A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions

The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.Other Information Published in: Computer Networks License: http://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://dx.doi.org/10.1016/j.comnet.2023.109605</p

Structural-based design of HD-TAC7 PROteolysis TArgeting chimeras (PROTACs) candidate transformations to abrogate SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for about 672 million infections and 6.85 million deaths worldwide. Upon SARS-CoV-2 infection, Histone deacetylases (HDACs) hyperactivate the pro-inflammatory response resulting in stimulation of Acetyl-Coenzyme A and cholesterol for viral entry. HDAC3 inhibition results in the anti-inflammatory activity and reduction of pro-inflammatory cytokines that may restrict COVID-19 progression. Here, we have designed 44 conformational ensembles of previously known HD-TAC7 by enumerating torsions of dihedral angles tested for their binding preferences against HDAC3. Through scrutinizing their placements at active site and binding affinities, three hits were isolated. Cereblon (CRBN) is a well-known E3 ligase that facilitates Proteolysis Targeting Chimeras (PROTACs) targeting. Three entities, including HDAC3-binding moiety (4-acetamido-N-(2-amino-4 fluorophenyl) benzamide), a 6-carbon linker, and CRBN binding ligand (pomalidomide) were assembled to design 4 PROTACs followed by energy minimization and docking against HDAC3 and CRBN, respectively. Subsequent molecular dynamics (MD) and free energy analyses corroborated similar binding trends and favorable energy values. Among all cases, Met88, GLu106, Pro352, Trp380 and Trp388 residues of CRBN, and Pro23, Arg28, Lys194, Phe199, Leu266, Thr299 and Ile346 residues of HDAC3 were engaged in PROTAC binding. Thus, conformational dynamics of both HDAC3 and CRBN moieties are essential for the placement of PROTAC, resulting in target degradation. Overall, the proposed bifunctional small molecules may effectively target HDAC3, stimulating innate immune response to restrict COVID-19 hyperinflammation. This study supports the basis for designing new PROTACs by limiting the conformational search space that may prove more efficient for targeting the protein of interest. Communicated by Ramaswamy H. Sarma</p

Identification of potential natural products derived from fungus growing termite, inhibiting <i>Pseudomonas aeruginosa</i> quorum sensing protein LasR using molecular docking and molecular dynamics simulation approach

Pseudomonas aeruginosa, the most common opportunistic pathogen, is becoming antibiotic-resistant worldwide. The fate of P. aeruginosa, a multidrug-resistant strain, can be determined by multidrug efflux pumps, enzyme synthesis, outer membrane protein depletion, and target alterations. Microbial niches have long used quorum sensing (QS) to synchronize virulence gene expression. Computational methods can aid in the development of novel P. aeruginosa drug-resistant treatments. The tripartite symbiosis in termites that grow fungus may help special microbes find new antimicrobial drugs. To find anti-quorum sensing natural products that could be used as alternative therapies, a library of 376 fungal-growing termite-associated natural products (NPs) was screened for their physicochemical properties, pharmacokinetics, and drug-likeness. Using GOLD, the top 74 NPs were docked to the QS transcriptional regulator LasR protein. The five lead NPs with the highest gold score and drug-like properties were chosen for a 200-ns molecular dynamics simulation to test the competitive activity of different compounds against negative catechin. Fridamycin and Daidzein had stable conformations, with mean RMSDs of 2.48 and 3.67 Å, respectively, which were similar to Catechin’s 3.22 Å. Fridamycin and Daidzein had absolute binding energies of −71.186 and −52.013 kcal/mol, respectively, which were higher than the control’s −42.75 kcal/mol. All the compounds within the active site of the LasR protein were kept intact by Trp54, Arg55, Asp67, and Ser123. These findings indicate that termite gut and fungus-associated NPs, specifically Fridamycin and Daidzein, are potent QS antagonists that can be used to treat P. aeruginosa’s multidrug resistance. Communicated by Ramaswamy H. Sarma</p

Effect of variation in permeability parameter <i>k</i>* on temperature profile <i>θ</i>(<i>η</i>) of Newtonian and non-Newtonian fluids.

<p>Effect of variation in permeability parameter <i>k</i>* on temperature profile <i>θ</i>(<i>η</i>) of Newtonian and non-Newtonian fluids.</p

Effect of variation in permeability parameter <i>k</i>* on velocity profile <i>f</i>′(<i>η</i>) of Newtonian and non-Newtonian fluids.

<p>Effect of variation in permeability parameter <i>k</i>* on velocity profile <i>f</i>′(<i>η</i>) of Newtonian and non-Newtonian fluids.</p

Effect of variation in temperature slip parameter <i>β</i> on temperature profile <i>θ</i>(<i>η</i>) of Newtonian and non-Newtonian fluids.

<p>Effect of variation in temperature slip parameter <i>β</i> on temperature profile <i>θ</i>(<i>η</i>) of Newtonian and non-Newtonian fluids.</p

Effect of variation in velocity slip parameter <i>δ</i> on velocity profile <i>f</i>′(<i>η</i>) of Newtonian and non-Newtonian fluids.

<p>Effect of variation in velocity slip parameter <i>δ</i> on velocity profile <i>f</i>′(<i>η</i>) of Newtonian and non-Newtonian fluids.</p

Effect of Prandtl number <i>P</i>_<i>r</i> on the temperature profile <i>θ</i>(<i>η</i>) of Newtonian and non-Newtonian fluids.

<p>Effect of Prandtl number <i>P</i>_<i>r</i> on the temperature profile <i>θ</i>(<i>η</i>) of Newtonian and non-Newtonian fluids.</p

The velocity and shear stress profiles for power-law index <i>n</i> = 1, permeability parameter <i>k</i>* = 0 and velocity slip parameter <i>δ</i> = 0.

<p>The velocity and shear stress profiles for power-law index <i>n</i> = 1, permeability parameter <i>k</i>* = 0 and velocity slip parameter <i>δ</i> = 0.</p

Effect of variation in suction/injection parameter <i>S</i> on temperature profile <i>θ</i>(<i>η</i>) of Newtonian and non-Newtonian fluids.

<p>Effect of variation in suction/injection parameter <i>S</i> on temperature profile <i>θ</i>(<i>η</i>) of Newtonian and non-Newtonian fluids.</p