Coronavirus: a comparative analysis of detection technologies in the wake of emerging variants.

An outbreak of the coronavirus disease caused by a novel pathogen created havoc and continues to affect the entire world. As the pandemic progressed, the scientific community was faced by the limitations of existing diagnostic methods. In this review, we have compared the existing diagnostic techniques such as reverse transcription polymerase chain reaction (RT-PCR), antigen and antibody detection, computed tomography scan, etc. and techniques in the research phase like microarray, artificial intelligence, and detection using novel materials; on the prospect of sample preparation, detection procedure (qualitative/quantitative), detection time, screening efficiency, cost-effectiveness, and ability to detect different variants. A detailed comparison of different techniques showed that RT-PCR is still the most widely used and accepted coronavirus detection method despite certain limitations (single gene targeting- in context to mutations). New methods with similar efficiency that could overcome the limitations of RT-PCR may increase the speed, simplicity, and affordability of diagnosis. In addition to existing devices, we have also discussed diagnostic devices in the research phase showing high potential for clinical use. Our approach would be of enormous benefit in selecting a diagnostic device under a given scenario, which would ultimately help in controlling the current pandemic caused by the coronavirus, which is still far from over with new variants emerging.

Subcutaneous Drug Delivery: A Review of the State-of-the-Art Modeling and Experimental Techniques.

Delivery of drug formulations through the subcutaneous route is a widely used modality for the treatment of several diseases, such as diabetes and auto-immune conditions. Subcutaneous injections are typically used to inject low-viscosity drugs in small doses. However, for new biologics, there is a need to deliver drugs of higher viscosity in large volumes. The response of subcutaneous tissue to such high-volume doses and higher viscosity injections is not well understood. Animal models have several drawbacks such as relevance to humans, lack of predictive power beyond the immediate population studied, cost, and ethical considerations. Therefore, a computational framework that can predict the tissue response to subcutaneous injections would be a valuable tool in the design and development of new devices. To model subcutaneous drug delivery accurately, one needs to consider: (a) the deformation and damage mechanics of skin layers due to needle penetration and (b) the coupled fluid flow and deformation of the hypodermis tissue due to drug delivery. The deformation of the skin is described by the anisotropic, hyper-elastic, and viscoelastic constitutive laws. The damage mechanics is modeled by using appropriate damage criteria and damage evolution laws in the modeling framework. The deformation of the subcutaneous space due to fluid flow is described by the poro-hyperelastic theory. The objective of this review is to provide a comprehensive overview of the methodologies used to model each of the above-mentioned aspects of subcutaneous drug delivery. We also present an overview of the experimental techniques used to obtain various model parameters.

A simple, robust and scalable route to prepare sub-50 nm soft PDMS nanoparticles for intracellular delivery of anticancer drugs.

Soft nanoparticles (NPs) have recently emerged as a promising material for intracellular drug delivery. In this regard, NPs derived from polydimethylsiloxane (PDMS), an FDA approved polymer can be a suitable alternative to conventional soft NPs due to their intrinsic organelle targeting ability. However, the available synthesis methods of PDMS NPs are complicated or require inorganic fillers, forming composite NPs and compromising their native softness. Herein, for the first time, we present a simple, robust and scalable strategy for preparation of virgin sub-50 nm PDMS NPs at room temperature. The NPs are soft in nature, hydrophobic and about 30 nm in size. They are stable in physiological medium for two months and biocompatible. The NPs have been successful in delivering anticancer drug doxorubicin to mitochondria and nucleus of cervical and breast cancer cells with more than four-fold decrease in IC50 value of doxorubicin as compared to its free form. Furthermore, evaluation of cytotoxicity in reactive oxygen species detection, DNA fragmentation, apoptosis-associated gene expression and tumor spheroid growth inhibition demonstrate the PDMS NPs to be an excellent candidate for delivery of anticancer drugs in mitochondria and nucleus of cancer cells.

Systematic Identification of Atom-Centered Symmetry Functions for the Development of Neural Network Potentials.

Neural network potentials are emerging as promising classical force fields that can enable long-time and large-length scale simulations at close to ab initio accuracies. They learn the underlying potential energy surface by mapping the Cartesian coordinates of atoms to system energies using elemental neural networks. To ensure invariance with respect to system translation, rotation, and atom index permutations, in the Behler-Parrinnello type of neural network potential (BP-NNP), the Cartesian coordinates of atoms are transformed into "structural fingerprints" using atom-centered symmetry functions (ACSFs). Development of an accurate BP-NNP for any chemical system critically relies on the choice of these ACSFs. In this work, we have proposed a systematic framework for the identification of an optimal set of ACSFs for any target system, which not only considers the diverse atomic environments present in the training dataset but also inter-ACSF correlations. Our method is applicable to different kinds of ACSFs and across diverse chemical systems. We demonstrate this by building accurate BP-NNPs for water and Cu2S systems.

Effect of temperature on concentrated electrolytes for advanced lithium ion batteries.

Salt-concentrated electrolytes are emerging as promising electrolytes for advanced lithium ion batteries (LIBs) that can offer high energy density and improved cycle life. To further improve these electrolytes, it is essential to understand their inherent behavior at various operating conditions of LIBs. Molecular dynamics (MD) simulations are extensively used to study various properties of electrolytes and explain the associated molecular-level phenomena. In this study, we use classical MD simulations to probe the properties of the concentrated electrolyte solution of 3 mol/kg lithium hexafluorophosphate (LiPF6) salt in the propylene carbonate solvent at various temperatures ranging from 298 to 378 K. Our results reveal that the properties such as ionic diffusivity and molar conductivity of a concentrated electrolyte are more sensitive to temperature compared to that of dilute electrolytes. The residence time analysis shows that temperature affects the Li+ ion solvation shell dynamics significantly. The effect of temperature on the transport and dynamic properties needs to be accounted carefully while designing better thermal management systems for batteries made with concentrated electrolytes to garner the advantages of these electrolytes.

Multiscale Modeling of Skin Electroporation.

Human skin, the largest external organ of the body, provides a selective barrier to therapeutics applied topically. The molecules having specific chemical and physical properties can only penetrate the deeper layer of the skin. However, the lag time for reaching a steady state in the deeper layer is generally of the order of hours. In order to deliver higher-molecular-weight, charged, and hydrophilic therapeutics in the deeper layer, the skin barrier must be breached. Electroporation is one of the methods used to breach the skin barrier for enhancement of drug permeation and reduction of lag time. However, the underlying mechanism responsible for the enhancement of drug permeation is not well understood. In this study, a multiscale model of skin electroporation is developed by connecting molecular phenomena to a macroscopic model. At the atomic scale, molecular dynamics simulations of the lipid matrix of the human stratum corneum (SC) were performed under the influence of an external electric field. The pores get formed during the electroporation process and the transport properties (diffusivity) of drug molecules are computed. The diffusion coefficient obtained during electroporation was found to be higher than passive diffusion. However, this alone could not explain the multifold increase in the drug flux on application of an electric field as observed in the experiments. Hence, a finite element method (FEM) model of the skin SC is also developed. The release of fentanyl through this model is compared with the available experimental results. Both experimental and simulated results of pore formation on application of an electric field and many folds' increase in drug flux are comparable. Once validated, the framework was used for the design of skin electroporation experiments (in silico) by changing the electric pulse parameters such as voltage, pulse duration, and number of pulses. This multiscale modeling framework provides valuable insight at the molecular and macroscopic levels to design the electroporation experiments. The framework can be utilized as a design tool for skin electroporation applications.

Interfacial structure in the liquid-liquid extraction of rare earth elements by phosphoric acid ligands: a molecular dynamics study.

Solvent extraction (SX), wherein two immiscible liquids, one containing the extractant molecules and the other containing the solute to be extracted are brought in contact to effect the phase transfer of the solute, underpins metal extraction and recovery processes. The interfacial region is of utmost importance in the SX process, since besides thermodynamics, the physical and chemical heterogeneity at the interface governs the kinetics of the process. Yet, a fundamental understanding of this heterogeneity and its implications for the extraction mechanism are currently lacking. We use molecular dynamics (MD) simulations to study the liquid-liquid interface under conditions relevant to the SX of Rare Earth Elements (REEs) by a phosphoric acid ligand. Simulations revealed that the extractant molecules and varying amounts of acid and metal ions partitioned to the interface. The presence of these species had a significant effect on the interfacial thickness, hydrogen bond life times and orientations of the water molecules at the interface. Deprotonation of the ligands was essential for the adsorption of the metal ions at the interface, with these ions forming a number of different complexes at the interface involving one to three extractant molecules and four to eight water molecules. Although the interface itself was rough, no obvious 'finger-like' water protrusions penetrating the organic phase were seen in our simulations. While the results of our work help us gain fundamental insights into the sequence of events leading to the formation of a variety of interfacial complexes, they also emphasize the need to carry out a more detailed atomic level study to understand the full mechanism of extraction of REEs from the aqueous to organic phases by phosphoric acid ligands.

Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases.

In the recent past, there has been proliferation in high-throughput density functional theory and data-driven explorations of materials motivated by a need to reduce physical testing and costly computations for materials discovery. This has, in conjunction with the development of open-access materials property databases, encouraged accelerated and more streamlined discovery and screening of technologically relevant materials. In this work, we report our results on the screening and DFT studies of one such class of materials, i.e. ABX3 inorganic halide perovskites (A, B and X representing the monovalent, divalent and halide ions respectively) using a coupled machine-learning (ML) and density functional theory (DFT) approach. Utilizing the support vector machine algorithm, we predict the formability of 454 inorganic halide compounds in the perovskite phase. Compounds with a formation probability P≥ 0.8 are further checked for thermodynamic stability in at least one of these three open materials databases - Materials Project (MP), Automatic FLOW for Materials Discovery (AFLOW) and Open Quantum Materials Database (OQMD). The shortlisted candidate perovskites are then considered for DFT computations. Taking input geometries from MP's structure predictor, the optimized lattice parameters and computed band gaps (BG) for all screened compounds are compared with predictions across all databases. Subsequently, detailed studies on low index surfaces are presented for two halide perovksites - RbSnCl3 and RbSnBr3- having band-gaps in the favourable range for photovoltaics (PV). Different possible (100), (110) and (111) surface terminations are investigated for each of these compositions and the atomic relaxations, surface energies and electronic band structures are reported for each termination. To the best of our knowledge, no surface studies have been reported in the literature for any of the halide perovskites present in our database. These studies, therefore, are an important step towards gaining a fundamental understanding of the interfacial properties of perovskites, which can help facilitate further breakthroughs in the PV technology.

Electroporation of Skin Stratum Corneum Lipid Bilayer and Molecular Mechanism of Drug Transport: A Molecular Dynamics Study.

The electroporation technique has been used significantly to increase drug permeation through the skin. This technique relies on the application of short-timed (microseconds to millisecond) electric fields (generally, order of 50--300 V) on the skin to create microscopic pores. However, the molecular mechanism of pore formation, resulting in an enhanced flux of active molecules through the skin, remains poorly understood. In this study, extensive atomistic molecular dynamics simulation of skin lipids [made up of ceramide (CER), cholesterol (CHOL), and free fatty acid (FFA)] has been performed at various external electric fields. We show for the first time the pore formation in the skin lipid bilayer during electroporation. We show the effect of the applied external electrical field (0.6-1.0 V/nm) on the pore formation dynamics in the lipid bilayer of different sizes (154, 616, and 2464 lipids) and compositions (CER/CHOL/FFA, 1:0:0, 1:0:1, 1:1:0, 1:1:1). The pore formation and resealing kinetics were different and were found to be highly dependent on the composition of the skin lipid bilayer. The pore formation time decreased with increase in the bilayer size. The pore sustaining electric field was found to be in the range of 0.20-0.25 V/nm for equimolar CER, CHOL, and FFA lipid bilayers. The skin lipid bilayer (1:1:1) sealed itself within 20 ns after the removal of the external electric field. We also present the molecular mechanism of enhancement of drug permeation in the presence of external field as compared to the passive diffusion. The molecular-level understanding obtained here could help in optimizing/designing the electroporation experiments for effective drug delivery. For a given skin composition and size of the drug molecule, the combination of pore formation time and pore growth model can be used to know a priori the desired electric field and time for the application of the electric field.

Effect of surface coverage and chemistry on self-assembly of monolayer protected gold nanoparticles: a molecular dynamics simulation study.

Gold nanoparticles (AuNP) are being used in a variety of applications ranging from drug delivery systems to nano-coolants. The structure and surface properties of AuNP can be manipulated using coatings of thiols, carrying different charges. Different densities of surface coverage can result in the formation of different structures. A molecular basis to quantify the interactions between AuNP covered with different densities (20, 60 and 100%) of surface coverage and various thiol chains (neutral, cationic and anionic) is obtained using potential of mean force (PMF) calculations. Further self-assembly simulations were performed to study the formation of aggregates/dispersed solutions with different densities of surface coverage (20, 40, 60, 80 and 100%). It is found that neutral coatings of all surface coverage densities studied, and charged coatings (anionic and cationic) of less than 60% surface coverage density result in the formation of aggregates. The aggregation and dispersion of AuNPs is explained in terms of an interplay between van der Waals and electrostatic forces. The results obtained are in good agreement with the data available in the literature and suggest that aggregation behaviour can be controlled by modifying the surface coverage and chemistry.

In-Silico Skin Model: A Multiscale Simulation Study of Drug Transport.

Accurate in-silico models are required to predict the release of drug molecules through skin in order to supplement the in-vivo experiments for faster development/testing of drugs. The upper most layer of the skin, stratum corneum (SC), offers the main resistance for permeation of actives. Most of the SC's molecular level models comprise cholesterol and phospholipids only, which is far from reality. In this study we have implemented a multiscale modeling framework to obtain the release profile of three drugs, namely, caffeine, fentanyl, and naphthol, through skin SC. We report for the first time diffusion of drugs through a realistic skin molecular model comprised of ceramides, cholesterol, and free fatty acid. The diffusion coefficients of drugs in the SC lipid matrix were determined from multiple constrained molecular dynamics simulations. The calculated diffusion coefficients were then used in the macroscopic models to predict the release profiles of drugs through the SC. The obtained release profiles were in good agreement with available experimental data. The partition coefficient exhibits a greater effect on the release profiles. The reported multiscale modeling framework would provide insight into the delivery mechanisms of the drugs through the skin and shall act as a guiding tool in performing targeted experiments to come up with a suitable delivery system.

Transdermal cellular membrane penetration of proteins with gold nanoparticles: a molecular dynamics study.

Transdermal delivery, where the skin acts as the route for local or systemic distribution, presents a lot of advantages over conventional routes such as oral and intravenous and intramuscular injections. However, the delivery of large biomolecules like proteins through the skin is challenging due to their size and structural properties. A molecular level understanding of their transport across the skin barrier is desirable to design successful formulations. We have employed constrained and unconstrained coarse grained molecular dynamics simulation techniques to obtain the molecular mechanism of penetration of the horseradish peroxidase (HRP) protein into the skin, in the presence and absence of gold nanoparticles (AuNPs). Unconstrained simulations show that HRP, when considered individually, was not able to breach the skin barrier, while in the presence of AuNPs, it first binds to the AuNPs and then breaches the barrier. The constrained simulations revealed that there was a free energy barrier for HRP to permeate inside the skin lipid layer when taken alone, while in the presence of gold nanoparticles, no barrier was found. Our study opens up the field of computational modeling based design of nanoparticle carriers for a given protein's transdermal delivery.

PDMS nanoparticles-decorated PDMS substrate promotes adhesion, proliferation and differentiation of skin cells.

Polydimethylsiloxane (PDMS) is known to be a common substrate for various cell culture-based applications. However, native PDMS is not very conducive for cell culture and hence, surface modification via cell adhesion moieties is generally needed to make it suitable especially for long-term cell culture. To address this issue, we propose to coat PDMS nanoparticles (NPs) on the surface of PDMS film to improve adhesion, proliferation and differentiation of skin cells. The proposed modification strategy introduces necessary nanotopography without altering the surface chemical properties of PDMS. Due to resemblance in the mechanical properties of PDMS with skin, PDMS NPs can recreate the native extracellular nanoenvironment of skin on the PDMS surface and provide anchoring sites for skin cells to adhere and grow. Human keratinocytes, representing 95% of the epidermal skin cells maintained their characteristic well-spread morphology with the formation of interconnected cell-sheets on this coated PDMS surface. Moreover, our in vitro immunofluorescence studies confirmed expression of distinctive epidermal protein markers on the coated surface indicating close resemblance with the native skin epidermis. Conclusively, our findings suggest that introducing nanotopography via PDMS NPs can be an effective strategy for emulating the native cellular functions of keratinocytes on PDMS based cell culture devices.

Coarse grained MD simulation of bulk and interfacial behavior of mixture of CTAB/SDS surfactants.

CONTEXT: This study involves simulating the process of inhibiting corrosion through the formation of micelles by surfactants and their deposition on iron (Fe) surfaces. The primary focus is on examining CTAB/SDS mixtures in aqueous solutions with different concentrations. Micelle properties, including size, shape, aggregation number, cluster size, and surfactant diffusion, were calculated and validated with experimental data. The coarse-grained Fe surface was modeled and validated against experimental water contact-angle data. Subsequently, the deposition of CTAB/SDS mixtures on the Fe surface and air-water interface was studied systematically. We found that the relative ratio of CTAB/SDS in the solution directly influences surfactant deposition behavior, which might impact the corrosion inhibition efficiency. METHODS: All the MD simulations were performed using the GROMACS software with MARTINI2 force field and Martini polar water. The molecules are packed using PACKMOL software. Both NVT and NPT simulations are caried out at temperature and pressure of 303 K and 1 bar respectively, with a nonbonded interaction cut-off (rcut) of 1.1 nm. The LJ potential was shifted from 0.9 nm to rcut, while the electrostatic potential was shifted from 0.0 nm to rcut. For electrostatics, reaction-field coulomb type is used, relative dielectric constant (epsilon-r) and the reaction field dielectric constant (epsilon-rf) are equal to 2.5 and infinity respectively. The dielectric constant below rcut is epsilon-r, and beyond the cut-off is epsilon-rf. Coulomb-modifier used as potential-shift which leads to shift in the coulomb potential by a constant such that it is zero at the rcut. This makes the potential of the integral of the force . The neighbor list was updated every 10 steps, employing a neighbor list cut-off equal to rcut. Using a polar water model, we used a constant time step of 0.02 ps throughout the simulation. The used epsilon-r = 2.5, is recommended for polar water.

Molecular dynamics simulation-based study to analyse the properties of entrapped water between gold and graphene 2D interfaces.

Heterostructures based on graphene and other 2D materials have received significant attention in recent years. However, it is challenging to fabricate them with an ultra-clean interface due to unwanted foreign molecules, which usually get introduced during their transfer to a desired substrate. Clean nanofabrication is critical for the utilization of these materials in 2D nanoelectronics devices and circuits, and therefore, it is important to understand the influence of the "non-ideal" interface. Inspired by the wet-transfer process of the CVD-grown graphene, herein, we present an atomistic simulation of the graphene-Au interface, where water molecules often get trapped during the transfer process. By using molecular dynamics (MD) simulations, we investigated the structural variations of the trapped water and the traction-separation curve derived from the graphene-Au interface at 300 K. We observed the formation of an ice-like structure with square-ice patterns when the thickness of the water film was <5 Å. This could cause undesirable strain in the graphene layer and hence affect the performance of devices developed from it. We also observed that at higher thicknesses the water film is predominantly present in the liquid state. The traction separation curve showed that the adhesion of graphene is better in the presence of an ice-like structure. This study explains the behaviour of water confined at the nanoscale region and advances our understanding of the graphene-Au interface in 2D nanoelectronics devices and circuits.

Modified variational autoencoder for inversely predicting plasmonic nanofeatures for generating structural color.

We apply a modified variational autoencoder (VAE) regressor for inversely retrieving the topological parameters of the building blocks of plasmonic composites for generating structural colors as per requirement. We demonstrate results of a comparison study between inverse models based on generative VAEs as well as conventional tandem networks that have been favored traditionally. We describe our strategy for improving the performance of our model by filtering the simulated dataset prior to training. The VAE- based inverse model links the electromagnetic response expressed as the structural color to the geometrical dimensions from the latent space using a multilayer perceptron regressor and shows better accuracy over a conventional tandem inverse model.

Elucidating collective translocation of nanoparticles across the skin lipid matrix: a molecular dynamics study.

The top layer of skin, the stratum corneum, provides a formidable barrier to the skin. Nanoparticles are utilized and further explored for personal and health care applications related to the skin. In the past few years, several researchers have studied the translocation and permeation of nanoparticles of various shapes, sizes, and surface chemistry through cell membranes. Most of these studies focused on a single nanoparticle and a simple bilayer system, whereas skin has a highly complex lipid membrane architecture. Moreover, it is highly unlikely that a nanoparticle formulation applied on the skin will not have multiple nanoparticle-nanoparticle and skin-nanoparticle interactions. In this study, we have utilized coarse-grained MARTINI molecular dynamics simulations to assess the interactions of two types (bare and dodecane-thiol coated) of nanoparticles with two models (single bilayer and double bilayer) of skin lipid membranes. The nanoparticles were found to be partitioned from the water layer to the lipid membrane as an individual entity as well as in the cluster form. It was discovered that each nanoparticle reached the interior of both single bilayer and double bilayer membranes irrespective of the nanoparticle type and concentration, though coated particles were observed to efficiently traverse across the bilayer when compared with bare particles. The coated nanoparticles also created a single large cluster inside the membrane, whereas the bare nanoparticles were found in small clusters. Both the nanoparticles exhibited preferential interactions with cholesterol molecules present in the lipid membrane as compared to other lipid components of the membrane. We have also observed that the single membrane model exhibited unrealistic instability at moderate to higher concentrations of nanoparticles, and hence for translocation study, a minimum double bilayer model should be employed.

Effects of Segregation on the Catalytic Properties of AgAuCuPdPt High-Entropy Alloy for CO Reduction Reaction.

Multicomponent alloys are promising catalysts for diverse chemical conversions, owing to the ability to tune their vast compositional space to maximize catalytic activity and product selectivity. However, elemental segregation, whereby the surface or grain boundaries of the material are enriched in a few elements, is a physically observed phenomenon in such alloys. Such segregation alters not only the composition but also the kinds of catalytically active sites present at the surface. Thus, elemental segregation, which can be achieved via various processing techniques, can be used as an additional knob in searching for alloy compositions that are both active and selective for a target chemical conversion. We demonstrate this using molecular simulations, machine learning, and Bayesian optimization to search for both random solid solution and "segregated" AgAuCuPdPt alloy compositions that are potentially active and selective for CO reduction reaction (CORR). Finally, we validate our findings by computing the reaction-free energy landscape for the CORR on the optimal alloy compositions via density functional theory calculations.

Classification of tastants: A deep learning based approach.

Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature. We analyzed the chemical characteristics of the molecules, with special focus on the presence of different functional groups. A deep neural network model based on molecular descriptors and a graph neural network model were trained for taste prediction. The class imbalance due to fewer umami molecules was tackled using special sampling techniques. Both models show comparable performance during evaluation, but the graph-based model can learn task-specific representations from the molecular structure without requiring handcrafted features. We further explain the deep neural network predictions using Shapley additive explanations. Finally, we demonstrated the applicability of the models by screening bitter, sweet, and umami molecules from a large food database. This study develops an in-silico approach to classify molecules based on their taste by leveraging the recent progress in deep learning, which can serve as a powerful tool for tastant design.

Integrated point-of-care RT-PCR methods during and after COVID-19 pandemic.

The COVID-19 pandemic has taken the world by surprise and people and organisations worldwide worked in some way or the other to combat the spread; isolate from the infected and get back to normal life, as it was before the pandemic hit. In this regard, the diagnosis of COVID-19 was at the centre of control and prevention and have seen a vehement change in every aspect, especially development of point-of-care testing for better and quick diagnosis. Among different types of techniques developed, the most important was the RT-PCR method of detection which detects nucleic acid of the virus in samples. RT-PCR is a laboratory-based method requiring trained professionals and precise steps for accurate testing. With the advent and spread of the pandemic, number of RT-PCR diagnostic centres rose significantly, and the detection process became less cumbersome, easy to use, ability to handle large volume of samples, more accurate, less time-consuming, and cost-effective. Different industries developed RT-PCR kits, reducing the efforts to prepare laboratory samples. Machines were employed for labour-driven tasks in PCR testing. In addition, new age technologies such as artificial intelligence, IoT, digital systems were combined with RT-PCR for accurate and easy testing. In this review, point-of-care RT-PCR methods, when the COVID-19 started, and the methods now, has been compared on the basis of technological advancements.