New strategies for surface modification of poly (vinyl alcohol) toward click chemistry applications

In this study, new synthesis strategies for poly (vinyl alcohol) (PVA) functionalized with azidation (Az) and alkynation (Alk) were carried out for surface functionality modification. These two functionalization routes utilize azide and alkyne groups, respectively, and offer simple way to enhance the PVA crosslinking capabilities toward producing new functional polymer-based materials. The Az functionalized PVA was prepared in two steps and denoted by PVA-Az, while Alk functionalized PVA was prepared in one step to produce PVA-Alk. Surfaces modification of PVA by using Az and Alk functional groups was confirmed by spectral analysis (Fourier transform infrared and ¹³C-NMR). Results showed that Alk prompted excellent chemical modifications on the surface of PVA while the thermal stability was enhanced by functionalizing the Az and Alk into PVA. Furthermore, X-ray photoelectron spectroscopy investigations showed a successful functionalization and new functional groups addition to PVA surface. Scanning electron microscope images were used to analyze the surface morphology of PVA by incorporating the Az and Alk groups, which indicated rougher surfaces compared to that of PVA backbone. In terms of efficiency and simplicity, the techniques used in this study appear to be entirely satisfactory.     

Effects of antimicrobial agents on the processing and performance properties of rubber material formulations with no cytotoxicity

The present work aims to develop antimicrobial rubber for safe industrial toys. For this purpose, natural rubber (NR) and synthetic rubber as styrene butadiene rubber (SBR) and ethylene propylene diene monomer (EPDM) were examined. Rubber and their ingredients as well as antimicrobial agents (doxycycline and cephalexin) were mixed in a rubber mixer. The rheological properties of compounded rubber were studied, and the curing time was determined. Mechanical properties and cytotoxicity were evaluated at optimally cured rubber compounds. Scanning electron micrographs of vulcanizates showed good dispersion of ingredients throughout the investigated matrices. Rheology study for the investigated vulcanizates in presence of tested antimicrobial species exhibited no significant change in their flow behaviors. It is significant to remember that the desired physical characteristics of rubber products, including their chemical and mechanical characteristics (elongation at break and tensile strength) enhanced when doxycycline and cephalexin are present, depending on their nature and concentration. Similar results were obtained for both the SBR and EPDM rubber vulcanizates. The cytotoxicity of the prepared vulcanizates towards human normal retina cell line (RPI-1) indicated good safety of these rubber products. Furthermore, developed rubber vulcanizates showed good antimicrobial efficacy towards the test bacteria and fungi strains.”     

Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early identification of breast cell malignancies formation of masses and Breast microcalcifications on the mammogram. When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network (ResNet50) for Breast Cancer Detection and Diagnosis, to obtain the Discriminative Localization, Convolutional Neural Network with Class Activation Map (CAM) has also been used to perform breast microcalcifications detection to find a specific class in the Histopathological image. The test results indicate that this method performed almost 225.15% better at determining the exact location of disease (Discriminative Localization) through breast microcalcifications images. ResNet50 seems to have the highest level of accuracy for images of Benign Tumour (BT)/Malignant Tumour (MT) cases at 97.11%. ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.