Bibliographic record
Abstract
PURPOSE OF REVIEW: Interleukin-6 (IL-6) has emerged as a cytokine involved in cachexia progression with some cancers. This review will present the recent breakthroughs in animal models and humans related to targeting IL-6 as a cancer cachexia therapy. RECENT FINDINGS: IL-6 can target adipose, skeletal muscle, gut, and liver tissue, which can all affect cachectic patient recovery. IL-6 trans-signaling through the soluble IL-6R has the potential to amplify IL-6 signaling in the cachectic patient. In the skeletal muscle, chronic IL-6 exposure induces proteasome and autophagy protein degradation pathways that lead to wasting. IL-6 is also indirectly associated with AMP-activated kinase (AMPK) and nuclear factor kappa B (NF-κB) activation. Several mouse cancer models have clearly demonstrated that blocking IL-6 and associated signaling can attenuate cachexia progression. Additionally, pharmaceuticals targeting IL-6 and associated signaling can relieve some cachectic symptoms in cancer patients. Research with cachectic mice has demonstrated that exercise and nutraceutical administration can interact with chronic IL-6 signaling during cachexia progression. SUMMARY: IL-6 remains a promising therapeutic strategy for attenuating cachexia progression with many types of cancer. However, improvement of this treatment will require a better understanding of the indirect and direct effects of IL-6 as well as its tissue-specific actions in the cancer patient.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".