The sweet side of sex as a biological variable
Bibliographic record
Abstract
Glycobiology as a field holds enormous potential for understanding human health and disease. However, few glycobiology studies adequately address the issue of sex differences in biology, which severely limits the conclusions that can be drawn. Numerous CAZymes, lectins, and other carbohydrate-associated molecules have the potential to be differentially expressed and regulated with sex, leading to differences in O-GlcNAc, N-glycan branching, fucosylation, sialylation, and proteoglycan structure, among others. Expression of proteins involved in glycosylation is influenced through hormones, miRNA, and gene dosage effects. In this review, we discuss the benefits of incorporating sex-based analysis in glycobiology research and the potential drivers of sex differences. We highlight examples of where incorporation of sex-based analysis has led to insights into glycobiology. Finally, we offer suggestions for how to proceed moving forward, even if the experiments are already complete. Properly incorporating sex based analyses into projects will substantially improve the accuracy and reproducibility of studies as well as accelerate the rate of discovery in the glycosciences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".