Baseline Naive CD4+ T-cell Level Predicting Immune Reconstitution in Treated HIV-infected Late Presenters
Bibliographic record
Abstract
BACKGROUND: Among HIV-infected patients initiating antiretroviral therapy (ART), early changes in CD4+ T-cell subsets are well described. However, HIV-infected late presenters initiating treatment present with a suboptimal CD4+ T-cell reconstitution and remain at a higher risk for AIDS and non-AIDS events. Therefore, factors associated with CD4+ T-cell reconstitution need to be determined in this population, which will allow designing effective immunotherapeutic strategies. METHODS: Thirty-one adult patients with baseline CD4+ T-cell count <350 cells/mm3 exhibiting viral suppression after ART initiation were followed in the HIV/AIDS research center of Peking Union Medical College Hospital in Beijing, China, from October 2002 to September 2013. Changes in T-cell subsets and associated determinants were measured. RESULTS: Median baseline CD4+ T-cell count was 70 cells/mm3. We found a biphasic reconstitution of T-cell subsets and immune activation: a rapid change during the first 6 months followed by a more gradual change over the subsequent 8 years. Baseline CD4+ T-cell count >200 cells/mm3 in comparison to CD4+ T-cell count ≤200 cells/mm3 was associated with more complete immune Reconstitution (77.8% vs. 27.3% respectively; P = 0.017) and normalized CD4/CD8 ratio. We showed that the baseline percentage of naive CD4+ T-cell was a predictive marker for complete immune reconstitution (area under receiver operating characteristic curve 0.907), and 12.4% as cutoff value had a sensitivity of 84.6% and a specificity of 88.2%. CONCLUSIONS: Baseline naive CD4+ T-cell percentage may serve as a predictive marker for optimal immune reconstitution during long-term therapy. Such study findings suggest that increasing thymic output should represent an avenue to improve patients who are diagnosed late in the course of infection.
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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) | 0.001 | 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".