Multiplexed microfluidic blotting of proteins and nucleic acids by parallel, serpentine microchannels
Bibliographic record
Abstract
This work develops a high-throughput, high-efficiency and straightforward microfluidic blotting method for analyzing proteins and nucleic acids. Sample solutions containing antibodies (for protein detection) or hybridization probes (for nucleic acid detection) are introduced into the parallel, serpentine microchannels to specifically recognize the immobilized targets on the substrate, achieving the identification of multiple targets in multiple samples simultaneously. The loading control, molecular weight markers, and antigen/antibody titration are designed and integrated into the microfluidic chip, thus allowing for the quantification of proteins and nucleic acids. Importantly, we could easily distinguish the adjacent blotting bands inside parallel microchannels, which may be difficult to achieve in conventional blotting. The small dimensions of microfluidic channels also help to reduce the amount of probing molecules and to accelerate the biochemical reaction. Our microfluidic blotting could bypass the steps of blocking and washing, further reducing the operation time and complexity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".