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Record W2901611542 · doi:10.1016/j.biopha.2018.10.107

Potential phytochemicals in the fight against skin cancer: Current landscape and future perspectives

2018· review· en· W2901611542 on OpenAlexaff
Javed Iqbal, Banzeer Ahsan Abbasi, Riaz Ahmad, Riffat Batool, Tariq Mahmood, Ali Talha Khalil, Sobia Kanwal, Sayed Afzal Shah, Muhammad Maqsood Alam, Sheeza Bashir, Hussain Badshah, Akhtar Munir

Bibliographic record

VenueBiomedicine & Pharmacotherapy · 2018
Typereview
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCancerSkin cancerMedicineMetastasisAngiogenesisDiseasePharmacologyTraditional medicineBioinformaticsBiologyCancer researchPathologyInternal medicine

Abstract

fetched live from OpenAlex

Skin cancer is a life threatening disease and their prevalence and risk has been increasing over the past three decades causing significant loss to human health worldwide. Mostly skin cancer has developed resistance against chemotherapy and radiotherapy. Therefore, development of novel, cost effective and efficient treatment methods are needed. Phytochemicals extracted from medicinal plants and dietary sources are often biologically active and has attracted the attention of researchers and pharmaceutical industries around the world. Many in vitro and in vivo studies of these bioactive compounds have shown potential antioxidant, anti-proliferative, anti-inflammatory and anti-angiogenic effects in the fight against skin cancer. These phytochemicals also regulate several other molecular processes such as angiogenesis, metastasis and cell cycle to combat skin cancer. The present review provides perspectives on the key phytochemicals, their therapeutic potentials, bioavailability and molecular mechanism of action in the cancer therapeutics. Current challenges and future directions for research are also critically discussed.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.388
Teacher spread0.360 · 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.

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

Citations100
Published2018
Admission routes1
Has abstractyes

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