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Record W4399684120 · doi:10.14814/phy2.16098

Inflammatory pathway communication with skeletal muscle—Does aging play a role? A topical review of the current evidence

2024· review· en· W4399684120 on OpenAlexaff
Stephen M. Cornish, Dean M. Cordingley

Bibliographic record

VenuePhysiological Reports · 2024
Typereview
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsPan Am ClinicUniversity of Manitoba
Fundersnot available
KeywordsCurrent (fluid)MedicineSkeletal muscleBioinformaticsInternal medicineBiologyEngineering

Abstract

fetched live from OpenAlex

Skeletal muscle plays an integral role in locomotion, but also as part of the integrative physiological system. Recent progress has identified crosstalk between skeletal muscle and various physiological systems, including the immune system. Both the musculoskeletal and immune systems are impacted by aging. Increased age is associated with decreased muscle mass and function, while the immune system undergoes "inflammaging" and immunosenescence. Exercise is identified as a preventative medicine that can mitigate loss of function for both systems. This review summarizes: (1) the inflammatory pathways active in skeletal muscle; and (2) the inflammatory and skeletal muscle response to unaccustomed exercise in younger and older adults. Compared to younger adults, it appears older individuals have a muted pro-inflammatory response and elevated anti-inflammatory response to exercise. This important difference could contribute to decreased regeneration and recovery following unaccustomed exercise in older adults, as well as in chronic disease. The current research provides specific information on the role inflammation plays in altering skeletal muscle form and function, and adaptation to exercise; however, the pursuit of more knowledge in this area will delineate specific interventions that may enhance skeletal muscle recovery and promote resiliency in this tissue particularly with aging.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.858
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.073
GPT teacher head0.383
Teacher spread0.310 · 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 designOther design
Domainnot available
GenreReview

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

Citations16
Published2024
Admission routes1
Has abstractyes

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