Exceptional T CD4 <sup>+</sup> Recovery Post-antiretroviral Is Linked to a Lower HIV Reservoir with a Specific Immune Differentiation Pattern
Bibliographic record
Abstract
We present a cohort of individuals who reached CD4+ T cell counts of greater than 1,000 cells/mm3 (Hypers) after starting antiretroviral treatment (ART) and compared them with those who reached between 350 and 999 CD4+ T cells/mm3 (Concordants). Demographic data, immune recovery kinetics, T CD4+ subset phenotypes, and integrated HIV DNA were analyzed. Data from individuals living with HIV on their first ART regimen and after 48 months of follow-up were obtained. Immune phenotype by Flow Cytometry analysis on whole blood was performed, cytokines were measured, and integrated HIV-1 DNA was measured by polymerase chain reaction. From a total of 424 individuals, 26 Hypers (6.1%), 314 Concordants (74.1%), and 84 (19.8%) discordants were identified. Hypers had a higher proportion of CD4+-naive (Nv) T cells (37.6 vs. 24.8, p < .05), and a low proportion of CD4+ effector memory T cells (27.9 vs. 39.4, p < .05), with similar results found in CD8+ T cells. Hypers demonstrated a higher percentage of CD4+CD45RA+CD31neg cells with a lower response to interleukin-2 stimulation and a lower integrated HIV-1 DNA/CD4 ratio (1.2 vs. 2.89, p < .05). In Hypers, T cell recovery occurs very early after initiation of ART. Following this initial recovery state, their CD4+ T cell level homeostasis seems to be driven by nonthymic-central-Nv cells. This exceptional recovery is associated with a lower HIV reservoir, which may be related to an increase in noninfected CD4+ T cells. These patients could then be eligible candidates for cure trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".