Molecular analysis of the inhibitory effect of N-acetyl-L-cysteine on the proliferation and invasiveness of pancreatic cancer cells
Bibliographic record
Abstract
Preliminary studies have suggested that the reactive oxygen species (ROS) scavenger N-acetyl-L-cysteine (NAC) may be effective in inhibiting the growth of pancreatic cancer cells. In-depth cellular and molecular analyses were carried out to determine NAC's mode of action in inhibiting the growth of a well-characterized pancreatic cancer cell line (AsPC-1). Standardized assays were used to monitor cellular growth, apoptosis, levels of ROS, cellular senescence, migration, and invasiveness. Cell stiffness was measured using atomic force microscopy. Gene expression was monitored by quantitative PCR. NAC significantly inhibits the growth and metastatic potential of AsPC-1 cells by inducing cell-cycle arrest in G1 and subsequent cellular senescence and decreased invasiveness. These anticancer properties are associated with an unexpected increase in the intracellular concentrations of ROS. NAC does not decrease the susceptibility of AsPC-1 cells to the anticancer drugs gemcitabine, mitomycin C, and doxorubicin. NAC-induced changes in gene expression are consistent with the onset of mesenchymal-to-epithelial transition. In conclusion, our findings indicate that NAC induces an integrated series of responses in AsPC-1 cells that make it a highly promising candidate for development as a pancreatic cancer therapeutic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".