Thrombin-responsive engineered nanoexcavator with full-thickness infiltration capability for pharmaceutical-free deep venous thrombosis theranostics
Bibliographic record
Abstract
Although nanotechnology has shown great promise for treating multiple vascular diseases in recent years, simultaneous noninvasive detection and efficient dissolution of deep venous thrombosis (DVT) still remains challenging. In particular, long blockage areas and large thrombus thicknesses in DVT cause enormous difficulties for site-specific deep-seated thrombus theranostics. Therefore, based on the unique components of DVT, the novel concept of a thrombin-responsive full-thickness infiltration nonpharmaceutical nanoplatform for DVT theranostics is proposed here. The penetration depth is innovatively enhanced with efficient targeting and accumulation in the whole thrombi. Herein, we report a thrombin-responsive phase-transition liposome incorporating a liquid perfluoropentane (PFP) core and modified with two binding peptides, activatable cell-penetrating peptide (ACPP) and fibrin-binding ligand (FTP), which contribute to efficient liposome targeting and accumulation within the thrombi. This targeted nanoplatform is constructed to dig out the thrombus with the assistance of low-intensity focused ultrasound (LIFU), performing the destructive function of an excavator via an acoustic droplet vaporization effect (acting as a "nanoexcavator" system), which can activate and vaporize into microbubbles to enhance LIFU efficacy. The resulting microbubbles enable real-time monitoring of the therapeutic process with ultrasound imaging and high performance photoacoustic imaging after loading DIR. This non-invasive nonpharmaceutical thrombolytic strategy is an improvement over existing clinical methods without systemic side effects.
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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) | 0.000 | 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".