Quantitative detection of pre-ovulatory luteinizing hormone surges in urine using the microfluidic vertical agitation approach
Bibliographic record
Abstract
Identifying the time of ovulation is an important process for women seeking and avoiding pregnancy. Luteinizing hormone (LH) plays an important role in ovulation, which is very important in the reproductive mechanism. Therefore, detecting the LH level is of great importance in monitoring ovulation. In this study, sensitive, rapid and selective electrochemical biosensors were developed to detect LH quantitatively from human urine samples and to monitor the ovulation period. Isopotential region and current density optimization studies revealed that sensors with an electrode width and spacing of 1 mm had the optimum performance. Electrochemical impedance spectra evidenced immobilization of DSP self-assembled monolayers and anti-LH-beta antibody on the surface. While the mobile phone vibrator led to a 3.5-fold enhancement in response signals, the agitation system developed resulted in a 10-fold improvement. The sensors displayed detection limits of 1.02 and 1.53 mIU/ml in the range of 0-40 mIU/ml LH concentration obtained using two statistical approaches. Additionally, the sensors showed no cross-reactivity to hCG, which is very similar in structure and is widely reported to have high cross-reactivity.
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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 | 0.001 |
| 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) | 0.000 | 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".