Coordinated roles for glycans in regulating the inhibitory function of CD22 on B cells
Bibliographic record
Abstract
CD22 is an inhibitory B cell co-receptor that recognizes sialic acid-containing glycoconjugates as ligands. Interactions with its glycan ligands are key to regulating the ability of CD22 to modulate B cell function, the most widely explored of which is antagonizing B cell receptor (BCR) signaling. Most importantly, interactions of CD22 with ligands on the same cell (cis) control the organization of CD22 on the cell surface, which minimizes co-localization with the BCR. In contrast with the modest ability of CD22 to intrinsically dampen BCR signaling, glycan ligands presented on another cell (trans) along with an antigen drawn CD22 and the BCR together within an immunological synapse, strongly inhibiting BCR signaling. New concepts are emerging for how CD22 controls B cell function, such as changes in glycosylation at different stages of B cell differentiation, specifically on GC B cells. Related to these changes, new players, such galectin-9, have been discovered that regulate cell surface nanoclusters of CD22. Roles of glycan ligands in controlling CD22 are the primary focus of this review as we highlight the ability of CD22 to modulate B cell function.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".