Microfluidic Wound-Healing Assay for ECM and Microenvironment Properties on Microglia BV2 Cells Migration
Bibliographic record
Abstract
Microglia cells, as the resident immune cells of the central nervous system (CNS), are highly motile and migratory in development and pathophysiological conditions. During their migration, microglia cells interact with their surroundings based on the various physical and chemical properties in the brain. Herein, a microfluidic wound-healing chip is developed to investigate microglial BV2 cell migration on the substrates coated with extracellular matrixes (ECMs) and substrates usually used for bio-applications on cell migration. In order to generate the cell-free space (wound), gravity was utilized as a driving force to flow the trypsin with the device. It was shown that, despite the scratch assay, the cell-free area was created without removing the extracellular matrix coating (fibronectin) using the microfluidic assay. It was found that the substrates coated with Poly-L-Lysine (PLL) and gelatin stimulated microglial BV2 migration, while collagen and fibronectin coatings had an inhibitory effect compared to the control conditions (uncoated glass substrate). In addition, the results showed that the polystyrene substrate induced higher cell migration than the PDMS and glass substrates. The microfluidic migration assay provides an in vitro microenvironment closer to in vivo conditions for further understanding the microglia migration mechanism in the brain, where the environment properties change under homeostatic and pathological conditions.
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) | 0.001 | 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) | 0.001 | 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".