Transcriptomic Features of Recurrence Rates in Idiopathic Subglottic Stenosis
Bibliographic record
Abstract
OBJECTIVE: Idiopathic subglottic stenosis (iSGS) is a rare disease characterized by narrowing of the upper airway and affects near-exclusively females. Patients often experience recurrent disease and require repeated surgical dilations. The pathophysiology underlying the broad spectrum of disease severity within iSGS remains unknown. In the current study, we sought to identify transcriptomic differences between iSGS patients with markedly different recurrence rates. METHODS: Prospectively collected clinical and bulk RNA sequencing data from subglottic tissues of 56 female iSGS patients with 1-4 years of follow-up were analyzed. DESeq2 was used to perform differential expression analysis, comparing samples from the highest (1.19-1.87 dilations/year) versus the lowest (0.30-0.65 dilations/year) quartile of surgical dilation rate (i.e., high vs. low recurrence groups). RESULTS: fold change > |1|). Pathway enrichment analyses showed that the high recurrence group had significantly increased expression of genes involved in adaptive immune responses (e.g., immunoglobulin subunit genes) and extracellular matrix organization (e.g., COMP, NID2) (adjusted p < 0.1). In contrast, the low recurrence group had significantly increased expression of genes involved in cilia structure and function (e.g., CFAP43, DNAI2) (adjusted p < 0.1), suggesting a relatively increased abundance of respiratory cilia. CONCLUSION: Transcriptomic profiling suggests that lower recurrence rates in iSGS are associated with retention of respiratory cilia, while adaptive immune responses and increased extracellular matrix deposition are present in those with higher recurrence rates. These results hold promise for the development of prognostic markers and identification of therapeutic targets for iSGS.
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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 | 0.001 | 0.001 |
| 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".