Statins as the Controlling Agents for Non-Hodgkin's Lymphomas via Increasing the Casein Kinase 2 Interacting Protein-1: A Hypothesis
Bibliographic record
Abstract
BACKGROUND: Non-Hodgkin's lymphomas (NHL), derived from B- or T-cell, consist of a heterogeneous group of malignant lymphoproliferative disorders. Knockdown of Casein kinase 2 interacting protein-1 (CKIP-1) in NHL promoted cell proliferation and inhibited apoptosis via enhancing phosphorylated Protein Kinase B (PKB or AKT) expression. Statins are the class of drugs that inhibit the ratelimiting step of the mevalonate pathway, which is essential for the biosynthesis of various compounds, including cholesterol. Also, statins have anticancer properties being mediated by different mechanisms. METHODS: A search on databases like Scopus and PubMed with keywords such as statin and non- Hodgkin's lymphomas was performed and Kyoto Encyclopedia of Genes and Genomes (KEGG) website was used to evaluate and reconfirm the involved cellular signaling pathway. RESULTS: CKIP-1 is involved in the regulation of cell proliferation and apoptosis while plays an important role in many cancers. We can hypothesize that statins may increase the expression levels of CKIP-1 which could contribute to the reductions in phospho-AKT level. Hence, they may ameliorate the NHL patients via suppressing AKT phosphorylation and increasing CKIP- expression. CONCLUSION: Present review confirms the positive effect of statins on NHL by increasing CKIP-1 and reducing cell proliferation, subsequently.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".