency-like state by Amphibian meiotic oocytes for stem cell therapy in humans. · full record | OpenQuestion