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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 OpenAlex
Zohra Nausheen Nizami, Hanan E. Aburawi, Abdelhabib Semlali, Khalid Muhammad, Rabah Iratni

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.331
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