Studying non-alcoholic fatty liver disease: the ins and outs of in vivo, ex vivo and in vitro human models
Bibliographic record
Abstract
The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing. Determining the pathogenesis and pathophysiology of human NAFLD will allow for evidence-based prevention strategies, and more targeted mechanistic investigations. Various in vivo, ex situ and in vitro models may be utilised to study NAFLD; but all come with their own specific caveats. Here, we review the human-based models and discuss their advantages and limitations in regards to studying the development and progression of NAFLD. Overall, in vivo whole-body human studies are advantageous in that they allow for investigation within the physiological setting, however, limited accessibility to the liver makes direct investigations challenging. Non-invasive imaging techniques are able to somewhat overcome this challenge, whilst the use of stable-isotope tracers enables mechanistic insight to be obtained. Recent technological advances (i.e. normothermic machine perfusion) have opened new opportunities to investigate whole-organ metabolism, thus ex situ livers can be investigated directly. Therefore, investigations that cannot be performed in vivo in humans have the potential to be undertaken. In vitro models offer the ability to perform investigations at a cellular level, aiding in elucidating the molecular mechanisms of NAFLD. However, a number of current models do not closely resemble the human condition and work is ongoing to optimise culturing parameters in order to recapitulate this. In summary, no single model currently provides insight into the development, pathophysiology and progression across the NAFLD spectrum, each experimental model has limitations, which need to be taken into consideration to ensure appropriate conclusions and extrapolation of findings are made.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".