Rapid Bispecific Antibodies Based Homogeneous Immunoassay for Detection of Prostate-Specific Antigen (PSA)
Bibliographic record
Abstract
Development of rapid and economical method for detection of prostate- specific antigen (PSA) in human blood. Methods: The usual procedure for the detection of prostate cancer markers in human is prostate-specific antigen (PSA) in blood (normal level ≤ 4 ng/mL) using heterogeneous immunoassay enzyme linked immunosorbent assay (ELISA). However, a rapid homogeneous immunoassay for the detection of PSA in serum, based on bispecific antibodies, is more convenient due to its speed, accuracy and obviating the need of multiple washing steps. The assay using bispecific antibody P57 (against PSA and peroxidase) and monospecific antibody B87 (against PSA) conjugated with glucose oxidase was developed in the presence of excess catalase. Similarly, in solid phase homogeneous immunoassay the monospecific antibody B87 (against PSA) and glucose oxidase were immobilized onto a solid support (plastic) and other reagents, bio-chemicals, and bispecific antibody P57 were taken in homogeneous solution. All variables, viz., glucose oxidase, peroxidase and catalase were optimized at different PSA concentrations. Results: Homogeneous immunoassay (HIA) showed linearity of PSA detection 1-10 ng/mL whereas, solid phase homogeneous immunoassay (SPHIA) showed in the range of 1-50 ng/mL suggesting SPHIA has a broader operating range, thus much better than HIA. Detection of PSA in a homogeneous solution can be completed in 90 minutes without involving any washing and incubation steps. Conclusions: Homogeneous assay is a rapid, economical method that eliminates all washing and incubation steps of conventional ELISA.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".