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The effect of high intensity interval training on muscle contractile function 8 weeks following chemically-induced ovarian failure

2024· article· en· W4398165537 on OpenAlexaff
Parastoo Mashouri, Avery Hinks, Benjamin E. Dalton, Luke D. Flewwelling, W. Glen Pyle, Arthur J. Cheng, Geoffrey A. Power

Bibliographic record

VenuePhysiology · 2024
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsYork UniversityUniversity of Guelph
Fundersnot available
KeywordsHigh-intensity interval trainingInterval trainingIntensity (physics)Internal medicineEndocrinologyFunction (biology)CardiologyMedicineChemistryBiologyAndrologyCell biologyPhysics

Abstract

fetched live from OpenAlex

The negative effects of ovariectomy on muscle contractile function have been well-characterized, however, the effects of gradual ovarian failure (i.e., perimenopausal transition into late-stage menopause) on muscle function over the lifespan have received less attention. Furthermore, whether exercise training can mitigate changes in muscle contractile function associated with menopause is unclear. The objective of this study, using a chemically-induced ovarian failure mouse model (4-vinylcyclohexene diepoxide; VCD), was to investigate time-course changes in muscle contractility of sedentary controls and the potential of high intensity interval training to mitigate any deleterious effects of ovarian failure. Starting at 11wks old, mice were injected with 160mg/kg/day of VCD for 15 days. Twenty-eight (VCD-trained: n=10, VCD-sedentary: n=10, control: n=8) CD1 female mice were used in this study, with the VCD-trained group beginning training at the onset of ovarian failure for a total of 8 weeks. Contractile properties of the plantar flexors were assessed using an in-vivo set-up 8 weeks following the onset of ovarian failure. As well, a fatigue task (repeated maximal isometric contractions until torque decreased by 60%) was performed. Recovery was measured immediately after, and up to 10min following task termination. Upon completion of mechanical testing, mice were sacrificed and intact muscle fibres were isolated from the flexor digitorum brevis, and myoplasmic free Ca 2+ (tetanic [Ca 2+ ] i ) concentrations were measured across stimulation frequencies of 10-200 Hz and throughout 50 tetanic contractions (70Hz) to replicate our fatigue task. There was no difference in pre-fatigue values across groups for peak twitch torque, peak 100Hz torque, RTD, 10:100Hz torque, tetanic [Ca 2+ ] i during low (10Hz) and high Hz (100Hz) stimulation, and all were reduced similarly immediately following the fatigue task. Repetitions to task failure was similar across groups and tetanic [Ca 2+ ] i during repetitive contractions (n=50) was reduced similarly across groups. As well, all groups recovered similarly across these measures. Torque and RTD did not recover fully by 10min for either measure, while 10Hz, 100Hz and 10:100Hz tetanic [Ca 2+ ] i was recovered immediately following the fatigue task. Given 10Hz torque was relatively maintained following the fatigue task, and there was a ~40% decline in 100Hz torque, the 10:100Hz ratio increased throughout recovery — this, combined with the [Ca 2+ ] data indicate that calcium sensitivity and release are unlikely contributors to impaired force production following the fatigue task across groups. The present study aimed to assess the impact of training on muscle contractility using a mouse model of gradual ovarian failure. Unlike other models of ovarian failure, there does not seem to be any impairment in muscular performance at the joint level in our VCD-mice, and they responded similarly across groups in response to repetitive fatiguing contractions and exercise training. Supported by NSERC. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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