Bibliographic record
Abstract
Natural antibodies are an innate-like subset of serum antibodies involved in host defense, tumor surveillance, homeostasis, and autoimmunity.Defining the natural antibody repertoire is critical for identifying biomarkers, developing vaccines, controlling and preventing autoimmunity, and understanding the development and organization of the immune system.While natural antibodies to protein antigens have been studied in depth, little is known about natural antibodies to carbohydrate antigens.To address this, we profiled IgM from umbilical cord blood and matched maternal sera on a glycan microarray.Since standard methods to detect maternal contamination in cord serum did not have sufficient sensitivity for our study, we developed a highly sensitive microarray-based assay.Using this method, we found that over 50% of the cord samples had unacceptable levels of maternal contamination.For the cord samples with high purity, anti-glycan IgM antibodies were prevalent and recognized a broad range of non-human and human glycans.Using principal component analysis and hierarchical clustering, cord IgM repertoires showed a high degree of similarity with each other but were distinct from maternal IgM repertoires.Our results demonstrate that many anti-glycan antibodies in human serum are natural antibodies and provide new insights into the development of anti-glycan antibody repertoires. AbbreviationsThe abbreviations used are: BG-A, blood group A; BG-B, blood group B; HSA, human serum albumin.LeX, Lewis X; LeC, Lewis C; SSEA4, stage-specific embryonic antigen 4; SSEA5, stage specific embryonic antigen 5. Monosaccharide residues are abbreviated as follows: Fuc, fucose; Gal, galactose; GalNAc, N-acetyl-galactosamine; Glc, glucose; GlcNAc, N-acetyl-glucosamine; Neu5Ac, N-acetylneuraminic acid.
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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) | 0.000 | 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".