<scp>RNA</scp> Sequencing and Pathways Analyses of Middle Ear Epithelia From Patients With Otitis Media
Bibliographic record
Abstract
OBJECTIVES: Otitis media (OM) is the most common pediatric diagnosis in the United States. However, our understanding of the molecular pathogenesis of OM remains relatively poor. Investigation of molecular pathways involved in OM may improve the understanding of this disease process and elucidate novel therapeutic targets. In this study, RNA sequencing (RNA-Seq) was used to discern cellular changes associated with OME compared to healthy middle ear epithelium (MEE). STUDY DESIGN: Ex vivo case-control translational. METHODS: Middle ear epithelia was collected from five pediatric patients diagnosed with OME undergoing tympanostomy tube placement and five otherwise healthy pediatric patients undergoing cochlear implantation. Specimens underwent RNA-Seq and pathways analyses. RESULTS: A total of 1,292 genes exhibited differential expression in MEE from OME patients compared to controls including genes involved in inflammation, immune response to bacterial OM pathogens, mucociliary clearance, regulation of proliferation and transformation, and auditory cell differentiation. Top networks identified in OME were organismal injury and abnormalities, cell morphology, and auditory disease. Top Ingenuity canonical pathways identified were axonal guidance signaling, which contains genes associated with auditory development and disease and nicotine degradation II and III pathways. Associated upstream regulators included β-estradiol, dexamethasone, and G-protein-coupled estrogen receptor-1 (GPER1), which are associated with otoprotection or inflammation during insult. CONCLUSIONS: RNA-Seq demonstrates differential gene expression in MEE from patients with OME compared to healthy controls with important implications for infection susceptibility, hearing loss, and a role for tobacco exposure in the development and/or severity of OME in pediatric patients. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:2590-2597, 2021.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".