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Record W4283368017 · doi:10.5772/intechopen.104386

The Novelty of miRNAs as a Clinical Biomarker for the Management of PCOS

2022· book-chapter· en· W4283368017 on OpenAlex
Rana Alhamdan, Juan Hernandez-Medrano

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIntechOpen eBooks · 2022
Typebook-chapter
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsAlberta Health
Fundersnot available
KeywordsmicroRNAPolycystic ovaryBiomarkerBioinformaticsEndocrine systemInfertilityBiologyEtiologyMedicineGeneInternal medicineEndocrinologyGeneticsInsulinInsulin resistanceHormonePregnancy

Abstract

fetched live from OpenAlex

Polycystic ovary syndrome (PCOS) is a common endocrine disorder that affects around 5–10% of women of reproductive age. The aetiology of PCOS is not fully understood with various genetics, iatrogenic (e.g. chemotherapy) and environmental factors have been proposed. microRNAs (miRNAs) are small non-coding single-stranded RNAs which are known to act as a regulator to gene expression at the post-transcriptional levels. Altered expression of miRNAs has been linked to several disorders including infertility. Recent reports demonstrated the expression of differential levels of miRNAs in the serum, ovarian follicular cells and follicular fluid of PCOS patients when compared with healthy women. Therefore, miRNAs may play important role in the pathogenesis of PCOS. The aim of this chapter is to summarise the current understanding pertaining to miRNAs and PCOS and to expedite its possible role in the diagnosis and management of this disorder.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.952
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0020.000

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 head0.082
GPT teacher head0.358
Teacher spread0.276 · 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