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Record W4307441992 · doi:10.1371/journal.pgen.1010434

Formation of benign tumors by stem cell deregulation

2022· review· en· W4307441992 on OpenAlexafffund
Matthieu Valet, Patrick Narbonne

Bibliographic record

VenuePLoS Genetics · 2022
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsStem cellBiologyStem cell nicheCell biologyCellular differentiationNicheCancer stem cellHomeostasisCell growthCellAdult stem cellProgenitor cellGeneticsGeneBiochemistry

Abstract

fetched live from OpenAlex

Within living organisms, stem cells respond to various cues, including to niche signals and growth factors. Niche signals originate from the stem cell's microenvironment and promote the undifferentiated state by preventing differentiation, allowing for stem cell self-renewal. On the other hand, growth factors promote stem cell growth and proliferation, while their sources comprise of a systemic input reflecting the animal's nutritional and metabolic status, and a localized, homeostatic feedback signal from the tissue that the stem cells serve. That homeostatic signal prevents unnecessary stem cell proliferation when the corresponding differentiated tissues already have optimal cell contents. Here, we recapitulate progresses made in our understanding of in vivo stem cell regulation, largely using simple models, and draw the conclusion that 2 types of stem cell deregulations can provoke the formation of benign tumors. Namely, constitutive niche signaling promotes the formation of undifferentiated "stem cell" tumors, while defective homeostatic signaling leads to the formation of differentiated tumors. Finally, we provide evidence that these general principles may be conserved in mammals and as such, may underlie benign tumor formation in humans, while benign tumors can evolve into cancer.

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 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 score0.987
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.087
GPT teacher head0.302
Teacher spread0.214 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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