FB-631 as a promising therapeutic tool for the treatment of non-small cell lung carcinoma 2469
Bibliographic record
Abstract
Abstract Description Aim Many NSCLC patients do not respond to current treatment options, or develop resistance in time. Therefore, developing therapeutic alternatives is an important focus of lung cancer research. Activation of the innate immunity response through stimulation of TLR7/8 is a promising strategy, but current TLR7/8 agonists are not safe for local administration in the lung. FB-631, a nanoparticle developed by Dr. Denis Leclerc, is a new TLR7 agonist, a potent activator of innate immunity, and was proven to be safe for administration via parenteral routes. Objective: Evaluate the impact of FB-631 on tumour development in a model of lung carcinoma. Methodology: Mice received FB-631 before or after the IV administration of CMT167 lung carcinoma cells. Cancer severity was reported upon sacrifice via the evaluation of lung index, and of a cancer score representative of the number and size of tumors. The pulmonary immune profile was also evaluated by flow cytometry. Results Cancer severity was significantly reduced in mice pre-treated with FB-631. This came with an increase in CD8 T cell and NK cell proportions in relation to tumor burden, and a modulation of regulatory molecules PD-L1/2 expression in vivo. In vitro, the stimulation of anticancer cDC1s with FB-631 increased IL-12 production and PD-L1/2 expression. Conclusion FB-631 can prevent the implantation of tumor cells and the development of tumors by supporting anti-tumor immunity, making it a promising tool for NSCLC treatment. Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".