Bibliographic record
Abstract
N-Glycan branching in the medial-Golgi generates ligands for lattice-forming lectins (e.g., galectins) that regulate surface levels of glycoproteins including epidermal growth factor (EGF) and transforming growth factor-beta (TGF-beta) receptors. Moreover, functional classes of glycoproteins differ in N-glycan multiplicities (number of N-glycans/peptide), a genetically encoded feature of glycoproteins that interacts with metabolic flux (UDP-GlcNAc) and N-glycan branching to differentially regulate surface levels. Oncogenesis increases beta1,6-N-acetylglucosaminyltransferase V (encoded by Mgat5) expression, and its high-affinity galectin ligands promote surface retention of growth receptors with a reduced dependence on UDP-GlcNAc. Mgat5(-/-) tumor cells are less metastatic in vivo and less responsive to cytokines in vitro, but undergo secondary changes that support tumor cell proliferation. These include loss of Caveolin-1, a negative regulator of EGF signaling, and increased reactive oxygen species, an inhibitor of phosphotyrosine phosphatases. These studies suggest a systems approach to cancer treatment where the surface distribution of receptors is targeted through metabolism and N-glycan branching to induce growth arrest.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".