Telomere Length in Human Natural Killer Cell Subsets
Bibliographic record
Abstract
Natural killer (NK) cells are cytotoxic cells that play a critical role in the innate immune response against infections and tumors. In the elderly, the cytotoxic function of NK cells is often compromised. Telomeres progressively shorten with each cell division and with age in most somatic cells eventually leading to chromosomal instability and cellular senescence. We studied the telomere length in NK cell subsets isolated from peripheral blood using "flow FISH," a method in which the hybridization of telomere probe in cells of interest is measured relative to internal controls in the same tube. We found that the average telomere length in human NK cells decreased with age as was previously found for human T lymphocytes. Separation of adult NK cells based on CD56 and CD16 expression revealed that the telomere length was significantly shorter in CD56(dim)CD16(+) (mature) NK cells compared to CD56(bright)CD16(-) (immature) NK cells from the same donor. Furthermore, sorting of NK cells based on expression of activation markers, such as NKG2D and LFA-1, revealed that NK cells expressing these markers have significantly shorter telomeres. Telomere fluorescence was very heterogeneous in NK cells expressing CD94, killer inhibitory receptor (KIR), NKG2A, or CD161. Our observations indicate that telomeric DNA in NK cells is lost with cell division and with age similar to what has been observed for most other hematopoietic cells. Telomere attrition in NK cells is a plausible cause for diminished NK cell function in the elderly.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".