Cutting-Edge Approach to Targeted Therapy: Repositioning of Old Drugs in Combination with Standard Clinical Chemotherapeutics Potentiates a Propitious Novel Targeted Therapy for Human Pancreatic Cancer
Bibliographic record
Abstract
Metastatic pancreatic cancer leads to a fatal outcome, with a median progression-free survival of approximately six months when utilizing the most successful combination of chemotherapeutic regimens. When drug resistance develops, it facilitates an increase in primary tumor growth and new and growing metastases. Patients inevitably and quickly succumb to their disease and die. Notably, chemotherapy has an unintended impact on the development of drug resistance through the enhancement of EMT development and the enrichment of cancer stem cells (CSC). Recent report discovered that neuraminidase-1 (Neu-1) regulates EMT induction, angiogenesis, and cellular proliferation by the activation of several receptor tyrosine kinases. Here, the continual therapeutic inhibition of Neu-1 through intravenous administration of oseltamivir phosphate (OP) and aspirin (ASA) alongside GEM treatment significantly inhibits tumor progression, crucial compensatory signaling pathways, EMT program, CSC, and metastasis progression in a preclinical RAG2xCy double mutant BALB/c mouse model of human PANC-1 pancreatic cancer. The tumorigenic and metastatic potential of the xenotumors from the animals treated with the experimental protocols were significantly ablated when transferred into the mammary fat pads of NSG (NOD SCID gamma) branded mice. Keywords: pancreatic cancer; chemoresistance; drug repurposing; EMT.
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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 | 0.000 |
| 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".