Volumetric Analysis of the Blood–Brain Barrier After Ischemic Stroke by Electron Tomography in Mice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Transmission electron microscopy (TEM) enables ultrastructural investigation of both organic and nonorganic samples. However, conventional TEM is limited by the acquisition of two-dimensional snapshots, restricting our volumetric understanding of complex ultrastructures. Electron tomography (ET) overcomes this limitation by offering detailed three-dimensional (3D) specimen representation. ET has been widely applied in biology; however, its use for blood-brain barrier (BBB) assessment has been overlooked. The BBB ensures proper brain function by limiting the entrance of blood-borne molecules into the brain and ensuring selective transport. The BBB is disrupted in several pathological conditions, resulting in neuronal damage. Understanding the fine changes underlying BBB disruption requires advanced imaging tools such as ET. METHODS: We developed a detailed room temperature electron tomography (RT-ET) method for sample preparation, tomogram generation, 3D segmentation, and applied this approach to assess ultrastructural changes in brain endothelial cells (ECs) after photothrombotic stroke in mice. RESULTS: Our findings identify altered transcytotic vesicle morphology, as well as remodeling of the endoplasmic reticulum, indicative of cellular stress and impaired vesicular trafficking. CONCLUSIONS: Our toolkit allows for reproducible, high-resolution analysis of brain microvascular pathology. This new RT-ET approach uncovers previously inaccessible ultrastructural alterations in ECs following ischemic stroke in mice, offering new insight into mechanisms of BBB disruption.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.005 |
| 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 it