The careful selection of zwitterionic nanoparticle coating results in rapid and efficient cell labeling for imaging‐based cell tracking
Bibliographic record
Abstract
Abstract The increased clinical application of cell‐based therapies has resulted in a parallel increase in the need for non‐invasive imaging‐based approaches for cell tracking, often through labeling with nanoparticles. An ideal nanoparticle for such applications must be biologically compatible as well as readily internalized by cells to ensure adequate and stable cell loading. Surface coatings have been used to make nanoparticle trackers suitable for these purposes, but those currently employed tend to have cytotoxic effects. Zwitterionic ligands are known to be biocompatible and antifouling; however, head‐to‐head evaluation of specific zwitterionic ligands for cell loading has not yet been explored. Magnetic particle imaging (MPI) detects superparamagnetic iron oxide nanoparticles (SPIONs) using time‐varying magnetic fields. Because MPI can produce high‐contrast, real‐time images with no tissue depth limitation, it is an ideal candidate for in vivo cell tracking. In this work, we have conjugated hard (permanently charged) and soft (pKa‐dependently charged) biomimetic zwitterionic ligands to SPIONs and characterized how these ligands changed SPION physicochemical properties. We have evaluated cellular uptake and subcellular localization between zwitterions, how the improvement in cell uptake generated stronger MPI signal for smaller numbers of cells, and how these cells can be tracked in an animal model with greater sensitivity for longer periods of time. Our best‐performing surface coating afforded high cell loading within 4 h, with full signal retention in vivo over 7 days.
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) | 0.001 | 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".