Thermal scribing to prototype plastic microfluidic devices, applied to study the formation of neutrophil extracellular traps
Bibliographic record
Abstract
Innovation in microfluidics-based biological research has been aided by the growing accessibility of versatile microscale fabrication techniques, particularly in rapid prototyping of elastomeric polydimethylsiloxane (PDMS) based devices. However, the use of PDMS presents considerable and often unexpected limitations, particularly in interpreting and validating biological data. To rapidly prototype microfluidic culture systems in conventional plastics commonly used in cell culture, we developed 'thermal scribing', a one-step micromachining technique in which thermoplastics are locally patterned by a heated tip, moving in user-controlled patterns. To demonstrate and study the thermal scribing process, we modified an inexpensive desktop hobby craft cutter with a soldering iron to scribe micropatterns on polystyrene substrates. The thermal scribing technique is useful for creating a variety of channel profiles and geometries, which cannot be readily achieved using other microfabrication approaches. The entire fabrication process, including post-processing operations needed to fabricate devices, can be completed within a few hours without the need for skilled engineering expertise or expensive equipment. We apply this technique to demonstrate that induction of functional neutrophil extracellular traps (NETs) can be significantly enhanced over previous studies, when experiments are conducted in microfluidic channels prototyped in an appropriate material. These results ultimately inform the design of neutrophil culture systems and suggest that the inherent ability of neutrophils to form NETs may have been significantly under-reported.
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 | 0.001 |
| 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".