Changes in cellular signaling patterns before and after stroke in the middle cerebral arteries of stroke prone spontaneously hypertensive rats
Bibliographic record
Abstract
Abstract Background Hemorrhagic stroke is associated with loss of middle cerebral artery (MCA) autoregulation in the stroke-prone spontaneously hypertensive rat (SHRsp). The signaling mechanism associated with the functional loss has yet to be defined. We hypothesize that physiological alterations coincide with changes to cerebrovascular inflammatory and contractile signaling and altered calcium signaling. METHODS: SHRsp rats were fed a high salt (4% NaCl) diet and sacrificed at 9 weeks of age for pre-stroke and after evidence of stroke for post-stroke samples. The MCAs were isolated for measuring protein levels using immunofluorescence (IF) & western blot (WB) for inflammatory signaling and contractile proteins. Tissues surrounding the MCA were analyzed for neuro-inflammation, neuronal damage, total and activated inflammatory proteins (ERK1/2 and p38MAPK), cerebrovascular contraction (PKC and MLC), and transient receptor potential V4 (TRPV4) expression. RESULTS: Our data show increase in activated inflammatory proteins after stroke with an associated decrease in expression of activated contractile proteins and TRPV4 channel expression compared to pre-stroke MCA. The post-stroke samples also show significant increase in neuro-inflammation and neuronal damage compared to pre-stroke samples. CONCLUSION An increase in activated/total (p38 MAPK &ERK1/2) is accompanied by a decrease in activated/total PKC & TRPV4 channel expression in post-stroke SHRsps. The decrease in vessel structural integrity and altered vascular tone of the MCAs may affect its ability to contract in response to pressure. Significant neuro-inflammation and neuronal damage in the brain tissues surrounding the MCA in post-stroke samples suggest MCA dysfunction is accompanied with neuronal and neural damage during stroke.
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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) | 0.000 | 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) | 0.002 | 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".