Current Developments of Iron Oxide Nanomaterials as MRI Theranostic Agents for Pancreatic Cancer
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive type of pancreatic cancer. PDAC is difficult to diagnose due to a lack of symptoms in early stages, resulting in a survival rate of less than 10%. Moreover, often cancerous tissues cannot be surgically resected due to their deep abdomen location. Therefore, early detection is the essential strategy enabling effective PDAC treatment. Over the past few years, the development of nanomaterials for Magnetic Resonance Imaging (MRI) has expanded and improved imaging quality and diagnostic accuracy. Nanomaterials can be currently designed, manufactured and synthesized with other structures to provide improved diagnosis and advanced therapy. Although MRI equipped with the innovative nanomaterials became a powerful tool for the diagnosis and treatment of patients with various cancers, the detection of PDAC remains challenging. Nevertheless, recent advancements in PDAC theranostics provided progress in the detection and treatment of this challenging type of cancer. Present research in this area is focused on suitable carriers, eliminating delivery barriers, and the development of efficient anti-cancer drugs. Herein we discuss the current applications of iron oxide nanoparticles to the MRI diagnosis and treatment of pancreatic cancer.
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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 | 0.001 | 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 | 0.001 |
| 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".