Engineering of fluorescent or photoactive Trojan probes for detection and eradication of β-Amyloids
Bibliographic record
Abstract
Trojan horse technology institutes a potentially promising strategy to bring together a diagnostic or cell-based drug design and a delivery platform. It provides the opportunity to re-engineer a novel multimodal, neurovascular detection probe, or medicine to fuse with blood-brain barrier (BBB) molecular Trojan horse. In Alzheimer's disease (AD) this could allow the targeted delivery of detection or therapeutic probes across the BBB to the sites of plaques and tangles development to image or decrease amyloid load, enhance perivascular Aβ clearance, and improve cerebral blood flow, owing principally to the significantly improved cerebral permeation. A Trojan horse can also be equipped with photosensitizers, nanoparticles, quantum dots, or fluorescent molecules to function as multiple targeting theranostic compounds that could be activated following changes in disease-specific processes of the diseased tissue such as pH and protease activity, or exogenous stimuli such as, light. This concept review theorizes the use of receptor-mediated transport-based platforms to transform such novel ideas to engineer systemic and smart Trojan detection or therapeutic probes to advance the neurodegenerative field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".