Perinatal Arterial Ischemic Stroke Is Associated to Materno-Fetal Immune Activation and Intracranial Arteritis
Bibliographic record
Abstract
The medium-size intra-cranial arteries arising from the carotid bifurcation are prone to perinatal arterial ischemic strokes (PAIS). PAIS’ physiopathology needs to be better understood to develop preventive and therapeutic interventions that are currently missing. We hypothesized that materno-fetal inflammation leads to a vasculitis affecting selectively the carotidian tree and promoting a focal thrombosis and subsequent stroke. Dams were injected with saline or lipopolysaccharide (LPS) from Escherichia coli. A prothrombotic stress was applied on LPS-exposed vs. saline (S)-exposed middle cerebral arteries (MCA). Immunolabeling detected the inflammatory markers of interest. In S-exposed newborn pups, a constitutive higher density of macrophages combined to higher expressions of tumor necrosis factor-α (TNF-α), and interleukin 1β (IL-1β) was observed within the wall of intra- vs. extra-cranial cervicocephalic arteries. LPS-induced maternal and placental inflammatory responses mediated by IL-1β, TNF-α and monocyte chemotactic protein 1 (MCP-1) were associated with: (i) increased density of pro-inflammatory macrophages (M1 phenotype); and (ii) pro-inflammatory orientation of the IL-1 system (IL-1β/IL-1 receptor antagonist (IL-1Ra) ratio) within the wall of LPS-, vs. S-exposed, intra-cranial arteries susceptible to PAIS. LPS plus photothrombosis, but not sole photothrombosis, triggered ischemic strokes and subsequent motor impairments. Based on these preclinical results, the combination of pro-thrombotic stress and selective intra-cranial arteritis arising from end gestational maternal immune activation seem to play a role in the pathophysiology of human PAIS.
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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) | 0.000 | 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) | 0.003 | 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".