Biological landscape and nanostructural view in development and reversal of oxaliplatin resistance in colorectal cancer
Bibliographic record
Abstract
The treatment of cancer patients has been mainly followed using chemotherapy and it is a gold standard in improving prognosis and survival rate of patients. Oxaliplatin (OXA) is a third-platinum anti-cancer agent that reduces DNA synthesis in cancer cells to interfere with their growth and cell cycle progression. In spite of promising results of using OXA in cancer chemotherapy, the process of drug resistance has made some challenges. OXA is commonly applied in treatment of colorectal cancer (CRC) as a malignancy of gastrointestinal tract and when CRC cells increase their proliferation and metastasis, they can obtain resistance to OXA chemotherapy. A number of molecular factors such as CHK2, SIRT1, c-Myc, LATS2 and FOXC1 have been considered as regulators of OXA response in CRC cells. The non-coding RNAs are able to function as master regulator of other molecular pathways in modulating OXA resistance. There is a close association between molecular mechanisms such as apoptosis, autophagy, glycolysis and EMT with OXA resistance, so that apoptosis inhibition, pro-survival autophagy induction and stimulation of EMT and glycolysis can induce OXA resistance in CRC cells. A number of anti-tumor compounds including astragaloside IV, resveratrol and nobiletin are able to enhance OXA sensitivity in CRC cells. Nanoparticles for increasing potential of OXA in CRC suppression and reversing OXA resistance have been employed in cancer chemotherapy. These subjects are covered in this review article to shed light on molecular factors resulting in OXA resistance.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".