Blood mRNA and miRNA transcriptome to predict chronic lung allograft dysfunction
Bibliographic record
Abstract
Chronic Lung Allograft Dysfunction (CLAD) manifests as Bronchiolitis Obliterans Syndrome (BOS) and the recently described Restrictive Allograft Syndrome (RAS). CLAD is unpredictable and irreversible. Thus predictive biomarkers of CLAD are needed to provide an early and personalized intervention. Our objective is to establish a predictive blood transcriptomic signature of CLAD. We hypothesized that gene and miRNA expression profiling of peripheral blood could uncover the early alterations of CLAD before the degradation of lung function. We selected 88 lung transplant recipients from the COLT (Cohort in Lung Transplantation) cohort. Patients were unequivocally phenotyped by an adjudication committee at 3 years post transplantation as stable (n=49), BOS (n=29) or RAS (n=10). Blood transcriptome (mRNA and microRNA) was investigated longitudinally at 6 months and 1 year post-transplantation, i.e. before the onset of CLAD. Integrated analysis of miRNA and mRNA expression was performed. Preliminary results show more than 500 differentially expressed genes (DEG) between Stable and BOS groups. Hierarchical clustering of DEG discriminates between stable and BOS patients with an accuracy approaching 80%. Gene ontology was conducted to categorize the function of the DEG. Analysis of KEGG pathways revealed several enrichment-related pathways including haematopoietic cell lineage or B cell receptor signaling pathway. As a conclusion, our work supports blood transcriptome analysis to predict and to explore the physiopathology of CLAD. Several biomarkers and pathways were identified. Investigations are ongoing to define the final gene-set predictor. Systems prediction of CLAD: http://www.sysclad.eu/
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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 | 0.000 |
| 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".