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Melatonin: Emerging Player in the Management of Oral Cancer

2023· review· en· W4380084871 on OpenAlexaff
Sagar Dholariya, Ragini Singh, Kinjal A. Patel

Bibliographic record

VenueCritical Reviews™ in Oncogenesis · 2023
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMelatoninCancerCarcinogenesisMedicineCarcinogenHormoneBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

Oral cancer (OC) has emerged as a major medical and social issue in many industrialized nations due to the high death rate. It is becoming increasingly common in people under the age of 45, although the underlying causes and mechanisms of this increase remain unclear. Melatonin, as a pleiotropic hormone, plays a pivotal role in a wide variety of cellular and physiological functions. Mounting evidence supports melatonin's ability to modify/influence oral carcinogenesis, help in the reduction of the incidence of OC, and increase chemo- and radiosensitivity. Despite its potential anti-carcinogenic effects, the precise function of melatonin in the management of OC is not well understood. This review summarizes the current knowledge regarding melatonin function in anti-carcinogenesis mechanisms for OC. In addition, clinical assessment and the potential therapeutic utility of melatonin in OC are discussed. This review will provide a basis for researchers to create new melatonin-based personalized medicines for treating and preventing OC.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.249
GPT teacher head0.470
Teacher spread0.220 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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