Alternol triggers immunogenic cell death <i>via</i> reactive oxygen species generation
Bibliographic record
Abstract
Alternol is a naturally occurring compound that exerts antitumor activity in several cancers. However, whether Alternol induces antitumor immune response remains unknown. In this study, we investigated whether Alternol induced immunogenic cell death (ICD) in prostate cancer cells. Alternol triggered ICD in prostate cancer cells, as evidenced by the release of damage-associated molecular patterns (DAMPs) (i.e., calreticulin, CALR; high mobility group protein B1, HMGB1; and adenosine triphosphate, ATP) and pro-inflammatory cytokine (i.e., interleukin [IL]-1α, IL-1β, IL-6, and IL-8) expression. Alternol facilitated tumor-associated antigen uptake and cross-presentation, CD8 + T-cell priming, and T-cell infiltration in tumor-draining lymph nodes (LNs) and tumors. The presence of Alternol fostered antitumor immune response in vivo, resulting in delayed tumor growth and prolonged survival. Moreover, inhibition of reactive oxygen species (ROS) generation blocked Alternol-induced upregulation of pre-inflammation cytokines, endoplasmic reticulum (ER) stress, and consequent antitumor immune response. Overall, our data indicate that Alternol triggers ICD in prostate cancer cells, which is mediated by ROS generation.
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 | 0.001 |
| 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".