<sup>68</sup>Ga/DOTA- and<sup>64</sup>Cu/NOTA-Phthalocyanine Conjugates as Fluorescent/PET Bimodal Imaging Probes
Bibliographic record
Abstract
In this paper, we describe the synthesis and characterization of a series of new bimodal probes combining water-soluble sulfonated zinc phthalocyanine (ZnPc) as a fluorescence imaging unit and either (68)Ga/1,4,7,10-tetraazocyclododecane-N,N'N″,N'″-tetraacetic acid (DOTA) or (64)Cu/1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) for PET imaging. The two moieties were linked through aliphatic chains of different lengths to modulate amphiphilicity. Labeling of DOTA- or NOTA-ZnPc conjugates with (68)Ga (t1/2 = 68 min) and (64)Cu (t1/2 = 12.7 h) was performed at 100 °C for 15 min with >90% efficiency for all conjugates. In vitro plasma stability assays demonstrated high stability of the (64)Cu/NOTA-ZnPc conjugate, which remained intact over a 24 h time period, and reasonably high stability of the (68)Ga/DOTA-ZnPc conjugate, which released up to 7% of free (68)Ga over a 3 h period. Based on in vitro plasma stability results, we performed biodistribution studies on two (64)Cu-labeled derivatives, which allowed us to select a single candidate for preliminary in vivo experiments. Fluorescence and PET imaging confirmed the potential of these novel conjugates to act as bimodal probes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".