Neutrophil Chemotaxis in Moving Gradients
Bibliographic record
Abstract
Abstract Neutrophils are known to rapidly migrate to sites of infection and injury, and track bacteria guided by spatiotemporally controlled chemokine gradients. Previous studies of neutrophil chemotaxis, using micropipettes and lately microfluidic devices, are limited to stationary sources and gradients. Thus, despite the well‐known ability of neutrophils to track bacteria in vitro, their response to defined moving gradients remains unknown. Here, a “floating” concentration gradient of interleukin‐8 is generated using a microfluidic quadrupole, and neutrophils cultured in a Petri dish are challenged with steep stationary and moving gradients. Individual neutrophils are tracked by live microscopy and their chemotaxis is analyzed. Interestingly, neutrophils are shown to enter the gradient region in a rolling‐like behavior, rapidly adhere to the bare dish, and polarize within 30 s, faster than what has been observed to date. Under stationary gradients, neutrophil migration length is maximal for cells located at the low end of the gradient, whereas under moving gradients, neutrophils migrate over longer distances and the length travelled is independent of their starting position. Furthermore, neutrophils are shown to initiate their migration at a maximum speed, slowing down when migrating deeper into the gradient and eventually stopping. This work lays the foundation for future chemotaxis assays with moving gradients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".