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Record W3110980882 · doi:10.37247/pamed2ed.2.2020.4

miRNA Profile and Role in the Immunological and Inflammatory Microenvironment of the Stroma

2020· book-chapter· en· W3110980882 on OpenAlexaff
Hussein Fayyad‐Kazan, Mohammad Fayyad‐Kazan, Douâa Moussa Agha, Bassam Badran, Dominique Bron, Nathalie Meuleman, Philippe Lewalle, Fadi Abdel‐Sater, Makram Merimi, Laurence Lagneaux, Mehdi Najar

Bibliographic record

VenueVide Leaf, Hyderabad eBooks · 2020
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsStromaBiologyImmunologyCell biologyPathologyMedicineImmunohistochemistry

Abstract

fetched live from OpenAlex

Background: Due to their self-renewal capacity, multi-lineage potential, and immunomodulatory properties, mesenchymal stromal cells (MSCs) are an attractive tool for different therapeutic strategies. Foreskin (FSK), considered as a biological waste material, has already been shown to be a valuable source of MSCs. Besides their typical fibroblast like morphology and International Society for cellular Therapy compliant phenotype, foreskin-MSCs (FSK-MSCs) are clonogenic, and highly proliferative cells with multi-lineage and strong immunomodulatory capacities. Of importance, FSK-MSCs properly adjust their fate following exposure to inflammatory signals. Being potent regulators of gene expression, miRNAs are www.videleaf.com involved in modulating nearly all cellular processes and in orchestrating the roles of different immune cells. In this study, we characterized the miRNome of FSK-MSCs by determining the expression profile of 380 different miRNAs in inflammation primed vs. control non-primed cells.

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 score0.554
Threshold uncertainty score0.613

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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