Mesenchymal Stromal (Stem) Cell Therapy Fails to Improve Outcomes in Experimental Severe Influenza
Bibliographic record
Abstract
RATIONALE: Severe influenza remains a major public health threat and is responsible for thousands of deaths annually. Increasing antiviral resistance and limited effectiveness of current therapies highlight the need for new approaches to influenza treatment. Extensive pre-clinical data have shown that mesenchymal stromal (stem) cell (MSC) therapy can induce anti-inflammatory effects and enhance repair of the injured lung. We hypothesized that MSC therapy would improve survival, dampen lung inflammation and decrease acute lung injury (ALI) in a murine model of severe influenza. METHODS: C57Bl/6 mice were infected with influenza A/PuertoRico/8/34 (mouse-adapted H1N1) or influenza A/Mexico/4108/2009 (swine-origin pandemic H1N1) and administered human or mouse MSCs via the tail vein, either pre- or post- infection. MSC efficacy was evaluated as both an independent and adjunctive treatment strategy in combination with the antiviral agent, oseltamivir. Weight loss and survival were monitored. Inflammatory cells, cytokine/chemokines (IFN-γ, CXCL10, CCL2 and CCL5) and markers of ALI (total protein and IgM), were measured in bronchoalveolar lavage fluid and lung parenchyma. RESULTS: Administration of murine MSCs or human MSCs in a prophylactic or therapeutic regimen failed to improve survival, decrease pulmonary inflammation/inflammatory cell counts or prevent ALI in influenza virus-infected mice. MSCs administered in combination with oseltamivir also failed to improve outcomes. CONCLUSIONS: Despite similarities in the clinical presentation and pathobiology of ALI and severe influenza, our findings suggest that MSC therapy may not be effective for prevention and/or treatment of acute severe influenza.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".