Phage Display Technology for Identifying Specific Antigens on Brain Endothelial Cells
Bibliographic record
Abstract
The development of efficient ways to deliver large molecules such as peptides, proteins, and nucleic acids across the blood-brain barrier (BBB) is crucial to future therapeutic strategies for treatment of central nervous system (CNS) disorders. The principal approach to deliver macromolecules across the BBB is the development of chimeric peptides ( 1 ). Ligands to various receptors that undergo transcytosis across brain capillary endothelium and are essential for physiological transport of proteins, including transferrin, insulin growth factor, and low-density lipoprotein, into the brain are used as vectors to deliver drugs or therapeutic peptides chemically linked to the ligand ( 1 , 2 ). This process is known as receptor-mediated endocytosis/transcytosis. An anti-transferrin receptor antibody (OX-26), for example, has been used to deliver endorphin, vasoactive intestinal peptide, and brain-derived neurotrophic factor ( 1 ), as well as oligonucleotides and plasmid deoxyribonucleic acid (DNA) ( 2 ) into the brain parenchyma. Further development of this approach requires rapid discovery of other suitable receptors/antigens expressed on human BBB endothelium that undergo transcytosis upon ligand binding. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
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".