Two-photon lithography-fabricated deterministic lateral displacement microfluidic system for efficient minicell purification in cancer therapy
Bibliographic record
Abstract
Chromosome-less minicells, derived from aberrant polar division events of bacterial cells, have emerged as promising nanocarriers for targeted cancer drug delivery due to their unique characteristics. A major challenge in their purification process lies in effectively isolating such spherical minicells (< 1 μm) from their rod-shaped parental cells (1-10 μm). This study investigates the use of Deterministic Lateral Displacement (DLD) microfluidic systems for minicell purification, leveraging Two-Photon Lithography (TPL) for the rapid prototyping of high-resolution designs optimized for this purpose. Under laminar flow conditions, we investigated key DLD design parameters including symmetric and asymmetric post gaps, outlet widths, dual post arrays, fluidic-resistance-optimized design. To enhance separation efficiency, we developed a two-stage microfluidic separation system combining a spiral inertial chip and an optimized DLD chip in series. Utilizing high-resolution TPL for chip fabrication of an inertial chip with 12 spirals and an asymmetric DLD chip with a 2 μm downstream post gap, we achieved a separation efficiency of 94%. This high efficiency achieved using microfluidics for the separation of cells differing in both shape and size, demonstrates the potential of advanced microfluidic systems in cell sorting.
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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) | 0.001 | 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".