Analysis of NKp30/NCR3 isoforms in untreated HIV-1-infected patients from the ANRS SEROCO cohort
Bibliographic record
Abstract
Natural killer (NK) cells play a prominent role at the intersection between innate and cognate immunity, thus influencing the development of multiple pathological conditions including HIV-1-induced AIDS. Not only NK cells directly kill HIV-1-infected cells, but also control the maturation and/or elimination of dendritic cells (DCs). These functions are regulated by the delicate balance between activating and inhibiting receptors expressed at the NK-cell surface. Among the former, NKp30 has raised significant interest since the alternative splicing of its intracellular domain leads to differential effector functions, dictating the prognosis of patients bearing gastrointestinal sarcoma, and B7-H6 has recently been identified as its main ligand. Since NKp30 is downregulated in CD56-/CD16+ NK cells expanded in viremic, chronically infected HIV-1+ patients, we decided to investigate the predictive value of NKp30 splice variants for spontaneous disease progression in 89 therapy-naïve HIV-1-infected individuals enrolled in an historical cohort of patients followed since diagnosis (ANRS SEROCO cohort). We found no difference in the representation of NK-cell subsets (CD56bright, CD56dim, CD56neg) in HIV-1-infected patients as compared with healthy subjects. NKp30 downregulation was detected in CD56dim and CD56neg NK-cell subsets, yet this did not convey any prognostic value. None of the NKp30 isoforms did affect disease progression, as measured in terms of time-to-loss of circulating CD4+ T cells, time-to-AIDS-defining events and overall survival. NKp30 isoforms do not seem to play a major role in the outcome of HIV-1 infection, but the heterogeneity of the immuno-virological status of patients at enrollment could have to be taken into account.
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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 | 0.000 | 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".