Bibliographic record
Abstract
Microfluidic systems have shown tremendous promise in biomedical applications; however, their clinical adoption has been slow. Particularly in fertility and pharmaceutical applications, the limited throughput of microfluidics is a barrier to translation. This thesis seeks to address barriers to scaling microfluidics throughput to meet the clinical application requirements. In many cases this involves the development of new architectures that extend interaction areas – of cells and structures, of cells and imaging area, and of fluid reactants for cell-free therapeutics. The first work demonstrates a 3D-structured device that processes a high volume of raw semen and selects the highest quality sperm with a sufficient quantity for downstream assisted reproduction treatments (Chapter 3). This work is further advanced toward the clinical adoption with a successful pilot test at a Toronto fertility center. Focusing on the male infertility diagnosis, an automated sperm DNA integrity assessment method is developed that quantifies DNA quality of thousands of sperm cells at the single-cell and population levels (Chapter 4). Building on the developed automated technique, a new method is developed that uniquely enables spatial mapping of the DNA quality of sperm inside microfluidic devices with a single-cell resolution (Chapter 5). In the last work, an exchange-membrane system is developed for therapeutic proteins synthesis at a clinically relevant throughput (Chapter 6).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.031 |
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".