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Record W4392154476 · doi:10.3390/antiox13030287

Redox System and Oxidative Stress-Targeted Therapeutic Approaches in Bladder Cancer

2024· article· en· W4392154476 on OpenAlexaff
George J. Dugbartey, Sydney Relouw, Liam McFarlane, Alp Şener

Bibliographic record

VenueAntioxidants · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsOxidative stressMedicineMalignancyRadiation therapyReactive oxygen speciesCancerCancer researchGenitourinary systemBladder cancerBioinformaticsOncologyImmunotherapyInternal medicineBiology

Abstract

fetched live from OpenAlex

Bladder cancer (BCa) is the most common genitourinary malignancy, with a high global incidence and recurrence rate that is paired with an increasing caregiver burden and higher financial cost, in addition to increasing morbidity and mortality worldwide. Histologically, BCa is categorized into non-muscle invasive, muscle invasive, and metastatic BCa, on the basis of which the therapeutic strategy is determined. Despite all innovations and recent advances in BCa research, conventional therapies such as chemotherapy, immunotherapy, radiotherapy, and surgery fall short in the complete management of this important malignancy. Besides this worrying trend, the molecular basis of BCa development also remains poorly understood. Burgeoning evidence from experimental and clinical studies suggests that oxidative stress resulting from an imbalance between reactive oxygen species (ROS) generation and the body's antioxidant production plays an integral role in BCa development and progression. Hence, ROS-induced oxidative stress-related pathways are currently under investigation as potential therapeutic targets of BCa. This review focuses on our current understanding regarding ROS-associated pathways in BCa pathogenesis and progression, as well as on antioxidants as potential adjuvants to conventional BCa therapy.

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 score0.025
Threshold uncertainty score0.754

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 head0.025
GPT teacher head0.268
Teacher spread0.243 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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