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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".