The Evaluation of Lipid Analysis for PXB-Cells LA as a Human Non-Alcoholic Fatty Liver Disease Model
Bibliographic record
Abstract
Fatty liver can progress into serious conditions, and the number of patients with fatty liver disease has risen globally in recent years. Various lipid metabolism disorders can cause fatty liver, and in vitro models, such as hepatoma cell lines, have been utilized in research related to lipid metabolism disorders, including the development of treatment strategies. We previously demonstrated that fresh hepatocytes (PXB-cells®) from chimeric mice with humanized livers display lipid metabolism similar to that of normal human hepatocytes. Additionally, we developed PXB-cells Lipid Analysis (PXB-cells LA) as a model of non-alcoholic fatty liver disease (NAFLD). PXB-cells LA exhibited increased levels of intracellular lipid droplets and lipids, especially triglycerides, compared to PXB-cells. Additionally, albumin secretion, drug metabolism, bile excretion transporters, mitochondria-derived oxidative phosphorylation, and intracellular adenosine triphosphate levels were attenuated in PXB-cells LA, while inflammatory marker levels were elevated. Collectively, these findings indicate hepatic dysfunction. Additionally, PXB-cells LA showed a fractional profile with a peak in very low-density lipoproteins, similar to PXB-cells. PXB-cells LA also secreted lipoproteins with a higher triglyceride content, associated with NAFLD. Taken together, these results suggest that PXB-cells LA is a useful cellular model of human NAFLD.
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 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 | 0.001 | 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) | 0.002 | 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".