Performances of a Pristine Graphene–Microbubble Hybrid Construct as Dual Imaging Contrast Agent and Assessment of Its Biodistribution by Photoacoustic Imaging
Bibliographic record
Abstract
Abstract Coupling near‐infrared (NIR) nanoscale absorbing materials with microbubbles (MBs) can generate a multifunctional dual imaging contrast agent. A new approach is presented for a hybrid photoacoustic/ultrasound contrast enhancer where pristine graphene is stably tethered to poly(vinyl alcohol) (PVA)‐based MBs. The main advantages of this approach are i) the preservation of optical and mechanical properties of intact graphene for an efficient photoacoustic (PA) enhancement and ii) the echogenicity and biocompatibility due to the robust anchoring of graphene to the bioinert PVA shell. PVA MBs provide ideal platforms for drug loading and ligand tethering for specific tumor targeting. One of the crucial goals toward this direction is optimizing this system in terms of balance between favorable acoustic/photoacoustic properties, immune shielding, and cytotoxicity. Such a combination strongly depends on the bridging moieties between graphene and the microbubble surface and can be easily tuned by PEGylation. The optimized graphene PVA MBs as contrast agent provide an efficient enhancement in vivo both in ultrasound and photoacoustic modes. The spectrally separable absorbance profile allows to a first demonstration of performing real‐time in vivo multiplexed photoacoustic imaging of graphene PVA MBs, and assessment of their full body biodistribution using a Vevo LAZR‐X photoacoustic imaging system.
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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".