Detection of Ovarian Cancer Biomarker Lysophosphatidic Acid Using a Label-Free Electrochemical Biosensor
Bibliographic record
Abstract
Electrochemical biosensors are valued for their sensitivity and selectivity in detecting biological molecules. Having the advantage of generating signals that can be directly or indirectly proportional to the concentration of the target analyte, these biosensors can achieve specificity by utilizing a specific biorecognition surface designed to recognize the target molecule. Electrochemical biosensors have garnered substantial attention, as they can be used to fabricate compact, cost-effective devices, making them promising candidates for point-of-care testing (POCT) devices. This study introduces a label-free electrochemical biosensor employing a gold screen-printed electrode (SPE) to detect lysophosphatidic acid (LPA), a potential early ovarian cancer biomarker. We employed the gelsolin–actin system, previously introduced by our group, in combination with fluorescence spectrometry, as a biorecognition element to detect LPA. By immobilizing a gelsolin–actin complex on an SPE, we were able to quantify changes in current intensity using cyclic voltammetry and differential pulse voltammetry, which was directly proportional to the LPA concentration in the solution. Our results demonstrate the high sensitivity of the developed biosensor for detecting LPA in goat serum, with a limit of detection (LOD) and a limit of quantification (LOQ) of 0.9 µM and 2.76 µM, respectively, highlighting its potential as a promising tool for early-stage diagnosis of ovarian cancer.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".