Desialylation of surface receptors as a new dimension in cell signaling
Bibliographic record
Abstract
Terminal sialic acid residues are found in abundance in glycan chains of glycoproteins and glycolipids on the surface of all live cells forming an outer layer of the cell originally known as glycocalyx. Their presence affects the molecular properties and structure of glycoconjugates, modifying their function and interactions with other molecules. Consequently, the sialylation state of glycoproteins and glycolipids has been recognized as a critical factor modulating molecular recognitions inside the cell, between the cells, between the cells and the extracellular matrix, and between the cells and certain exogenous pathogens. Until recently sialyltransferases that catalyze transfer of sialic acid residues to the glycan chains in the process of their biosynthesis were thought to be mainly responsible for the creation and maintenance of a temporal and spatial diversity of sialylated moieties. However, the growing evidence suggests that in mammalian cells, at least equally important roles belong to sialidases/neuraminidases, which are located on the cell surface and in intracellular compartments, and may either initiate the catabolism of sialoglycoconjugates or just cleave their sialic acid residues, and thereby contribute to temporal changes in their structure and functions. The current review summarizes emerging data demonstrating that mammalian neuraminidase 1, well known for its lysosomal catabolic function, is also targeted to the cell surface and assumes the previously unrecognized role as a structural and functional modulator of cellular receptors.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".