Bibliographic record
Abstract
Significance: A wide range of cell types can migrate in response to physiological or externally applied direct current electric field (dcEF), a process termed electrotaxis. In particular, electrotaxis of epithelial cells to wound-generated dcEF for mediating wound healing is a well-accepted mechanism. In addition, various immune cells have been demonstrated to undergo electrotaxis, suggesting a link between electrotaxis and inflammatory responses in wound healing. Electrotaxis research will generate important insight into the electrical guiding mechanism for cell migration thereby providing the scientific basis to further develop clinical applications for wound care. Development of advanced electrotaxis assays will critically enable in-depth experimental electrotaxis studies in vitro. Recent Advances: Recently, a number of new electrotaxis assays or new uses of previously developed assays for electrotaxis studies have been reported. These new developments provide improved solutions for experimental throughput, configuration of three-dimensional cell migration environments and coexisting guiding signals, measurements of collective electrotactic cell migration, and sorting electrotactic populations. Critical Issues: These new developments face the challenge of playing a more important role to better understand the biological mechanisms underlying electrotaxis, in addition to making a stronger impact on relevant applications. Future Directions: On one hand, specific electrotaxis assays should be further developed to improve its function and tested for a broader range of experimental conditions and electrotactic populations. On the other hand, joint efforts among electrotaxis researchers are needed to integrate the unique features of specific electrotaxis assays, allowing more advanced and efficient electrotaxis analyses to answer both basic science and clinical questions.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".