Characterization of a mouse model to study the relationship between apical periodontitis and atherosclerosis
Bibliographic record
Abstract
AIM: First, to determine the feasibility of using the low-density lipoprotein receptor knockout (LDLR KO) mouse model to study apical periodontitis (AP). Secondly, to investigate the causal relationship between AP and atherosclerosis. It was hypothesized that it would be feasible to induce AP and atherosclerosis in LDLR KO mice and find a difference in atherosclerosis between AP and Sham groups. METHODOLOGY: Using a published methodology, AP was induced in LDLR KO mice by exposing the dental pulp of the four first molars (Tx). Shams received only anaesthesia. Mice were fed a high fat, Western-type diet (WTD), to induce atherosclerosis. At 16 weeks, mice were euthanized and aortas collected to measure atherosclerosis lesion burden (oil red O staining). Periapical lesions were validated using micro-CT and histology. Systemic inflammation was measured using a cytokine array. RESULTS: Both groups developed a similar degree of atherosclerosis (mean lesion area 7.46 ± 0.44% in the Tx group compared with 7.65 ± 0.46%, in the Sham group, P = 0.77), and a similar degree of inflammation. Periapical lesions (PALs) in all four molars were only identified in a small subset of Tx mice. CONCLUSIONS: A novel mouse model, which combines AP and CVD, was created. This model allows investigation of the relationship between the two diseases, whilst avoiding other potential common confounders. Although no difference in the degree of atherosclerosis was found between the groups, more studies in which the number of periapical lesions, changes in systemic inflammation and the degree of atherosclerosis are correlated are necessary to ultimately determine the impact of AP on CVD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".