Rabies Virus Targeting NIR-II Phototheranostics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Rabies is a viral disease with an almost 100% fatality rate, primarily transmitted through bites from infected animals, with a long incubation period and no effective clinical treatments to date. Herein, we developed the first fluorescent nanotheranostic probe in the second near-infrared (NIR) window capable of efficiently crossing the blood–brain barrier (BBB), precisely targeting rabies virus (RABV), and enabling safe photodynamic therapy (PDT). This probe is based on a novel NIR-II organic polyacetylene fluorophore, DK, which self-assembles via a click reaction with a nanoparticle carrier, N3-PEG2000-R, that we synthesized with a high biocompatibility and BBB permeability. The probe surface is further modified with an aptamer that specifically binds to RABV glycoprotein (RVG), resulting in our final nanotheranostic probe, DK@RA-PEG . Upon intravenous injection into mice, it effectively crosses the BBB, localizes to the infection site, and binds to the RVG, allowing for real-time NIR-II fluorescence imaging. Additionally, it efficiently converts light energy into chemical energy without generating thermal effects, ensuring safe and effective PDT. This advanced nanotheranostic probe integrates precise targeting, deep-tissue imaging, and safe therapy, making it a promising candidate for future clinical applications in rabies treatment.
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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 | 0.000 |
| 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".