Enhanced Sensitivity of Cancer Stem Cells to Chemotherapy Using Functionalized Mesoporous Silica Nanoparticles

Cancer stem cells (CSCs) are responsible for cancer drug resistance with high expression of ABCG2, which pumps the internalized chemotherapeutic out to escape drug-induced cytotoxicity. Here, we established a functionalized mesoporous silica nanoparticle (MSN) system to deliver shABCG2 and doxorubicin (Dox) synergistically. With excellent cell uptake and endosomal escape capacities, the dual-delivery carriers internalized shABCG2 and Dox into CSCs efficiently. ABCG2 depletion increased intracellular and intranuclear Dox enrichment, drove vigorous Dox-induced cell death, and impaired the self-renewal of CSCs. Additionally, the nanoparticles eliminated tumors efficiently and reduced tumor initiation by CSCs <i>in vivo</i>, with negligible side effects. Our findings suggest that well-designed delivery systems for conventional chemotherapeutic agents are promising for CSC therapy

ROC curve of APACHE II score on admission versus APACHE II score on day 1 in predicting POF.

<p>ROC curve of APACHE II score on admission versus APACHE II score on day 1 in predicting POF.</p

ROC analysis of CD4⁺ T cell proportion, CD4⁺ / CD8⁺ ratio and APACHE II scores in diagnosing POF.

<p>AUC: area under the curve; CI: confidence intervals; PPV: positive predictive value; NPV: negative predictive value.</p><p>ROC analysis of CD4⁺ T cell proportion, CD4⁺ / CD8⁺ ratio and APACHE II scores in diagnosing POF.</p

ROC curve of CD4⁺ T cell proportion versus CD4⁺/CD8⁺ ratio in predicting POF.

<p>ROC curve of CD4⁺ T cell proportion versus CD4⁺/CD8⁺ ratio in predicting POF.</p

Baseline characteristics of the AP patients.

<p>Data are presented in either means and standard deviations or frequencies and percentages.</p><p>Baseline characteristics of the AP patients.</p

Prediction of Severe Acute Pancreatitis Using a Decision Tree Model Based on the Revised Atlanta Classification of Acute Pancreatitis

<div><p>Objective</p><p>To develop a model for the early prediction of severe acute pancreatitis based on the revised Atlanta classification of acute pancreatitis.</p><p>Methods</p><p>Clinical data of 1308 patients with acute pancreatitis (AP) were included in the retrospective study. A total of 603 patients who were admitted to the hospital within 36 hours of the onset of the disease were included at last according to the inclusion criteria. The clinical data were collected within 12 hours after admission. All the patients were classified as having mild acute pancreatitis (MAP), moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP) based on the revised Atlanta classification of acute pancreatitis. All the 603 patients were randomly divided into training group (402 cases) and test group (201 cases). Univariate and multiple regression analyses were used to identify the independent risk factors for the development of SAP in the training group. Then the prediction model was constructed using the decision tree method, and this model was applied to the test group to evaluate its validity.</p><p>Results</p><p>The decision tree model was developed using creatinine, lactate dehydrogenase, and oxygenation index to predict SAP. The diagnostic sensitivity and specificity of SAP in the training group were 80.9% and 90.0%, respectively, and the sensitivity and specificity in the test group were 88.6% and 90.4%, respectively.</p><p>Conclusions</p><p>The decision tree model based on creatinine, lactate dehydrogenase, and oxygenation index is more likely to predict the occurrence of SAP.</p></div

Multivariate regression analyses.

<p>Multivariate regression analyses.</p

Receiver operating characteristic curves for classification and regression tree (CART model) and Bedside Index for Severity in Acute Pancreatitis(BISAP model).

<p>Receiver operating characteristic curves for classification and regression tree (CART model) and Bedside Index for Severity in Acute Pancreatitis(BISAP model).</p

Patients stratified by the tree model in the training and the test groups.

<p>Patients stratified by the tree model in the training and the test groups.</p

A decision tree model for the prediction of severe acute pancreatitis (SAP) generated by classification and regression tree (CART) analysis in the training set of 402 patients.

<p>A decision tree model for the prediction of severe acute pancreatitis (SAP) generated by classification and regression tree (CART) analysis in the training set of 402 patients.</p