Development and immunochemical evaluation of a novel chicken IgY antibody specific for KLK6
Bibliographic record
Abstract
BACKGROUND: Human kallikrein-related peptidase 6 (KLK6) has been implicated in various types of cancer and in neurodegenerative and demyelinating diseases including multiple sclerosis. Further, anti-KLK6 antibodies attenuated disease manifestations in the mouse model of multiple sclerosis. Availability of specific antibodies against KLK6 is fundamental to the development of improved diagnostic and/or immunotherapeutic applications. Here, we exploited the enhanced immunogenicity of mammalian proteins in avian species to generate a polyclonal antibody against KLK6. RESULTS: Chicken were immunized with recombinant KLK6 and antibodies Y (IgYs) were purified from egg yolk with a simple procedure and evaluated for KLK6 detection by ELISA and Western blot using recombinant proteins and human cell lysates and supernatants. The anti-KLK6 Y polyclonal exhibited high affinity for KLK6 with a detection limit of 30 fmol. On the other hand, the widely used rabbit polyclonal antibody that was raised against the same recombinant KLK6 had a detection limit of 300 fmol. Moreover, the IgYs did not display any crossreactivity with recombinant KLKs or endogenous KLKs and other cellular proteins. CONCLUSIONS: Based on its high specificity and sensitivity the developed anti-KLK6 IgY is expected to aid the development of improved diagnostic tools for the detection of KLK6 in biological and clinical samples.
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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 | 0.000 |
| 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".