Blocking TGF-β Expression Attenuates Tumor Growth in Lung Cancers, Potentially Mediated by Skewing Development of Neutrophils
Bibliographic record
Abstract
In the tumor microenvironment (TME), cells secrete a cytokine known as transforming growth factor-β (TGF-β), which polarizes tumor-associated neutrophils (TANs) towards a protumor phenotype. In this work, C57BL/6 mice with TGF-β1 gene knocked out selectively in myofibroblasts receive orthotopic implantation of Lewis lung carcinoma (LLC). Then, TANs’ differentiation and tumor growth are studied both in vivo and in vitro, to examine the potential effects of TGF-β levels in TME on neutrophil polarization and cancer progression. Possible results are anticipated and discussed from various aspects. Though tumor suppression via inhibition of TGF-β signaling has been widely studied in this field, this study is the first to present a detailed experimental design for evaluating the potential antitumor effects of blocking TGF-β expression. This work provides a creative approach for cancer treatment targeting specific cytokines, and the experimental design presented here may apply to future research on other cytokines, promoting the development of novel cancer-treating strategies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".