A Preclinical Evaluation of <i>Antrodia camphorata</i> Alcohol Extracts in the Treatment of Non‐Small Cell Lung Cancer Using Non‐Invasive Molecular Imaging
Bibliographic record
Abstract
This study was carried out to provide a platform for the pre-clinical evaluation of anti-cancer properties of a unique CAM (complementary and alternative medicine) agent, Antrodia camphorata alcohol extract (ACAE), in a mouse model with the advantageous non-invasive in vivo bioluminescence molecular imaging technology. In vitro analyses on the proliferation, migration/invasion, cell cycle and apoptosis were performed on ACAE-treated non-small cell lung cancer cells, H441GL and control CGL1 cells. In vivo, immune-deficient mice were inoculated subcutaneously with H441GL followed by oral gavages of ACAE. The effect of ACAE on tumor progression was monitored by non-invasive bioluminescence imaging. The proliferation and migration/invasion of H441GL cells were inhibited by ACAE in a dose-dependent manner. In addition, ACAE induced cell cycle arrest at G0/G1 phase and apoptosis in H441GL cells as shown by flow cytometric analysis, Annexin-V immunoflourescence and DNA fragmentation. In vivo bioluminescence imaging revealed that tumorigenesis was significantly retarded by oral treatment of ACAE in a dose-dependent fashion. Based on our experimental data, ACAE contains anti-cancer properties and could be considered as a potential CAM agent in future clinical evaluation.
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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) | 0.001 | 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) | 0.002 | 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".