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Record W2312332112 · doi:10.1189/jlb.3ma0915-433rr

Tumor necrosis factor drives increased splenic monopoiesis in old mice

2016· article· en· W2312332112 on OpenAlexafffund
Dessi Loukov, Avee Naidoo, Alicja Puchta, Jorge L Arredondo Marin, Dawn M. E. Bowdish

Bibliographic record

VenueJournal of Leukocyte Biology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsBiologySpleenTumor necrosis factor alphaTumor necrosis factor αNecrosisEndocrinologyInternal medicineCancer researchImmunologyPathologyGenetics

Abstract

fetched live from OpenAlex

Aging is accompanied by changes in hematopoiesis and consequently in leukocyte phenotype and function. Although age-related changes in bone marrow hematopoiesis are fairly well documented, changes in extramedullary hematopoiesis are less well described. We observed that 18-22-mo-old mice had larger spleens than young controls and found that the enlargement was caused by increased monopoiesis. Because extramedullary hematopoiesis is often driven by inflammation, we hypothesized that the chronic, low-level inflammation that occurs with age is a causal agent in splenomegaly. To test this theory, we compared the number of monocytes in 18-mo-old tumor necrosis factor-knockout mice, which are protected from age-associated inflammation, and found that they did not have increased extramedullary monopoiesis. To determine whether increased splenic monopoiesis is caused by intrinsic changes in the myeloid precursors that occur with age or by the aging microenvironment, we created heterochronic bone marrow chimeras. Increased splenic monopoiesis occurred in old recipient mice, regardless of the age of the donor mouse, but not in young recipient mice, demonstrating that these cells respond to signals from the microenvironment. These data suggest that decreasing the inflammatory microenvironment with age would be an effective strategy for reducing inflammatory diseases propagated by cells of myeloid lineage, which increase in number with age.

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 score0.317
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.014
GPT teacher head0.249
Teacher spread0.234 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

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