PSA Fluoroimmunoassays Using Anti-PSA ScFv and Quantum-Dot Conjugates
Bibliographic record
Abstract
AIMS: The conjugates of monoclonal antibodies and luminescent nanoparticles (quantum dots [Qdots]) have a large number of potential applications in both fluoroimmunoassays and biological imaging; however, conjugating full-length antibody monoclonal antibodies directly to Qdots or other inorganic nanoparticles often results in the irreversible formation of oligomeric monoclonal antibody-nanoparticle complexes, which leads to dramatically reduced binding activities. This study demonstrated that the use of single-chain antibody fragments (scFvs) appears to have a number of advantages, in terms of solubility, activity, ease of preparation and ease of structure-based genetic engineering. MATERIALS & METHODS: Two antiprostate-specific antigen scFvs mutants--one with an 11-residue c-myc (referred as scFvB80-M1) and the other with a lysine-enriched His 6-tagging peptide attached to their C-termini (referred as scFvB80-M2)--were prepared. These two scFv mutants were conjugated directly with CdSe/ZnS Qdots and their binding activities were measured and compared. RESULTS & DISCUSSION: Both scFv mutants can be conjugated covalently with CdSe/ZnS Qdots; however, the resulting conjugates exhibit significantly different affinities in the prostate-specific antigen fluoroimmunoassays--the binding activity of scFvB80-M2/Qdots is equivalent of that of free scFvB80 and four times of that of scFvB80-M1/Qdots. CONCLUSION: This study demonstrates that binding activity of scFv/Qdot conjugates can be improved through structure-based genetic engineering of the scFv.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".