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Record W3133660370 · doi:10.3389/fcell.2021.634512

The Interplay Between Non-coding RNAs and Insulin-Like Growth Factor Signaling in the Pathogenesis of Neoplasia

2021· review· en· W3133660370 on OpenAlexaff
Soudeh Ghafouri‐Fard, Atefe Abak, Mahdi Mohaqiq, Hamed Shoorei, Mohammad Taheri

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsCentennial College
Fundersnot available
KeywordsPathogenesisPI3K/AKT/mTOR pathwayBiologyInsulin resistanceSignal transductionInsulin-like growth factorMAPK/ERK pathwayBioinformaticsGrowth factorInsulinCancer researchNeuroscienceGeneticsImmunologyEndocrinologyReceptor

Abstract

fetched live from OpenAlex

The insulin-like growth factors (IGFs) are polypeptides with similar sequences with insulin. These factors regulate cell growth, development, maturation, and aging via different processes including the interplay with MAPK, Akt, and PI3K. IGF signaling participates in the pathogenesis of neoplasia, insulin resistance, diabetes mellitus, polycystic ovarian syndrome, cerebral ischemic injury, fatty liver disease, and several other conditions. Recent investigations have demonstrated the interplay between non-coding RNAs and IGF signaling. This interplay has fundamental roles in the development of the mentioned disorders. We designed the current study to search the available data about the role of IGF-associated non-coding RNAs in the evolution of neoplasia and other conditions. As novel therapeutic strategies have been designed for modification of IGF signaling, identification of the impact of non-coding RNAs in this pathway is necessary for the prediction of response to these modalities.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0010.000
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 head0.016
GPT teacher head0.289
Teacher spread0.273 · 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 designOther design
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

Citations13
Published2021
Admission routes1
Has abstractyes

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