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Record W2044173312 · doi:10.1055/s-0033-1343407

Effect of Muscle Unloading, Reloading and Exercise on Inflammation during a Head-down Bed Rest

2013· article· en· W2044173312 on OpenAlexfundno aff
M. Mutin-Carnino, A. Carnino, Sandrine Roffino, Angèle Chopard

Bibliographic record

VenueInternational Journal of Sports Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsnot available
FundersCanadian Space AgencyEuropean Space AgencyNational Aeronautics and Space Administration
KeywordsBed restInflammationRest (music)MedicineInterleukin 6Internal medicineC-reactive proteinEndocrinology

Abstract

fetched live from OpenAlex

Muscles are affected by unloading during head-down bed rests and by reloading through normal reambulation. This study investigated the effects of a 60 days head-down long-term bed rest with or without predefined exercise countermeasures on the development of an inflammatory reaction.Blood samples were taken before, during and after bed rest in control and exercise groups of women. They were assayed for soluble ICAM-1, VCAM-1, E- and L-selectin, IL1β, IL6, TNFα and CRP as markers of inflammation with ELISA.Head-down long-term bed rest induced plasma volume variations which had an impact on the concentrations of the inflammatory factors and led to data corrections for a reliable analysis of the results. None of the marker of inflammation, except IL6 in control group, showed a significant change from baseline during bed rest. The main results were obtained during recovery. VCAM-1 increased in all groups, ICAM-1, in the control group, and L-selectin, in the exercise group. Peaks of IL6 and CRP were observed on day 59 of bed rest for IL6 and on day 2 of recovery for CRP in the control group. Exercise during bed rest prevented the augmentation of IL6, CRP and ICAM-1. These results might suggest a shift towards pro-inflammatory conditions, prevented in part by exercises.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.288
Teacher spread0.281 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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