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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".