SIV infection induces alterations in gene expression and loss of interneurons in Rhesus Macaque frontal cortex during early systemic infection
Bibliographic record
Abstract
Understanding the neurobiological mechanisms underlying HIV-associated neurocognitive decline in people living with HIV is frequently complicated by an inability to analyze changes across the course of the infection and frequent presence of comorbid psychiatric and substance use disorders. Preclinical non-human primate simian immunodeficiency virus (SIV) models help address these shortcomings. However, SIV studies frequently target protracted endpoints, limiting our understanding of the neuromolecular alterations during the early post-infection window. To begin to address this knowledge gap, we utilized single nuclei transcriptomics to examine frontal cortex samples of rhesus macaques 10- and 20-days post-SIV infection, compared to non-infected controls. We identify and validated a decrease in inhibitory neurons during the early post infection window, representing a potential substrate of longer-term injury and neurocognitive impairment in people living with HIV. Differential expression identified alterations in cellular subtype gene expression that persisted over the 20-day time course and short-lived differences only detected at 10-days post-SIV infection. In silico predicted regulatory mechanisms and dysregulated neural signaling pathways are presented. Analysis of cell-cell interaction networks identify altered signal pathways in the frontal cortex that may represent regional alterations in cell-cell communications. In total, these results identify cell type-specific molecular mechanisms putatively capable of underlying long-term neurocognitive alterations in persons living with HIV.
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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 | 0.000 |
| 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".