Iodinated gadolinium-gold nanomaterial as a multimodal contrast agent for cartilage tissue imaging
Bibliographic record
Abstract
Cartilage damage, a common cause of osteoarthritis, requires medical imaging for accurate diagnosis of pathological changes. However, current instruments can acquire limited imaging information due to sensitivity and resolution issues. Therefore, multimodal imaging is considered an alternative strategy to provide valuable images and analyzes from different perspectives. Among all biomaterials, gold nanomaterials not only exhibit outstanding benefits as drug carriers, in vitro diagnostics, and radiosensitizers, but are also widely used as contrast agents, particularly for tumors. However, their potential for imaging cartilage damage is rarely discussed. In this study, we developed a versatile iodinated gadolinium-gold nanomaterial, AuNC@BSA-Gd-I, and its radiolabeled derivative, AuNC@BSA-Gd-131I, for cartilage detection. With its small size, negative charge, and multimodal capacities, the probe can penetrate damaged cartilage and be detected or visualized by computed tomography, MRI, IVIS, and gamma counter. Additionally, the multimodal imaging potential of AuNC@BSA-Gd-I was compared to current multifunctional gold nanomaterials containing similar components, including anionic AuNC@BSA, AuNC@BSA-I, and AuNC@BSA-Gd as well as cationic AuNC@CBSA. Due to their high atomic numbers and fluorescent emission, AuNC@BSA nanomaterials could provide fundamental multifunctionality for imaging. By further modifying AuNC@BSA with additional imaging materials, their application could be extended to various types of medical imaging instruments. Nonetheless, our findings showed that each of the current nanomaterials exhibited excellent abilities for imaging cartilage with their predominant imaging modalities, but their versatility was not comparable to that of AuNC@BSA-Gd-I. Thus, AuNC@BSA-Gd-I could be served as a valuable tool in multimodal imaging strategies for cartilage assessment.
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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 | 0.001 | 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".