Artificial Blood Vessel Frameworks from 3D Printing-Based Super-Assembly as <i>In Vitro</i> Models for Early Diagnosis of Intracranial Aneurysms
Bibliographic record
Abstract
Intracranial aneurysm (IA) is a bulge from the weak area in the wall of cerebral blood vessels and can cause serious diseases, such as hemorrhagic stroke and other neurologic diseases. Experimental and computational results demonstrated that the different flow fields of blood had a great influence on the formation, growth, and rupture of IAs. Therefore, it is crucial to acquire flow field of blood for fully characterizing the hemodynamics. In this study, six transparent models of artificial blood vessels with different growth stages of IAs by 3D printing-based super-assembly technology were first fabricated. Epoxy-based resin was used to form a 3D pipeline structure, and it played an important role in restoring the appearance of IAs and comparing with the medical image. Phase contrast-magnetic resonance imaging (PC-MRI) and computational fluid dynamics (CFD) were used to assess flow fields of IA during growth. The internal flow and wall shear stress (WSS) of inner IAs showed a very low level in the cardiac cycle compared with normal blood vessels. The CFD and PC-MRI demonstrated that the internal flow of IA gradually interfered with intravascular flow because IAs formed, and this interference gradually reduced after a mid-term stage. Meanwhile, the growth and rupture points of side IAs mainly located in the efferent region of IAs may result from the blood flow becoming extremely slow in this area. This proposed 3D printing-based super-assembly technology reduced the replica size by at least 80% and provided a visual internal structure to obtain MR imaging data.
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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 | 0.001 |
| 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 | 0.001 | 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".