Immunological Screening And Characterization of Highly Specific Monoclonal Antibodies Against 20 Kda Hgh
Bibliographic record
Abstract
BACKGROUND: hGH has been widely abused as a doping agent in sports for many years. There are some important approaches for the detection of hGH doping, and the ratio of 22:20 kDa GH was considered one of the most suitable detection indicators of GH abuse. Currently, effective anti-GH antibodies and related reagents are needed to develop a detection method, in particular, highly specific anti-20 kDa hGH monoclonal antibodies are a prerequisite. Herein we constructed the expression vector of 20 kDa hGH and prepared the corresponding antibodies by the immunization of the recombinant human 20 kDa into mice. Positive clones that can specifically recognize 20 kDa hGH were screened and characterized by enzyme immunoassay, Dot-ELISA and surface plasmon resonance. In total, 14 specific monoclonal cell lines were screened out. RESULTS: By a series of characterization, it was found that the 6C8, 44H3, 12G7 and 33Y19 clones were showing much higher specificity and affinity to 20 kDa hGH, and P3H9 could recognize both 20 and 22 kDa hGH isoforms. 6C8 and 44H3 matched well with P3H9 in the surface plasmon resonance testing. The 12G7 clone had the best surface properties with an association constant of 3.4 × 10(9) M(-1) and a dissociation constant of 2.95 × 10(10) M. CONCLUSION: Highly specific monoclonal antibodies against 20 kDa hGH were generated, and also two paired antibodies (P3H9 and 6C8 or P3H9 and 44H3) were characterized, which can serve as the potential components for 22:20 kDa detection kit.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".