Pipetting-driven microfluidic immunohistochemistry to facilitate enhanced immunoreaction and effective use of antibodies
Bibliographic record
Abstract
Immunohistochemistry (IHC), which has been used to detect antigens in cells of a tissue section using an immunoreaction between an antibody and an antigen, is a practical tool for identifying the type and stage of diseases in cancer diagnosis and scientific research. However, conventional IHC requires long, laborious process times and high costs. Although microfluidic IHC platforms have been developed to overcome these limitations, the application of microfluidic IHC in real-world environments is still limited due to the additional equipment needed to operate the microfluidic systems. In addition, continuous flow in a microfluidic channel leads to a waste of unbound antibodies. In this study, we demonstrate a novel and easy-to-use microfluidic IHC platform operated only using a manual pipette that is commonly available in research laboratories or hospitals. No other device such as a pump or a controller is required to operate our system. Bidirectional flows of the antibody solution in a microfluidic device are induced by repetitive manual pipetting which facilitates the enhanced antigen-antibody reaction and enables the effective use of a limited amount of antibody. When breast cancer cell and tissue sections are reacted with antibodies using our platform, pipetting for less than 2 min is sufficient to obtain immunostaining results without damaging the sample. The staining intensity by our method is similar to that of the sample stained for 1 h by a conventional batch process. We believe that this pipetting-based approach to the operation of a microfluidic system allows end users to use microfluidic IHC more conveniently and easily in real-world environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".