Highly Proton-Conducting Membranes Based on Poly(arylene ether)s with Densely Sulfonated and Partially Fluorinated Multiphenyl for Fuel Cell Applications.

Series of partially fluorinated sulfonated poly(arylene ether)s were synthesized through nucleophilic substitution polycondensation from three types of diols and superhydrophobic tetra-trifluoromethyl-substituted difluoro monomers with postsulfonation to obtain densely sulfonated ionomers. The membranes had similar ion exchange capacities of 2.92 ± 0.20 mmol g-1 and favorable mechanical properties (Young's moduli of 1.60-1.83 GPa). The membranes exhibited considerable dimensional stability (43.1-122.3% change in area and 42.1-61.5% change in thickness at 80 °C) and oxidative stability (~55.5%). The proton conductivity of the membranes, higher (174.3-301.8 mS cm-1) than that of Nafion 211 (123.8 mS cm-1), was the percent conducting volume corresponding to the water uptake. The membranes were observed to comprise isolated to tailed ionic clusters of size 15-45 nm and 3-8 nm, respectively, in transmission electron microscopy images. A fuel cell containing one such material exhibited high single-cell performance-a maximum power density of 1.32 W cm2 and current density of >1600 mA cm-2 at 0.6 V. The results indicate that the material is a candidate for proton exchange membranes in fuel cell applications.

Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks.

The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in detecting PCa. However, prostate MRI assessment relies on human experts and specialized training with considerable inter-reader variability. Deep learning may be a more robust approach for prostate MRI assessment. Here we present a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model. We used PROSTATEx dataset to train the model and combined different sequences into three channels of a single image. For each subject, all slices that contained the transition zone (TZ), peripheral zone (PZ), and PCa region were selected. The datasets were produced using different combinations of images, including T2-weighted (T2W) images, diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) images. Among these groups, the T2W + DWI + ADC images exhibited the best performance with a dice similarity coefficient of 90.45% for the TZ, 70.04% for the PZ, and 52.73% for the PCa region. Image sequence analysis with a DCNN model has the potential to assist PCa diagnosis.

Histogram analysis of prostate cancer on dynamic contrast-enhanced magnetic resonance imaging: A preliminary study emphasizing on zonal difference.

BACKGROUND: This study evaluated the performance of histogram analysis in the time course of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for differentiating cancerous tissues from benign tissues in the prostate. METHODS: We retrospectively analyzed the histograms of DCE-MRI of 30 patients. Histograms within regions of interest(ROI) in the peripheral zone (PZ) and transitional zone (TZ) were separately analyzed. The maximum difference wash-in slope (MWS) and delay phase slope (DPS) were defined for each voxel. Differences in histogram parameters, namely the mean, standard deviation (SD), the coefficient of variation (CV), kurtosis, skewness, interquartile range (IQR), percentile (P10, P25, P75, P90, and P90P10), Range, and modified full width at half-maximum (mFWHM) between cancerous and benign tissues were assessed. RESULTS: In the TZ, CV for ROIs of 7.5 and 10mm was the only significantly different parameter of the MWS (P = 0.034 and P = 0.004, respectively), whereas many parameters of the DPS (mean, skewness, P10, P25, P50, P75 and P90) differed significantly (P = <0.001-0.016 and area under the curve [AUC] = 0.73-0.822). In the PZ, all parameters of the MWS exhibited significant differences, except kurtosis and skewness in the ROI of 7.5mm(P = <0.001-0.017 and AUC = 0.865-0.898). SD, IQR, mFWHM, P90P10 and Range were also significant differences in the DPS (P = 0.001-0.035). CONCLUSION: The histogram analysis of DCE-MRI is a potentially useful approach for differentiating prostate cancer from normal tissues. Different histogram parameters of the MWS and DPS should be applied in the TZ and PZ.

Design and Synthesis of Enantiomerically Pure Decahydroquinoxalines as Potent and Selective κ-Opioid Receptor Agonists with Anti-Inflammatory Activity in Vivo.

In order to develop novel κ agonists restricted to the periphery, a diastereo- and enantioselective synthesis of (4aR,5S,8aS)-configured decahydroquinoxalines 5-8 was developed. Physicochemical and pharmacological properties were fine-tuned by structural modifications in the arylacetamide and amine part of the pharmacophore as well as in the amine part outside the pharmacophore. The decahydroquinoxalines 5-8 show single-digit nanomolar to subnanomolar κ-opioid receptor affinity, full κ agonistic activity in the [35S]GTPγS assay, and high selectivity over µ, δ, σ1, and σ2 receptors as well as the PCP binding site of the NMDA receptor. Several analogues were selective for the periphery. The anti-inflammatory activity of 5-8 after topical application was investigated in two mouse models of dermatitis. The methanesulfonamide 8a containing the (S)-configured hydroxypyrrolidine ring was identified as a potent (Ki = 0.63 nM) and highly selective κ agonist (EC50 = 1.8 nM) selective for the periphery with dose-dependent anti-inflammatory activity in acute and chronic skin inflammation.