High-Resolution Analysis of Antibodies to Post-Translational Modifications Using Peptide Nanosensor Microarrays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Autoantibodies are a hallmark of autoimmune diseases such as lupus and have the potential to be used as biomarkers for diverse diseases, including immunodeficiency, infectious disease, and cancer. More precise detection of antibodies to specific targets is needed to improve diagnosis of such diseases. Here, we report the development of reusable peptide microarrays, based on giant magnetoresistive (GMR) nanosensors optimized for sensitively detecting magnetic nanoparticle labels, for the detection of antibodies with a resolution of a single post-translationally modified amino acid. We have also developed a chemical regeneration scheme to perform multiplex assays with a high level of reproducibility, resulting in greatly reduced experimental costs. In addition, we show that peptides synthesized directly on the nanosensors are approximately two times more sensitive than directly spotted peptides. Reusable peptide nanosensor microarrays enable precise detection of autoantibodies with high resolution and sensitivity and show promise for investigating antibody-mediated immune responses to autoantigens, vaccines, and pathogen-derived antigens as well as other fundamental peptide-protein interactions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it