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Record W3186794219 · doi:10.82308/43175

Role of plasma composition on bone healing and implant osseointegration

2021· article· en· W3186794219 on OpenAlexfundno aff
Faez Al Hamed

Bibliographic record

VenueeScholarship@McGill (McGill) · 2021
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéMcGill University Health CentreAlpha Omega FoundationRéseau de Recherche en Santé Buccodentaire et OsseuseFaculty of Dentistry, McGill UniversityMcGill University
KeywordsOsseointegrationDentistryBone healingImplantMedicineSurgery

Abstract

fetched live from OpenAlex

Blood plasma plays a key role in bone regeneration, and plasma-derived products such as platelet concentrates (PCs) have been used to enhance bone healing in oral and orthopedic surgeries. In this thesis, we hypothesized that changes in plasma composition affect its ability to regenerate bone. Accordingly, the goal of this thesis was to study how changes in plasma composition affect bone regeneration and implant osseointegration. This manuscript-based dissertation includes four interconnected manuscripts. In the first study (review study), we summarized the clinical research on the effect of PCs on oral and craniofacial regeneration. We concluded that PCs could enhance soft tissue and bone healing, although, these beneficial outcomes are clinically inconsistent. Also, there is a lack of standardized clinical and experimental studies assessing how changes in PC composition affect bone regeneration. In the second study, we assessed how changes in PC composition affect bone healing. We compared PCs with different concentrations of platelets and metabolites in a bone defect rat model. We showed that differences in the composition of PCs, especially the concentration of platelets, affect their ability to regenerate bone. In the third study, we assessed how changes in plasma composition due to aging affect bone regeneration by applying plasma derived from young or old rats in a bone defect model. We showed that plasma composition changes with age and this seems to affect its ability to regenerate bone. Also, we have discovered that plasma aging modifies the proteomes of bone defects. Young plasma upregulated pathways essential for bone healing while old plasma upregulated pathways involved in disease and inflammation. In the fourth study, we assessed how changes in plasma composition due to the short-term administration of donepezil, anti-Alzihmer’s drug known to reduce the plasma concentrations of adrenaline and nor-adrenaline, affect bone healing and implant osseointegration. We have shown that short-term administration of donepezil hindered bone healing and implant osseointegration. In conclusion, this thesis presents fundamental knowledge regarding the effect of changes in plasma composition on bone healing and implant osseointegration. These results could help develop a new biological material with better regenerative potential

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

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