High frequencies of adaptive NK cells are associated with absence of coronary plaque in cytomegalovirus infected people living with HIV
Bibliographic record
Abstract
The objective of this study was to evaluate whether adaptive NKG2C+CD57+ natural killer (adapNK) cell frequencies are associated with pre-clinical coronary atherosclerosis in participants of the Canadian HIV and Aging Cohort Study. This cross-sectional study included 194 Canadian HIV and Aging Cohort Study participants aged ≥ 40 years of which 128 were cytomegalovirus (CMV)+ people living with HIV (PLWH), 8 were CMV-PLWH, 37 were CMV mono-infected individuals, and 21 were neither human immunodeficiency virus nor CMV infected. Participants were evaluated for the frequency of their adapNK cells and total plaque volume (TPV). TPV was assessed using cardiac computed tomography. Participants were classified as free of, or having, coronary atherosclerosis if their TPV was "0" and ">0," respectively. The frequency of adapNK cells was categorized as low, intermediate or high if they constituted <4.6%, between ≥4.6% and 20% and >20%, respectively, of the total frequency of CD3-CD56dim NK cells. The association between adapNK cell frequency and TPV was assessed using an adjusted Poisson regression analysis. A greater proportion of CMV+PLWH with TPV = 0 had high adapNK cell frequencies than those with TPV > 0 (61.90% vs 39.53%, P = .03) with a similar non-significant trend for CMV mono-infected participants (46.15% vs 34.78%). The frequency of adapNK cells was negatively correlated with TPV. A high frequency of adapNK cells was associated with a relative risk of 0.75 (95% confidence intervals 0.58, 0.97, P = .03) for presence of coronary atherosclerosis. This observation suggests that adapNK cells play a protective role in the development of coronary atherosclerotic plaques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".