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Record W2035892137 · doi:10.1111/jicd.12020

Caffeine alters mitochondrial dehydrogenase and alkaline phosphatase activity of human gingival fibroblasts <i>in vitro</i>

2012· article· en· W2035892137 on OpenAlexaff
Shabnam Soltani Bozchaloei, Siew‐Ging Gong, Ahmad Reza Dehpour, Parisa Farrokh, Mohammad Reza Khoshayand, Mahvash Oskoui

Bibliographic record

VenueJournal of Investigative and Clinical Dentistry · 2012
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsUniversity of Toronto
FundersPasteur Institute of Iran
KeywordsCaffeineAlkaline phosphataseViability assayEnzyme assayChemistryLactate dehydrogenaseIn vitroDehydrogenaseEnzymeEndocrinologyBiochemistryInternal medicineBiologyPharmacologyMedicine

Abstract

fetched live from OpenAlex

AIM: Caffeine is one of the most widely consumed behaviorally active substances in the world. Although its effects on the central nervous system and bone metabolism have been documented, as yet there is no report on its effect on tissues in the oral cavity. In this study we analyzed the viability of human gingival fibroblasts (HGF) and alkaline phosphatase (ALP) enzyme activity after exposure to different concentrations of caffeine for different exposure time periods. METHODS: The HGF were cultured with different concentrations of caffeine. Viability of cells exposed to caffeine was analyzed by the 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) assay to assess mitochondrial dehydrogenase activity. The activity of ALP was analyzed at specific time intervals after caffeine addition. RESULTS: Our results showed that caffeine of concentrations <1 mm did not affect the viability of HGF and the ALP enzyme activity. Nevertheless, caffeine at 5 and 10 mm dramatically decreased the viability and ALP activity of the cells after 4 days such that, by day 9, the viability of cells declined to near zero in the 10 mm group. CONCLUSION: These results provided evidence that caffeine in high concentrations can decrease cellular viability and ALP activity in HGF.

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)0.002
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 score0.179
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.069
GPT teacher head0.399
Teacher spread0.330 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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