Association between methylation in nasal epithelial TSLP gene and chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
Abstract Background This study was performed to determine whether there was any association between abnormal DNA methylation of a thymic stromal lymphopoietin (TSLP) locus and pathogenesis of chronic rhinosinusitis (CRS). Methods A total of 48 CRS patients with nasal polyps (CRSwNP), 28 CRS patients without nasal polyps (CRSsNP) and 21 control subjects were enrolled into the study; and evaluated for serum total IgE level, olfactory score and nasal resistance. Samples were obtained from nasal polyps of CRSwNP patients, ethmoid mucosae of CRSsNP patients and inferior turbinate (IT) mucosa of control subjects during surgery, and used to isolate purified primary human nasal epithelial cells (HNECs). Genomic DNA was extracted from purified primary HNECs of each subject and DNA methylation ratios for a selected region of the TSLP gene were screened the using MassARRAY EpiTYPER. Results A total of 17 CpG units were analyzed; of which two CpG units (CpG3 and 22:23:24) had increased methylation ratios in the CRSwNP patients compared to the CRSsNP and control subjects after correction for false discovery rate (FDR) (Q < 0.1). The methylation ratios at both CpG3 and CpG22:23:24 units were positively correlated with olfactory score (r = 0.41, P = 0.0001; r = 0.25, P = 0.021) and unilateral nasal resistance at 75 Pa (r = 0.24, P = 0.04; r = 0.24, P = 0.036) and 150 Pa (r = 0.34, P = 0.004; r = 0.25, P = 0.031). Total nasal resistance at 75 Pa/150 Pa or serum total IgE levels were not correlated with the methylation ratios at either CpG unit. Conclusions Increased DNA methylation at the TSLP locus is likely to be associated with CRSwNP pathogenesis; however these findings need to be confirmed in larger multicentre group studies.
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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 | 0.001 |
| 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) | 0.002 | 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".