Ultrastructural analysis of the brain endothelium by electron tomography
Bibliographic record
Abstract
Abstract Transmission electron microscopy (TEM) is a powerful imaging technique, yielding ultrastructural investigation of organic and non-organic samples. Despite its ability to reach nanoscale resolutions, conventional TEM presents a major disadvantage by only acquiring two-dimensional snapshots, thus hindering our volumetric understanding of samples. Electron tomography (ET) overcomes this limitation by offering detailed views of a thin specimen in 3 dimensions (3D). This technique is widely used in biology and has expanded our understanding of mitochondrial structure or synaptic organization. Proper brain functioning is highly reliable on a constant nutritional support through its microvasculature lined by endothelial cells. These unique cells form a selective and protective barrier, known as the blood-brain barrier (BBB), which limits the entrance of blood-borne molecules into the brain. In pathological conditions, the BBB is disrupted, resulting in neuronal damage. Understanding the fine changes underlying BBB disruption requires advanced imaging tools such as ET, to detect the finest changes in endothelial ultrastructure. This manuscript briefly explains how TEM and ET function, and then provides a detailed, didactic method for sample preparation, tomogram generation and 3D segmentation of brain endothelial cells using ET.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".