<scp>I</scp> g <scp>E</scp> glycosylation and impact on structure and function: A systematic review
Bibliographic record
Abstract
The impact of human IgE glycosylation on structure, function and disease mechanisms is not fully elucidated, and heterogeneity in different studies renders drawing conclusions challenging. Previous reviews discussed IgE glycosylation focusing on specific topics such as health versus disease, FcεR binding or impact on function. We present the first systematic review of human IgE glycosylation conducted utilizing the PRISMA guidelines. We sought to define the current consensus concerning the roles of glycosylation on structure, biology and disease. Despite diverse analytical methodologies, source, expression systems and the sparsity of data on IgE antibodies from non-allergic individuals, collectively evidence suggests differential glycosylation profiles, particularly in allergic diseases compared with healthy states, and indicates functional impact, and contributions to IgE-mediated hypersensitivities and atopic diseases. Beyond allergic diseases, dysregulated terminal glycan structures, including sialic acid, may regulate IgE metabolism. Glycan sites such as N394 may contribute to stabilizing IgE structure, with alterations in these glycans likely influencing both structure and IgE-FcεR interactions. This systematic review therefore highlights critical IgE glycosylation attributes in health and disease that may be exploitable for therapeutic intervention, and the need for novel analytics to explore pertinent research avenues.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".