Access to gem-Difluoroalkenes via Organic Photoredox-Catalyzed gem-Difluoroallylation of Alkyl Iodides.

An organic photoredox-catalyzed gem-difluoroallylation of α-trifluoromethyl alkenes with alkyl iodides via C-F bond cleavage for the synthesis of gem-difluoroalkene derivatives is reported. This transition-metal-free transformation utilized a readily available organic dye 4CzIPN as the sole photocatalyst and employed a common chemical N,N,N',N'-tetramethylethylenediamine as the radical activator of alkyl iodides via halogen-atom transfer. In addition, a variety of iodides, including primary, secondary, and tertiary alkyl iodides, were tolerated and provided good to high yields.

TBENet:A two-branch boundary enhancement Network for cerebrovascular segmentation.

Cerebrovascular segmentation in digital subtraction angiography (DSA) images is the gold standard for clinical diagnosis. However, owing to the complexity of cerebrovascular, automatic cerebrovascular segmentation in DSA images is a challenging task. In this paper, we propose a CNN-based Two-branch Boundary Enhancement Network (TBENet) for automatic segmentation of cerebrovascular in DSA images. The TBENet is inspired by U-Net and designed as an encoder-decoder architecture. We propose an additional boundary branch to segment the boundary of cerebrovascular and a Main and Boundary branches Fusion Module (MBFM) to integrate the boundary branch outcome with the main branch outcome to achieve better segmentation performance. The TBENet was evaluated on HMCDSA (an in-house DSA cerebrovascular dataset), and reaches 0.9611, 0.7486, 0.7152, 0.9860 and 0.9556 in Accuracy, F1 score, Sensitivity, Specificity, and AUC, respectively. Meanwhile, we tested our TBENet on the public vessel segmentation benchmark DRIVE, and the results show that our TBENet can be extended to diverse vessel segmentation tasks.

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning.

Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas. Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split. Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model. Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.

The Cu-MOF-199/single-walled carbon nanotubes modified electrode for simultaneous determination of hydroquinone and catechol with extended linear ranges and lower detection limits.

A novel electrochemical sensor based on Cu-MOF-199 [Cu-MOF-199 = Cu3(BTC)2 (BTC = 1,3,5-benzenetricarboxylicacid)] and SWCNTs (single-walled carbon nanotubes) was fabricated for the simultaneous determination of hydroquinone (HQ) and catechol (CT). The modification procedure was carried out through casting SWCNTs on the bare glassy carbon electrode (GCE) and followed by the electrodeposition of Cu-MOF-199 on the SWCNTs modified electrode. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM) were performed to characterize the electrochemical performance and surface characteristics of the as-prepared sensor. The composite electrode exhibited an excellent electrocatalytic activity with increased electrochemical signals towards the oxidation of HQ and CT, owing to the synergistic effect of SWCNTs and Cu-MOF-199. Under the optimized condition, the linear response range were from 0.1 to 1453 µmol L(-1) (RHQ = 0.9999) for HQ and 0.1-1150 µmol L(-1) (RCT = 0.9990) for CT. The detection limits for HQ and CT were as low as 0.08 and 0.1 µmol L(-1), respectively. Moreover, the modified electrode presented the good reproducibility and the excellent anti-interference performance. The analytical performance of the developed sensor for the simultaneous detection of HQ and CT had been evaluated in practical samples with satisfying results.