Current Signal Transduction Therapy - Volume 14, Issue 2, 2019

Background: This review presents the exhaustive exploration of 1,3,5-triazine scaffold for development of analogs of anticancer drugs, over the last century. In the recent years, striazine moiety has been one of the most studied moiety, showing broad-spectrum pharmacological activities such as antibacterial, antifungal, analgesic, anti-HIV, antileishmanial, antitrypanosomal, antimalarial and antiviral. Nowadays, many boffins are have become interested in novel synthesis of s-triazine derivatives because of low cost and ease of availability. Methods: This scaffold has been extensively investigated mainly in the past decade. Many products have been synthesized from different starting materials and these synthetic products possess anticancer potential against various cell lines. Results: Many 1,3,5-triazine analogs exhibited significant anticancer activity in various models and cell lines exhibiting different mechanisms. Some analogs have also shown good pharmacokinetic parameters with less IC50 values. Conclusion: Various 1,3,5-triazine analogs have shown potent activities and may be regarded as clinical candidates for future anticancer formulations. This review may be helpful to those researchers seeking required information with regard to the drug design and medicinal properties of 1,3,5-triazine derivatives for selected targets. This review may also offer help to find and improve clinically viable anticancer molecules.

Background: The humans can be affected by more than 100 types of cancers in which about 22 % cancer death are caused by tobacco, 10% due to alcohol and obesity, 5-10 % by genetic defects and 20 % by infections. Rheumatoid arthritis, an autoimmune disorder, occurs mostly in middle age, affects 2.5 times more to females than males and till 2015, more than 24.5 Million people get affected from this disorder. The deaths due to rheumatoid arthritis were 28000 in 1990 and increased to 38000 in 2013. Parkinson’s disease, a neurodegenerative disorder of central nervous system affects about 6.2 million people in 2015 and responsible for approximately 117400 deaths worldwide. Parkinson’s disease occurs mainly over the age of 60 and males get more affected than females. Methods: Bibliographic database has created by mendeley desktop software for available literature in peer reviewed research articles especially by titles and disease names as keywords with AND Boolean operator (title AND year or author AND year). The intervention and findings of quality papers were extracted by detailed study and a conceptual framework has developed. Results: Total 121 research and review articles are cited in this review to produce high impact in literature for pathophysiology and receptors involved in all three diseases. Changes in enzyme action, prohibition of angiogenesis and inhibition of microtubule are the main areas where anticancer molecules may perform significant effect. The immune system is not a good target for rheumatic treatment due to many complications that occur in body but fibroblast, like synoviocytes, proteases which are responsible for cartilage destruction and osteoclast differentiation may be the beneficial targets for pharmacoactive molecules in the treatment of rheumatoid arthritis. In Parkinson’s disease, supply of dopamine to brain from outside results in brain dopamine synthesis decrement which increase drug dependency. The compounds which stimulate secretion, reuptake inhibitor and increment in dopaminergic neurons may be good targets. Conclusion: Alteration of signal transduction by a drug is the goal of chemogenomics, a new branch formed by combination of chemistry and genomics. The proliferation, angiogenesis and apoptosis of cancer cells are regulated by cellular signaling of transcription factors, protein kinases, transmembrane receptors, extracellular ligands and some external factors like oncogenic mutations, ubiquitin-proteasome pathway with epigenetic changes. Traditional anticancer drugs either alter DNA synthesis or control cell division while new drugs retard tumor growth or induce apoptosis. The deterioration of dopaminergic neurons in substantia nigra results in Parkinson’s disease with mental confusion, cognitive dysfunction and sleep disorder. Rheumatoid arthritis is characterized by inflammation, autoimmunity, joint destruction, deformity and premature mortality and treated mainly by anti-inflammatory and antirheumatic drugs. This review provides a comprehensive summary of objects which may act as potential targets for many health disorders.

Background: Neurodegenerative diseases are age-related ailments which are characterized by progressive neuronal damage and loss. These diseases can be caused by both genetic and environmental factors. Alzheimer’s and Parkinson’s are the most predominant neurodegenerative diseases. Though various research strategies have been employed to eliminate the cause of the disease, till date successful strategies available are symptomatic. Various compounds have been designed against the targets, such as BACE1, acetylcholinesterase, glycogen synthase kinase, muscarinic acetylcholine receptor etc. Methods: This review consists of information gathered from various research articles and review papers in the concerned field. An attempt was made to identify important findings from these papers. Important in silico techniques used in the identification of drug candidates and newly designed compounds as therapeutics for neurodegenerative diseases were summarized. Results: Sixty papers were included in this review. A comprehensive overview of computer aided drug designing techniques used aimed at the identification of new drug candidates is provided. Ligand based drug design approaches such as QSAR, virtual screening and pharmacophore have been described. Current therapies used against Alzheimer’s and Parkinson’s have summarized. New compounds against the targets of for Alzheimer’s and Parkinson’s identified by computational screening of compounds have been summarized. Conclusion: The findings of this review confirm that therapies and current successful strategies for neurodegenerative disease are mainly symptomatic. Current research is mainly focused on preventing the progress of neurodegeneration. Various in silico techniques; ligand-based methods such as QSAR, virtual screening, pharmacophore mapping and structure-based methods such as homology modeling, docking studies have been used to identify therapeutic compounds for Alzheimer’s and Parkinson’s.

Background: Data mining is an emerged and promising technology, and it utilized in the software engineering development process. It does not only enhance the accurateness, it also improves the reliability of the software. A software development application error is a fault or bug in a computer program. It produces wrong or unexpected outcomes. In traditional software development, faults manually triaged through a specialist developer, i.e., a human being triaged. Methods: Manual fault triage takes long time and produce low accuracy for the huge amount of faults. To resolve above issues, An Efficient Integration of Instance and Aspect Preferment Algorithm (EIIAPA) is proposed to decrease the scale of fault report data concurrently and to enhance the accurateness of data. Results & Conclusion: The proposed technique helps to validate & verify software application in effective way. Reduction of data on fault triage aims to construct a high-superiority set of fault data in the small-scale system through eliminating the fault report. To applying an algorithm, fault data set and attributes are extracted from every fault data set and train a predictive model based on the historical dataset. Based on Experimental evaluations, proposed methodology reduces 0.06 ET (Execution Time), and improves 0.5 P (Precision), 0.75 R (Recall), 0.39 F-M (F-Measure) and 5.07% (accuracy) compared than existing methodologies.

Background: In a Wireless Sensor Network, one of the important issues is minimizing energy consumption without losing accuracy during data transmission. Communication must be released in an optimized way, which enhances energy efficiency in the networks. Methods: By applying various techniques especially node balance set, the network lifetime is increased and delay is minimized. To accomplish node balance set, Cluster Head (CH) determination mechanism is implemented and clustering based load balanced is established. Further, the enactment of the anticipated designs is established through simulations in the circumstance of scalable data transmission in a WSN. Results & Conclusion: Hypothetical study and experimental simulations are studied by various performance evaluation metrics namely clock offset, number of transmitted messages, delayed messages and Residual Energy. The results confirm that clustering with node balance set saves more energy in WSN and also it reduces synchronization errors.

Background: To transmit the secret data are in a secure manner or to prevent the intruder/ third party activities while transmitting secret data’s through the public networks are challenging task now. In order to deal with these situations, this paper presents an encryption/encoding technique of MSI (Modified Steganography for Image) for secret data before transmitting over the network. Methods: The MSI technique is classified into two phases, one is stegano image creation by encode process and another one is reconstructing the secret data from the stegano image by decode process. In encode process, the grayscale cover and secret images are considering as an input. In addition, segregation, 8-bit binary conversion, substitution and decimal conversion processes are doing a vital role in hiding the secret image into a cover image. The MSI encoded stegno image is almost equal to the cover image and it's not an easy to identify by the Human Visual Attack (HVA). Results & Conclusion: To evaluate the proposed MSI encoding process the time, signal to noise ratio, complexity and strength are considering as the parameters. In result, the MSI encode technique is providing good reconstruction image quality, strong against the pixel attack and HVA, less execution time than the conventional schemes.

Background: This paper presents an image security system by combining bit rotation with block based chaotic maps cryptography. Methods: The system uses permutation technique that divides the image into blocks before applying right/left rotation of bits to the pixel values based on a randomly generated key. Then, the image blocks are fused together. A scrambling operation followed by chaotic map is applied on the rotated image to diffuse the image pixels using another randomly generated key. The chaotic map scatters all the pixel positions in the image. The decryption is the complete reversal operation of the encryption process. Results & Conclusion: The performance of the proposed technique is evaluated using several metrics: Histograms of both original and cipher images, correlation of adjacent pixels and correlation between the original and cipher images, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The results indicate a very secure technique to protect all types of images.

Background: Identifying user suitability plays a vital role in various modalities like neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper analysis the user suitability based on neural networks (NN), subjects, age groups and gender for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand. Six parametric feature extraction algorithms are used to extract the features from sEMG signals such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural Network. Methods: sEMG signals generated from forearm muscles of the participants are collected through an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the user is identified in the sEMG recognition module using signal processing, feature extraction and machine learning techniques. The information about the identified movement is passed to microcontroller wherein a control is developed to command the prosthetic hand to emulate the identified movement. Results: From the six feature extraction algorithms and two neural network models used in the study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best suited for this study as the neural network model is specially designed for pattern matching problem. Moreover, it has simple architecture and low computational complexity. AR Burg is found to be the best feature extraction technique in this study due to its high resolution for short data records and its ability to always produce a stable model. In all the neural network models, the maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are best suited for the study due to their better muscle contractions. Better muscle fatigue resistance has contributed for better performance of female subjects as compared to male subjects. From the single trial analysis, it can be observed that the hand close movement has achieved best recognition rate for all neural network models. Conclusion: In this paper a study was conducted to identify user suitability for designing hand prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements. The suitability of the user was identified using two neural networks with six parametric features. From the result, it was concluded thatfit women doing regular physical exercises aged between 26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition Neural Network with AR Burg extraction features using extension movements will be a better way to design the HMI. However, Signal acquisition based on wireless method is worth considering for the future.