Role of Hyperplasia of Gingival Lymphatics in Periodontal Inflammation
Bibliographic record
Abstract
Lymphatic vessels are important for maintenance of tissue fluid homeostasis and afferent antigen transport. In chronic inflammation, lymphangiogenesis takes place and is characterized by lymphatic endothelial cell proliferation and lymphatic hyperplasia. Vascular endothelial growth factor C (VEGFC) is the main known lymphangiogenic growth factor, and its expression is increased in periodontitis, a common chronic infectious disease that results in tissue destruction and alveolar bone loss. The role of lymphangiogenesis during development of periodontitis is unknown. Here, we test if transgenic overexpression of epithelial VEGFC in a murine model is followed by hyperplasia of lymphatic vessels in oral mucosa and if the lymphatic drainage capacity is altered. We also test if lymphatic hyperplasia protects against periodontal disease development. Transgenic keratin 14 (K14)-VEGFC mice had significant hyperplasia of lymphatics in oral mucosa, including gingiva, without changes in blood vessel vasculature. The basal lymph flow was normal but slightly lower than in wild-type mice when oral mucosa was challenged with lipopolysaccharide from Porphyromonas gingivalis. Under normal conditions, K14-VEGFC mice exhibited an increased number of neutrophils in gingiva, demonstrated enhanced phagocyte recruitment in the cervical lymph nodes, and had more alveolar bone when compared with their wild-type littermates. After induction of periodontitis, no strain differences were observed in the periodontal tissues with respect to granulocyte recruitment, bone resorption, angiogenesis, cytokines, and bone-related protein expressions or in draining lymph node immune cell proportions and vascularization. We conclude that overexpression of VEGFC results in hyperplastic lymphatics, which do not enhance lymphatic drainage capacity but facilitate phagocyte transport to draining lymph nodes. Hyperplasia of lymphatics does not protect against development of ligature-induced periodontitis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".