Protective Role of Apelin in a Mouse Model of Post–Intensive Care Syndrome
Bibliographic record
Abstract
Post-intensive care syndrome (PICS) is a serious condition involving physical weakness, depression, and cognitive impairment that develop during or after an ICU stay, often resulting in long-term declines in quality of life. Patients with acute respiratory distress syndrome and severe coronavirus disease (COVID-19) are at particularly high risk, yet the molecular mechanisms underlying PICS remain poorly understood. Here, we identify impaired Apelin-APJ signaling as a potential contributor to PICS pathogenesis through the disruption of interorgan homeostasis. Using a mouse model combining acute lung injury and hindlimb immobilization, we observed PICS-like features, including muscle atrophy, lung inflammation, and neurobehavioral abnormalities such as anxiety-like behavior and special working memory. Single-cell RNA sequencing in brain revealed upregulation of gene programs associated with Alzheimer's disease, depression, and neuroinflammation, particularly in endothelial cells and microglia. Concurrently, Apelin-APJ signaling was downregulated in skeletal muscle. These changes were exacerbated in Apelin-deficient mice and attenuated by muscle-specific Apelin overexpression, which also reduced systemic IL-6 and restored circulating Apelin levels. In survivors of ARDS who had severe COVID-19, ICU-acquired weakness was associated with reduced plasma Apelin and elevated IL-6 levels. Transcriptomic profiling of peripheral blood mononuclear cells from patients with ICU-acquired weakness showed gene expression signatures linked to depression and neurodegeneration, mirroring murine findings. These data suggest that impaired Apelin-APJ signaling may play a role in PICS pathophysiology. Although skeletal muscle appears to contribute to systemic Apelin levels, further studies are needed to clarify tissue-specific roles. Modulating this pathway could offer a therapeutic strategy to mitigate long-term outcomes in ICU survivors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".