Engineering physical microenvironments to study innate immune cell biophysics
Bibliographic record
Abstract
Innate immunity forms the core of the human body's defense system against infection, injury, and foreign objects. It aims to maintain homeostasis by promoting inflammation and then initiating tissue repair, but it can also lead to disease when dysregulated. Although innate immune cells respond to their physical microenvironment and carry out intrinsically mechanical actions such as migration and phagocytosis, we still do not have a complete biophysical description of innate immunity. Here, we review how engineering tools can be used to study innate immune cell biophysics. We first provide an overview of innate immunity from a biophysical perspective, review the biophysical factors that affect the innate immune system, and then explore innate immune cell biophysics in the context of migration, phagocytosis, and phenotype polarization. Throughout the review, we highlight how physical microenvironments can be designed to probe the innate immune system, discuss how biophysical insight gained from these studies can be used to generate a more comprehensive description of innate immunity, and briefly comment on how this insight could be used to develop mechanical immune biomarkers and immunomodulatory therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".