Immune Microenvironment on the Molecular Mechanisms and Therapeutic Targets of MAFLD
Bibliographic record
Abstract
Zhonghao Jiang,1,* Baolin Qian,1,* Tongjie Xu,1,2 Junjie Bai,1 Wenguang Fu1,3 1Department of Biliary-Pancreatic Center, The Affiliated Hospital of Southwest Medical University, Luzhou, People’s Republic of China; 2Department of Vascular Surgery, The Affiliated Hospital, Southwest Medical University, Luzhou, 646000, People’s Republic of China; 3Metabolic Hepatobiliary and Pancreatic Diseases Key Laboratory of Luzhou City, The Affiliated Hospital, Southwest Medical University, Luzhou, 646000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wenguang Fu, Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital, Southwest Medical University, Luzhou, 646000, People’s Republic of China, Email fuwg@swmu.edu.cnAbstract: Metabolic dysfunction-associated fatty liver disease (MAFLD) is one of the most prevalent chronic liver diseases worldwide. It is characterized by hepatic steatosis in the absence of significant alcohol consumption, and can progress to liver fibrosis, cirrhosis, and even hepatocellular carcinoma (HCC). Despite its widespread impact, treatment options remain limited, and effective therapies targeting the underlying disease mechanisms are lacking. Recent studies have highlighted the critical role of the liver’s immune microenvironment in the onset and progression of MAFLD. However, research into immune-based therapies remains in its early stages. Most existing studies have focused on understanding the immune mechanisms involved, but specific immune targets and therapeutic strategies have yet to be fully explored. This gap has hindered the development of targeted immunotherapies for MAFLD. This review aims to examine the molecular mechanisms of the immune microenvironment in MAFLD and identify potential therapeutic targets, offering insights for future clinical and scientific advancements.Keywords: MAFLD, immune microenvironment, pathway, therapeutic targets
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".