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Record W4393935452 · doi:10.3390/ijms25074017

Skeletal Muscle Heat Shock Protein Content and the Repeated Bout Effect

2024· article· en· W4393935452 on OpenAlexaff
Marius Locke, Giovanni Bruccoleri

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSkeletal muscleHeat shock proteinChemistryInternal medicineEndocrinologyShock (circulatory)Tibialis anterior muscleHsp70Muscle tissueMedicineBiochemistry

Abstract

fetched live from OpenAlex

The “Repeated Bout Effect” (RBE) occurs when a skeletal muscle is preconditioned with a few lengthening contractions (LC) prior to exposing the muscle to a greater number of LC. The preconditioning (PC) results in significantly less damage and preservation of force. Since it takes only a few LC to increase muscle heat shock protein (HSP) content, it was of interest to examine the relationship between HSPs and the RBE. To do this, one tibialis anterior (TA) muscle from Sprague–Dawley rats (n = 5/group) was preconditioned with either 0, 5, or 15 lengthening contractions (LC) and exposed to a treatment of 60 LC 48 h later. Preconditioning TA muscles with 15 LC, but not 5 LC, significantly elevated muscle αB-crystallin (p < 0.05), HSP25 (p < 0.05), and HSP72 content (p < 0.001). These preconditioned TA muscles also showed a significantly (p < 0.05) reduced loss of active torque throughout the subsequent 60 LC. While there was a trend for all preconditioned muscles to maintain higher peak torque levels throughout the 60 LC, no significant differences were detected between the groups. Morphologically, preconditioned muscles appeared to show less discernible muscle fiber damage. In conclusion, an elevated skeletal muscle HSP content from preconditioning may contribute to the RBE.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.140

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.021
GPT teacher head0.317
Teacher spread0.296 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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