Exploring an alternative explanation for the second phase of viral decay: Infection of short-lived cells in a drug-limited compartment during HAART
Bibliographic record
Abstract
Most HIV-infected patients who initiate combination antiretroviral therapy experience a viral load decline in several phases. These phases are characterized by different rates of viral load decay that decrease when transitioning from one phase to the next. There is no consensus as to the origin of these phases. One hypothesis put forward is that short- and long-lived infected cells are responsible for the first and second phases of decay, respectively. However, significant differences in drug concentrations are observed in monocytes from various tissues, suggesting the first two phases of decay in viral loads could instead be attributed to short-lived cells being differently exposed to drugs. Compared to a well-exposed compartment, new cell infection can be expected in a compartment with limited drug exposure, thus leading to a slower viral load decay with potential virologic failure and drug resistance. In the current study, the latter hypothesis was investigated using a model of viral kinetics. Empirical datasets were involved in model elaboration and parameter estimation. In particular, susceptibility assay data was used for an in vitro to in vivo extrapolation based on the expected drug concentrations inside physiological compartments. Results from numerical experiments of the short-term evolution of viral loads can reproduce the first two phases of viral decay when allowing new short-lived cell infections in an unidentified drug-limited compartment. Model long-term predictions are however less consistent with clinical observations. For the hypothesis to hold, efavirenz, tenofovir and emtricitabine drug exposure in the drug-limited compartment would have to be very low compared to exposure in peripheral blood. This would lead to significant long-term viral growth and the frequent development of resistant strains, a prediction not supported by clinical observations. This suggests that the existence of a drug-limited anatomical compartment is unlikely, by itself, to explain the second phase of viral load decay.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".