In silico modeling of the specific inhibitory potential of thiophene-2,3-dihydro-1,5-benzothiazepine against BChE in the formation of β-amyloid plaques associated with Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Alzheimer's disease, known to be associated with the gradual loss of memory, is characterized by low concentration of acetylcholine in the hippocampus and cortex part of the brain. Inhibition of acetylcholinesterase has successfully been used as a drug target to treat Alzheimer's disease but drug resistance shown by butyrylcholinesterase remains a matter of concern in treating Alzheimer's disease. Apart from the many other reasons for Alzheimer's disease, its association with the genesis of fibrils by β-amyloid plaques is closely related to the increased activity of butyrylcholinesterase. Although few data are available on the inhibition of butyrylcholinesterase, studies have shown that that butyrylcholinesterase is a genetically validated drug target and its selective inhibition reduces the formation of β-amyloid plaques.</p> <p>Rationale</p> <p>We previously reported the inhibition of cholinesterases by 2,3-dihydro-1, 5-benzothiazepines, and considered this class of compounds as promising inhibitors for the cure of Alzheimer's disease. One compound from the same series, when substituted with a hydroxy group at C-3 in ring A and 2-thienyl moiety as ring B, showed greater activity against butyrylcholinesterase than to acetylcholinesterase. To provide insight into the binding mode of this compound (Compound A), molecular docking in combination with molecular dynamics simulation of 5000 ps in an explicit solvent system was carried out for both cholinesterases.</p> <p>Conclusion</p> <p>Molecular docking studies revealed that the potential of Compound A to inhibit cholinesterases was attributable to the cumulative effects of strong hydrogen bonds, cationic-π, π-π interactions and hydrophobic interactions. A comparison of the docking results of Compound A against both cholinesterases showed that amino acid residues in different sub-sites were engaged to stabilize the docked complex. The relatively high affinity of Compound A for butyrylcholinesterase was due to the additional hydrophobic interaction between the 2-thiophene moiety of Compound A and Ile69. The involvement of one catalytic triad residue (His438) of butyrylcholinesterase with the 3'-hydroxy group on ring A increases the selectivity of Compound A. C-C bond rotation around ring A also stabilizes and enhances the interaction of Compound A with butyrylcholinesterase. Furthermore, the classical network of hydrogen bonding interactions as formed by the catalytic triad of butyrylcholinesterase is disturbed by Compound A. This study may open a new avenue for structure-based drug design for Alzheimer's disease by considering the 3D-pharmacophoric features of the complex responsible for discriminating these two closely-related cholinesterases.</p

4D printing of smart magnetic-based robotic materials

Four-dimensional (4D) printing has been rapidly emerging in robotic manufacturing. In order to tackle delicate jobs, in various adaptive environments, such as sorting food, manipulating human organs and tissues, and locomotion in inaccessible terrain, stiff actuators, sensors, and even central controller units are often replaced with soft and flexible materials in traditional robots. Additive manufacturing of robots gained researchers' attention in recent years. Soft polymer materials, like poly-dimethyl siloxane (PDMS), have been developed and additive manufacturing has made it easier to practice them. Recently, a broader range of smart robotic materials with the capability of response and adaptation to external stimuli have been introduced in additive manufacturing. Soft robotics may now be actuated by external stimuli of electric, magnetic, chemical, thermal, or photonic origin thanks to the improved functionalities of smart materials that induce programmable modulations in size, shape, and stiffness. In operating soft robots, a magnetic field provides unique benefits, such as penetrating a variety of materials, remote commanding, and fast response time, with applications in an enclosed location, such as surgical catheters or untethered drug-delivering robots working in the human body. This chapter presents and categorizes numerous state-of-the-art electromagnetic robotics materials made using 4D printing. The operating principles, manufacturing processes, and their associated benefits and disadvantages, as well as future potential, are outlined

Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition

Personalized movie summarization is demand of the current era due to an exponential growth in movies production. The employed methods for movies summarization fail to satisfy the user’s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user-preference based movie summarization scheme. First, we segmented movie into shots using a novel entropy-based shots segmentation mechanism. Next, temporal saliency of shots is computed, resulting in highly salient shots in which character faces are detected. The resultant shots are then forward propagated to our trained deep CNN model for facial expression recognition (FER) to analyze the emotional state of the characters. The final summary is generated based on user-preferred emotional moments from the seven emotions, i.e., afraid, angry, disgust, happy, neutral, sad, and surprise. The subjective evaluation over five Hollywood movies proves the effectiveness of our proposed scheme in terms of user satisfaction. Furthermore, the objective evaluation verifies the superiority of the proposed scheme over state-of-the-art movie summarization methods

Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies

Movies have become one of the major sources of entertainment in the current era, which are based on diverse ideas. Action movies have received the most attention in last few years, which contain violent scenes, because it is one of the undesirable features for some individuals that is used to create charm and fantasy. However, these violent scenes have had a negative impact on kids, and they are not comfortable even for mature age people. The best way to stop under aged people from watching violent scenes in movies is to eliminate these scenes. In this paper, we proposed a violence detection scheme for movies that is comprised of three steps. First, the entire movie is segmented into shots, and then a representative frame from each shot is selected based on the level of saliency. Next, these selected frames are passed from a light-weight deep learning model, which is fine-tuned using a transfer learning approach to classify violence and non-violence shots in a movie. Finally, all the non-violence scenes are merged in a sequence to generate a violence-free movie that can be watched by children and as well violence paranoid people. The proposed model is evaluated on three violence benchmark datasets, and it is experimentally proved that the proposed scheme provides a fast and accurate detection of violent scenes in movies compared to the state-of-the-art methods