Progranulin protects against the tissue damage of acute ischaemic stroke: Figure 1
Bibliographic record
Abstract
This scientific commentary refers to ‘Multiple therapeutic effects of progranulin on experimental acute ischaemic stroke’, by Kanazawa et al. (doi:10.1093/brain/awv079). Stroke is one of the commonest causes of death, and survivors often suffer debilitating impairments of speech, memory, movement, swallowing and other neurological functions. About 80% of strokes are ischaemic, due to thrombosis or embolism. The resultant loss of oxygen to the affected tissue depletes cells of energy and initiates a series of biological events that include the release of toxic levels of glutamate, the production of free radicals, inflammation, and blood vessel disruption. The infarct, or the region of cell death at the ischaemic core, is surrounded by an area of damaged but still living brain tissue known as the penumbra. The injured but living cells of the penumbra can be restored to functionality if the infarct is prevented from expanding into this region by, for example, restoring blood flow through early administration of the thrombolytic protein tissue plasminogen activator (tPA). Very few other drugs have proven beneficial in limiting stroke-related damage in the human brain (Donnan et al. , 2008). The penumbra has been described as a transition zone where processes of cell death and injury radiate outwards from the infarct to confront counteracting mechanisms of repair that are mobilized by the brain to protect the damaged tissue (Lo, 2008). In this issue of Brain , Kanazawa and colleagues provide important insights into how a secreted protein, progranulin, protects the brain from further damage after an ischaemic stroke and suggest that progranulin could be a valuable new weapon in the development of treatments that will protect the brain from stroke-related injury (Kanazawa et al. …
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.031 | 0.018 |
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".