Bibliographic record
Abstract
Front cover: Guest Editor: Lisa M. Münter McGill University, Montreal, QC, Canada. E-mail: lisa.munter@mcgill.ca Imaging the Microvascular Network of the Cortical Mantle. This review discusses how small cerebral venules could be a locus of occlusion during vascular dementia, contributing to cerebral microinfarcts, and potentially adding to brain dysfunction during cognitive impairment. Rodent models have provided clues to the consequence of occluding single penetrating venules in cortex. The image on the left is an extracted rat brain after transcardially perfusing a fluorescent gel. It shows both arteriole and venous networks on the dorsal surface of the brain. The image in the middle was captured in vivo through a cranial window using two-photon microscopy. It shows pseudocolored arterioles (red) and venules (blue) on the pial surface of the rat somatosensory cortex. The image on the right shows a magnified view of in vivo two-photon imaging data. Note how the pial arterioles and venules often branch and then end. These branch endings are points of penetrating into the cortical mantle to feed the underlying capillaries of the parechyma (white). Preclinical studies have shown that the obstruction of either penetrating arteriole or penetrating venule leads to cortical microinfarction. Image Source: Pictures taken by the authors. Read the full article ‘Does the pathology of small venules contribute to brain microinfarcts and dementia?’ by D. A. Hartmann, H. I. Hyacinth, F-F. Liao, A. Y. Shih (J. Neurochem. 2018, vol. 144(5), pp. 517–526) on doi: 10.1111/jnc.14228
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.791 | 0.723 |
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".