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Record W2161962677 · doi:10.1002/bit.22708

Adventures in time and space: Nonlinearity and complexity of cytokine effects on stem cell fate decisions

2010· article· en· W2161962677 on OpenAlexaff
Julie Audet

Bibliographic record

VenueBiotechnology and Bioengineering · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStem cellAdventureNonlinear systemCytokineCell biologyBiologyComputational biologyComputer scienceImmunologyPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Cytokines are central factors in the control of stem cell fate decisions and, as such, they are invaluable to those interested in the manipulation of stem and progenitor cells for clinical or research purposes. In their in vivo niches or in optimized cultures, stem cells are exposed to multiple cytokines, matrix proteins and other cell types that provide individual and combinatorial signals that influence their self-renewal, proliferation and differentiation. Although the individual effects of cytokines are well-characterized in terms of increases or decreases in stem cell expansion or in the production of specific cell lineages, their interactions are often overlooked. Factorial design experiments in association with multiple linear regression is a powerful multivariate approach to derive response-surface models and to obtain a quantitative understanding of cytokine dose and interactions effects. On the other hand, cytokine interactions detected in stem cell processes can be difficult to interpret due to the fact that the cell populations examined are often heterogeneous, that cytokines can exhibit pleiotropy and redundancy and that they can also be endogenously produced. This perspective piece presents a list of possible biological mechanisms that can give rise to positive and negative two-way factor interactions in the context of in vivo and in vitro stem cell-based processes. These interpretations are based on insights provided by recent studies examining intra- and extra-cellular signaling pathways in adult and embryonic stem cells. Cytokine interactions have been classified according to four main types of molecular and cellular mechanisms: (i) interactions due to co-signaling; (ii) interactions due to sequential actions; (iii) interactions due to high-dose saturation and inhibition; and (iv) interactions due to intercellular signaling networks. For each mechanism, possible patterns of regression coefficients corresponding to the cytokine main effects, quadratic effects and two-way interactions effects are provided. Finally, directions for future mechanistic studies are presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations14
Published2010
Admission routes1
Has abstractyes

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