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Record W2110679389 · doi:10.1096/fj.02-0077fje

Differential effects of leptin on thermoregulation and uncoupling protein abundance in the neonatal lamb

2002· article· en· W2110679389 on OpenAlexaff
A. Mostyn, J. Bispham, Sarah Pearce, Yvonne Evens, N Raver, D. H. Keisler, R. Webb, Terence Stephenson, Michael Symonds

Bibliographic record

VenueThe FASEB Journal · 2002
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsQueen's University
FundersBiotechnology and Biological Sciences Research CouncilUniversity of Nottingham
KeywordsLeptinThermogeninInternal medicineEndocrinologyAdipose tissueThermoregulationBrown adipose tissueThermogenesisUncoupling proteinAdipokineBiologyChemistryObesityMedicine

Abstract

fetched live from OpenAlex

As the role of leptin in energy balance in neonate is unknown, we investigated the effect of acute (2 h) and chronic (7 days) administration of leptin (100 microg/day) on thermoregulation and mitochondrial protein abundance in adipose tissue. The concentration of uncoupling protein (UCP)1 and voltage-dependent anion channel (VDAC) located on the inner and outer mitochondrial membranes, respectively, were measured. Administration of leptin prevented the normal decline in colonic temperature over the first few hours and days after birth. It subsequently accelerated the loss of both mRNA and protein for UCP1 but had no effect on VDAC abundance. At seven days of age, colonic temperature was correlated strongly with both mRNA abundance and thermogenic potential of UCP1 in leptin-treated but not control lambs, indicating more effective use of UCP1 for heat production following leptin administration. Leptin had no effect on weight gain or adipose tissue deposition; at one day of age only, leptin mRNA was correlated positively with adipose tissue weight. In conclusion, leptin administration to neonatal lambs improves thermoregulation and promotes the loss of UCP1 in brown adipose tissue.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations41
Published2002
Admission routes1
Has abstractyes

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