Thyroid hormone protects human lung epithelial cells from cold preservation and warm reperfusion-induced injury
Bibliographic record
Abstract
BACKGROUND: Cellular stress associated with static-cold storage (SCS) and warm reperfusion of donor lungs can contribute to ischemia-reperfusion (IR) injury during transplantation. Adding cytoprotective agents to the preservation solution may be conducive to reducing graft deterioration and improving post-transplant outcomes. METHODS: SCS and warm reperfusion were simulated in human lung epithelial cells (BEAS-2B) by exposing cells to low potassium dextran glucose solution at 4 °C for different periods and then switching back to serum-containing culture medium at 37 °C. Transcriptomic analysis was used to explore potential cytoprotective agents. Based on its results, cell viability, caspase activity, cell morphology, mitochondrial function, and inflammatory gene expression were examined under simulated IR conditions with or without thyroid hormones (THs). RESULTS: After 18 h SCS followed by 2 h warm reperfusion, genes related to inflammation and cell death were upregulated, and genes related to protein synthesis and metabolism were downregulated in BEAS-2B cells, which closely mirrored gene profiles found in thyroid glands of mice with congenital hypothyroidism. The addition of THs (T3 or T4) to the preservation solution increases cell viability, inhibits activation of caspase 3, 8 and 9, preserves cell morphology, enhances mitochondrial membrane potential, reduces mitochondrial superoxide production, and suppresses inflammatory gene expression. CONCLUSION: Adding THs to lung preservation solutions may protect lung cells during SCS by promoting mitochondrial function, reducing apoptosis, and inhibiting pro-inflammatory pathways. Further in vivo testing is warranted to determine the potential clinical application of adding THs as therapeutics in lung preservation solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".