Immune system transcriptome in gingival tissues of young nonhuman primates
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Young/adolescent humans harbor many microorganisms associated with periodontal disease in adults and show substantial gingival inflammatory responses. However, younger individuals do not demonstrate the soft- and hard-tissue destruction that hallmark periodontitis. MATERIAL AND METHODS: This study evaluated responses to the oral microbial ecology in gingival tissues from clinically healthy young Macaca mulatta (< 3 years of age) compared with older animals (5-23 years of age). RNA was isolated from the tissues and analyzed for the transcriptome using the Rhesus Macaque GeneChip (Affymetrix). RESULTS: Global transcriptional profiling of four age groups revealed a subset of 159 genes that were differentially expressed across at least one of the age comparisons. Correlation metrics generated a relevance network abstraction of these genes. Partitioning of the relevance network revealed seven distinct communities comprising functionally related genes associated with host inflammatory and immune responses. A group of genes was identified that were selectively increased/decreased or positively/negatively correlated with gingival profiles in the animals. A principal components analysis created metagenes of expression profiles for classifying the 23 animals. CONCLUSION: The results provide novel system-level insights into gene-expression differences in gingival tissues from healthy young animals, weighted toward host responses associated with anti-inflammatory biomolecules or those linked with T-cell regulation of responses. The combination of the regulated microenvironment may help to explain the apparent 'resistance' of younger individuals to developing periodontal disease.
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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 | 0.000 |
| 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".