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Record W4294391246 · doi:10.1038/s41536-022-00246-3

Harnessing conserved signaling and metabolic pathways to enhance the maturation of functional engineered tissues

2022· review· en· W4294391246 on OpenAlexafffund
Neal I. Callaghan, Lauren J. Durland, Ronald G. Ireland, J. Paul Santerre, Craig A. Simmons, Locke Davenport Huyer

Bibliographic record

Venuenpj Regenerative Medicine · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity of TorontoDalhousie University
FundersCanadian Institutes of Health ResearchDalhousie UniversityNatural Sciences and Engineering Research Council of CanadaDalhousie Medical Research Foundation
KeywordsInduced pluripotent stem cellBiologyDrug discoveryNeuroscienceFunction (biology)Regenerative medicineComputational biologySignal transductionStem cellCellDrug developmentBioinformaticsCell biologyDrugEmbryonic stem cellPharmacologyGenetics

Abstract

fetched live from OpenAlex

The development of induced-pluripotent stem cell (iPSC)-derived cell types offers promise for basic science, drug testing, disease modeling, personalized medicine, and translatable cell therapies across many tissue types. However, in practice many iPSC-derived cells have presented as immature in physiological function, and despite efforts to recapitulate adult maturity, most have yet to meet the necessary benchmarks for the intended tissues. Here, we summarize the available state of knowledge surrounding the physiological mechanisms underlying cell maturation in several key tissues. Common signaling consolidators, as well as potential synergies between critical signaling pathways are explored. Finally, current practices in physiologically relevant tissue engineering and experimental design are critically examined, with the goal of integrating greater decision paradigms and frameworks towards achieving efficient maturation strategies, which in turn may produce higher-valued iPSC-derived tissues.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.071
GPT teacher head0.342
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2022
Admission routes2
Has abstractyes

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