Bibliographic record
Abstract
Chemotherapy is one of the main therapies in cancer and plays an important role in controlling tumor progression, which can offer a longer overall survival (OS) for patients. But as the accumulation of drugs used in vivo, cancer cells develop drug resistance, even multi-drug resistance (MDR), that can cause failure of the whole therapy. The similar phenomenon can be observed in vitro. There are several mechanisms of drug resistance such as drug efflux, mediated by extracellular vesicles. Exosomes, a subset of extracellular vesicles (EVs), can be secreted by many types of cells and transfer proteins, lipids, and miRNA/mRNA/DNAs between cells in vitro and in vivo. Particularly cancer cells secrete more exosomes than healthy cells and resistance cells secrete more exosomes than sensitive cells. Exosomes have function of intercellular communication and molecular transfer, both associated with tumor growth, invasion, metastasis, angiogenesis, and drug resistance. In this paper, we will review the current knowledge regarding the emerging roles of exosomes and its cargo in drug resistance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".