Tim-3 marks human natural killer cell maturation and suppresses cell-mediated cytotoxicity
Bibliographic record
Abstract
Natural killer (NK) cells are innate lymphocytes that play an important role against viral infections and cancer. This effect is achieved through a complex mosaic of inhibitory and activating receptors expressed by NK cells that ultimately determine the magnitude of the NK-cell response. The T-cell immunoglobulin- and mucin domain-containing (Tim)-3 receptor was initially identified as a T-helper 1-specific type I membrane protein involved in regulating T-cell responses. Human NK cells transcribe the highest amounts of Tim-3 among lymphocytes. Tim-3 protein is expressed on essentially all mature CD56(dim)CD16(+) NK cells and is expressed heterogeneously in the immature CD56(bright)CD16(-) NK-cell subset in blood from healthy adults and in cord blood. Tim-3 expression was induced on CD56(bright)CD16(-) NK cells after stimulation with IL-15 or IL-12 and IL-18 in vitro, suggesting that Tim-3 is a maturation marker on NK cells. Whereas Tim-3 has been used to identify dysfunctional T cells, NK cells expressing high amounts of Tim-3 are fully responsive with respect to cytokine production and cytotoxicity. However, when Tim-3 was cross-linked with antibodies it suppressed NK cell-mediated cytotoxicity. These findings suggest that NK-cell responses may be negatively regulated when NK cells encounter target cells expressing cognate ligands of Tim-3.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".