Review—Recent Advances in Electrochemical Detection of Prostate Specific Antigen (PSA) in Clinically-Relevant Samples
Bibliographic record
Abstract
Electrochemical biosensors hold great promise for enabling clinical analysis of biomarkers at the point-of-care. This is particularly of interest for cancer management due to the importance of early diagnostics as well as the critical need for frequent treatment monitoring. We have reviewed clinically-relevant electrochemical biosensors that have been developed over the past five years for the analysis of prostate specific antigen (PSA), a model protein target for prostate cancer management. We have critically evaluated the key performance metrics of these biosensors for clinical translation: limit-of-detection, linear range, and recovery rate in bodily fluids. These PSA electrochemical biosensors can be broadly categorized as sandwich assays, direct detection assays, and indirect detection assays. Among these, indirect detection assays deliver the lowest limit-of-detection. We have identified the development of multiplexed assays for detecting a panel of cancer biomarkers that includes a combination of protein and nucleic acids targets as a key priority for future development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".