Microfluidic sieve and detector for rapid ultrasensitive assays with single-molecule sensitivity
Bibliographic record
Abstract
The advent of ultrasensitive assays enabled many new research and clinical applications, but the time-to-result is hours and has not significantly improved over time, limiting their potential. We introduce the microfluidic sieve and detector (MSD) that can sieve 200 microlitres in just one minute, and quantify captured analytes in four minutes, down to zeptomolar (10 -19 M) concentration. The MSD comprises half a million 8-μm-diameter pores that bind analytes upon Brownian motion-induced wall collision. Digital sandwich assays are completed by sequentially flowing the sample, reagents, partitioning the pores, and digitally revealing single ‘trapped’ analytes by enzymatic amplification. The MSD tests are specific and reproducible, exhibit a large dynamic range, and are easy-to-use, affordable, tuneable and versatile, enabling measurement of different proteins including influenza A nucleoprotein in clinical samples, and even nucleic acids while being both faster and more sensitive than PCR. As such, the MSD could open a new chapter for analysis and diagnostics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".