Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
Bibliographic record
Abstract
The main advantages of studying the pathophysiology of periodontal disease utilizing murine models are the reduced cost of animals, array of genetically modified strains, the vast number of analyses that can be performed on harvested soft and hard tissues. However, many of these systems are subject to procedural criticisms. As an alternative, the ligature-induced model of periodontal disease, driven by the localized development and retention of a dysbiotic oral microbiome, can be employed, which is rapidly induced and relatively reliable. Unfortunately, the variants of ligature-induced murine periodontitis protocol are isolated to focal regions of the periodontium and subject to premature avulsion of the installed ligature. This minimizes the amount of tissue available for subsequent analyses and increases the number of animals required for study. This protocol describes the precise manipulations required to place extended molar ligatures with improved retention and usage of a novel rinse technique to recover oral neutrophils in mice with an alternative approach that mitigates the aforementioned technical challenges.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".