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Record W3119520381 · doi:10.3390/antiox10010049

Disrupted H2S Signaling by Cigarette Smoking and Alcohol Drinking: Evidence from Cellular, Animal, and Clinical Studies

2021· review· en· W3119520381 on OpenAlexafffund
Ethan Read, Jiechun Zhu, Guangdong Yang

Bibliographic record

VenueAntioxidants · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSulfur Compounds in Biology
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsOxidative stressMedicineAlcoholAntioxidantCigarette smokingAddictionAlcohol addictionAnimal studiesAlcohol and healthBioinformaticsPhysiologyPharmacologyPsychiatryAlcohol consumptionInternal medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

The role of endogenous hydrogen sulfide (H2S) as an antioxidant regulator has sparked interest in its function within inflammatory diseases. Cigarette and alcohol use are major causes of premature death, resulting from chronic oxidative stress and subsequent tissue damage. The activation of the Nrf2 antioxidant response by H2S suggests that this novel gasotransmitter may function to prevent or potentially reverse disease progression caused by cigarette smoking or alcohol use. The purpose of this study is to review the interrelationship between H2S signaling and cigarette smoking or alcohol drinking. Based on the databases of cellular, animal, and clinical studies from Pubmed using the keywords of H2S, smoking, and/or alcohol, this review article provides a comprehensive insight into disrupted H2S signaling by alcohol drinking and cigarette smoking-caused disorders. Major signaling and metabolic pathways involved in H2S-derived antioxidant and anti-inflammatory responses are further reviewed. H2S supplementation may prove to be an invaluable asset in treating or preventing diseases in those suffering from cigarette or alcohol addiction.

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)0.001
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 score0.910
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.134
GPT teacher head0.416
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

Citations20
Published2021
Admission routes2
Has abstractyes

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