Current Signal Transduction Therapy - Volume 17, Issue 1, 2022

Electromyogram (EMG) signals are produced by the human body and are used in prosthetic design due to their significant functionality with human biomechanics. Engineers are capable of developing a variety of prosthetic limbs with the advancement of technology in the domain of biomedical signal processing, as limb amputees can restore their lives with the help of prosthetic limbs. This current review aimed at determining the signals that are used to monitor the device, explaining the various steps and techniques involved (such as data acquisition, feature vector conversion after noise, and redundant data removal), and reviewing previously developed electromyogram- based prosthetic controls. Furthermore, this study also focuses on a variety of electromyogram- controlled applications

Background: The development of multi-resistant strains of the Plasmodium parasite has become a global problem. Therefore, designing of new antimalarial agents is an exclusive solution. Objective: To improve the activity and identify potentially efficacious new antimalarial agents, integrated computational perspectives such as pharmacophore mapping, 3D-QSAR and docking study have been applied to a series of indolo-quinoline derivatives. Methods: The pharmacophore mapping generated various hypotheses based on key functional features and the best hypothesis ADRRR_1 revealed that indolo-quinoline scaffold is essential for antimalarial activity. 3D-QSAR model was established based on CoMFA and CoMSIA models by using 30 indoloquinoline analogues as training set and the rest of 19 as test set. Results: The molecular field analysis (MFA) with PLS (partial least-squares) method was used to develop significant CoMFA (q²=0.756, r²=0.996) and CoMSIA (q²=0.703, r²=0.812) models. The CoMFA and CoMSIA models showed good predictive ability with r2 pred values of 0.9623 and 0.9214, respectively. Docking studies were performed by using pfLDH to identify structural insight into the active site and results signify that the quinoline nitrogen acts as a hydrogen bond acceptor region to facilitate interaction with Glu122. Finally, designed molecules were screened through the ADMET tool to evaluate the pharmacokinetic and drug-likeness parameters. Conclusion: Thus, these studies suggested that established models have good predictability and would help in the optimization of newly designed molecules that may produce potent antimalarial activity.

Background: Thiosemicarbazones belong to the group of semicarbazides, which contains a sulfur atom instead of an oxygen atom. Several studies have shown that they are effective against extracellular protozoans like Trichomonas vaginalis, Plasmodium falciparum, Trypanosoma cruzi, and other parasites. Objective: The current research involves pharmacophore model design, 3-D-QSAR, virtual screening, and docking studies, all of which are evaluated using various parameters. Methods: The present study was performed by Schrodinger software. A total of 40 ligands were selected for the development of 3D QSAR models. To predict the pIC50 values in 3D-QSAR analysis, the entire dataset was divided into two sets, training, and test sets, in a 7:3 ratio. The selected pharmacophore hypothesis has been used for the virtual screening study. Results: DHHRR_1 emerged as the best pharmacophore model with a survival score of 5.80. The 3D QSAR study showed a significant model with R2 = 0.91 and Q2 = 0.73. The series top-scoring compound 7e had a docking score of -10.44 and showed interactions with the amino acids, ARG- 265, PHE-227, and LEU-531, required for activity. The developed pharmacophore model had been used for the screening of ZINC compounds, where ZINC26244107, ZINC13469100, ZINC01290725, and ZINC01350173 showed the best XP docking scores (-11.60, -11.27, -11.35, - 10.52, respectively) while binding important amino acids ARG265, HIE185 and LEU 531 against plasmodium falciparum, PDB ID:5TBO. The docking study was further evaluated by taking standard drug chloroquine, showing similar binding interactions as shown by other compounds. The ADME studies showed the drug-likeness properties of the compounds. Conclusion: The results of the present study may be helpful for the future development of antimalarial compounds against Plasmodium falciparum.

Aim: On multiple muscle locations, Surface Electromyography (sEMG) signals were recorded to predict the effect of different hand movements. Background: Myoelectric information is a non-stationary signal, so extracting correct features is important to boost any myoelectric control devices' performance. Myoelectric signal is an electrical activity recorded by a surface electrode at various movements of the muscles. Objective: The study presented pattern recognition classification methods to select an excellent algorithm for controlling the SEMG signal. Methods: Various time domain and frequency domain parameters were extracted prior to conducting the classifier test. Results: For the evaluation of the results for the recorded data (of all six movements), confusion matrix for neural network, Support Vector Machine (SVM), DT, and Linear Discriminant Analysis (LDA) classifiers is presented. Conclusion: This present study will be a step ahead in analyzing different problems for developing lower limb prosthesis.