Regulation of Lentivirus Neurovirulence by Lipopolysaccharide Conditioning: Suppression of CXCL10 in the Brain by IL-10
Bibliographic record
Abstract
Lentivirus infections including HIV and feline immunodeficiency virus (FIV) cause neurovirulence, which is largely mediated by innate immunity. To investigate the interactions between neurovirulence and repeated conditioning by innate immune activation, models of lentivirus infection were exposed to LPS. Gene expression in HIV-infected (HIV+) and control (HIV-) patient brains was compared by real time RT-PCR and immunocytochemistry. Supernatants from mock and HIV-infected monocyte-derived macrophages exposed to LPS were applied to human neurons. FIV-infected (FIV+) and control (FIV-) animals were exposed repeatedly to LPS postinfection together with concurrent neurobehavioral testing, viral load, and host gene analyses. Brains from HIV+ individuals exhibited induction of CD3epsilon, CXCL10, and granzyme A expression (p < 0.05). Supernatants from HIV+ monocyte-derived macrophages induced CXCL10 expression in neurons, which was diminished by IL-10 treatment (p < 0.05). LPS-exposed FIV+ animals demonstrated lower plasma and brain viral loads (p < 0.05). Neuronal CXCL10 expression was increased in FIV+ animals but was suppressed by LPS exposure, together with reduced brain CD3epsilon and granzyme A expression (p < 0.05). In conjunction with preserved NeuN-positive neuronal counts in parietal cortex (p < 0.05), FIV+ animals exposed to LPS also showed less severe neurobehavioral deficits (p < 0.05). Repeated LPS exposures suppressed CXCL10 in the brain and ensuing T cell infiltration with a concomitant reduction in neurovirulence. Thus, innate immune chronic conditioning exerted beneficial effects on neurovirulence through suppression of a specific chemotactic factor, CXCL10, mediated by IL-10, leading to reduced leukocyte infiltration and release of neurotoxic factors.
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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".