Lanthanide-Containing Polymer Microspheres by Multiple-Stage Dispersion Polymerization for Highly Multiplexed Bioassays
Bibliographic record
Abstract
We describe the synthesis and characterization of metal-encoded polystyrene microspheres by multiple-stage dispersion polymerization with diameters on the order of 2 mum and a very narrow size distribution. Different lanthanides were loaded into these microspheres through the addition of a mixture of lanthanide salts (LnCl(3)) and excess acrylic acid (AA) or acetoacetylethyl methacrylate (AAEM) dissolved in ethanol to the reaction after about 10% conversion of styrene, that is, well after the particle nucleation stage was complete. Individual microspheres contain ca. 10(6)-10(8) chelated lanthanide ions, of either a single element or a mixture of elements. These microspheres were characterized one-by-one utilizing a novel mass cytometer with an inductively coupled plasma (ICP) ionization source and time-of-flight (TOF) mass spectrometry detection. Microspheres containing a range of different metals at different levels of concentration were synthesized to meet the requirements of binary encoding and enumeration encoding protocols. With four different metals at five levels of concentration, we could achieve a variability of 624, and the strategy we report should allow one to obtain much larger variability. To demonstrate the usefulness of element-encoded beads for highly multiplexed immunoassays, we carried out a proof-of-principle model bioassay involving conjugation of mouse IgG to the surface of La and Tm containing particles and its detection by an antimouse IgG bearing a metal-chelating polymer with Pr.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".