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Record W4388713811 · doi:10.1016/j.crtox.2023.100137

Chaga mushroom triterpenoids as adjuncts to minimally invasive cancer therapies: A review

2023· review· en· W4388713811 on OpenAlexaff
Selina Plehn, Sajeev Wagle, H.P. Vasantha Rupasinghe

Bibliographic record

VenueCurrent Research in Toxicology · 2023
Typereview
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMushroomInonotus obliquusTriterpenoidNutraceuticalCancerTraditional medicineBioavailabilityMedicinePharmacologyCancer cellCytotoxicityBiologyIn vitroBiochemistryBotanyInternal medicine

Abstract

fetched live from OpenAlex

Cancer has become the second leading cause of death in the world. Integrative cancer therapy management is continuously evolving to enhance treatment outcomes. Chaga mushroom (Inonotus obliquus) is a parasitic fungus acclaimed to contain pharmaceutical and nutraceutical value in the fight against cancer. In particular, triterpenoid constituents derived from Chaga mushrooms have been recognized for their anti-cancer activity after distinguished cytotoxicity was repeatedly observed in cancer cells treated in vitro with lipophilic fractions of extract compared to aqueous ones. Studies that investigate the anti-cancer activity of Chaga mushroom triterpenoids are reviewed in this article to determine which cancer cell lines demonstrate the greatest susceptibility to them while highlighting the structure-activity relationships that are involved. Triterpenoid supplementation as an adjunct to cancer treatment may be a viable option as inotodiol and 3-β-22 α-dihydroxylanosta-8, 25-diene-24-one have been shown to exhibit anti-cancer activity similar to that of conventional drugs. Advances in addressing bioavailability challenges are also included in this review as studies include in vivo components.

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 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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.005

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 head0.467
GPT teacher head0.594
Teacher spread0.128 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2023
Admission routes1
Has abstractyes

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