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Record W2772855961 · doi:10.1111/jcmm.13417

Physical forces modulate cell differentiation and proliferation processes

2017· review· en· W2772855961 on OpenAlexaff
Laurent Schwartz, Jorgelindo da Veiga Moreira, Mario Jolicœur

Bibliographic record

VenueJournal of Cellular and Molecular Medicine · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCellular differentiationBiologyCellCell metabolismProcess (computing)MitochondrionIntracellularCell biologyComplement (music)Cell growthComputational biologyNeuroscienceGeneticsGeneComputer sciencePhenotype

Abstract

fetched live from OpenAlex

Currently, the predominant hypothesis explains cellular differentiation and behaviour as an essentially genetically driven intracellular process, suggesting a gene-centrism paradigm. However, although many living species genetic has now been described, there is still a large gap between the genetic information interpretation and cell behaviour prediction. Indeed, the physical mechanisms underlying the cell differentiation and proliferation, which are now known or suspected to guide such as the flow of energy through cells and tissues, have been often overlooked. We thus here propose a complementary conceptual framework towards the development of an energy-oriented classification of cell properties, that is, a mitochondria-centrism hypothesis based on physical forces-driven principles. A literature review on the physical-biological interactions in a number of various biological processes is analysed from the point of view of the fluid and solid mechanics, electricity and thermodynamics. There is consistent evidence that physical forces control cell proliferation and differentiation. We propose that physical forces interfere with the cell metabolism mostly at the level of the mitochondria, which in turn control gene expression. The present perspective points towards a paradigm shift complement in biology.

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 score0.975
Threshold uncertainty score0.726

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 head0.024
GPT teacher head0.306
Teacher spread0.281 · 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

Citations52
Published2017
Admission routes1
Has abstractyes

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