Malignant function of nuclear factor-kappaB axis in prostate cancer: Molecular interactions and regulation by non-coding RNAs
Bibliographic record
Abstract
Prostate carcinoma is a malignant situation that arises from genomic alterations in the prostate, leading to changes in tumorigenesis. The NF-κB pathway modulates various biological mechanisms, including inflammation and immune responses. Dysregulation of NF-κB promotes carcinogenesis, including increased proliferation, invasion, and therapy resistance. As an incurable disease globally, prostate cancer is a significant health concern, and research into genetic mutations and NF-κB function has the efficacy to facilitate the introduction of novel therapies. NF-κB upregulation is observed during prostate cancer progression, resulting in increased cell cycle progression and proliferation rates. Additionally, NF-κB endorses resistance to cell death and enhances the capacity for metastasis, particularly bone metastasis. Overexpression of NF-κB triggers chemoresistance and radio-resistance, and inhibition of NF-κB by anti-tumor compounds can reduce cancer progression. Interestingly, non-coding RNA transcripts can regulate NF-κB level and its nuclear transfer, offering a potential avenue for modulating prostate cancer progression.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".