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Record W2912956439 · doi:10.15407/ubj91.01.053

Intermittent fasting causes metabolic stress and leucopenia in young mice

2019· article· en· W2912956439 on OpenAlexafffund
Oksana M. Sorochynska, Maria M. Bayliak, Yulia V. Vasylyk, Oksana V. Kuzniak, Ivanna Z. Drohomyretska, Andriy Ya. Klonovskyi, Janet M. Storey, Kenneth B. Storey, Volodymyr I. Lushchak

Bibliographic record

VenueThe Ukrainian Biochemical Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education and Science of UkraineVolkswagen Foundation
KeywordsPhysiologyMedicineInternal medicineEndocrinologyBiology

Abstract

fetched live from OpenAlex

Overweight and obesity became the worldwide epidemic resulting from overeating especially when a so-called Western diet rich in carbohydrates and fats is used. It is widely accepted that limitation of food consumption could help to withstand such state of adult organism, but information about younger groups is contradictory. The present study was undertaken to characterize the effects of intermittent fasting, using an every other day (EOD) fasting/feeding protocol, on hematological parameters and biochemical blood plasma indices in young mice from one to two months old. It was shown that intermittently fasted mice were characterized by a reduced body weight, reduced total number of blood leucocytes, lower glucose and lactate levels and higher activity of alanine aminotransferase and aspartate aminotransferase in blood plasma as compared with the age-matched control mice. To gain the same mass EOD animals needed to eat more food than ad libitum fed animals. These differences may probably be explained by a need to expend certain resources to combat stress induced by intermittent fasting. Our data showed that EOD feeding at a young age may negatively influence young mammals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.016
GPT teacher head0.286
Teacher spread0.271 · 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 designObservational
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

Citations22
Published2019
Admission routes2
Has abstractyes

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