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Record W4224293391 · doi:10.1155/2022/3254420

Molecular Mechanisms of Coffee on Prostate Cancer Prevention

2022· review· en· W4224293391 on OpenAlexaff
Júlia Montenegro, Otniel Freitas‐Silva, Anderson Junger Teodoro

Bibliographic record

VenueBioMed Research International · 2022
Typereview
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsUniversity of Alberta
FundersUniversidade Federal do Estado do Rio de JaneiroFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsProstate cancerOxidative stressSex hormone-binding globulinPeroxiredoxinAntioxidantBiologyTestosterone (patch)HormoneEstrogenCancerEndocrinologyBiochemistryAndrogenEnzymeGenetics

Abstract

fetched live from OpenAlex

Prostate cancer (PCa) is one of the most common types of cancer among men, and coffee is associated with a reduced risk of developing PCa. Therefore, we aim to review possible coffee molecular mechanisms that contribute to PCa prevention. Coffee has an important antioxidant capacity that reduces oxidative stress, leading to a reduced mutation in cells. Beyond direct antioxidant activity, coffee stimulates phase II enzymatic activity, which is related to the detoxification of reactive metabolites. The anti‐inflammatory effects of coffee reduce tissue damage related to PCa development. Coffee induces autophagy, regulates the NF‐ κ B pathway, and reduces the expression of iNOS and inflammatory mediators, such as TNF‐ α , IL‐6, IL‐8, and CRP. Also, coffee modulates transcriptional factors and pathways. It has been shown that coffee increases testosterone and reduces sex hormone‐binding globulin, estrogen, and prostate‐specific antigen. Coffee also enhances insulin resistance and glucose metabolism. All these effects may contribute to protection against PCa development.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.267
GPT teacher head0.549
Teacher spread0.282 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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