An All-on-chip Method for Rapid Neutrophil Chemotaxis Analysis Directly from a Drop of Blood
Bibliographic record
Abstract
Neutrophil migration and chemotaxis are critical for our body's immune system. Microfluidic devices are increasingly used for investigating neutrophil migration and chemotaxis owing to their advantages in real-time visualization, precise control of chemical concentration gradient generation, and reduced reagent and sample consumption. Recently, a growing effort has been made by the microfluidic researchers toward developing integrated and easily operated microfluidic chemotaxis analysis systems, directly from whole blood. In this direction, the first all-on-chip method was developed for integrating the magnetic negative purification of neutrophils and the chemotaxis assay from small blood volume samples. This new method permits a rapid sample-to-result neutrophil chemotaxis test in 25 min. In this paper, we provide detailed construction, operation and data analysis method for this all-on-chip chemotaxis assay with a discussion on troubleshooting strategies, limitations and future directions. Representative results of the neutrophil chemotaxis assay testing a defined chemoattractant, N-Formyl-Met-Leu-Phe (fMLP), and sputum from a chronic obstructive pulmonary disease (COPD) patient, using this all-on-chip method are shown. This method is applicable to many cell migration-related investigations and clinical applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".