Selective Recognition of Rituximab-Functionalized Gold Nanoparticles by Lymphoma Cells Studied with 3D Imaging
Bibliographic record
Abstract
Several types of multivalent therapeutic antibody constructs have recently been described which exhibit an increased efficacy compared to free, bivalent antibody. For example, it has been shown that rituximab-coupled liposomes (devoid of encapsulated drug) show a stronger response than equal amounts of monomeric rituximab, supposedly due to the ability of the multivalent complex to hyper-cross-link its target in the plasma membrane. We sought to create a new type of multivalent antibody construct using gold nanoparticles, where rituximab is bound to the particle surface through a strong covalent bond. In the present study, rituximab-conjugated gold particles have been prepared with the aim of identifying suitable formulations for use in studies assessing the therapeutic potential of these novel formulations. Different types of rituximab-conjugated particles are prepared and characterized. The size of the particles, as well as the type of functionalization, is varied. In vitro studies with CD-20 positive human mantle cell lymphoma cells and CD-20 negative breast cancer cells combined with three-dimensional (3D) imaging allowed us to select an optimized rituximab−gold system for further studies. Selective recognition of these rituximab nanocarriers by lymphoma cells is demonstrated.
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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 | 0.000 |
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".