Cadherin peptide‐induced enhancement of blood brain barrier (BBB) permeability
Bibliographic record
Abstract
Objective To examine the effects of two cadherin peptides, a short linear peptide containing His‐Ala‐Val (HAV) sequence and a cyclic peptide containing the Ala‐Asp‐Thr (cADT) sequence, on BBB integrity. Methods The effects of the HAV and cADT peptides on BBB permeability were examined in Balb/c mice. Blood‐brain barrier permeability was assessed with gadolinium contrast agent (Gad; a small hydrophilic permeability marker), IRdye 800cw PEG (a large hydrophilic permeability marker), and Rhodamine 800 (a P‐gp substrate) under control conditions and following exposure to HAV (1.0–32 mM/kg) or cADT peptides (1mM/kg) using magnetic resonance imaging (MRI) and near infrared fluorescence imaging. Results Mice treated with HAV displayed a dose‐dependent increase in BBB permeability as assessed with Gad enhanced MRI with doses of 1.0 mM/kg having no effect and maximal increases (18‐fold) observed with 32 mM/kg. Increases in BBB permeability were also observed with IRdye 800cw PEG (3‐fold) and Rhodamine 800 (60% increase). The increase in permeability was transient (return to normal within 1 hr). Increases in BBB permeability were also observed with cADT, albeit at lower dose (1.0 mM/kg). Conclusions Cadherin peptides produced a rapid and reversible increase in BBB permeability and the combination of these peptides with therapeutic agents can enhance drug delivery to the brain. Funding: Supported by NSERC and NIH.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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 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".