Optimization of In vivo Imaging Provides a First Look at Mouse Model of Non-Alcoholic Fatty Liver Disease (NAFLD) Using Intravital Microscopy
Bibliographic record
Abstract
Non-alcoholic fatty liver disease is a spectrum of liver pathology ranging from simple steatosis to steatohepatitis and can progress to diseases associated with poor outcomes including cirrhosis and hepatocellular carcinoma (HCC). NAFLD research has typically focused on the pathophysiology associated with lipid metabolism, using traditional measures such as histology and serum transaminase assessment; these methods have provided key information regarding NAFLD progression. Although valuable, these techniques are limited in providing further insight into the mechanistic details of inflammation associated with NAFLD. Intravital microscopy (IVM) is an advanced tool that allows for real-time visualization of cellular behavior and interaction in a living animal. Extensive IVM imaging has been conducted in liver, but, in the context of NAFLD, this technique has been regularly avoided due to significant tissue autofluorescence, a phenomenon that is exacerbated with steatosis. Here, we demonstrate that, using multiple imaging platforms and optimization techniques to minimize autofluorescence, IVM in fatty liver is possible. Successful fatty liver intravital imaging provides details on cell trafficking, recruitment, function, and behavior in addition to information about blood flow and vessel dynamics, information which was previously difficult to obtain. As more than 30% of the global population is overweight/obese, there is a significant proportion of the population at risk for NAFLD and complications due to NAFLD (liver decompensation, cirrhosis, HCC). IVM has the potential to elucidate the poorly understood mechanisms surrounding liver inflammation and NAFLD progression and possesses the potential to identify key processes that may be targeted for future therapeutic interventions in NAFLD patients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".