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Record W1999045871 · doi:10.1089/152308602753625979

Attenuation of the Development of Murine Solid Leukemia Tumor by Physical Exercise

2002· article· en· W1999045871 on OpenAlexaff
Zsolt Radák, D. Gaál, Albert W. Taylor, Takao Kaneko, Shunichi Tahara, Hideko Nakamoto, Sataro Goto

Bibliographic record

VenueAntioxidants and Redox Signaling · 2002
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsWestern University
FundersJapan Society for the Promotion of ScienceMagyar Tudományos Akadémia
KeywordsLeukemiaTransplantationInternal medicineLipid peroxidationPhysical exerciseEndocrinologyCancer researchBiologyMedicineChemistryImmunologyOxidative stress

Abstract

fetched live from OpenAlex

The active involvement of physical exercise in the evolution of a variety of cancers is well documented. However, its role in solid leukemia tumor development is essentially unknown. Solid leukemia tumor cells were transplanted into 21 hybrid BDF1 control mice, exercise-trained mice that did not exercise during leukemia and exercise-trained mice that exercised during leukemia. The tumor size of the continuously exercising group was ~50% of that of control and exercise-terminated animals 18 days after the transplantation. The activity of antioxidant enzymes and the levels of lipid peroxidation and 8-hydroxy-2'-deoxyguanosine were not different in the tumors of the three groups. The level of carbonylated proteins was smaller in tumors of continuously exercising animals. The mutant form of cell regulatory protein p53 and vascular endothelial growth factor were present in similar amounts in the tumor cells of each group. On the other hand, the protooncogene Ras and I-kappaB proteins were present in higher concentrations in tumors of continuously exercising rats. The present data suggest that exercise during leukemia attenuates the development of tumors in mice. The selective alteration of regulatory proteins might play a role in the beneficial effects of exercise during leukemia.

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)0.000
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 score0.031
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.272
Teacher spread0.251 · 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

Citations20
Published2002
Admission routes1
Has abstractyes

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