Supraparticle Assemblies of Magnetic Nanoparticles and Quantum Dots for Selective Cell Isolation and Counting on a Smartphone-Based Imaging Platform
Bibliographic record
Abstract
There are numerous diagnostic and therapeutic applications for the detection and enumeration of specific cell types. Flow cytometry is the gold standard technique for this purpose but is poorly suited to point-of-need assays. The ideal platform for these assays would combine the immunocytochemical capabilities of flow cytometry with low-cost, portable instrumentation, and a simple and rapid assay workflow. Here, we present a smartphone-based imaging platform (SIP) in tandem with magnetic-fluorescent suprananoparticle assemblies as a step toward these ideal criteria. The assemblies (MNP@QD) are magnetic iron oxide nanoparticles surrounded by a dense corona of many brightly luminescent semiconductor quantum dots (QDs), where both the assemblies and their immunoconjugates are prepared by self-assembly. As proof of concept, we show that the MNP@QD and SIP pairing is able to selectively isolate, fluorescently immunolabel, and count breast cancer cells that are positive for human epidermal growth factor receptor 2 (HER2). These results are an important foundation for future point-of-need diagnostics capable of multiplexed isolation, counting, and immunoprofiling of cells on a smartphone, enabled by the highly advantageous optical properties of QDs.
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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 | 0.001 |
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".