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Record W4296163606 · doi:10.1177/01926233221123523

How Does Lifestyle Affect Hematopoiesis and the Bone Marrow Microenvironment?

2022· review· en· W4296163606 on OpenAlexaff
James J. Vanhie, Michael De Lisio

Bibliographic record

VenueToxicologic Pathology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBone marrowMyelopoiesisHaematopoiesisInflammationMedicineStem cellAdipose tissuePhysical exerciseObesityCytokineImmunologyMyeloidBiologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Lifestyle factors are modifiable behavioral factors that have a significant impact on health and longevity. Diet-induced obesity and physical activity/exercise are two prevalent lifestyle factors that have strong relationships to overall health. The mechanisms linking obesity to negative health outcomes and the mechanisms linking increased participation in physical activity/exercise to positive health outcomes are beginning to be elucidated. Chronic inflammation, due in part to overproduction of myeloid cells from hematopoietic stem cells (HSCs) in the bone marrow, is an established mechanism responsible for the negative health effects of obesity. Recent work has shown that exercise training can reverse the aberrant myelopoiesis present in obesity in part by restoring the bone marrow microenvironment. Specifically, exercise training reduces marrow adipose tissue, increases HSC retention factor expression, and reduces pro-inflammatory cytokine levels in the bone marrow. Other, novel mechanistic factors responsible for these exercise-induced effects, including intercellular communication using extracellular vesicles (EVs), is beginning to be explored. This review will summarize the recent literature describing the effects of exercise on hematopoiesis in individuals with obesity and introduce the potential contribution of EVs to this process.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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.

Study designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

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