Impact of pre-existing neuroendocrine tumors on brain tissue compliance following intracerebral hemorrhage in old spontaneously hypertensive rats
Bibliographic record
Abstract
Objective Conditions associated with mass effect, such as intracranial tumors and hemorrhagic stroke (intracerebral hemorrhage; ICH), disrupt intracranial pressure (ICP) regulation by exhausting the brain’s compliance reserves. Displacement of cerebrospinal fluid, blood, and brain tissue (“tissue compliance”) can buffer ICP elevations, but these reserves are limited. Brain tumor patients face elevated ICH risk, yet how pre-existing mass effect influences acute intracranial compliance remains unclear. This study examined how chronic intracranial mass effect alters tissue compliance following acute ICH. Methods In an aged spontaneously hypertensive rat (SHR) cohort, a high incidence of spontaneous intracranial tumors were discovered. This prompted an exploratory analysis in 18 SHRs (n = 6/group: ICH-24 h, ICH-72 h, sham) to evaluate the impact of chronic mass effect on post-ICH tissue compliance. Following collagenase striatal hemorrhage, brains were collected for macroscopic and microscopic volumetric morphological analysis. Results Tissue compliance persisted in tumor-bearing animals, reflected by reduced contralateral hemisphere volume (∼6% at 24; ∼14 % at 72 h) and neuronal soma shrinkage in hippocampal and cortical regions. Despite smaller hematoma volumes, rats with tumors exhibited nearly twofold greater tissue compliance responses than non-tumor animals. Conclusion Pre-existing mass effect may modify the recruitment of intracranial compliance reserves following stroke in aged animals.
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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 | 0.000 | 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) | 0.001 | 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".