Mixed and Matched Metallo‐Nanotexaphyrin for Customizable Biomedical Imaging
Bibliographic record
Abstract
Abstract The discovery and synthesis of multifunctional organic building blocks for nanoparticles have remained challenging. Texaphyrin macrocycles are multifunctional, all‐organic compounds that possess versatile metal‐chelation capabilities and unique theranostics properties for biomedical applications. Unfortunately, there are significant difficulties associated with the synthesis of texaphyrin‐based subunits capable of forming nanoparticles. Herein, the detailed synthesis of a texaphyrin‐phospholipid building block is reported via a key 1,2‐dinitrophenyl‐phospholipid intermediate, along with stable chelation of two clinically relevant metal ions into texaphyrin‐lipid without compromising their self‐assembly into texaphyrin nanoparticles or nanotexaphyrin. A postinsertion methodology to quantitatively insert a variety of metal‐ions into preformed nanotexaphyrins is developed and employed to synthesize a structurally stable, mixed 111 indium‐manganese‐nanotexaphyrin for dual modal single‐photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI). In vivo dual SPECT/MRI imaging of 111 In‐Mn‐nanotexaphyrins in an orthotopic prostatic PC3 mouse model demonstrates complementary signal enhancement in the tumor with both modalities at 22 h post intravenous administration. This result highlights the utility of hybrid metallo‐nanotexaphyrins to achieve sensitive and accurate detection of tumors by accommodating multiple imaging modalities. The power of this mixed and matched metallo‐nanotexaphyrin strategy can be unleashed to allow a diverse range of multifunctional biomedical imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".