A toolkit of thread-based microfluidics, sensors, and electronics for 3D tissue embedding for medical diagnostics
Bibliographic record
Abstract
Abstract Threads, traditionally used in the apparel industry, have recently emerged as a promising material for the creation of tissue constructs and biomedical implants for organ replacement and repair. The wicking property and flexibility of threads also make them promising candidates for the creation of three-dimensional (3D) microfluidic circuits. In this paper, we report on thread-based microfluidic networks that interface intimately with biological tissues in three dimensions. We have also developed a suite of physical and chemical sensors integrated with microfluidic networks to monitor physiochemical tissue properties, all made from thread, for direct integration with tissues toward the realization of a thread-based diagnostic device (TDD) platform. The physical and chemical sensors are fabricated from nanomaterial-infused conductive threads and are connected to electronic circuitry using thread-based flexible interconnects for readout, signal conditioning, and wireless transmission. To demonstrate the suite of integrated sensors, we utilized TDD platforms to measure strain, as well as gastric and subcutaneous pH in vitro and in vivo.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".