Sex-specific impacts of lung viral infection on brain microglia and astrocytes 2565
Bibliographic record
Abstract
Abstract Description Introduction Recent studies suggest respiratory infections like SARS-CoV-2 can influence brain glial cell behavior. Early-life stress, such as neonatal stress, may predispose the nervous system to an exacerbated response during immune challenges. Thus, we investigated how early-life stress modifies glial cell activation and function during Sendai virus-induced lung inflammation, and assess its impact on neuroimmune responses and susceptibility to respiratory diseases in adulthood. Methods This study used a neonatal maternal separation (NMS) rat model in which Sprague Dawley rats were separated from their mother for 3 hours daily from postnatal days 3 to 12. At 8-10 weeks, rats were infected intranasally with Sendai virus, and brain glial cells were analyzed by flow cytometry post-infection. Results Activation markers of the microglia and astrocytes are modulated by Sendaï infection differently in response to stress and sex. Viral infection increased CD200 on astrocytes 4 days post-infection. At the same time, activated microglia MHCII levels were lower, with differences across sexes and between NMS and control groups. Conclusion Respiratory viral infections may affect glial cell activation, showing a pattern aligning with human SARS-CoV-2 observations. Early-life stress may further influence the glial response to such infections, emphasizing the role of developmental factors and immune challenges in shaping neuroimmune responses. Funding Sources Supported by CIHR, FRQS, Quebec Respiratory Health Network and Fondation IUCPQ. Topic Categories Neuroimmunology (NEUR)
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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) | 0.005 | 0.001 |
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".