Potential Influence of Antifungal-drug Resistant Pathogens in Patients with Cholangiocarcinoma and the Application of Nanoparticle Mechanisms as Novel Antifungal and Anticancer Agents
Bibliographic record
Abstract
Abstract Purpose of Review Cholangiocarcinoma (CCA) is the most lethal and common malignant tumours that occur in the bile ducts. Although it is relatively rare, it is prevalent with an annual incidence rate of 0.3–6% per 100,000 people globally. The onset of CCA can be influenced by several risk factors, including exposure to invasive fungal pathogens. Immunocompromised patients with CCA that undergo endoscopic retrograde cholangiopancreatography (ERCP) are susceptible to invasive fungal infections (IFIs) caused by the World Health Organization (WHO) priority list of critical fungal pathogens. This potentially leads to harbouring of antifungal-drug resistant pathogens (AFDRPs) that could have a detrimental impact on disease treatment as a result of their resistance mechanisms. Recent Findings In this article, we reviewed the prevalence and impact of AFDRP colonisation in CCA patients undergoing ERCP. The potential influence of AFDRPs on the development of CCA tumours or the response to treatment. Lastly, we addressed the potential application of drug delivery systems based on nanoparticles for the targeted delivery of antifungal and anticancer agents to AFDRPs in CCA patients. Summary Understanding the mechanisms of nanoparticles (NPs) in manipulating fungal cells and CCA cells as these interactions are complex. Therefore, the development effective NP-based strategies as antifungal and anticancer agents in important for the treatment of cancer and fungal infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".