Mapping Semaphorins and Netrins in the Pathogenesis of Human Thoracic Aortic Aneurysms
Bibliographic record
Abstract
Thoracic aortic aneurysm (TAA) is a complex life-threatening disease characterized by extensive extracellular matrix (ECM) fragmentation and persistent inflammation, culminating in a weakened aorta. Although evidence suggests defective canonical signaling pathways in TAA, the full spectrum of mechanisms contributing to TAA is poorly understood, therefore limiting the scope of drug-based treatment. Here, we used a sensitive RNA sequencing approach to profile the transcriptomic atlas of human TAA. Pathway analysis revealed upregulation of key matrix-degrading enzymes and inflammation coincident with the axonal guidance pathway. We uncovered their novel association with TAA and focused on the expression of Semaphorins and Netrins. Comprehensive analysis of this pathway showed that several members were differentially expressed in TAA compared to controls. Immunohistochemistry revealed that Semaphorin4D and its receptor PlexinB1, similar to Netrin-1 proteins were highly expressed in damaged areas of TAA tissues but faintly detected in the vessel wall of non-diseased sections. It should be considered that the current study is limited by its sample size and the use of internal thoracic artery as control for TAA for the sequencing dataset. Our data determines important neuronal regulators of vascular inflammatory events and suggest Netrins and Semaphorins as potential key contributors of ECM degradation in TAA.
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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 | 0.001 | 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) | 0.000 | 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".