Current Signal Transduction Therapy - Volume 18, Issue 1, 2023

Background: Heart disease is considered one of the complex diseases that has affected a large number of people around the world. It is important to detect and identify cardiac diseases at early stages. Objective: A large number of methods are already present that detect various heart diseases; however, there are some limitations to these methods, that have degraded their overall performance. Methods: In this paper, an effective and efficient method based on a convolutional neural network (CNN) and a feed-forward artificial neural network (FFANN) is proposed that can effectively detect cardiac diseases after analysing the electrocardiogram (ECG) signals. In this ongoing study, the transformed signals are used to extract the information from the processed data. The extracted features are then passed to the proposed CNN-FFANN classifiers for training and testing purposes. Results: The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices. Conclusion: The simulated outcomes have proved the proposed CNN-FFANN model to be more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications.

Background: Hypertension is a global public health issue that is becoming more prevalent. It is a non-communicable disease and a great public health problem affecting almost half of the world’s adult population. Being multifactorial, hypertension is a key risk factor for stroke, coronary artery disease, heart failure, and chronic renal failure. However, the cellular and molecular mechanisms that regulate it remain mostly unknown. According to numerous studies, microRNAs (miRNAs) have been implicated in a range of cellular processes in the development of illnesses. The current study aims to identify miRNAs in hypertension from genome sequences found in public genomic databases. Materials and Methods: In this study, we have used bioinformatic approaches to identify miR-26a- 1 for hypertension using the NCBI database, miRBase and target scan. Finally, the RNA fold was used to create the secondary structure of miR-26a-1. Results and Discussion: Careful evaluation of secondary structure result showed that hsa-miR- 26a-1 has a minimum free energy of - 37.30 kcal. The correlation between miR-26a-1 and hypertension genome sequence was identified. Conclusion: These computational approaches have concluded that miR-26a-1 can be used as a diagnosis, prognosis and effective therapeutic target for treating hypertension. Thus, further research could enlighten the role of miR-26a-1 in hypertension.

Despite enormous advances in medical technology, the prognosis of Brain Tumour (BT) remains extremely time-consuming and troublesome for physicians. Early and precise brain tumour identification effectively results and leads to an increased survival rate. This paper examines various techniques in order of priority to classify clinical images to analyse various research gaps and highlights their costs and benefits. Human mortality can be reduced by using an automatic classification scheme. The automatic classification of brain tumours is difficult due to the large spatial and structural variability of the brain tumor’s surrounding region. The latest developments have been investigated in image characterization strategies for diagnosing human body disease and addressing the classification of nuclear medical imaging identification techniques like Convolution Neural Network (CNN), Support Vector Machine (SVM), Histogram technique, K-Means Clustering (KMC) etc., just as the respective parameters like the image modalities employed, the dataset and the trade-offs have been compared for each technique. Among these techniques, the CNN model accomplished the highest accuracy of 99% for two sets of data: Brain Tumour Segmentation (BTS) and BD-brain tumour and high average susceptibility of 0.99 for all datasets. Finally, the review demonstrated that improving image order strategies regarding the accuracy, sensitivity value, and feasibility of Computer-Aided Diagnosis (CAD) is a significant challenge and an open research area.

Introduction: The prolonged antimalarial therapy with the marketed drug has developed multi-resistant strains of Plasmodium parasites that emerge as a consequential global problem. Therefore, designing new antimalarial agents is an exclusive solution to overcome the alarming situation. Methods: The integrated computational perspectives, such as pharmacophore mapping, 3D-QSAR and docking studies have been applied to improve the activity of the imidazo-quinoline scaffold. The best hypothesis AARRR_1 (Survival score 5.4609) obtained through pharmacophore mapping revealed that imidazo-quinoline scaffold is found to be vital for antimalarial activity. The significant CoMFA (q² = 0.728, r² = 0.909) and CoMSIA (q² = 0.633, r² = 0.729) models, developed by using molecular field analysis with the PLS method, showed good predictive ability with r²pred values of 0.9127 and 0.7726, respectively. Docking studies were performed using Schrodinger and GOLD software with the Plasmodium falciparum purine nucleoside phosphorylase enzyme (PDB ID-5ZNC) and results indicated that the imidazo-quinoline moiety facilitates the interaction with Tyr 160. Results: In addition, some compounds are screened from the ZINC database based on structural requirements to verify the relevance of the research. Finally, designed molecules and ZINC database compounds were screened through the ADMET tool to evaluate pharmacokinetic and druglikeness parameters. Conclusion: Thus, these exhaustive studies suggested that established models have good predictability and would help in the optimization of newly designed molecules that may lead to potent antimalarial activity for getting rid of resistance issues.

Aims: COVID-19 is a widespread infectious disease that affects millions of people worldwide. On account of the alarming rate of the spread of COVID-19, scientists are looking for new strategies for the diagnosis of this disease. X-rays are much more affordable and widely available compared to CT screening. The PCR testing process is time-consuming and experiences false negative rates, these traditional medical imaging modalities play a vital role in the control of the pandemic. In this paper, we have developed and examined different CNN models to identify the best method for diaognosing this disease. Background and Objective: The efforts of providing testing kits have increased due to the transmission of COVID 19. The preparation of these kits are complicated, rare, and expensive moreover, the difficulty of using them is another issue. The results have shown that the testing kits take crucial time to diagnose the virus, in addition to the fact that they have a 30 % loss rate. Methods: In this article, we have studied the usage of ubiquitous X-ray imaging, for the classification of COVID-19 chest images, using existing convolutional neural networks (CNNs). Different CNN architectures, including VGG19, Densnet-121, and Xception are applied to train the network by chest X-rays of infected patients but not the infected ones. Results: After applying these methods the results showed different accuracies but were more precise than the state-of-the-art models. The DenseNet-121 network obtained 97% accuracy, 98% precision, and 96% F1 score. Conclusion: COVID-19 is a widespread infectious disease that affects millions of people worldwide. On account of the alarming rate of the spread of COVID-19 scientists are looking for new strategies for the diagnosis of this disease. In this article, we have examined the performance of different CNN models to identify the best method for the classification of this disease. The VGG 19 method showed 93 % accuracy.

Background: Computational modeling is used to develop solutions by formulating and modeling real-world problems. This research article presents an innovative approach to using a computational model, as well as an evaluation of software interfaces for usability. Methods: In this work, a machine learning technique is used to classify different mitogenic activated protein kinases (MAPK), namely extracellular signal-regulated kinase (ERK), c-Jun amino (N)- terminal kinases (JNK), and mitogenic kinase (MK2) proteins. A deficiency of ERK and JNK leads to neurodegenerative diseases, such as Parkinson's disease, Alzheimer's disease (AD), and prion diseases, while the deficiency of MK2 leads to atherosclerosis. In this study, images from a heat map were normalized, scaled, smoothed, and sharpened. Different feature extraction methods have been used for various attributes, while principal component analysis was used as a feature selection technique. These features were extracted with machine learning algorithms to produce promising results for clinical applications. Results: The results show that ANN achieves 97.09%, 96.82%, and 96.01% accuracy for JNK, ERK, and MK2 proteins, respectively, whereas CNN achieves 97.60%, 97.36%, and 96.81% accuracy for the same proteins. When CNN is used, the best results are obtained for JNK protein, with a training accuracy of 97.06% and a testing accuracy of 97.6%. Conclusion: The proposed computational model is validated using a convolution neural network (CNN). The effect of the hidden layer on different activation functions has been then observed using ANN and CNN. The proposed model may assist in the detection of various MAPK proteins, yielding promising results for clinical diagnostic applications.