Multiphoton microscopy can visualize zonal damage and decreased cellular metabolic activity in hepatic ischemia-reperfusion injury in rats
Bibliographic record
Abstract
Ischemia-reperfusion (I/R) injury is a common occurrence in liver surgery. In orthotopic transplantation, the donor liver is exposed to periods of ischemia and when oxygenated blood is reintroduced to the liver, oxidative stress may develop and lead to graft failure. The aim of this project was to investigate whether noninvasive multiphoton and fluorescence lifetime imaging microscopy, without external markers, were useful in detecting early liver damage caused by I/R injury. Localized hepatic ischemia was induced in rats for 1 h followed by 4 h reperfusion. Multiphoton and fluorescence lifetime imaging microscopy was conducted prior to ischemia and up to 4 h of reperfusion and compared to morphological and biochemical assessment of liver damage. Liver function was significantly impaired at 2 and 4 h of reperfusion. Multiphoton microscopy detected liver damage at 1 h of reperfusion, manifested by vacuolated cells and heterogeneous spread of damage over the liver. The damage was mainly localized in the midzonal region of the liver acinus. In addition, fluorescence lifetime imaging showed a decrease in cellular metabolic activity. Multiphoton and fluorescence lifetime imaging microscopy detected evidence of early I/R injury both structurally and functionally. This provides a simple noninvasive technique useful for following progressive liver injury without external markers.
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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) | 0.000 | 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) | 0.001 | 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 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".