Molecular Engineering of Acoustic Protein Nanostructures
Bibliographic record
Abstract
Ultrasound is among the most widely used biomedical imaging modalities, but has limited ability to image specific molecular targets due to the lack of suitable nanoscale contrast agents. Gas vesicles-genetically encoded protein nanostructures isolated from buoyant photosynthetic microbes-have recently been identified as nanoscale reporters for ultrasound. Their unique physical properties give gas vesicles significant advantages over conventional microbubble contrast agents, including nanoscale dimensions and inherent physical stability. Furthermore, as a genetically encoded material, gas vesicles present the possibility that the nanoscale mechanical, acoustic, and targeting properties of an imaging agent can be engineered at the level of its constituent proteins. Here, we demonstrate that genetic engineering of gas vesicles results in nanostructures with new mechanical, acoustic, surface, and functional properties to enable harmonic, multiplexed, and multimodal ultrasound imaging as well as cell-specific molecular targeting. These results establish a biomolecular platform for the engineering of acoustic nanomaterials.
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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) | 0.001 | 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".