Technology-based approaches toward a better understanding of neuro-coagulation in brain homeostasis
Bibliographic record
Abstract
Blood coagulation factors can enter the brain under pathological conditions that affect the blood-brain interface. Besides their contribution to pathological brain states, such as neural hyperexcitability, neurodegeneration, and scar formation, coagulation factors have been linked to several physiological brain functions. It is for example well established that the coagulation factor thrombin modulates synaptic plasticity; it affects neural excitability and induces epileptic seizures via activation of protease-activated receptors in the brain. However, major limitations of current experimental and clinical approaches have prevented us from obtaining a profound mechanistic understanding of "neuro-coagulation" in health and disease. Here, we present how novel human relevant models, i.e., Organ-on-Chips equipped with advanced sensors, can help overcoming some of the limitations in the field, thus providing a perspective toward a better understanding of neuro-coagulation in brain homeostasis.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".