The functional diversity of tissue‐resident natural killer cells against infection
Bibliographic record
Abstract
For decades, studies of natural killer (NK) cells have focused on those found in peripheral blood (PBNK cells) as the prototype for NK cell biology. Only recently have researchers begun to explore the diversity of tissue-resident NK (tr-NK) cells. While tr-NK cells were initially identified from mice parabiosis and intravascular staining experiments, they can also be identified by tissue retention markers such as CD69, CD103 and others. More importantly, tr-NK cells have distinct functions compared to PBNK cells. Within the liver, there are diverse subsets of tr-NK cells expressing different combinations of tissue-retention markers and transcription factors, the clinical relevance of which are still unclear. Functionally, liver tr-NK are primed with immediate responsiveness to infection and equipped with regulatory mechanisms to prevent liver damage. When decidual NK (dNK) cells were first discovered, they were mainly characterized by their reduced cytotoxicity and functions related to placental development. Recent studies, however, revealed different mechanisms by which dNK cells prevent uterine infections. The lungs are one of the most highly exposed sites for infection due to their role in oxygen exchange. Upon influenza infection, lung tr-NK cells can degranulate and produce more inflammatory cytokines than PBNK cells. Less understood are gut tr-NK cells which were recently characterized in infants and adults for their functional differences. In this mini-review, we aim to provide a brief overview of the most recent discoveries on how several tr-NK cells are implicated in the immune response against infection.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".