Obstructive Sleep Apnea and Cardiovascular Disease
Bibliographic record
Abstract
Rationale: Cytomegalovirus (CMV)-related morbidities remain one of the most common complications after lung transplantation and have been linked to allograft dysfunction, but the factors that predict high risk for CMV complications and effective immunity are incompletely understood.Objectives: To determine if short telomeres in idiopathic pulmonary fibrosis (IPF) lung transplant recipients (LTRs) predict the risk for CMV-specific T-cell immunity and viral control.Methods: We studied IPF-LTRs (n = 42) and age-matched non-IPF-LTRs (n = 42) and assessed CMV outcomes.We measured lymphocyte telomere length and DNA sequencing, and assessed CMV-specific T-cell immunity in LTRs at high risk for CMV events, using flow cytometry and fluorescence in situ hybridization. Measurements and Main Results:We identified a high prevalence of relapsing CMV viremia in IPF-LTRs compared with non-IPF-LTRs (69% vs. 31%; odds ratio, 4.98; 95% confidence interval, 1.95-12.50;P , 0.001).Within this subset, IPF-LTRs who had short telomeres had the highest risk of CMV complications (P , 0.01) including relapsing-viremia episodes, end-organ disease, and CMV resistance to therapy, as well as shorter time to viremia versus age-matched non-IPF control subjects (P , 0.001).The short telomere defect in IPF-LTRs was associated with significantly impaired CMV-specific proliferative responses, T-cell effector functions, and induction of the major type-1 transcription factor T-bet (T-box 21;TBX21).Conclusions: Because the short telomere defect has been linked to the pathogenesis of IPF in some cases, our data indicate that impaired CMV immunity may be a systemic manifestation of telomere-mediated disease in these patients.Identifying this high-risk subset of LTRs has implications for risk assessment, management, and potential strategies for averting post-transplant CMV morbidities.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.034 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".