Abstract 13601: Distinct Proteomic Profiles in Human Thoracic Aortic Aneurysm by Segment May Drive Prognostic Differences: Conventional & Machine Learning Analysis
Bibliographic record
Abstract
Background: Acute Type A Aortic Dissection (ATAD) is a surgical emergency with 18% mortality. Anatomic segment (root, ascending, arch, descending) impacts aneurysm natural history but mechanisms remain unclear. Aim: To compare proteomic profiles of human thoracic aortic segments that could account for distinct phenotypes and clinical outcomes; to analyze the largest cohort to date with enhanced depth of coverage. Methods: Aortic tissues were collected (N=148) from 82 unique individuals and analyzed using our customized proteomics protocol ( Figure 1A ). Conventional statistics, machine learning (t-distributed Stochastic Neighbour Embedding, t-SNE), and functional enrichment analyses were used to characterize significant phenotypic differences by segment. Differential protein expression was validated using immunofluorescence. Results: From all samples, 7251 proteins were identified (5660 quantified), exceeding literature (by 100s-1000s). Significant differences were greatest in comparisons of root, ascending, and descending aorta ( Figure 1B-C ). Root vs descending and ascending vs descending comparisons had clear separation in t-SNE analysis; root and ascending samples also clustered modestly ( Figure 1D ). MFAP4, a cellular-binding protein previously associated with descending thoracic dissection in Marfan patients, was significantly elevated in ascending aortic segments compared to root and descending. This was validated by immunofluorescence ( Figure 1E ). Conclusions: We extensively profiled thoracic aneurysm tissue using enhanced coverage, customized proteomics from the largest known cohort of human samples. Thoracic aneurysm phenotype differs by aortic segment as a function of intrinsic biochemical (proteomic) processes. Potential thoracic aneurysm biomarkers likely must account for aortic segment. Multiomic integrative analysis (DNA, RNA, post-translational data) are underway.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".