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Record W4378574660 · doi:10.3390/antiox12061159

Oxidative Stress Inducers in Cancer Therapy: Preclinical and Clinical Evidence

2023· review· en· W4378574660 on OpenAlexaff
Zohra Nausheen Nizami, Hanan E. Aburawi, Abdelhabib Semlali, Khalid Muhammad, Rabah Iratni

Bibliographic record

VenueAntioxidants · 2023
Typereview
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOxidative stressReactive oxygen speciesCancerPharmacologyCancer cellDoxorubicinChemistryOxidative phosphorylationCancer researchBiologyBiochemistryChemotherapy

Abstract

fetched live from OpenAlex

Reactive oxygen species (ROS) are metabolic byproducts that regulate various cellular processes. However, at high levels, ROS induce oxidative stress, which in turn can trigger cell death. Cancer cells alter the redox homeostasis to facilitate protumorigenic processes; however, this leaves them vulnerable to further increases in ROS levels. This paradox has been exploited as a cancer therapeutic strategy with the use of pro-oxidative drugs. Many chemotherapeutic drugs presently in clinical use, such as cisplatin and doxorubicin, induce ROS as one of their mechanisms of action. Further, various drugs, including phytochemicals and small molecules, that are presently being investigated in preclinical and clinical studies attribute their anticancer activity to ROS induction. Consistently, this review aims to highlight selected pro-oxidative drugs whose anticancer potential has been characterized with specific focus on phytochemicals, mechanisms of ROS induction, and anticancer effects downstream of ROS induction.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.331
GPT teacher head0.492
Teacher spread0.161 · 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 designNot applicable
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

Citations62
Published2023
Admission routes1
Has abstractyes

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