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Record W2947755685 · doi:10.1186/s12906-019-2505-9

Hibiscus flower extract selectively induces apoptosis in breast cancer cells and positively interacts with common chemotherapeutics

2019· article· en· W2947755685 on OpenAlexafffund
Christopher Nguyen, Kiruthika Baskaran, Alaina Pupulin, Ivan Ruvinov, Ola Zaitoon, Sahibjot Grewal, Benjamin Scaria, Ali Mehaidli, Caleb Vegh, Siyaram Pandey

Bibliographic record

VenueBMC Complementary and Alternative Medicine · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicHibiscus Plant Research Studies
Canadian institutionsUniversity of Windsor
FundersLotte and John Hecht Memorial Foundation
KeywordsHibiscusBreast cancerPharmacologyMedicineCancerCancer cellApoptosisPhytochemicalCisplatinMCF-7Cancer researchTraditional medicineEstrogen receptorChemotherapyBiologyInternal medicineBiochemistryBotanyHuman breast

Abstract

fetched live from OpenAlex

BACKGROUND: Current therapeutic approaches to treat metastatic breast cancer, although effective, have shown many inadvertent side effects such as genotoxicity due to a lack of selectivity. Thus, these treatment plans are not suitable for long-term usage. Natural health product extracts are safe for long-term consumption and some have shown to be medicinally active containing multiple bioactive compounds able target multiple vulnerabilities in cancer. One of which, Hibiscus rosa-sinesis (hibiscus) extract, has been reported to have many medicinal and anticancer properties due to its antioxidant and hypolipidemic effects. However, its efficacy against breast cancer has not been fully investigated and characterized. If effective against cancer, hibiscus extract could potentially be combined with chemotherapeutic treatments in adjuvant therapy to reduce chemotherapy-inducing side effects. METHOD: We have investigated aqueous hibiscus flower extract anticancer efficacy, selectivity, and interactions with chemotherapeutics taxol, cisplatin, and tamoxifen in estrogen-receptor positive breast cancer cells, triple-negative human breast cancer cells, and normal non-cancerous cells. Apoptotic morphology and biochemical marker expression were assessed to determine the extent anticancer efficacy of hibiscus. Mitochondrial membrane potential reduction and reactive oxygen species generation were quantified using fluorogenic dyes to determine the mechanism of hibiscus extract action. RESULTS: Hibiscus extract was able to selectively induce apoptosis in both triple-negative and estrogen-receptor positive breast cancer cells in a dosage-dependent manner. Most importantly, addition of hibiscus extract was found to enhance the induction of apoptosis of chemotherapy treatments (taxol and cisplatin) in triple-negative breast cancer cells when compared to treatment alone. Moreover, hibiscus extract addition to chemotherapy treatment was able to increase oxidative stress and decrease mitochondrial membrane potential compared to individual treatments. CONCLUSION: Hibiscus extract is effective on breast cancer, most notably on generally resistant triple-negative breast cancer, while being selective for normal healthy cells. Hibiscus extract could supplement chemotherapeutic regimens as an adjuvant and lead to a more efficacious treatment approach to reduce chemotherapy dosages and related toxicity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.109
GPT teacher head0.441
Teacher spread0.332 · 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 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

Citations80
Published2019
Admission routes2
Has abstractyes

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