Protective Effect of Willow (Salix babylonica L.) on Fish Resistance to Vibrio parahaemolyticus and Vibrio alginolyticus.

Vibrio spp. cause vibriosis in many saltwater and freshwater aquatic species, such as fish, crustaceans, and mollusks. Vibrio parahaemolyticus and Vibrio alginolyticus are among the few Vibrio species commonly found in infections in fish. This study aimed at investigating the chemical composition and evaluating the antibacterial activities of Salix babylonica L. The ethyl acetate (LL2) and methanolic (LL3) extracts were used to evaluate the resistance of strains as V. parahaemolyticus LBT6 and VTCC 12233, and two strains of V. alginolyticus, NG20 and ATCC 17749, and compared their efficacy with cefotaxime in order to find an alternative to antibiotics in the treatment of vibriosis. The obtained results show that the LL2 extract, with its major components identified as chrysoeriol, luteolin, and ß-sitosterol, exhibited a bacteriostatic effect against all the tested strains. In parallel, the LL3 extract, with the four major compounds luteolin-7-O-ß-D-glucopyranoside, salicin, p-hydroxy benzoic acid, and ß-sitosterol-3-O-ß-D-glucopyranoside, showed significant bactericidal activity against these four strains; the minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) varied from 2.0 to 3.0 µg/mL and from 3.5 to 5.0 µg/mL, respectively. Moreover, the LL3 extract could effectively increase the survival rate of the challenged fish at a dose of 5% (w/w) for the zebrafish (Danio rerio) and 3% (w/w) for the sea bass (Lates calcarifer). The LL3 extract showed a potential application of S. babylonica L. in the prevention and treatment of vibriosis in fish.

TMD-Unet: Triple-Unet with Multi-Scale Input Features and Dense Skip Connection for Medical Image Segmentation.

Deep learning is one of the most effective approaches to medical image processing applications. Network models are being studied more and more for medical image segmentation challenges. The encoder-decoder structure is achieving great success, in particular the Unet architecture, which is used as a baseline architecture for the medical image segmentation networks. Traditional Unet and Unet-based networks still have a limitation that is not able to fully exploit the output features of the convolutional units in the node. In this study, we proposed a new network model named TMD-Unet, which had three main enhancements in comparison with Unet: (1) modifying the interconnection of the network node, (2) using dilated convolution instead of the standard convolution, and (3) integrating the multi-scale input features on the input side of the model and applying a dense skip connection instead of a regular skip connection. Our experiments were performed on seven datasets, including many different medical image modalities such as colonoscopy, electron microscopy (EM), dermoscopy, computed tomography (CT), and magnetic resonance imaging (MRI). The segmentation applications implemented in the paper include EM, nuclei, polyp, skin lesion, left atrium, spleen, and liver segmentation. The dice score of our proposed models achieved 96.43% for liver segmentation, 95.51% for spleen segmentation, 92.65% for polyp segmentation, 94.11% for EM segmentation, 92.49% for nuclei segmentation, 91.81% for left atrium segmentation, and 87.27% for skin lesion segmentation. The experimental results showed that the proposed model was superior to the popular models for all seven applications, which demonstrates the high generality of the proposed model.

A Label-Free Impedimetric DNA Sensor Based on a Nanoporous SnO2 Film: Fabrication and Detection Performance.

Nanoporous SnO2 thin films were elaborated to serve as sensing electrodes for label-free DNA detection using electrochemical impedance spectroscopy (EIS). Films were deposited by an electrodeposition process (EDP). Then the non-Faradic EIS behaviour was thoroughly investigated during some different steps of functionalization up to DNA hybridization. The results have shown a systematic decrease of the impedance upon DNA hybridization. The impedance decrease is attributed to an enhanced penetration of ionic species within the film volume. Besides, the comparison of impedance variations upon DNA hybridization between the liquid and vapour phase processes for organosilane (APTES) grafting on the nanoporous SnO2 films showed that vapour-phase method is more efficient. This is due to the fact that the vapour is more effective than the solution in penetrating the nanopores of the films. As a result, the DNA sensors built from vapour-treated silane layer exhibit a higher sensitivity than those produced from liquid-treated silane, in the range of tested target DNA concentration going to 10 nM. Finally, the impedance and fluorescence response signals strongly depend on the types of target DNA molecules, demonstrating a high selectivity of the process on nanoporous SnO2 films.