The lung proteome in HIV-associated obstructive lung disease
Bibliographic record
Abstract
Rationale Obstructive lung disease is increasingly common among persons living with HIV (PLWH). There are currently no validated biomarkers that identify individuals at risk of developing obstructive lung disease (OLD), and specific mechanisms contributing to HIV-associated OLD remain elusive, independent of smoking. We sought to identify biomarkers and biological pathways associated with OLD using a broad proteomic approach. Methods We performed tandem mass tagging and mass spectrometry (MS) analysis on bronchoalveolar lavage fluid samples from persons living with HIV with OLD (n=26) and without OLD (n=26). We combined untargeted MS with a targeted SomaScan aptamer-based approach. We used Pearson correlation tests to identify associations between each protein and lung function (forced expiratory volume in 1 s (FEV1) % pred). We adjusted for multiple comparisons using a false discovery rate adjustment. Significant proteins were entered into a pathway over-representation analysis. Protein-driven endotypes were constructed using K-means clustering. Measurements and main results We identified over 3800 proteins by MS and identified 254 proteins that correlated with FEV1% pred when we combined the MS and SomaScan proteomes when adjusting for smoking status. Pathway analysis revealed cell adhesion molecules as significant. Conclusions Protein expression differs in the lung of PLWH and decreased lung function (FEV1% pred). Pathway analysis reveals cell adhesion molecules having potentially important roles in this process.
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 | 0.000 |
| 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".