In Vivo Impairment of Neutrophil Recruitment during Lentivirus Infection
Bibliographic record
Abstract
Evidence indicates that the lentivirus, HIV, infection affects neutrophil response to bacteria and bacterial products in vitro. We used a novel model of rapid onset immunosuppression following infection with a similar lentivirus, feline immunodeficiency virus (FIV), in cats to examine neutrophil function within the microvasculature in vivo and to determine the steps that are impaired in the neutrophil recruitment cascade. In uninfected cats and cats infected neonatally with FIV, the mesentery was exteriorized, but remained autoperfused during intravital microscopy for 4 h. When the tissue was superfused with 10 micro g/ml of LPS for 4 h, intravital microscopy displayed a profound increase in neutrophil rolling at both 8 and 12 wk of age in uninfected cats. At 12 wk of age, FIV-infected animals showed a profound decrease in the number of rolling neutrophils. In vitro studies revealed that neutrophils from infected and uninfected animals rolled equally well on surrogate selectin substrata. In addition, in vivo neutrophil adhesion and emigration out of the vasculature were severely reduced, and in vitro neutrophil chemotaxis from FIV-infected animals was significantly impaired in response to fMLP or IL-8. However, FIV infection of neutrophils could not be detected. In summary, in vivo lentivirus infection with immunosuppression leads to a severe impairment in neutrophil rolling, adhesion, and emigration in response to bacterial stimulants potentially involving both endothelial and neutrophil dysfunction. These in vivo studies also indicate that neutrophil dysfunction should be taken into account when treating infections and tissue injury.
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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".