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Record W4388714065 · doi:10.1016/j.dnarep.2023.103595

The entanglement of DNA damage and pattern recognition receptor signaling

2023· article· en· W4388714065 on OpenAlexafffund
Cindy T. Ha, Maha M. Tageldein, Shane M. Harding

Bibliographic record

VenueDNA repair · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchTerry Fox Research InstitutePrincess Margaret Cancer Foundation
KeywordsDNA damageBiologyDNA repairCell biologyDNAMitosisMutagenesisCell cycleGenome instabilityMitotic catastropheApoptosisGeneticsGeneMutation

Abstract

fetched live from OpenAlex

Cells are under constant pressure to suppress DNA damage originating from both exogenous and endogenous sources. Cellular responses to DNA damage help to prevent mutagenesis and cell death that arises when DNA damage is either left unrepaired or repaired inaccurately. During the "acute phase" of DNA damage signaling, lesions are recognized, processed, and repaired to restore the primary DNA sequence whilst cell cycle checkpoints delay mitotic progression, cell death and the propagation of errors to daughter cells. Increasingly, there is recognition of a "chronic phase" of DNA damage signaling, exemplified by the secretion of dozens of cytokines days after the inciting damage event. In this review, we focus on the cellular origin of these chronic responses, the molecular pathways that control them and the increasing appreciation for the interconnection between acute and chronic DNA damage responses.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.240
Teacher spread0.226 · 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 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

Citations4
Published2023
Admission routes2
Has abstractyes

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