Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provides the most helpful neuroimaging tool for detecting AD. In this paper, we employ a DenseNet-201 based transfer learning technique for diagnosing different Alzheimer's stages as Non-Demented (ND), Moderate Demented (MOD), Mild Demented (MD), Very Mild Demented (VMD), and Severe Demented (SD). The suggested method for a dataset of MRI scans for Alzheimer's disease is divided into five classes. Data augmentation methods were used to expand the size of the dataset and increase DenseNet-201's accuracy. It was found that the proposed strategy provides a very high classification accuracy. This practical and reliable model delivers a success rate of 98.24%. The findings of the experiments demonstrate that the suggested deep learning approach is more accurate and performs well compared to existing techniques and state-of-the-art methods.
Flaxseed (Linum usitatissimum), commonly known as linseed is an oilseed crop, emerging as an important and functional ingredient of food and has been paid more attention due to its nutritional value as well as beneficial effects. It is mainly rich in is α-linolenic acid (ALA, omega-3 fatty acid), fibres and lignans that have potential health benefits in reducing cardiovascular diseases, diabetes, osteoporosis, atherosclerosis, cancer, arthritis, neurological and autoimmune disorders. Due to its richness in omega-3 fatty acid, a group of enzymes known as fatty acid desaturases (FADs) mainly introduce double bonds into fatty acids' (FAs) hydrocarbon chains that produce unsaturated fatty acids. Fatty acid desaturase 3 (FAD3), the commonest microsomal enzyme of omega-3 fatty acid, synthesizes linolenic acid (C18:3) from linoleic acid located in endoplasmic reticulum (ER) facing towards the cytosol. The emerging field of bioinformatics and large number of databases of bioactive peptides, helps in providing time-saving and efficient method for identification of potential bioactivities of any protein. In this study, 10 unique sequences of FAD3 from flaxseed protein have been used for in silico proteolysis and releasing of various bioactive peptides using three plant proteases, namely ficin, papain and stem bromelain, that are evaluated with the help of BIOPEP database. Overall, 20 biological activities were identified from these proteins. The results showed that FAD3 protein is a potential source of peptides with angiotensin-I-converting enzyme (ACE) inhibitory and dipeptidyl peptidase-IV (DPP-IV) activities, and also various parameters such as ∑A, ∑B, AE, W, BE, V and DHt were also calculated. Furthermore, PeptideRanker have been used for screening of novel promising bioactive peptides. Various bioinformatics tools also used to study protein's physicochemical properties, peptide's score, toxicity, allergenicity aggregation, water solubility, and drug likeliness. The present work suggests that flaxseed protein can be a good source of bioactive peptides for the synthesis of good quality and quantity of oil, and in silico method helps in investigating and production of functional peptides.