Design and Characterization of a Dual-Protein Strategy for an Early-Stage Assay of Ovarian Cancer Biomarker Lysophosphatidic Acid
Bibliographic record
Abstract
The overall 5-year survival rate of ovarian cancer (OC) is generally low as the disease is often diagnosed at an advanced stage of progression. To save lives, OC must be identified in its early stages when treatment is most effective. Early-stage OC causes the upregulation of lysophosphatidic acid (LPA), making the molecule a promising biomarker for early-stage detection. An LPA assay can additionally stage the disease since LPA levels increase with OC progression. This work presents two methods that demonstrate the prospective application for detecting LPA: the electromagnetic piezoelectric acoustic sensor (EMPAS) and a chemiluminescence-based iron oxide nanoparticle (IONP) approach. Both methods incorporate the protein complex gelsolin-actin, which enables testing for detection of the biomarker as the binding of LPA to the complex results in the separation of gelsolin from actin. The EMPAS was characterized with contact angle goniometry and atomic force microscopy, while gelsolin-actin-functionalized IONPs were characterized with transmission electron microscopy and Fourier transform infrared spectroscopy. In addition to characterization, LPA detection was demonstrated as a proof-of-concept in Milli-Q water, buffer, or human serum, highlighting various LPA assays that can be developed for the early-stage detection of OC.
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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".