Current approaches to evaluate the function of cytotoxic T-cells in non-human primates
Bibliographic record
Abstract
Cytotoxic T-lymphocytes (CTL) are a subset of T-cells that play a critical role in protecting against intracellular infections and cancer, and have the ability to identify and kill infected or transformed cells expressing non-self peptides associated with major histocompatibility (MHC) Class I molecules. Conversely, aberrant CTL activity can contribute to immune-related pathology under conditions of overwhelming infection or autoimmunity. Disease-modifying therapeutics can have unintended effects on CTL, and a growing number of therapeutics are intended to either suppress or enhance CTL or their functions. The susceptibility of CTL to unintended effects from common therapeutic modalities underscores the need for a better understanding of the impact that such therapies have on CTL function and the associated safety implications. While there are reliable ways of quantifying CTL, notably via flow cytometric analysis of specific CTL markers, it has been a greater challenge to implement fit-for-purpose methods measuring CTL function in the context of safety studies of therapeutics. This review focuses on methods for measuring CTL responses in the context of drug safety and pharmacology testing, with the goals of informing the reader about current approaches, evaluating their pros and cons, and providing perspectives on the utility of these approaches for safety evaluation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".