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Progressive exercise training protects bone marrow stem cells from radiation‐induced damage

2008· article· en· W2258247492 on OpenAlexafffund
Michael De Lisio, Nghi Phan, Doug Boreham, Gianni Parise

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsBasal (medicine)Bone marrowMedicineTreadmillStem cellAdaptive responseBone Marrow Stem CellInternal medicineBiologyCell biologyGenetics

Abstract

fetched live from OpenAlex

Exercise training is known to induce an adaptive response in numerous tissues, however, the effects of exercise training on bone marrow stem cells (BMSC) is unknown. The purpose of this study was to determine if exercise training produced adaptations in BMSC and if this adaptive response could protect against subsequent high levels of ROS. C57Bl/6 mice were divided into the following groups (n=6 per group): treadmill running (EX), treadmill running and low dose radiation (ExLDR), and control (CON). Following 10 weeks of intervention half the mice in each group were exposed to a high dose radiation (HDR) challenge. Exercise training did not affect the basal number of double strand breaks (DSB) as determined by Gamma H2AX foci, but resulted in significantly fewer DSB in response to HDR, in vitro . Furthermore, exercise training significantly decreased both the basal number of micronucleated reticulocytes (MN‐RET) as well as the number of MN‐RET in response to HDR, in vivo . Interestingly, exercise was able to prevent the increase in basal MN‐RET in the mice exposed to 10 weeks of LDR. In conclusion, progressive exercise training was sufficient to protect BMSC from high doses of radiation. This research was funded by US DOE, Low Dose Research Program (DE‐FG02‐07ER64343) and NSERC.

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)0.000
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 score0.076
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.255
Teacher spread0.225 · 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
Published2008
Admission routes2
Has abstractyes

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