<sup>99m</sup>Tc-Functionalized Single-Walled Carbon Nanotubes for Bone Targeting
Bibliographic record
Abstract
Tailoring carbon nanotube surface chemistry is essential for biomedical applications. Bisphosphonate–carbon nanotube conjugates were prepared to target regions of active bone metabolism. The conjugates were synthesized using covalent functionalization ( G1 ) or latently reactive polymer–nanotube complexes ( G2 ), and it was found that the noncovalent G2 method afforded higher quality colloidal dispersions. The in vitro cytotoxicity tests against C2C12 cells were carried out and the results revealed low cytotoxicity and good biocompatibility profile for both bisphosphonate–carbon nanotube conjugates. The conjugates were radiolabeled with 99m Tc in high radiochemical yield (80–92%). In vitro binding studies to hydroxyapatite showed binding of 77 and 36% for G1 and G2, respectively, after 1 h incubation at room temperature. A biodistribution study of G1 and G2 in vivo in a Balb/c mouse model demonstrated rapid blood clearance after 1 h and superior bone localization of G2 conjugates as compared to G1 conjugates. Photoacoustic imaging of nanotube conjugates confirmed superior in vivo bone localization of G2 as compared to G1 . Together, biodistribution studies of radiolabeled complexes and photoacoustic imaging demonstrated that latently reactive polymer–nanotube complexes are a promising platform to modulate carbon nanotube surface chemistry for targeted diagnostic and drug delivery.
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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 | 0.001 |
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".