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Record W7133094530

Periodontal fibroblast– macrophage interactions and intervention dynamics in root resorption

2024· dissertation· W7133094530 on OpenAlexfundno aff
Rajeshwari Hadagalu Revana Siddappa

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldHealth Professions
TopicDental Trauma and Treatments
Canadian institutionsnot available
FundersUniversity of TorontoInternational Association for Dental Research
KeywordsCementumOsteoclastResorptionParacrine signallingMacrophageBone resorptionCellular differentiationPeriodontitisCementoblast
DOInot available

Abstract

fetched live from OpenAlex

Root resorption following dental avulsion is a complex process characterized by osteoclast mediated root dentin and cementum degradation. While osteoclasts are the key resorbing cells, their differentiation from precursor macrophages (Mφ) is spatiotemporally regulated by resident periodontal fibroblasts (PDLF). Mφ can polarize into pro-inflammatory (M1) or anti-inflammatory (M2) phenotype based on the microenvironmental cues to regulate the disease or healing outcomes. There is no standard treatment for root resorption owing to the poor understanding of the disease mechanism. This study aims to (1) characterize in-vitro 2-dimensional PDLF-Mφ direct coculture and 3-dimensional collagen-based PDLF-Mφ tissue graft models to study the effect of PDLF on osteoclastic differentiation of Mφ and (2) study the effect of engineered chitosan nanoparticles (CSNP), CSNP functionalized with photosensitizer Rose Bengal (CSRB), and CSNP functionalized with dexamethasone (CS-DEX) on osteoclastic differentiation of Mφ using in-vitro coculture and in-vivo delayed replantation models. 2-dimensional PDLF-Mφ direct coculture showed more significant clastic activity than Mφ monoculture and Mφ grown in conditioned media of PDLF in inflammatory and resorptive stimuli, highlighting the juxtacrine effects (p<0.05). There was a temporal effect of TNF-α, IL-1β, MMP9, MMP2, and NFATc1, STAT6 in root resorption. 3-dimensional PDLF-Mφ tissue graft model consisted of a PDLF core surrounded by Mφ with a total diameter of 3.8mm ± 0.3 and a thickness range of 300-500μm providing a platform for juxtacrine and paracrine interactions. CSNP and CS-DEX treatment reduced osteoclastic differentiation of Mφ by downregulating CD80, NFATc1, STAT6 and MMP9 and upregulating IL10. CSNP showed a more significant effect on upregulating TGF-β1, periostin and OPG than CS-DEX (p<0.05). Root surface treatment with CSDEX/CSRB in delayed replantation showed a significant reduction in resorption, ankylosis, TRAP activity, osteocalcin expression, and increased periostin (p<0.05). CS-DEX/CSRB effectively reduced CD80, NFATc1 and STAT6. CSRB upregulated TGFβ1, periostin and downregulated osteocalcin and MMP9. In conclusion, 2D and 3D in-vitro experiments demonstrated the role of PDLF on clastic differentiation of Mφ with key mediators TNFα, STAT6, MMP9 and MMP2. Additionally, the animal study revealed that matrix stabilizing CSRB and sustained DEX-releasing CSDEX can be viable therapeutic options to hinder root resorption/ankylosis while enhancing healing outcomes in avulsed teeth.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.037
GPT teacher head0.459
Teacher spread0.422 · 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
Published2024
Admission routes1
Has abstractyes

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