HIV-1 Latency-Reversing Agents Prostratin and Bryostatin-1 Induce Blood–Brain Barrier Disruption/Inflammation and Modulate Leukocyte Adhesion/Transmigration
Bibliographic record
Abstract
Abstract A shock-and-kill approach involving the simultaneous treatment of HIV-1–infected patients with latency-reversing agents (LRAs) and combination antiretroviral therapy was proposed as a means to eradicate viral reservoirs. Currently available LRAs cannot discriminate between HIV-1–infected and uninfected cells. Therefore, the risks and benefits of using broad-spectrum LRAs need to be carefully evaluated, particularly in the CNS, where inflammation and leukocyte transmigration must be tightly regulated. We used a real-time impedance-sensing system to dynamically record the impact of different classes of LRAs on the integrity of tight monolayers of the immortalized human cerebral microvascular endothelial cell line hCMEC/D3. Results show that prostratin and bryostatin-1 can significantly damage the integrity of an endothelial monolayer. Moreover, prostratin and bryostatin-1 induce secretion of some proinflammatory cytokines and an increase of ICAM-1 expression. Additional studies demonstrated that prostratin and bryostatin-1 also affect adhesion and transmigration of CD4+ and CD8+ T cells as well as monocytes in an in vitro human blood–brain barrier (BBB) model. Prostratin and bryostatin-1 could thus be considered as potent regulators of BBB permeability and inflammation that influence leukocyte transport across the BBB. Altogether, these findings contribute to a better understanding of the potential risks and benefits of using a shock-and-kill approach with LRAs on the normal physiological functions of the BBB.
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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 | 0.000 |
| 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 | 0.001 |
| 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".