The bitter side of sweet: the role of Galectin-9 in immunopathogenesis of viral infections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, a critical role for β-galactoside-binding protein, Galectin-9 (Gal-9) has emerged in infectious disease, autoimmunity, and cancer. It is a ligand for T cell immunoglobulin mucin domain 3 (Tim-3), a type-I glycoprotein that is persistently expressed on dysfunctional T cells during chronic viral infections. Gal-9 exerts its pivotal immunomodulatory effects by inducing apoptosis or suppressing effector functions via engagement with its receptor, Tim-3. Recent studies report elevation of circulating Gal-9 in humans infected with different viral infections. Interaction of soluble Gal-9 with Tim-3 expressed on the surface of activated CD4+ T cells renders them less susceptible to HIV-1 infection, while enhanced HIV infection occurs when Gal-9 interacts with a different receptor than Tim-3. This indicates the versatile role of Gal-9 in viral pathogenesis. For instance, higher expression of Tim-3 during chronic viral infection and elevation of plasma Gal-9 may have evolved to limit persistent immune activation and pathogenic T cells activity. In contrast, Gal-9 can suppress the effectiveness of immunity against viral infections. In agreement, Gal-9 knockout mice mount a more robust and vigorous virus-specific immune response in acute and chronic viral infections resulting in rapid viral clearance. In line with this observation, blocking Gal-9 signals to Tim-3-expressing T cells result in improved immune responses. Here we review the biological and immunological properties of Gal-9 in viral infections (HIV, HCV, HBV, HSV, CMV, influenza, and dengue virus). Manipulating Gal-9 signals may have immunotherapeutic potential and could represent an alternative approach for improving immune responses to viral infections/vaccines.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it