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Record W2766468001 · doi:10.1139/bcb-2017-0118

Skeletal muscle and fetal alcohol spectrum disorder

2017· review· en· W2766468001 on OpenAlexaffvenue
Semone B. Myrie, Mark A. Pinder

Bibliographic record

VenueBiochemistry and Cell Biology · 2017
Typereview
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSkeletal muscleOffspringEndocrinologyInternal medicineMyopathyGlucose uptakeBiologyInsulin resistancePhysiologyMedicinePregnancyDiabetes mellitusInsulinGenetics

Abstract

fetched live from OpenAlex

Skeletal muscle is critical for mobility and many metabolic functions integral to survival and long-term health. Alcohol can affect skeletal muscle physiology and metabolism, which will have immediate and long-term consequences on health. While skeletal muscle abnormalities, including morphological, biochemical, and functional impairments, are well-documented in adults that excessively consume alcohol, there is a scarcity of information about the skeletal muscle in the offspring prenatally exposed to alcohol ("prenatal alcohol exposure"; PAE). This minireview examines the available studies addressing skeletal muscle abnormalities due to PAE. Growth restriction, fetal alcohol myopathy, and abnormalities in the neuromuscular system, which contribute to deficits in locomotion, are some direct, immediate consequences of PAE on skeletal muscle morphology and function. Long-term health consequences of PAE-related skeletal abnormalities include impaired glucose metabolism in the skeletal muscle, resulting in glucose intolerance and insulin resistance, leading to an increased risk of type 2 diabetes. In general, there is limited information on the morphological, biochemical, and functional features of skeletal abnormalities in PAE offspring. There is a need to understand how PAE affects muscle growth and function at the cellular level during early development to improve the immediate and long-term health of offspring suffering from PAE.

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 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 score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.307
Teacher spread0.283 · 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

Citations15
Published2017
Admission routes2
Has abstractyes

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