Rapid Multiplexed Flow Cytometric Assay for Botulinum Neurotoxin Detection Using an Automated Fluidic Microbead-Trapping Flow Cell for Enhanced Sensitivity
Bibliographic record
Abstract
A bead-based sandwich immunoassay for botulinum neurotoxin serotype A (BoNT/A) has been developed and demonstrated using a recombinant 50 kDa fragment (BoNT/A-HC-fragment) of the BoNT/A heavy chain (BoNT/A-HC) as a structurally valid simulant. Three different anti-BoNT/A antibodies were attached to three different fluorescent dye encoded flow cytometry beads for multiplexing. The assay was conducted in two formats: a manual microcentrifuge tube format and an automated fluidic system format. Flow cytometry detection was used for both formats. The fluidic system used a novel microbead-trapping flow cell to capture antibody-coupled beads with subsequent sequential perfusion of sample, wash, dye-labeled reporter antibody, and final wash solutions. After the reaction period, the beads were collected for analysis by flow cytometry. Sandwich assays performed on the fluidic system gave median fluorescence intensity signals on the flow cytometer that were 2-4 times higher than assays performed manually in the same amount of time. Limits of detection were estimated at 1 pM (approximately 50 pg/mL for BoNT/A-HC-fragment) for the 15 min fluidic assay in buffer.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".