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Record W3010467867 · doi:10.1242/jcs.238394

Bone marrow adipose cells – cellular interactions and changes with obesity

2020· review· en· W3010467867 on OpenAlexafffund
Parastoo Boroumand, Amira Klip

Bibliographic record

VenueJournal of Cell Science · 2020
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenUniversity of Toronto
KeywordsBiologyAdipose tissueBone marrowObesityCell biologyImmunologyEndocrinology

Abstract

fetched live from OpenAlex

The bone marrow is a spatially restricted niche, housing cells of the hematopoietic and mesenchymal lineages in various hierarchical commitment states. Although highly localized, cells within this niche are also subject to regulation by environmental and/or circulatory changes through extensive vascularization. Bone marrow adipocytes, derived from mesenchymal stem cells and once known as marrow space fillers, are a heterogeneous population. These cells reside in distinct niches within the bone marrow and interact with proximal cells, such as hematopoietic precursors and lineage-committed cells. In this diverse cellular milieu, bone marrow adipocytes influence commitment decisions and cellular lineage selection by interacting with stem and progenitor cells. In addition, bone marrow adipocytes respond to environmental changes, such as obesity, by undergoing hypertrophy, hyperplasia or adoption of characteristics resembling those of peripheral brown, beige or white adipocytes. Here, we review recent findings and concepts on the influence of bone marrow adipocytes on hematopoietic and other cellular lineages within this niche. We discuss how changes in local, systemic, cellular and secreted signals impact on mesenchymal stem cell expansion, differentiation and lineage commitment. Furthermore, we highlight that bone marrow adipocytes may be intermediaries conveying environmental cues to influence hematopoietic cellular survival, proliferation and preferential differentiation.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.056
GPT teacher head0.347
Teacher spread0.291 · 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 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

Citations33
Published2020
Admission routes2
Has abstractyes

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