Bibliographic record
Abstract
Infertility is a worldwide disease. Approximately 48.5 million couples worldwide are infertile, including one in six Canadian couples. Male infertility, which affects approximately 7% of the male population, is responsible for an estimated 40% to 50% of all infertility cases. Male infertility is often attributed to poor semen quality with suboptimal sperm motility, limited concentration, or abnormal morphology. The total number of spermatozoa per ejaculate and the sperm concentration are related to both time to pregnancy and pregnancy rates and are predictors of conception. Since sperm concentration is an important criterion to estimate semen quality, a direct relationship between sperm concentration and signal intensity is desired for establishment through enzyme linked immunosorbent assays that measure sperm protein 10 (SP-10) concentration.This thesis focuses on developing three types of microfluidic platforms for SP-10 measurement. Firstly, a nitrocellulose paper-based multi-well plate platform was developed with a LOD of 88.84 ng/ml. The developed nitrocellulose paper plate has 96 independent wells for multiplexing ELISA assay and is compatible with standard laboratory microplates and instruments. The nitrocellulose plate is fabricated through a flexible one-step laser micromachining process, which can simply and rapidly produce a low-cost nitrocellulose paper-based multi-well plate with different sizes for ELISA experiments. Next, a lateral flow immunoassay platform was developed for home-based SP-10 measurement. This simple, rapid, and cost-effective platform replies on capillarity of a nitrocellulose strip to transport fluids and produces a measurable signal within 15 minutes. Gold nanoparticles and gold nanoshells were both applied on this platform as colorimetric labels; it was demonstrated that the LOD with gold nanoshells (5.16 ng/ml) was 5 times lower than that with gold nanoparticles (25.03 ng/ml). In addition, a single magnetic bead system integrated with a microfluidic device was developed for SP-10 measurement in laboratory with a LOD of 1.18 µg/ml. By applying a rotating magnetic field, the single magnetic bead moves freely in the chamber of the microfluidic device to actively capture the SP-10 antigen linked to the fluorescent-labeled SP-10 antibody. SP-10 concentration was measured by comparing the fluorescent intensity of the bead before and after swimming in the chamber.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".