THE DEVELOPMENT OF AN IN VITRO MODEL TO EXAMINE AND MODULATE HEPATIC ISCHEMIA AND REPERFUSION RESPONSES
Bibliographic record
Abstract
Transplantation is the optimal form of therapy for patients with end-stage liver disease; however, the use of organs with hepatic steatosis is often associated with increased risks for poor function and graft loss. In addition, ischemia reperfusion (IR) injury leads to cellular damage that can culminate in functional impairment and loss of graft. Furthermore, IR injury is aggravated by pre-existing steatosis and may involve additional mechanisms and mediators of cellular damage. Current models to study IR in vitro are not well defined and may overlook periods of injury that are involved in transplantation. In this thesis, I present an in vitro model for IR injury that includes multiple phases of injury and leads to the upregulation of heme oxygenase-1 (HO-1), and possibly enhances the expression of matrix metalloproteinase-9 (MMP-9). As graft HO-1 expression correlates positively with reduced injury, but MMP-9 expression is associated with increased injury, I therefore examined the utility of in vitro gene therapies to affect the expression of these proteins. We conclude that the in vitro model of ischemia and reperfusion is a promising tool to study the cellular response to IR and may provide a platform for the development of future therapies which could have clinical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".