Targeted MRI and optical molecular imaging using gadolinium loaded small unilamellar vesicles
Bibliographic record
Abstract
Noninvasive investigation of cellular and molecular processes becomes possible through the novel techniques, one of which is molecular imaging, where enhanced sensitivity is a key component for clinic translation of the technique. In this presentation, spontaneously forming, small unilamellar vesicles (ULVs) (30 nm in diameter) were used as a platform to build a bi-modal [i.e., optical and Magnetic Resonance Imaging (MRI)] targeted contrast agent for the molecular imaging of brain tumors. Small ULVs were loaded with a gadolinium (Gd) chelated lipid (Gd-DPTA-BOA), functionalized with targeting antibodies (anti-EGFR monoclonal and anti-IGFBP7 single domain), and incorporated a near infrared dye (Cy5.5). The resultant ULVs were characterized in vitro using small angle neutron scattering (SANS), in phantom MRI and dynamic light scattering (DLS). Targeted (with antibodies) and nontargeted-Gd loaded sULVs labeled with Cy5.5 were assessed in vivo in a mice brain tumor model using both optical imaging and MRI. The results demonstrated that a spontaneously forming, nanosized ULV loaded with a high payload of Gd can selectively target and image, using MR and optical imaging, brain tumor vessels when functionalized with antibodies. The unique features of these targeting ULVs make them promising molecular MRI contrast agents.
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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".