Nucleic Acid Detection Immunoassay for Prostate-Specific Antigen Based on Immuno-PCR Methodology
Bibliographic record
Abstract
BACKGROUND: Serum prostate-specific antigen (PSA) concentrations after radical prostatectomy typically become undetectable with the use of current immunometric assay methods. Despite modern surgical techniques, 15%-30% of prostate cancer patients undergoing radical prostatectomy develop a biochemical recurrence during follow-up. Unfortunately, poor analytical sensitivity of standard PSA assays delays biochemical recurrence detection, and because of day-to-day assay imprecision ultrasensitive PSA assays cannot assess PSA kinetics. We developed an immuno-PCR assay for total PSA that has a limit of quantification >10 times lower than current ultrasensitive assays. METHODS: The 2-site immunometric assay for total PSA employed 2 monoclonal antibodies, one conjugated to a double-stranded DNA label and the other bound to paramagnetic microparticles. After several washing steps, quantification cycles were determined and values were converted to PSA concentrations. We characterized analytical performance and compared accuracy with a commercially available total PSA assay. RESULTS: The limit of quantification was 0.65 ng/L and the assay was linear in the range of 0.25-152.0 ng/L. Total imprecision estimates at PSA concentrations of 3.8, 24.1, and 69.1 ng/L were <15.2%, <9.4%, and <10.6%, respectively. Recovery of supplemented PSA ranged from 87.5% to 119.2% (mean 100.3%). Dilution recovery ranged from 96.4% to 115.3% (mean 102.3%). There was no high-dose hook effect up to 50 000 ng/L of PSA. Comparison with the commercial PSA assay showed a regression slope of 1.06 and a correlation coefficient of 0.996. CONCLUSIONS: The analytical characteristics of the assay support the use of this assay for the accurate and precise measurement of serum PSA, even at sub-nanogram-per-liter concentrations.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".