Transcriptional events in a clinical model of oral mucosal tissue injury and repair
Bibliographic record
Abstract
Tissue injury in the oral mucosa activates a cascade of transcriptional events important during the healing process that are not yet clearly defined. To characterize these events and identify potential gene targets for future studies, we used cDNA expression arrays in a clinical model of tissue injury. Mucosal biopsies were taken before third molar extraction, 2-4 hours postoperatively, or at 48 hours. Hybridization patterns were analyzed and validated using real-time polymerase chain reaction. Prior to extraction, the biopsied mucosal tissues were characterized by a panoply of genes that were constitutively expressed. After injury, analysis revealed differential expression of genes involved in transcription, inflammation, and remodeling. At 2-4 hours after injury, genes such as Fos, Jun, and early growth response protein were up-regulated, while genes responsible for intercellular adhesion were down-regulated. At 48 hours after injury, the gene profile had shifted toward tissue remodeling. Here we identify genes constitutively expressed in normal oral mucosa and transcriptional events following mucosal tissue injury, which may be useful in identifying new therapeutic targets.
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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) | 0.000 | 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 | 0.001 | 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".