Targeting mixed lineage kinase domain-like protein's non-necroptosis role: A new horizon in anti-inflammatory therapy for alcoholic liver disease
Bibliographic record
Abstract
Although mixed lineage kinase domain-like protein (MLKL) is widely recognized as a critical effector in the necroptotic signaling pathway, MLKL plays broader regulatory roles beyond programmed necroptosis. Notably, Xuan Yuan et al demonstrated that CPD4, an ATP-binding pocket inhibitor of MLKL, significantly reduces liver inflammation and improves liver function by inhibiting NF-κB signaling, suggesting its use as a potential therapeutic candidate for alcoholic liver disease. However, the pharmacokinetic properties and long-term toxicity of CPD4 require further evaluation. Moreover, a single therapeutic strategy targeting MLKL may not be sufficient. Future studies should focus on the precise regulation of MLKL and develop combination therapies to achieve dual intervention of inflammatory and cell death pathways. This paper provides an important theoretical foundation for translational research on MLKL-targeted therapy. However, its clinical translation requires overcoming existing limitations and further elucidating the regulatory network of MLKL in complex microenvironments.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".