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Of mice and men (and women) and the acylation-stimulating protein pathway

2000· review· en· W2334558306 on OpenAlexaff
Allan D. Sniderman, Magdalena Maslowska, Katherine Cianflone

Bibliographic record

VenueCurrent Opinion in Lipidology · 2000
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsLipoprotein lipaseChemistryHormoneHormone-sensitive lipaseLeptinInsulinEndocrinologyAdipocyteInternal medicineClearanceLipaseAdipose tissueBiochemistryEnzymeBiologyMedicineObesity

Abstract

fetched live from OpenAlex

The storage and release of energy by adipocytes is of fundamental biologic importance. Not surprisingly, therefore, the rate at which these processes occur can be modulated by a variety of physiologic molecules. A newly recognized participant is produced by adipocytes themselves: acylation-stimulating protein (ASP). This article focuses on the most recent in-vivo evidence regarding how the ASP pathway may influence energy storage and release. In brief, the rate at which triglycerides are cleared from plasma (i.e. the rate at which they are hydrolysed) is determined by lipoprotein lipase and insulin, which is the principal hormone that regulates lipoprotein lipase. By contrast, the ASP pathway modulates the rate at which fatty acids are taken up and converted to triglycerides by adipocytes. Under certain circumstances, however, reduction of activity of the ASP pathway may negatively impact on the first step of the process. ASP also influences the rate at which fatty acids are released by adipocytes, and it is clear that insulin and ASP interact in a variety of ways that involve energy storage and release. Accordingly, to understand the impact of any intervention on energy storage and release by adipocytes, the effects of both insulin and ASP must be taken into account.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.579

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 integrity0.0000.000
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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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 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

Citations61
Published2000
Admission routes1
Has abstractyes

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