In-vitro Modulation of mTOR-HIF-1α Axis by TLR7/8 Agonist (Resiquimod) in B-Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
Abstract Targeting toll-like receptors (TLRs), via TLR agonists, has been implicated in the regulation of immunometabolism. B-chronic lymphocytic leukemia (B-CLL) represents a suitable model for B-cell derived malignancies with shifted metabolic adaptations. Several signaling pathways have been found to be critical in metabolic reprogramming of CLL, including mechanistic target of rapamycin- hypoxia inducible factor-1α (mTOR- HIF-1α) pathway, the main metabolic regulator of glycolysis. Here, we investigated the effect of TLR7/8 agonist (Resiquimod) on the expression of mTOR and HIF-1α in patients with CLL. B cells were purified using Rosettesep Human B cell Enrichment Cocktail (Stem cell Technologies, Vancouver, BC, Canada#15,024) from peripheral venous blood of CLL patients (n = 20) and healthy individuals (n = 15). Isolated B cells were then cultured in both presence and absence of Resiquimod. Gene expression of mTOR and HIF-1α were assessed using qRT-PCR. Resiquimod significantly decreased mTOR and HIF-1α gene expression in both CLL ( p < 0.001and p < 0.001, respectively) and Normal B cells ( p = 0.004 and p = 0.001, respectively). Resiquimod may reprogram immunometabolism of malignant B-CLL cells via down-regulation of key glycolytic metabolic actors, mTOR and HIF-1α genes. Accordingly, Resiquimod may be an adjuvant as a therapeutic tool for CLL, which needs to be studied further.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".