MétaCan
Menu
Back to cohort
Record W2278696051 · doi:10.1080/15384101.2015.1121322

BCL10 is recruited to sites of DNA damage to facilitate DNA double-strand break repair

2016· article· en· W2278696051 on OpenAlexafffund
Ismail Hassan Ismail, Ashley Dronyk, Xiuying Hu, Michael J. Hendzel, Andrew Shaw

Bibliographic record

VenueCell Cycle · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsDNA damageBiologyBCL10DNA repairDNARAD51CarcinogenesisGeneticsCell biologyCancer researchMolecular biologyCancerGene

Abstract

fetched live from OpenAlex

Recent studies have found BCL10 can localize to the nucleus and that this is linked to tumor aggression and poorer prognosis. These studies suggest that BCL10 localization plays a novel role in the nucleus that may contribute to cellular transformation and carcinogenesis. In this study, we show that BCL10 functions as part of the DNA damage response (DDR). We found that BCL10 facilitates the rapid recruitment of RPA, BRCA1 and RAD51 to sites of DNA damage. Furthermore, we also found that ATM phosphorylates BCL10 in response to DNA damage. Functionally, BCL10 promoted DNA double-strand breaks repair, enhancing cell survival after DNA damage. Taken together our results suggest a novel role for BCL10 in the repair of DNA lesions.

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 score0.047
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.249
Teacher spread0.222 · 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

Citations17
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueCell CycleSame topicDNA Repair MechanismsFrench-language works237,207