Lipase-based MIL-100(Fe) biocomposites as chiral stationary phase for high-efficiency capillary electrochromatographic enantioseparation.

A novel chiral porous column was fabricated by lipase immobilized MIL-100(Fe) biocomposites as chiral stationary phase through covalent coupling and applied to capillary electrochromatographic enantioseparation. MOF-based lipase biocomposites not only enhance stereoselective activities but also improve the stability and applicability of the enzyme. The functionalized porous columns were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, and powder X-ray diffraction. The performance of the porous column was evaluated by enantioseparating amino acid enantiomers, affording high resolution over 2.0. Besides, the enantio-resolutions of phenylephrine, phenylsuccinic acid, chloroquine, and zopiclone were also greater than 2.0. The relative standard deviations of run-to-run, intra-, and inter-day repeatability were within 4.0% in terms of resolution and retention time, exhibiting excellent stability of the column. Conceivably, the results show that MOF-based lipase composites as chiral stationary phase offer a highly efficient means for enantioseparation in capillary electrochromatography, attributing to the enhanced enantioselective activities of lipase by highly ordered frameworks.

Clinical application of deep learning-based synthetic CT from real MRI to improve dose planning accuracy in Gamma Knife radiosurgery: a proof of concept study.

Dose planning for Gamma Knife radiosurgery (GKRS) uses the magnetic resonance (MR)-based tissue maximum ratio (TMR) algorithm, which calculates radiation dose without considering heterogeneous radiation attenuation in the tissue. In order to plan the dose considering the radiation attenuation, the Convolution algorithm should be used, and additional radiation exposure for computed tomography (CT) and registration errors between MR and CT are entailed. This study investigated the clinical feasibility of synthetic CT (sCT) from GKRS planning MR using deep learning. The model was trained using frame-based contrast-enhanced T1-weighted MR images and corresponding CT slices from 54 training subjects acquired for GKRS planning. The model was applied prospectively to 60 lesions in 43 patients including benign tumor such as meningioma and pituitary adenoma, metastatic brain tumors, and vascular disease of various location for evaluating the model and its application. We evaluated the sCT and compared between treatment plans made with MR only (TMR 10 plan), MR and real CT (rCT; Convolution with rCT [Conv-rCT] plan), and MR and synthetic CT (Convolution with sCT [Conv-sCT] plan). The mean absolute error (MAE) of 43 sCT was 107.35 ± 16.47 Hounsfield units. The TMR 10 treatment plan differed significantly from plans made by Conv-sCT and Conv-rCT. However, the Conv-sCT and Conv-rCT plans were similar. This study showed the practical applicability of deep learning based on sCT in GKRS. Our results support the possibility of formulating GKRS treatment plans while considering radiation attenuation in the tissue using GKRS planning MR and no radiation exposure.

Utilization of ionic deep eutectic solvent as sustainable mobile phase additive in high-performance liquid chromatography for improving the separation of biogenic amines.

Biogenic amines are present in large quantities in fermented foods and are a key marker for assessing food safety. This paper proposes a novel method for high-performance liquid chromatography analysis of biogenic amines using deep eutectic solvent as a mobile phase additive. After screening eight synthetic deep eutectic solvents and comparing them with several common additives, deep eutectic solvents based on choline chloride and ethylene glycol showed significant effects in improving the separation of biogenic amines. A Box-Behnken design of 17 runs was used to screen and optimize the key chromatographic parameters, resulting in an expected composition of the mobile phase of 0.73% deep eutectic solvent, 65% acetonitrile, and a column temperature of 28°C. The proposed method exhibited excellent linearity (0.1-50 µg/ml, R2 ≥ 0.9987), limit of detection (0.007-0.031 µg/ml), precision (1.28%-5.34%) and accuracy (87.2%-110.6%). The method can be applied successfully to the separation and analysis of biogenic amines in cooking wine samples.

Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium.

PURPOSE: We aimed to develop a deep learning (DL)-based algorithm for automated quantification of aortic valve calcium (AVC) from non-enhanced electrocardiogram-gated cardiac CT scans and compare performance of DL-measured AVC volume and Agatston score with those of visual gradings by radiologist readers for classification of AVC severity. METHOD: A total of 589 CT examinations performed at a single center between March 2010 and August 2017 were retrospectively included. The DL algorithm was designed to segment AVC and to quantify AVC volume, and Agatston score was calculated using attenuation values. Manually measured AVC volume and Agatston score were used as ground truth. To validate AVC segmentation performance, the Dice coefficient was calculated. For observer performance testing, four radiologists determined AVC grade in two reading rounds. The diagnostic performance of DL-measured AVC volume and Agaston score for classifying severe AVC was compared with that of each reader's assessment. RESULTS: After applying the DL algorithm, the Dice coefficient score was 0.807. In patients with AVC, accuracy of DL-measured AVC volume for AVC grading was 97.0 % with area under the curve (AUC) of 0.964 (95 % confidence interval [CI] 0.923-1) in the test set, which was better than the radiologist readers (accuracy 69.7 %-91.9 %, AUC 0.762-0.923) with manually measured AVC volume as ground truth. When manually measured AVC Agatston score was used as ground truth, accuracy of DL-measured AVC Agatston score for AVC grading was 92.9 % with AUC of 0.933 (95 % CI 0.885-0.981) in the test set, which was also better than the radiologist readers (accuracy 77.8-89.9 %, AUC 0.791-0.903). CONCLUSIONS: DL-based automated AVC quantification may be comparable with manual measurements. The diagnostic performance of the DL-measured AVC volume and Agatston score for classification of severe AVC outperforms radiologist readers.

Zinc Oxide Nanoparticles Induce Autophagy and Apoptosis via Oxidative Injury and Pro-Inflammatory Cytokines in Primary Astrocyte Cultures.

The present study examined the potential toxic concentrations of zinc oxide nanoparticles (ZnO NPs) and associated autophagy and apoptosis-related injuries in primary neocortical astrocyte cultures. Concentrations of ZnO NPs ≥3 µg/mL induced significant toxicity in the astrocytes. At 24 h after exposure to the ZnO NPs, transmission electron microscopy revealed swelling of the endoplasmic reticulum (ER) and increased numbers of autophagolysosomes in the cultured astrocytes, and increased levels of LC3 (microtubule-associated protein 1 light chain 3)-mediated autophagy were identified by flow cytometry. Apoptosis induced by ZnO NP exposure was confirmed by the elevation of caspase-3/7 activity and 4',6'-diamidino-2-phenylindole (DAPI) staining. Significant (p < 0.05) changes in the levels of glutathione peroxidase, superoxide dismutase, tumor necrosis factor (TNF-α), and interleukin-6 were observed by enzyme-linked immunoassay (ELISA) assay following the exposure of astrocyte cultures to ZnO NPs. Phosphatidylinositol 3-kinase (PI3K)/mitogen-activated protein kinase (MAPK) dual activation was induced by ZnO NPs in a dose-dependent manner. Additionally, the Akt (protein kinase B) inhibitor BML257 and the mTOR (mammalian target of rapamycin) inhibitor rapamycin contributed to the survival of astrocytes. Inhibitors of cyclooxygenase-2 and lipoxygenase attenuated ZnO NP-induced toxicity. Calcium-modulating compounds, antioxidants, and zinc/iron chelators also decreased ZnO NP-induced toxicity. Together, these results suggest that ZnO NP-induced autophagy and apoptosis may be associated with oxidative stress and the inflammatory process in primary astrocyte cultures.

Vibrations Generated by Several Nickel-titanium Endodontic File Systems during Canal Shaping in an Ex Vivo Model.

INTRODUCTION: This study aimed to compare the vibration generated by several nickel-titanium (NiTi) file systems and transmitted to teeth under 2 different motions (continuous rotation motion and reciprocating motion). METHODS: Sixty J-shaped resin blocks (Endo Training Bloc-J; Dentsply Maillefer, Ballaigues, Switzerland) were trimmed to a root-shaped form and divided into 2 groups according to the types of electric motors: WaveOne motor (WOM, Dentsply Maillefer) and X-Smart Plus motor (XSM, Dentsply Maillefer). Each group was further subdivided into 3 subgroups (n = 10 each) according to the designated file systems: ProTaper Next (PTN, Dentsply Maillefer), ProTaper Universal (PTU, Dentsply Maillefer), and WaveOne (WOP, Dentsply Maillefer) systems. Vibration was measured during the pecking motion using an accelerometer attached to a predetermined consistent position. The average vibration values were subjected to 2-way analysis of variance as well as the t test and Duncan test for post hoc comparison at the 95% confidence interval. RESULTS: Both motor types and instrument types produced significantly different ranges of average vibrations. Regardless of the instrument types, the WOM group generated greater vibration than the XSM group (P < .05). Although PTN and PTU did not show significant differences, the WOP group showed significantly greater vibration than the other groups regardless of motor types (P < .05). CONCLUSIONS: Under the limitations of this study design, the reciprocating NiTi file system may generate greater vibration than the continuous rotation NiTi file systems. The motor type also has a significant effect to amplify the vibrations.