Spongiotic gingival hyperplasia: identifying new mechanisms and a survey of clinical approach
Bibliographic record
Abstract
OBJECTIVE: Spongiotic gingival hyperplasia (SGH) or localized juvenile spongiotic gingival hyperplasia (LJSGH) is a rare, benign, soft tissue oral lesion that shares overlapping clinical characteristics with more common reactive gingival lesions found in adolescents. SGH is clinically unrelated to the accumulation of plaque, and treatment by periodontal debridement and improved oral hygiene measures are ineffective. Currently, the etiology and pathogenesis of SGH are unknown. The objectives of this study are: (1) to assess the ability of clinicians to recognize and manage SGH compared with other gingival lesions, and (2) to identify the underlying mechanisms of SGH pathogenesis using messenger RNA (mRNA) sequencing. STUDY DESIGN: Orthodontic clinicians were surveyed using 4 representative clinical images (SGH, pyogenic granuloma, plaque-induced gingivitis, and squamous cell carcinoma). RNA was extracted from 3 cases of SGH and 2 cases of gingival inflamed fibromas (control), followed by differential gene expression analyses. RESULTS: There is limited awareness among clinicians regarding SGH. Expression analyses revealed that SGH is characterized by overexpression of members of the IL-17 and TNF pathways, among others. Pathway analyses revealed upregulation of genes associated with angiogenesis, neutrophil activation, cell proliferation, and apoptosis, along with downregulation of pathways associated with keratinization. CONCLUSIONS: Increased education focused on oral pathology may be beneficial and bring more awareness to this unique entity. Genetic analyses suggest a reactive origin, with unique IL-17 and TNF activation and proliferative pathways driving SGH growth even in the absence of plaque, and further studies are needed to generate specific treatments targeting these immune pathways.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".