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Record W2004692124 · doi:10.1155/2012/742439

Peri-Implant Crestal Bone Loss: A Putative Mechanism

2012· article· en· W2004692124 on OpenAlexafffund
Yuko Ujiie, Reynaldo Todescan, John E. Davies

Bibliographic record

VenueInternational Journal of Dentistry · 2012
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchBiomet 3i
KeywordsPeriDentistryMechanism (biology)ImplantPeri-implantitisMedicineOrthodonticsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Purpose. The immunological mechanisms of peri-implant crestal bone loss have, hitherto, not been elucidated. We hypothesized that bacterial products from the microgap cause upregulation of cytokines in otherwise healthy peri-implant cells, which results in osteoclast formation and, ultimately, in bone resorption. Materials and Methods. We used RT-PCR and ELISA to assay mediators of osteoclastogenesis in rat and human macrophages (r-and hMO); bone marrow derived stromal cells (r-and hBMCs); and human gingival fibroblasts (hGF)-with or without stimulation by LPS. TRAP positive multinucleate cells were assessed for their resorptive ability. Results. We show that IL-1α, IL-1β, and IL-6 were expressed by all examined cell types, and TNF-α was upregulated in hGF. Secretion of IL-1α and IL-1β proteins was stimulated in hMO by LPS, and IL-6 protein secretion was highly stimulated in hBMCs and hGF. Both LPS and RANKL stimulated macrophages to form osteoclast-like TRAP positive cells, which resorbed calcium phosphate substrates. Conclusion. Taken together, the results of our study support the hypothesis that bacterial endotoxins upregulate enhanced mediators of osteoclastogenesis in resident cells found in the healthy peri-implant compartment and that the local synergistic action of cytokines secreted by such cells results in the genesis of resorptively active osteoclasts.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.788

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.344
Teacher spread0.318 · 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 designCase report
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

Citations17
Published2012
Admission routes2
Has abstractyes

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