Detection of Ovarian Cancer Biomarker Lysophosphatidic Acid Using a Label-Free Electrochemical Biosensor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it