Efficacy of hyperthermia in human colon adenocarcinoma cells is improved by auraptene
Bibliographic record
Abstract
Colon adenocarcinoma is one of the most common cancers worldwide, and resistance to current therapeutic modalities is a serious drawback in its treatment. Auraptene is a natural coumarin with considerable anticancer effects. The goal of this study was to introduce a novel combinatorial approach for treatment against colon adenocarcinoma cells. To do so, HT29 cells were pretreated with nontoxic auraptene and then hyperthermia was induced. Afterwards, the viability of the cells was assessed, changes induced in the cell cycle were analyzed, and the expression patterns of candidate genes were studied. Results from the MTT assay demonstrated significant (p < 0.01) decreases in cell viability when 20 μg/mL auraptene was used for 72 h, heat shock was induced, and cells were allowed to recover for 24 h. Flow cytometry analysis also indicated considerable changes in the distribution of cells between the sub-G1/G1 and G2/M phases of cell cycle after the combinatorial treatment. Real-time RT–PCR studies revealed significant (p < 0.01) up-regulation of P21 in the cells pretreated with auraptene after heat shock, whereas no significant change was observed in HSP27 expression. Our findings not only indicate, for the first time, that the efficacy of hyperthermia was improved by auraptene pretreatment, but also suggest that this coumarin could be used in the future to achieve more effective therapeutic outcomes.
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".