Neutrophil Targeting Platform Reduces Neutrophil Extracellular Traps for Improved Traumatic Brain Injury and Stroke Theranostics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traumatic brain injuries (TBI) and stroke are major causes of morbidity and mortality in both developing and developed countries. The complex and heterogeneous pathophysiology of TBI and cerebral ischemia-reperfusion injury (CIRI), in addition to the blood-brain barrier (BBB) resistance, is a major barrier to the advancement of diagnostics and therapeutics. Clinical data showed that the severity of TBI and stroke is positively correlated with the number of neutrophils in peripheral blood and brain injury sites. Furthermore, neutrophil extracellular traps (NETs) released by neutrophils correlate with worse TBI and stroke outcomes by impairing revascularization and vascular remodeling. Therefore, targeting neutrophils to deliver NETs inhibitors to brain injury sites and reduce the formation of NETs can be an optimal strategy for TBI and stroke therapy. Herein, the study designs and synthesizes a reactive oxygen species (ROS)-responsive neutrophil-targeting delivery system loaded with peptidyl arginine deiminase 4 (PAD4) inhibitor, GSK484, to prevent the formation of NETs in brain injury sites, which significantly inhibited neuroinflammation and improved neurological deficits, and improved the survival rate of TBI and CIRI. This strategy may provide a groundwork for the development of targeted theranostics of TBI and stroke.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it