Microfluidic determination of lymphocyte vascular deformability: effects of intracellular complexity and early immune activation
Bibliographic record
Abstract
Despite the critical importance of mechanical (rheological + extrudability) deformability in the vascular flow of lymphocytes, it has been poorly investigated due to the limitations of existing technological tools. Microfluidics analysis of leukocyte deformability offers significant advantages in that it offers high throughput, large sample population and the ability to analyze a heterogeneous population. These advantages are in stark contrast to previous approaches that focused on single cell measurements. Importantly, the flow characteristics of microfluidic devices more closely model vascular deformability in that shear stress is applied forcing leukocyte passage through micropores of designed size. The modeling of vascular flow has been further enhanced by the development of a microfluidic ratchet device that introduced an oscillatory flow. As demonstrated in this study, the microfluidic ratchet device was able to separate human peripheral blood leukocyte subsets (i.e., monocytes and lymphocytes) based on differential deformability profiles. Furthermore, morphologically similar lymphocyte subsets (CD4, CD8 and NK) could also be separated. The subset separation was observed to be largely due to differences in their intracellular complexity (i.e., granule content) with granule-positive T lymphocytes and NK cells being less deformable than granule-negative lymphocytes. Moreover, upon immune activation, deformability of the de-granulated lymphocytes increased consequent to the decrease in cytoplasmic granularity/viscosity. This study for the first time demonstrates that leukocytes subsets have differential deformability profiles and that intracellular granularity/degranulation significantly impacts the lymphocytes' mechanical properties. These findings could be of clinical value as biomarkers of lymphocyte activation state and potential disease processes.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".