Editorial: Glycotherapeutics: Design, synthesis, function and biomedical application of agents emerging from glycochemistry and glycobiology
Bibliographic record
Abstract
Glycotherapeutics: Design, synthesis, function and biomedical application of agents emerging from glycochemistry and glycobiologyPost-translational glycosylation is a non-template driven process for the addition of carbohydrates, or glycans, to biomolecules such as protein and lipids.It is well recognized that glycosylation impacts many cellular processes including, but not limited to, protein folding, trafficking, receptor binding, signaling, inflammation, and cell-to-cell/matrix adhesion (Varki 2017).Additionally, glycans can be used in the targeting of drugs in diseases such as cancer (Diniz et al., 2022) and lysosomal storage disorders (Do et al., 2019) as well as modulating pharmacokinetics and immunogenicity of therapeutic proteins and antibodies (Liu 2015;Archer et al., 2022;Dammen-Brower et al., 2022).The goal of this Research Topic was to assemble a collection of articles highlighting recent advancements pertaining to diagnostic tools and therapeutics utilizing multidisciplinary approaches in glycobiology and glycochemistry.Collectively, four articles submitted by 21 experts in glycoscience were peer-reviewed and accepted for publication in this Research Topic to emphasize the importance of glycans in human health and disease, in addition to presenting rapidly evolving technologies used in the analysis of carbohydrate-modified molecules and the enzymes that synthesize or degrade them.A review article by Loaeza-Reyes et al. provides a comprehensive overview of protein N-linked and O-linked glycosylation and their implications in cardiovascular function and disease, including an analysis of cardiovascular
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.020 |
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".