Bibliographic record
Abstract
Abstract Chronic viral infection and the tumor microenvironment are two settings in which persistent immune stimulation can occur. Under such conditions, T cell activation becomes downregulated, likely in order to reduce immune pathology, resulting in a phenotype known as T cell ‘exhaustion’. Functionally exhausted T cells are characterized by reduced cytokine production, proliferative capability, and cytotoxic activity, with a corresponding expression of checkpoint receptors, including PD-1, CTLA-4, LAG-3 and Tim-3. While PD-1, CTLA-4, and LAG-3 have known mechanisms for reducing T cell effector function, the intrinsic effects of Tim-3 on T cell activation are largely unknown. In fact, recent evidence reveals divergent effects of Tim-3 in various infectious models. Data from our lab indicate that Tim-3 can enhance acute TCR signaling, while much of the existing literature associates Tim-3 expression with diminished effector function. Current approaches to study Tim-3 require persistent antigen stimulation or ectopic expression. We have developed a novel mouse model where the expression of Tim-3 is induced in vivo by Cre-mediated recombination. We hypothesize that inducible Tim-3 expression may cause T cells to become dysfunctional earlier by enhancing the initial response to stimulation. Using this model, we have found that the presence of Tim-3 enhances T cell activation after acute in vitro stimulation. We are currently testing how induction of Tim-3 will affect responses to a murine model of chronic viral infection, LCMV-clone13. Results from this study will address knowledge gaps about the effects of Tim-3 on T cells and will have practical applications in both chronic viral infection and cancer immunotherapy.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".