MétaCan
Menu
Back to cohort
Record W4401209608 · doi:10.3390/biom14080922

Complex Interplay between DNA Damage and Autophagy in Disease and Therapy

2024· review· en· W4401209608 on OpenAlexafffund
Aman Singh, Naresh Ravendranathan, Jefferson C. Frisbee, Krishna K. Singh

Bibliographic record

VenueBiomolecules · 2024
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsWestern University
FundersHeart and Stroke Foundation of Canada
KeywordsAutophagyDNA damageContext (archaeology)BiologyDNA repairCancerDiseaseCancer researchProgrammed cell deathBioinformaticsMedicineDNAGeneticsApoptosisInternal medicine

Abstract

fetched live from OpenAlex

Cancer, a multifactorial disease characterized by uncontrolled cellular proliferation, remains a global health challenge with significant morbidity and mortality. Genomic and molecular aberrations, coupled with environmental factors, contribute to its heterogeneity and complexity. Chemotherapeutic agents like doxorubicin (Dox) have shown efficacy against various cancers but are hindered by dose-dependent cytotoxicity, particularly on vital organs like the heart and brain. Autophagy, a cellular process involved in self-degradation and recycling, emerges as a promising therapeutic target in cancer therapy and neurodegenerative diseases. Dysregulation of autophagy contributes to cancer progression and drug resistance, while its modulation holds the potential to enhance treatment outcomes and mitigate adverse effects. Additionally, emerging evidence suggests a potential link between autophagy, DNA damage, and caretaker breast cancer genes BRCA1/2, highlighting the interplay between DNA repair mechanisms and cellular homeostasis. This review explores the intricate relationship between cancer, Dox-induced cytotoxicity, autophagy modulation, and the potential implications of autophagy in DNA damage repair pathways, particularly in the context of BRCA1/2 mutations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

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)0.0000.000

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 head0.067
GPT teacher head0.411
Teacher spread0.343 · 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.

Study designOther design
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

Citations10
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueBiomoleculesSame topicAutophagy in Disease and TherapyFrench-language works237,207