Biological and Behavioral Patterns of Post-Stroke Depression in Rats
Bibliographic record
Abstract
BACKGROUND: Post-stroke depression (PSD) is the most frequent psychiatric complication following ischemic stroke. It affects up to 60% of all patients and is associated with increased morbidity and mortality following ischemic stroke. The pathophysiology of PSD remains elusive and appears to be multifactorial, rather than "purely" biological or psychosocial in origin. Thus, valid animal models of PSD would contribute to the study of the etiology (and treatment) of this disorder. METHODS: The present study depicts a rat model for PSD, using middle cerebral artery occlusion (MCAO). The two-way shuttle avoidance task, Porsolt forced-swim test, and sucrose preference test were employed to assess any depression-like behavior. Localized brain expressions of brain-derived neurotrophic factor (BDNF) protein levels were evaluated to examine the possible involvement of the brain neuronal plasticity in the observed behavioral syndrome. The raw data were subjected to unsupervised fuzzy clustering (UFC) algorithms to assess the sensitivity of bio-behavioral measures indicative of depressive symptoms post MCAO. RESULTS: About 56% of the rats developed significant depressive-like behavioral disruptions as a result of MCAO compared with 4% in the sham-operated control rats. A pattern of a depressive-like behavioral response was common to all affected MCAO animals, characterized by significantly more escape failures and reduced number of total avoidance shuttles, a significant elevation in immobility duration, and reduced sucrose preference. Significant downregulations of BDNF protein levels in the hippocampal sub-regions, frontal cortex, and hypothalamus were observed in all affected MCAO animals. CONCLUSION: The UFC analysis supports the behavioral analysis and thus, lends validity to our results.
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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) | 0.001 | 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".