Serial Evolution of TCR <i>β</i> Chain Transcript Mobilization in HIV Type-1-Infected Patients Following Vaccine Immune Stimulation and HAART Interruption
Bibliographic record
Abstract
In this article, we studied the T cell receptor (TCR)beta chain transcript mobilization in peripheral blood lymphocytes harvested from HIV-1-infected patients before and after vaccination with a mixture of six lipopeptides and at the moment and serially after highly active antiretroviral therapy (HAART) interruption. This study was performed by using a combined qualitative and quantitative assessment of Vbeta mRNA alterations at the level of complementary determining region 3 length distribution (CDR3-LD) of the TCR. Whereas healthy individuals displayed both stable CDR3-LD profiles and Vbeta transcript accumulations over time, the four HIV-1-infected patients in a quiescent disease phase under HAART have a highly significantly biased CDR3-LD. In addition, they displayed a significant further increase of alterations of their beta CDR3-LD profile after vaccination and both a more altered CDR3-LD (p < 0.05) and an increased transcript accumulation of some Vbeta families after HAART interruption. These modifications mostly concerned the CD8(+ve) T cells. Such a global approach of TCR alterations may help to follow the immune response of these patients and allow targeting of more complex in vivo studies by identifying the T cells with a selected repertoire.
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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".