3D printing of beta-amyloid protein deposits along capillary walls
Bibliographic record
Abstract
Abnormally high levels of neocortical beta-amyloid protein (A+) reflect Alzheimer’s disease (AD) pathology in persons with clinical evidence of dementia or mild cognitive impairment (MCI). The abnormal aggregation of beta-amyloid protein in the brain neuropil may lead to either diffuse plaques and/or concentrated neuritic plaques, with the latter form of deposits often present in the vicinity of the cerebral microvasculature. The A protein, with its crystalline molecular structure, infiltrates the vessel walls and compromises the blood-brain barrier (BBB). The physical basis for this was elegantly shown by Meyer and colleagues, in a 2008 publication showing two-dimensional (2D) confocal laser scanning microscopic imaging of vascular A protein deposits in APP23 transgenic mice. These investigators showed accumulations of “tuft-life structures”, also referred to as “pompons” with protruding spikes on – and embedded within – microvessels. These pompons of beta-amyloid protein “consist of fibrillar structures, [and] can encircle and constrict capillaries, and are often associated with distortion of capillaries”.We were struck by both the heuristic value of the imaging of Meyer and colleagues, in explaining the root cause of both the amyloid-related alterations in the vascular bed, and depicting minute protein deposits that are both geometrically complex, seemingly delicate and fragile, and yet tenacious in their embedding within small vessel walls. We re-created these images with standard 3D printing technology (extruded plastic) for both educational/teaching and artistic purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".