Pathophysiology of the Neurovascular Unit: Disease Cause or Consequence?
Bibliographic record
Abstract
Pathophysiology of the neurovascular unit (NVU) is commonly seen in neurological diseases. The typical features of NVU pathophysiology include tissue hypoxia, inflammatory and angiogenic activation, as well as initiation of complex molecular interactions between cellular (brain endothelial cells, astroctyes, pericytes, inflammatory cells, and neurons) and acellular (basal lamina) components of the NVU, jointly resulting in increased blood-brain barrier permeability, brain edema, neurovascular uncoupling, and neuronal dysfunction and damage. The evidence of important role of the brain vascular compartment in disease pathogenesis has elicited the debate whether the primary vascular events may be a cause of the neurological disease, as opposed to a mere participant recruited by a primary neuronal origin of pathology? Whereas some hereditary and acquired cerebral angiopathies could be considered a primary cause of neurological symptoms of the disease, the epidemiological studies showing a high degree of comorbidity among vascular disease and dementias, including Alzheimer's disease, as well as migraine and epilepsy, suggested that primary vascular pathology may be etiological factor causing neuronal dysfunction or degeneration in these diseases. This review focuses on recent hypotheses and evidence, suggesting that pathophysiology of the NVU may be initiating trigger for neuronal pathology and subsequent neurological manifestations of the disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".