Mouse strain-dependent variation in metabolic associated fatty liver disease (MAFLD): a comprehensive resource tool for pre-clinical studies
Bibliographic record
Abstract
Non-alcoholic steatohepatitis (NASH), characterized as the joint presence of steatosis, hepatocellular ballooning and lobular inflammation, and liver fibrosis are strong contributors to liver-related and overall mortality. Despite the high global prevalence of NASH and the substantial healthcare burden, there are currently no FDA-approved therapies for preventing or reversing NASH and/or liver fibrosis. Importantly, despite nearly 200 pharmacotherapies in different phases of pre-clinical and clinical assessment, most therapeutic approaches that succeed from pre-clinical rodent models to the clinical stage fail in subsequent Phase I-III trials. In this respect, one major weakness is the lack of adequate mouse models of NASH that also show metabolic comorbidities commonly observed in NASH patients, including obesity, type 2 diabetes and dyslipidaemia. This study provides an in-depth comparison of NASH pathology and deep metabolic profiling in eight common inbred mouse strains (A/J, BALB/c, C3H/HeJ, C57BL/6J, CBA/CaH, DBA/2J, FVB/N and NOD/ShiLtJ) fed a western-style diet enriched in fat, sucrose, fructose and cholesterol for eight months. Combined analysis of histopathology and hepatic lipid metabolism, as well as measures of obesity, glycaemic control and insulin sensitivity, dyslipidaemia, adipose tissue lipolysis, systemic inflammation and whole-body energy metabolism points to the FVB/N mouse strain as the most adequate diet-induced mouse model for the recapitulation of metabolic (dysfunction) associated fatty liver disease (MAFLD) and NASH. With efforts in the pharmaceutical industry now focussed on developing multi-faceted therapies; that is, therapies that improve NASH and/or liver fibrosis, and concomitantly treat other metabolic comorbidities, this mouse model is ideally suited for such pre-clinical use.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".