Influence of <i><scp>SIGLEC9</scp></i> polymorphisms on <scp>COPD</scp> phenotypes including exacerbation frequency
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The exacerbation-prone phenotype of COPD is particularly important, as exacerbations lead to poor quality of life and disease progression. We previously found that COPD patients who lack Siglec-14, a myeloid cell protein that recognizes bacteria and triggers inflammatory responses, are less prone to exacerbation. We hypothesized that the variations in other SIGLEC genes could also influence COPD exacerbation frequency, and investigated the association between SIGLEC9 polymorphisms and the exacerbation-prone phenotype of COPD. METHODS: We examined whether SIGLEC9 polymorphisms affect the frequency of COPD exacerbation in 135 subjects within our study population, and also analysed the correlation between the genotypes and the severity of airflow obstruction and emphysema in 362 Japanese smokers including 244 COPD patients. The association between these single nucleotide polymorphisms (SNPs) and COPD phenotypes were also assessed in a Caucasian population of ECLIPSE study. The effects of these coding SNPs (cSNPs) on Siglec-9 protein functions were analysed using in vitro assays. RESULTS: The G allele of rs2075803 and rs2075803 G/rs2258983 A(GA) haplotype in SIGLEC9 was associated with higher frequency of exacerbations and the extent of emphysema in COPD. These results did not replicate in the ECLIPSE study. A myeloid cell line expressing the Siglec-9 variant corresponding to GA haplotype produced more TNF-α than the one expressing the variant corresponding to the other major haplotype. CONCLUSION: The SIGLEC9 rs2075803 G/rs2258983 A haplotype, which corresponds to a Siglec-9 variant that is less effective at suppressing inflammatory response, may be a risk factor for the development of emphysema.
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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) | 0.001 | 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".