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Record W4388383236 · doi:10.1016/j.dnarep.2023.103591

Telomeres as hotspots for innate immunity and inflammation

2023· article· en· W4388383236 on OpenAlexfundno aff
Joe Nassour, Sara Przetocka, Jan Karlseder

Bibliographic record

VenueDNA repair · 2023
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsnot available
FundersHewitt FoundationNational Institute on AgingSamuel Waxman Cancer Research FoundationNational Institute of General Medical SciencesAmerican Heart AssociationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean Molecular Biology OrganizationGlenn Foundation for Medical ResearchNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsTelomereBiologyInflammationInnate immune systemImmunitySenescenceImmunologyPermissiveNeuroscienceGeneticsImmune systemDNA

Abstract

fetched live from OpenAlex

Aging is marked by the gradual accumulation of deleterious changes that disrupt organ function, creating an altered physiological state that is permissive for the onset of prevalent human diseases. While the exact mechanisms governing aging remain a subject of ongoing research, there are several cellular and molecular hallmarks that contribute to this biological process. This review focuses on two factors, namely telomere dysfunction and inflammation, which have emerged as crucial contributors to the aging process. We aim to discuss the mechanistic connections between these two distinct hallmarks and provide compelling evidence highlighting the loss of telomere protection as a driver of pro-inflammatory states associated with aging. By reevaluating the interplay between telomeres, innate immunity, and inflammation, we present novel perspectives on the etiology of aging and its associated diseases.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.297
Teacher spread0.274 · 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 designObservational
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

Citations29
Published2023
Admission routes1
Has abstractno

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