Multifunctional Carbon-Based Nanomaterials: Applications in Biomolecular Imaging and Therapy
Bibliographic record
Abstract
Molecular imaging has been widely used not only as an important detection technology in the field of medical imaging for cancer diagnosis but also as a theranostic approach for cancer in recent years. Multifunctional carbon-based nanomaterials (MCBNs), characterized by unparalleled optical, electronic, and thermal properties, have attracted increasing interest and demonstrably hold the greatest promise in biomolecular imaging and therapy. As such, it should come as no surprise that MCBNs have already revealed a great deal of potential applications in biomedical areas, such as bioimaging, drug delivery, and tumor therapy. Carbon nanomaterials can be categorized as graphene, single-walled carbon nanotubes, mesoporous carbon, nanodiamonds, fullerenes, or carbon dots on the basis of their morphologies. In this article, reports of the use of MCBNs in various chemical conjugation/functionalization strategies, focusing on their applications in cancer molecular imaging and imaging-guided therapy, will be comprehensively summarized. MCBNs show the possibility to serve as optimal candidates for precise cancer biotheranostics.
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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 | 0.001 | 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) | 0.001 | 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".