Present Status and Future Challenges of New Therapeutic Targets in Preclinical Models of Stroke in Aged Animals with/without Comorbidities
Bibliographic record
Abstract
The aging process, comorbidities, and age-associated diseases are closely dependent on each other. Cerebral ischemia impacts a wide range of systems in an age-dependent manner. However, the aging process has many facets which are influenced by the genetic background and epigenetic or environmental factors, which can explain why some people age differently than others. Therefore, there is an urgent need to identify age-related changes in body functions or structures that increase the risk for stroke and which are associated with a poor outcome. Multimodal imaging, electrophysiology, cell biology, proteomics, and transcriptomics, offer a useful approach to link structural and functional changes in the aging brain, with or without comorbidities, to post-stroke rehabilitation. This can help us to improve our knowledge about senescence firstly, and in this context, aids in elucidating the pathophysiology of age-related diseases that allows us to develop therapeutic strategies or prevent diseases. These processes, including potential therapeutical interventions, need to be studied first in relevant preclinical models using aged animals, with and without comorbidities. Therefore, preclinical research on ischemic stroke should consider age as the most important risk factor for cerebral ischemia. Furthermore, the identification of effective therapeutic strategies, corroborated with successful translational studies, will have a dramatic impact on the lives of millions of people with cerebrovascular diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".