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Record W4413409594 · doi:10.1093/stmcls/sxaf042

A molecular systems architecture of the mesenchymal stromal cell microenvironment

2025· article· en· W4413409594 on OpenAlexaff
V. A. Shiva Ayyadurai, Prabhakar Deonikar, Armand Keating

Bibliographic record

VenueStem Cells · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMesenchymal stem cellBiologyStromal cellIn silicoComputational biologyCrosstalkRegenerative medicineTumor microenvironmentCell biologyStem cellCancer researchImmunologyImmune systemGeneticsEngineering

Abstract

fetched live from OpenAlex

A systems-level understanding of immunomodulatory, regenerative, and pro-/antifibrosis functions of mesenchymal stromal cells (MSCs) is critical to advance MSCs as a viable therapeutic option. Given the complexity of MSCs and their interactions with microenvironmental cells, a systems biology approach may enable such understanding to achieve practical objectives such as target identification, combination therapeutics, clinical strategies, and avoidance of adverse effects. In this study, a molecular systems architecture of MSCs microenvironment is developed to organize the complexity of biomolecular interactions between MSCs and other microenvironmental cells. This architecture provides a visual mapping of MSC interactions, identifies the complex crosstalk between MSCs and cells in the microenvironment, reveals potential targets, and offers a framework for creating future predictive, quantitative computational (in silico) models of the MSC microenvironment. The development of combination therapeutics, clinical strategies to improve therapeutic efficacy, and avoidance of adverse effects can be facilitated by such in silico models. However, it must all begin with a molecular systems architecture of MSCs-the objective and result of this study.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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