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Record W4312019325 · doi:10.1111/php.13768

Toxic Interaction Between Solar Radiation and Cigarette Smoke on Primary Human Keratinocytes

2022· article· en· W4312019325 on OpenAlexafffund
Alexe Grenier, Mathieu C. Morissette, Patrick J. Rochette, Roxane Pouliot

Bibliographic record

VenuePhotochemistry and Photobiology · 2022
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPrimary (astronomy)Cigarette smokeRadiationSmokeChemistryEnvironmental scienceToxicologyBiologyPhysicsOpticsAstrophysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Solar radiation and cigarette smoke are two environmental risk factors known to affect skin integrity. Although the toxic effects of these factors on skin have been widely studied separately, few studies have focused on their interaction. The objective of this study was to evaluate and understand the synergistic harmful effects of cigarette smoke and solar rays on human primary keratinocytes. The keratinocytes were exposed to cigarette smoke extract (CSE) and then irradiated with a solar simulator light (SSL). The viability, as determined by measuring metabolic activity of skin cells, and the levels of global reactive oxygen species (ROS) were evaluated after exposure to CSE and SSL. The combination of 3% CSE with 29 kJ m −2 UVA caused a decrease of 81% in cell viability, while with 10% to 20% CSE, the cell viability was null. This phototoxicity was accompanied by an increase in singlet oxygen but a decrease in type I ROS when CSE and SSL were combined in vitro . Surprisingly, an increase in the CSE's total antioxidant capacity was also observed. These results suggest a synergy between the two environmental factors in their effect on skin cells, and more precisely a phototoxicity causing a drastic decrease in cell viability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations3
Published2022
Admission routes2
Has abstractyes

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