The spatial cerebral damage caused by larger infarct and β‐amyloid toxicity is driven by the anatomical/functional connectivity
Bibliographic record
Abstract
Large cerebral infarctions are major predictors of death and severe disability from stroke. Conversely, data concerning these types of infarctions and the affected adjacent brain circuits are scarce. It remains to be determined if the co-morbid concurrence of large infarct and β-amyloid (Aβ) toxicity can precipitate the early development of dementia. Here, we described a dose-dependent effect of a unilateral striatal injection of vasoconstrictive endothelin-1 (ET-1) along with Aβ toxicity on CNS pathogenesis; driven by the anatomical and functional networks within a brain circuit. After 21 days of treatment, a high dose (60 pmol) of ET-1 (E60) alone caused the greatest increase in neuroinflammation, mainly in the ipsilateral striatum and distant regions with synaptic links to the striatal lesion such as white matter (subcortical white matter, corpus callosum, internal capsule, anterior commissure), gray matter (globus pallidus, thalamus), and cortices (cingulate, motor, somatosensory, entorhinal). The combined E60 + Aβ treatment also extended perturbation in the contralateral hemisphere of these rats, such as increased deposition of amyloid precursor protein fragments associated with the appearance of degenerating cells and the leakage of laminin from the basement membrane across a compromised blood-brain barrier. However, the cerebral damage induced by the 6 pmol ET-1 (E6), Aβ and E6 + Aβ rats was not detrimental enough to injure the complete network. The appreciation of the causal interactions among distinct anatomical units in the brain after ischemia and Aβ toxicity will help in the design of effective and alternative therapeutics that may disassociate the synergistic or additive association between the infarcts and Aβ toxicity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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 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".