The Internet of Things (IoT) uses wireless networks without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental factors. There are a variety of WSN uses, and some well-known application factors include energy consumption and lifespan duration for routing purposes. The sensors have detecting, processing, and communication capabilities. In this paper, an intelligent healthcare system is proposed which consists of nano sensors that collect real-time health status and transfer it to the doctor’s server. Time consumption and various attacks are major concerns, and some existing techniques contain stumbling blocks. Therefore, in this research, a genetic-based encryption method is advocated to protect data transmitted over a wireless channel using sensors to avoid an uncomfortable data transmission environment. An authentication procedure is also proposed for legitimate users to access the data channel. Results show that the proposed algorithm is lightweight and energy efficient, and time consumption is 90% lower with a higher security ratio.

γ-Aminobutyric acid (GABA) Transporters (GATs) belong to sodium and chloride dependent-transporter family and are widely expressed throughout the brain. Notably, GAT1 is accountable for sustaining 75% of the synaptic GABA concentration and entails its transport to the GABA_A receptors to initiate the receptor-mediated inhibition of post-synaptic neurons. Imbalance in ion homeostasis has been associated with several neurological disorders related to the GABAergic system. However, inhibition of the GABA uptake by these transporters has been accepted as an effective approach to enhance GABAergic inhibitory neurotransmission in the treatment of seizures in epileptic and other neurological disorders. Here, we reviewed computational methodologies including molecular modeling, docking, and molecular dynamic simulations studies to underscore the structure and function of GAT1 in the GABAergic system. Additionally, various SAR and QSAR methodologies have been reviewed to probe the 3D structural features of inhibitors required to modulate GATs activity. Overall, present review provides an overview of crucial role of GAT1 in GABAergic system and its modulation to evade neurological disorders.

The hERG (human ether-a-go-go-related gene) encoded potassium ion (K⁺) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC₅₀) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC₅₀) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC₅₀) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC₅₀) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.

Cytochrome P450 (CYP450) enzymes belong to a superfamily of heme-containing proteins that are involved in the metabolism of structurally diverse endogenous and exogenous compounds. Various proof-of-concept studies indicate that metabolic stability and intrinsic clearance of CYP450 substrates are linked with the respective lipophilicity (log P or log D). This necessitates the normalization of lipophilicity (log P or log D) of a given CYP450 substrate with respect to its metabolic stability (LipMetE) and intrinsic clearance (log₁₀CL_int,u). Therefore, in this article, the LipMetE values of already known substrates of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, including some marketed drugs, have been calculated to elucidate the relationship between lipophilicity (log D_7.4) and in vitro clearance. Moreover, various drug efficiency metrics including lipophilic efficiency (LipE) and ligand efficiency (LE) have been evaluated, and the thresholds of these parameters have been defined for the CYP450 substrates exhibiting normalized LipMetE. Our results indicate that for a given range of LipMetE, greater the log D value of the substrate the more avidly it binds to a given CYP450 enzyme and shows more intrinsic clearance (log₁₀CL_int,u). Overall, the majority of the model substrates of CYP450 isoforms and already marketed drugs in our datasets exhibit log D_7.4 values of ∼2.5 with LipMetE values in the range of 0–2.5 and LipE values of ≤3. Overall, consideration of these parameters in ADME profiling could aid in reducing the drug failure rate in the later stages of clinical investigations.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (I_Kr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal arrhythmias. Several marketed drugs have been withdrawn for this reason. Therefore, there is considerable interest in developing better tests for predicting drugs which can block the hERG channel. The drug-binding pocket in hERG channels, which lies below the selectivity filter, normally contains K⁺ ions and water molecules. In this study, we test the hypothesis that these water molecules impact drug binding to hERG. We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. The ligand–protein interaction fingerprints (PLIF) scheme was used to summarize and compare the interactions. All models delineated similar 3D hERG binding features, however, small deviations of about ~0.4 Å were observed between important hotspots of molecular interaction fields (MIFs) between solvated and non-solvated hERG models. These small changes in conformations do not affect the performance and predictive power of the model to any significant extent. The model that exhibits the best statistical values was attained with a cryo_EM structure of the hERG channel in open state without water. This model also showed the best R² of 0.58 and 0.51 for the internal and external validation test sets respectively. Our results suggest that the inclusion of water molecules during the docking process has little effect on conformations and this conformational change does not impact the predictive ability of the 3D QSAR models.