Cell-compatible, integrin-targeted cryptophane-129XeNMR biosensors
Bibliographic record
Abstract
Peptide-modified cryptophane enables sensitive detection ofproteinanalytes using hyperpolarized 129Xe NMR spectroscopy. Here we report improved targeting and delivery of cryptophane to cells expressing αvβ3 integrin receptor, which is overexpressed in many human cancers. Cryptophane was functionalized with cyclic RGDyKpeptide and Alexa Fluor 488dye, and cellular internalization was monitored by confocal laser scanning microscopy. Competitive blocking assays confirmed cryptophaneendocytosis through an αvβ3 integrin receptor-mediated pathway. The peptide–cryptophane conjugate was determined to be nontoxic in normal human lung fibroblasts by MTT assay at the micromolar cryptophane concentrations typically used for hyperpolarized 129Xe NMR biosensing experiments. Flow cytometry revealed 4-fold higher cellular internalization in cancer cells overexpressing the integrin receptor compared to normal cells. Nanomolar inhibitory concentrations (IC50 = 20–30 nM) were measured for cryptophane biosensors against vitronectin binding to αvβ3 integrin and fibrinogen binding to αIIbβ3 integrin. Functionalization of the conjugate with two propionic acidgroups improved water solubility for hyperpolarized 129Xe NMR spectroscopic studies, which revealed a single resonance at 67 ppm for the 129Xe-cryptophane–cyclic RGDyK biosensor. Introduction of αIIbβ3 integrin receptor in detergent solution generated a new “ bound” 129Xe biosensor peak that was shifted 4 ppm downfield from the “free” 129Xe biosensor.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".