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Synergistic enhancement of collagenous protein synthesis by human gingival fibroblasts exposed to nifedipine and interleukin‐1‐beta <i>in vitro</i>

2000· article· en· W2077370709 on OpenAlexaff
Roger B. Johnson, E.J. Zebrowski, Xiaowei Dai

Bibliographic record

VenueJournal of Oral Pathology and Medicine · 2000
Typearticle
Languageen
FieldHealth Professions
TopicOral and gingival health research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNifedipineCytokineConnective tissueBETA (programming language)In vitroInterleukinMedicineChemistryCalciumEndocrinologyInternal medicineMolecular biologyPathologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Gingival overgrowth commonly occurs coincident to therapy with calcium channel blockers. The biologic mechanism for this condition is unknown; however, many clinicians suggest that poor oral hygiene may contribute to development of the overgrowth. This study tests the hypothesis that collagenous protein synthesis by gingival fibroblasts is synergistically enhanced when they are exposed to both nifedipine (N) and the pro-inflammatory cytokine, interleukin-1-beta, a cytokine expressed in inflamed gingiva. Human gingival fibroblasts were isolated from biopsies of normal gingiva and cells separated into two groups. Group 1 was exposed to media containing 0, 5, 50, or 500 pg/ml IL-1-beta, or 10(-7) M N for 7 days; Group 2 was exposed to those concentrations of IL-1-beta +10(-7) M N. [3H]-proline was added to the medium for the final 24 h. Cells and matrix were harvested and radioactivity determined by liquid scintillation analysis. Means (d.p.m./10(3) cells) were compared by factorial ANOVA and Scheffé comparisons. Collagenous protein synthesis was significantly reduced by 5 pg/ml IL-1-beta +10(-7) M N and enhanced by 500 pg/ml IL-1-beta +10(-7) M N as compared to N or IL-1-beta alone. Thus, patients may be more susceptible to gingival overgrowth coincident to nifedipine therapy as a result of the synergistic enhancement of connective tissue synthesis by these agents.

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)0.000
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 score0.246
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.419
Teacher spread0.363 · 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

Citations28
Published2000
Admission routes1
Has abstractyes

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