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Record W2770273110 · doi:10.1182/blood-2017-07-746453

Age-related clonal hematopoiesis

2017· review· en· W2770273110 on OpenAlexaff
Liran I. Shlush

Bibliographic record

VenueBlood · 2017
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsHaematopoiesisBiologyProgenitor cellStem cellSomatic cellImmunologyStem cell theory of agingCancerSomatic evolution in cancerGeneticsStem cell factorGene

Abstract

fetched live from OpenAlex

Age-related alterations in the human blood system occur in B cells, T cells, cells of the innate system, as well as hematopoietic stem and progenitor cells (HSPCs). Interestingly, age-related, reduced genetic diversity can be identified at the stem cell level and also independently in B cells and T cells. This reduced diversity is most probably related to somatic mutations or to changes in the microenvironmental niche. Either process can select for specific clones or cause repeated evolutionary bottlenecks. This review discusses the age-related clonal expansions in the human HSPC pool, which was termed in the past age-related clonal hematopoiesis (ARCH). ARCH is defined as the gradual, clonal expansion of HSPCs carrying specific, disruptive, and recurrent genetic variants, in individuals without clear diagnosis of hematological malignancies. ARCH is associated not just with chronological aging but also with several other, age-related pathological conditions, including inflammation, vascular diseases, cancer mortality, and high risk for hematological malignancies. Although it remains unclear whether ARCH is a marker of aging or plays an active role in these various pathophysiologies, it is suggested here that treating or even preventing ARCH may prove to be beneficial for human health. This review also describes a decision tree for the diagnosis and follow-up for ARCH in a research setting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.108
GPT teacher head0.403
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations283
Published2017
Admission routes1
Has abstractyes

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