Transgenic mouse models to study the physiological and pathophysiological roles of human Siglecs
Bibliographic record
Abstract
Sialic acid-binding immunoglobulin-like lectins (Siglecs) are important immunomodulatory receptors. Due to differences between human and mouse Siglecs, defining the in vivo roles for human Siglecs (hSiglecs) can be challenging. One solution is the development and use of hSiglec transgenic mice to assess the physiological roles of hSiglecs in health and disease. These transgenic mice can also serve as important models for the pre-clinical testing of immunomodulatory approaches that are based on targeting hSiglecs. Four general methods have been used to create hSiglec-expressing transgenic mice, each with associated advantages and disadvantages. To date, transgenic mouse models expressing hSiglec-2 (CD22), -3 (CD33), -7, -8, -9, -11, and -16 have been created. This review focuses on both the generation of these hSiglec transgenic mice, along with the important findings that have been made through their study. Cumulatively, hSiglec transgenic mouse models are providing a deeper understanding of the differences between human and mice orthologs/paralogs, mechanisms by which Siglecs regulate immune cell signaling, physiological roles of Siglecs in disease, and different paradigms where targeting Siglecs may be therapeutically advantageous.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".