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Record W2617651358 · doi:10.1089/hum.2016.167

Induced Cell Turnover: A Novel Therapeutic Modality for <i>In Situ</i> Tissue Regeneration

2017· review· en· W2617651358 on OpenAlexaff
Francesco Albert Bosco Cortese, Sebastián Aguiar, Giovanni Santostasi

Bibliographic record

VenueHuman Gene Therapy · 2017
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsQueen's University
Fundersnot available
KeywordsHoming (biology)TransplantationRegenerative medicineRegeneration (biology)Cell biologyEx vivoTissue engineeringSelf-healing hydrogelsBiologyGenetic enhancementInduced pluripotent stem cellStem cellCancer researchIn vivoMedicineChemistryEmbryonic stem cellBiotechnologySurgeryGeneBiochemistryGenetics

Abstract

fetched live from OpenAlex

Induced cell turnover (ICT) is a theoretical intervention in which the targeted ablation of damaged, diseased, and/or nonfunctional cells is coupled with replacement by partially differentiated induced pluripotent stem cells in a gradual and multiphasic manner. Tissue-specific ablation can be achieved using pro-apoptotic small molecule cocktails, peptide mimetics, and/or tissue-tropic adeno-associated virus-delivered suicide genes driven by cell type-specific promoters. Replenishment with new cells can be mediated by systemic administration of cells engineered for homing, robustness, and even enhanced function and disease resistance. Otherwise, the controlled release of cells can be achieved using implanted biodegradable scaffolds, hydrogels, and polymer matrixes. In theory, ICT would enable in situ tissue regeneration without the need for surgical transplantation of organs produced ex vivo, and addresses non-transplantable tissues (such as the vasculature, lymph nodes, and the nervous system). This article outlines several complimentary strategies for overcoming barriers to ICT in an effort to stimulate further research at this promising interface of cell therapy, tissue engineering, and regenerative medicine.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.235
GPT teacher head0.421
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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