Nanomaterial‐Based Bioimaging Probes
Bibliographic record
Abstract
Non-invasive medical imaging techniques play an important role in the diagnosis of many diseases and in the assessment of therapeutic outcome. To visualize the underlying biological processes or conditions on a molecular level, exogenous bioimaging probes are used to provide a signal detectable with magnetic (MRI), nuclear (CT, SPECT, PET), or optical (luminescence imaging) methods. The introduction of bioimaging probes that are based on nanomaterials opened up new opportunities for drug targeting and molecular imaging. Of special interest are multifunctional bioimaging probes, which combine different imaging modalities or unite a diagnostic probe with a therapeutic functionality, thus forming a theranostic conjugate. Further benefits of nanoprobes comprise the possibility of incorporating a targeting vector or modifying the overall physicochemical properties such that the nanomaterial is transported to parts of the body that the unaltered material would not reach. This chapter provides an overview of the different approaches that are taken to develop nanomaterial-based imaging probes, discusses important design criteria, and points to future trends for nanoprobes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".