A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform \u3e70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients

In Vitro Evaluation of pH-Responsive Nanoscale Hydrogels for the Oral Delivery of Hydrophobic Therapeutics

About 70% of pharmaceutical drug candidates are poorly soluble and suffer from low oral bioavailability. Additionally, a large number of therapeutics are also substrates for P-glycoprotein (P-gp) receptors present on the intestinal cell lining and undergo efflux that further reduces their oral bioavailability drastically. Nanoscale hydrogels are promising candidates for oral delivery of hydrophobic therapeutics as they hold immense potential in improving solubility and increasing intestinal permeability of such therapeutics. In this report, we describe the in vitro evaluation and comparison of four novel, pH-responsive poly­(methacrylic acid-<i>g</i>-polyethylene glycol-<i>co</i>-hydrophobic monomer) nanoscale hydrogels for their capacity to load and release chemotherapeutic doxorubicin, as well as their ability to modulate permeability in vitro for improving doxorubicin transport. The resulting nanoscale formulations showed appreciable loading, and in vitro release studies demonstrated excellent pH-triggered release kinetics. These nanoscale hydrogels can serve as carriers for oral delivery of doxorubicin, achieving drug loading efficiencies of 56–70%, and releasing up to 95% of drug within 6 h. Powder X-ray diffraction studies revealed a change from the crystalline nature of doxorubicin to an amorphous form when encapsulated within formulations, illustrating their potential of enhancing solubility and stability for oral delivery of the hydrophobic therapeutic. Furthermore, their ability to modulate in vitro intestinal permeability was also studied using transport studies with Caco-2 cells, and was complemented by assessing their antitumor activity against P-gp overexpressing, DOX-resistant H69/LX4 cancer cells. In vitro cell culture tests demonstrated up to 50% reduction in cellular proliferation in the case of poly­(methacrylic acid-<i>g</i>-polyethylene glycol-<i>co</i>-methyl methacrylate), suggesting that these carriers are most suitable as hydrophobic drug carriers that can potentially overcome solubility and permeability limitations typically faced by hydrophobic therapeutics in the gastrointestinal (GI) tract