c‐MET pathway involvement in chronic rhinosinusitis: A genetic association analysis
Bibliographic record
Abstract
OBJECTIVE: The c-MET receptor and its ligand hepatocyte growth factor (HGF) has been shown to be overexpressed in tissue from chronic rhinosinusitis (CRS) patients with nasal polyps compared with that from controls. We assessed the genetic association of polymorphisms in the met proto-oncogene (MET) gene with CRS. STUDY DESIGN: Case-control genetic association study. SETTING: Tertiary-care university hospital. SUBJECTS AND METHODS: A total of 206 unrelated Canadian patients with CRS and 196 control subjects were enrolled. Subjects were genotyped for 33 polymorphisms in the MET gene. RESULTS: The allelic association analysis showed eight single nucleotide polymorphisms in the MET gene (rs38850, rs38855, rs38857, rs2237717, rs2402118, rs193688, rs1621, rs42336) with a statistically significant association with CRS. The rs38850 T allele showed the strongest association and the highest risk for CRS (P = 0.004; odds ratio 1.65, 95% confidence interval 1.18-2.32); the association did not reach statistical significance after adjustment for genomic control (P = 0.06). The haplotype TGG constructed of markers rs38850, rs38855, and rs38857 represented a risk haplotype, resulting in a P value of 0.003 that remained significant after correction for multiple testing (P = 0.018). CONCLUSION: These data suggest that polymorphisms in the MET gene may play a role in the susceptibility to develop CRS. Study findings apply to patients with severe CRS unresponsive to surgery.
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 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 | 0.001 |
| 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) | 0.003 | 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".