Towards new functional nanostructures for medical imaging
Bibliographic record
Abstract
Nanostructures represent a promising new type of contrast agent for clinical medical imaging modalities, including magnetic resonance imaging, x-ray computed tomography, ultrasound, and nuclear imaging. Currently, most nanostructures are simple, single-purpose imaging agents based on spherical constructs (e.g., liposomes, micelles, nanoemulsions, macromolecules, dendrimers, and solid nanoparticle structures). In the next decade, new clinical imaging nanostructures will be designed as multi-functional constructs, to both amplify imaging signals at disease sites and deliver localized therapy. Proposals for nanostructures to fulfill these new functions will be outlined. New functional nanostructures are expected to develop in five main directions: Modular nanostructures with additive functionality; cooperative nanostructures with synergistic functionality; nanostructures activated by their in vivo environment; nanostructures activated by sources outside the patient; and novel, nonspherical nanostructures and components. The development and clinical translation of next-generation nanostructures will be facilitated by a combination of improved clarity of the in vivo imaging and biological challenges and the requirements to successfully overcome them; development of standardized characterization and validation systems tailored for the preclinical assessment of nanostructure agents; and development of streamlined commercialization strategies and pipelines tailored for nanostructure-based agents for their efficient translation to the clinic.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".