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Record W4220770386 · doi:10.3389/fgene.2022.868598

Small Non-Coding RNAs in the Human Placenta: Regulatory Roles and Clinical Utility

2022· review· en· W4220770386 on OpenAlexafffund
Nikita Telkar, Greg L. Stewart, Michelle E. Pewarchuk, David E. Cohn, Wendy P. Robinson, Wan L. Lam

Bibliographic record

VenueFrontiers in Genetics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchNational Institutes of HealthUniversity of British Columbia
KeywordsSmall nucleolar RNAmicroRNABiologyPlacentaRegulation of gene expressionGene silencingGeneGene expressionRNAGeneticsLong non-coding RNAComputational biologyBioinformaticsNon-coding RNAPregnancyFetus

Abstract

fetched live from OpenAlex

The placenta is a vital organ formed during pregnancy, and being the interface between the mother and fetus, it is paramount that placental functioning is strictly controlled. Gene expression in the placenta is finely tuned-with aberrant expression causing placental pathologies and inducing stress on both mother and fetus. Gene regulation is brought upon by several mechanisms, and small non-coding RNAs (sncRNAs) have recently been appreciated for their contribution in gene repression. Their dysregulation has been implicated in a range of somatic and inherited disorders, highlighting their importance in maintaining healthy organ function. Their specific roles within the placenta, however, are not well understood, and require further exploration. To this end, we summarize the mechanisms of microRNAs (miRNAs), Piwi-interacting RNAs (piRNAs), small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), and transfer RNAs (tRNAs), their known contributions to human placental health and disease, the relevance of sncRNAs as promising biomarkers throughout pregnancy, and the current challenges faced by placental sncRNA studies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.332
Teacher spread0.277 · 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.

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

Citations11
Published2022
Admission routes2
Has abstractyes

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