Structure, organization, and function of glycosphingolipids in membrane
Bibliographic record
Abstract
A large variety of glycosylation patterns in combination with different ceramide structures in glycosphingolipids provide a basis for cell type-specific glycosphingolipid pattern in membrane, which essentially reflects the composition of glycosphingolipid-enriched microdomains. Functions of glycosphingolipids as antigens, mediators of cell adhesion, and modulators of signal transduction are all based on such organization. Of particular importance is the assembly of glycosphingolipids with signal transducers and other membrane proteins to form a functional unit termed a, through which glycosylation-dependent cell adhesion coupled with signal transduction takes place. The microenvironment formed by interfacing glycosynapses of interacting cells plays a central role in defining phenotypic changes after cell adhesion, as occur in ontogenic development and cancer progression. These basic functional features of glycosphingolipids in membrane can also be considered roles of glycosphingolipids and gangliosides characteristic of neutrophils, myelocytes, and other blood cells. A series of sialyl fucosyl poly-N-acetylgalactosamine gangliosides without the sialyl-Le epitope, collectively termed, have been shown to mediate E-selectin-dependent rolling and tethering under dynamic flow with physiologic shear stress conditions. Functional roles of myeloglycan in neutrophils during inflammatory processes are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".