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Bioactive Compounds from Curcuma amada and Their Effect on Acute Myeloid Leukemia

2022· review· en· W4316036088 on OpenAlexaff
Ajila Chandran, Varsha Jayasankar, Paul A. Spagnuolo, Jayasankar Subramanian

Bibliographic record

VenueCritical Reviews™ in Oncogenesis · 2022
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsVineland Research and Innovation CentreUniversity of GuelphUniversity of Calgary
Fundersnot available
KeywordsMyeloid leukemiaApoptosisAutophagyLeukemiaViability assayCancerCell cycle checkpointCancer researchCell cycleAntioxidantPharmacologyMyeloidCancer cellChemistryBiologyMedicineImmunologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Acute myeloid leukemia (AML) is an aggressive blood cancer with limited chemotherapy options and negative patient outcomes. Investigations with bioactive compounds from dietary sources against cancer have increased in the recent years, which highlight the need for novel therapeutic approaches and new anti-leukemic agents possessing higher efficacy and selectivity for AML cells and fewer negative side effects. Bioactive compounds demonstrated the ability to induce cell cycle blockage and apoptosis or autophagy in cancer cells, as well as inhibition of proliferation/migration and tumor progression, etc. Bioactive compounds isolated from dietary sources such as mango ginger show promise for AML treatment. Curcuma amada roots have been used in traditional medicine and showed antioxidant, antimicrobial and anticancer properties. Bioactive molecules isolated from C. amada showed effects on the mitochondrial metabolism and reduced the viability of multiple leukemic cell lines.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.061
GPT teacher head0.396
Teacher spread0.334 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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