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Record W3123460211 · doi:10.3389/fcell.2020.620089

Senescence and Apoptosis: Architects of Mammalian Development

2021· review· en· W3123460211 on OpenAlexafffund
Emma Wanner, Harikrishnan Thoppil, Karl Riabowol

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2021
Typereview
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsBiologyCell biologySenescenceTotipotentProgrammed cell deathCarcinogenesisEpigeneticsCell fate determinationEmbryonic stem cellApoptosisGeneticsTranscription factorGene

Abstract

fetched live from OpenAlex

Mammalian development involves an exquisite choreography of cell division, differentiation, locomotion, programmed cell death, and senescence that directs the transformation of a single cell zygote to a mature organism containing on the order of 40 trillion cells in humans. How a single totipotent zygote undergoes the rapid stages of embryonic development to form over 200 different cell types is complex in the extreme and remains the focus of active research. Processes such as programmed cell death or apoptosis has long been known to occur during development to help sculpt organs and tissue systems. Other processes such as cellular senescence, long thought to only occur in pathologic states such as aging and tumorigenesis have been recently reported to play a vital role in development. In this review, we focus on apoptosis and senescence; the former as an integral mechanism that plays a critical role not only in mature organisms, but that is also essential in shaping mammalian development. The latter as a well-defined feature of aging for which some reports indicate a function in development. We will dissect the dual roles of major gene families, pathways such as Hox, Rb, p53, and epigenetic regulators such as the ING proteins in both early and the late stages and how they play antagonistic roles by increasing fitness and decreasing mortality early in life but contribute to deleterious effects and pathologies later in life.

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)0.000
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 score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.279
Teacher spread0.253 · 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

Citations47
Published2021
Admission routes2
Has abstractyes

Explore more

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