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Record W4318562430 · doi:10.1002/ptr.7738

Targeting regulated cell death with plant natural compounds for cancer therapy: A revisited review of apoptosis, autophagy‐dependent cell death, and necroptosis

2023· review· en· W4318562430 on OpenAlex
Yuanyuan Yang, Yanmei Chen, Jun Hao Wu, Yueting Ren, Bo Liu, Yan Zhang, Haiyang Yu

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

VenuePhytotherapy Research · 2023
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsNecroptosisAutophagyProgrammed cell deathApoptosisCancer cellCell biologyCancerBiologySmall moleculeCancer researchBiochemistry

Abstract

fetched live from OpenAlex

Regulated cell death (RCD) refers to programmed cell death regulated by various protein molecules, such as apoptosis, autophagy-dependent cell death, and necroptosis. Accumulating evidence has recently revealed that RCD subroutines have several links to many types of human cancer; therefore, targeting RCD with pharmacological small-molecule compounds would be a promising therapeutic strategy. Moreover, plant natural compounds, small-molecule compounds synthesized from plant sources, and their derivatives have been widely reported to regulate different RCD subroutines to improve potential cancer therapy. Thus, in this review, we focus on updating the intricate mechanisms of apoptosis, autophagy-dependent cell death, and necroptosis in cancer. Moreover, we further discuss several representative plant natural compounds and their derivatives that regulate the above-mentioned three subroutines of RCD, and their potential as candidate small-molecule drugs for the future cancer treatment.

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)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.742
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.112
GPT teacher head0.419
Teacher spread0.307 · 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