NIH workshop report on the trans-agency blood–brain interface workshop 2016: exploring key challenges and opportunities associated with the blood, brain and their interface
Bibliographic record
Abstract
A trans-agency workshop on the blood-brain interface (BBI), sponsored by the National Heart, Lung and Blood Institute, the National Cancer Institute and the Combat Casualty Care Research Program at the Department of Defense, was conducted in Bethesda MD on June 7-8, 2016. The workshop was structured into four sessions: (1) blood sciences; (2) exosome therapeutics; (3) next generation in vitro blood-brain barrier (BBB) models; and (4) BBB delivery and targeting. The first day of the workshop focused on the physiology of the blood and neuro-vascular unit, blood or biofluid-based molecular markers, extracellular vesicles associated with brain injury, and how these entities can be employed to better evaluate injury states and/or deliver therapeutics. The second day of the workshop focused on technical advances in in vitro models, BBB manipulations and nanoparticle-based drug carrier designs, with the goal of improving drug delivery to the central nervous system. The presentations and discussions underscored the role of the BBI in brain injury, as well as the role of the BBB as both a limiting factor and a potential conduit for drug delivery to the brain. At the conclusion of the meeting, the participants discussed challenges and opportunities confronting BBI translational researchers. In particular, the participants recommended using BBI translational research to stimulate advances in diagnostics, as well as targeted delivery approaches for detection and therapy of both brain injury and disease.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.021 |
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".