The deaf-mutes population always feels helpless when they are not understood by others and vice versa. This is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing "Within Blocks" and "Before Classifier" methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time are recorded to evaluate the models' efficiency. The experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. The CBAM-ResNet "Before Classifier" models are more efficient than "Within Blocks" CBAM-ResNet models. Thus, the best trained model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results indicated that the "Before Classifier" of CBAMResNet models is more efficient in recognising MSL and it is worth for future research.
To establish a productive infection in host cells, viruses often use one or multiple host membrane glycoproteins as their receptors. For Influenza A virus (IAV) such a glycoprotein receptor has not been described, to date. Here we show that IAV is using the host membrane glycoprotein CD66c as a receptor for entry into human epithelial lung cells. Neuraminidase (NA), a viral spike protein, binds to CD66c on the cell surface during IAV entry into the host cells. Lung cells overexpressing CD66c showed an increase in virus binding and subsequent entry into the cell. Upon comparison, CD66c demonstrated higher binding capacity than other membrane glycoproteins (EGFR and DC-SIGN) reported earlier to facilitate IAV entry into host cells. siRNA mediated knockdown of CD66c from lung cells inhibited virus binding on cell surface and entry into cells. Blocking CD66c by antibody on the cell surface resulted in decreased virus entry. We found that CD66c is a specific glycoprotein receptor for influenza A virus that did not affect entry of non-IAV RNA virus (Hepatitis C virus). Finally, IAV pre-incubated with recombinant CD66c protein when administered intranasally in mice showed decreased cytopathic effects in mice lungs. This publication is the first to report CD66c (Carcinoembryonic cell adhesion molecule 6 or CEACAM6) as a glycoprotein receptor for Influenza A virus.
Nuclear factor-kappa B, involved in inflammation, host immune response, cell adhesion, growth signals, cell proliferation, cell differentiation, and apoptosis defense, is a dimeric transcription factor. Inflammation is a key component of many common respiratory disorders, including asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, and acute respiratory distress syndrome. Many basic transcription factors are found in NF-κB signaling, which is a member of the Rel protein family. Five members of this family c-REL, NF-κB2 (p100/p52), RelA (p65), NF-κB1 (p105/p50), RelB, and RelA (p65) produce 5 transcriptionally active molecules. Proinflammatory cytokines, T lymphocyte, and B lymphocyte cell mitogens, lipopolysaccharides, bacteria, viral proteins, viruses, double-stranded RNA, oxidative stress, physical exertion, various chemotherapeutics are the stimulus responsible for NF-κB activation. NF-κB act as a principal component for several common respiratory illnesses, such as asthma, lung cancer, pulmonary fibrosis, COPD as well as infectious diseases like pneumonia, tuberculosis, COVID-19. Inflammatory lung disease, especially COVID-19, can make NF-κB a key target for drug production.