Delayed viral clearance and altered inflammatory responses affect severity of SARS-CoV-2 infection in aged mice
Why this work is in the frame
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Bibliographic record
Abstract
Epidemiological investigations consistently demonstrate an overrepresentation of the elderly in COVID-19 hospitalizations and fatalities, making the advanced age as a major predictor of disease severity. Despite this, a comprehensive understanding of the cellular and molecular mechanisms explaining how old age represents a major risk factor remain elusive. To investigate this, we compared SARS-CoV-2 infection outcomes in young adults (2 months) and geriatric (15–22 months) mice. Both groups of K18-ACE2 mice were intranasally infected with 500 TCID50 of SARS-CoV-2 Delta variant with analyses performed on days 3, 5, and 7 post-infection (DPI). Analyses included pulmonary cytokines, lung RNA-seq, viral loads, lipidomic profiles, and histological assessments, with a concurrent evaluation of the percentage of mice reaching humane endpoints. The findings unveiled notable differences, with aged mice exhibiting impaired viral clearance, reduced survival, and failure to recover weight loss due to infection. RNA-seq data suggested greater lung damage and reduced respiratory function in infected aged mice. Additionally, elderly-infected mice exhibited a deficient antiviral response characterized by reduced Th1-associated mediators (IFNγ, CCL2, CCL3, CXCL9) and diminished number of macrophages, NK cells, and T cells. Furthermore, mass-spectrometry analysis of the lung lipidome indicated altered expression of several lipids with immunomodulatory and pro-resolution effects in aged mice such as Resolvin, HOTrEs, and NeuroP, but also DiHOMEs-related ARDS. These findings indicate that aging affects antiviral immunity, leading to prolonged infection, greater lung damage, and poorer clinical outcomes. This underscores the potential efficacy of immunomodulatory treatments for elderly subjects experiencing symptoms of severe COVID-19.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.022 |
| 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) | 0.000 | 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 it