Prostaglandin F<sub>2α</sub> synthase promotes oxaliplatin resistance in colorectal cancer through prostaglandin F<sub>2α</sub>-dependent and F<sub>2α</sub>-independent mechanism
Bibliographic record
Abstract
Small extracellular vesicles (exosomes) are important components of the tumor microenvironment. They are small membrane-bound vesicles derived from almost all cell types and play an important role in intercellular communication. Exosomes transmit biological molecules obtained from parent cells, such as proteins, lipids, and nucleic acids, and are involved in cancer development. MicroRNAs (miRNAs), the most abundant contents in exosomes, are selectively packaged into exosomes to carry out their biological functions. Recent studies have revealed that exosome-delivered miRNAs play crucial roles in the tumorigenesis, progression, and drug resistance of hepatocellular carcinoma (HCC). In addition, exosomes have great industrial prospects in the diagnosis, treatment, and prognosis of patients with HCC. This review summarized the composition and function of exosomal miRNAs of different cell origins in HCC and highlighted the association between exosomal miRNAs from stromal cells and immune cells in the tumor microenvironment and the progression of HCC. Finally, we described the potential applicability of exosomal miRNAs derived from mesenchymal stem cells in the treatment of HCC.
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".