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Record W4391509193 · doi:10.53555/sfs.v10i1s.2114

Regulation of uncontrolled cell growth (cancer) by curcumin and piperine- A Review

2023· review· en· W4391509193 on OpenAlexvenueno aff
Soma Bhattacharjee, Sonali Das, Srayasree Ghosh, Doyel Maity, Papiya Saha, Swarnali Roy, R. Mitra

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPiperaceae Chemical and Biological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPiperineCurcuminChemistryCancer researchPharmacologyTraditional medicineMedicine

Abstract

fetched live from OpenAlex

Cancer is a clinical condition of uncontrolled growth of cells. Today one in six people have been suffering from this life threatening disease. It may be benign (localized) or malignant (spread). Besides the common treatment therapies like surgery, chemotherapy and radiotherapy naturally occurring spices like curcumin, found in turmeric(Curcuma longa) and piperine obtained from black peeper (Piper nigrum L) are getting importance for regulation of cancer proliferation. Curcumin and piperine both have the properties to regulate several intracellular signaling pathways acting as antioxidant and antiproliferative agent. According to literature glucuronidation of curcumin leads to formation of other compound that makes the curcumin unavailable and tend to be excreted in feces. On the other hand, piperine increases bioavailability of curcumin by inhibiting glucuronidation of curcumin in the liver and small intestine. So, using both spices together in food and beverages daily can increase the bioavailability of curcumin that could be blessings for cancer patients. In this paper an initiative has been made to illustrate the functions of both curcumin and piperine in inhibition of cell growth proliferation. The knowledge of this paper may be helpful in bio-medical applications and in formulation of new drug development therapy.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.805
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.546
GPT teacher head0.474
Teacher spread0.072 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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