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Record W2992231601

IS FAST FOOD SPEEDING UP THE AGING PROCESS? COMPARING THE SKELETAL MUSCLE INFLAMMATORY ENVIRONMENT IN DIET INDUCED OBESITY AND OLD RATS

2014· article· en· W2992231601 on OpenAlexvenueaboutno aff
Charmi Dholakia, Christine Waters‐Banker, Geoffrey A. Power, Walter Herzog

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2014
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSarcopeniaSkeletal muscleInflammationMedicineAtrophyInternal medicineDiabetes mellitusPopulationMuscle atrophyEndocrinologyObesityType 2 diabetesPathologyPhysiology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Obesity, linked to various deleterious health problems such as heart disease and Type II diabetes, is becoming a public health concern and economic burden worldwide [1].  Loss of skeletal muscle content, or atrophy, and fat infiltration are often reported in obese individuals [2,3]. In a separate population, elderly adults also exhibit a loss of muscle mass commonly referred to as sarcopenia [4]. Similarly, both of these populations are characterized by systemic inflammation [5,6]. However, it is not yet known whether skeletal muscle inflammation is similar in obese and aging individuals, or if obesity may be an accelerated model of aging. Therefore, the purpose of this study was to compare the general cellular infiltration associated with inflammation in the tibialis anterior (TA) muscle of a diet induced obesity model, and an established aging model, against a young healthy model, in rats. METHODS Six male Sprague Dawley rats provided with a high fat/high sucrose diet for 6 months were analyzed for this study. Additionally, The TA muscles from the non-perturbed contralateral limb of 12 Fisher 344 x Brown Norway rats (old (n=6) and young (n=6)), were collected following mechanical testing. All TA muscles were harvested and flash frozen in liquid nitrogen. Four 8μm cryosections were stained with Hematoxylin and Eosin (H&E), and imaged using light microscope. Because skeletal muscle consists of resident ‘anti-inflammatory’ macrophages, stereological point counting techniques were used to count clusters of immune cells likely associated with a ‘pro-inflammatory’ response. These clusters were normalized per 100 muscle fibres. All procedures were performed according to the guidelines of the Canadian Council on Animal Care and were approved by the Animal Care Committee of the University of Calgary. A Kruskal-Wallis One-Way ANOVA was used to determine differences between groups. RESULTS Obese rats had a significantly greater number of immune cell clusters per 100 muscle fibres compared to Young rats (p=0.027) between Obese and Old rats. DISCUSSION AND CONCLUSIONS The inflammatory environment within the skeletal muscle of Obese rats more closely resembled Old than the Young rats. This was surprising considering the average age difference between the Obese and Young rats at time of sacrifice was only one month (9 and 8 months, respectively), compared to Old rats at 36 months. Additionally, similar gross morphological differences were observed in the Old and Obese rats, such as inflammatory cell mediated fibre breakdown and fat infiltration, not observed in the Young tissue. The inflammatory process in skeletal muscle is crucial to the maintenance, repair, and regeneration of skeletal muscle. Dysregulation of this response may have severe consequences on skeletal muscle health, especially as young obese individuals progress into old age.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.377
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2014
Admission routes2
Has abstractyes

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