Isolation, Detection and Analysis of Circulating Tumour Cells: A Nanotechnological Bioscope.

Cancer is one of the dreaded diseases to which a sizeable proportion of the population succumbs every year. Despite the tremendous growth of the health sector, spanning diagnostics to treatment, early diagnosis is still in its infancy. In this regard, circulating tumour cells (CTCs) have of late grabbed the attention of researchers in the detection of metastasis and there has been a huge surge in the surrounding research activities. Acting as a biomarker, CTCs prove beneficial in a variety of aspects. Nanomaterial-based strategies have been devised to have a tremendous impact on the early and rapid examination of tumor cells. This review provides a panoramic overview of the different nanotechnological methodologies employed along with the pharmaceutical purview of cancer. Initiating from fundamentals, the recent nanotechnological developments toward the detection, isolation, and analysis of CTCs are comprehensively delineated. The review also includes state-of-the-art implementations of nanotechnological advances in the enumeration of CTCs, along with future challenges and recommendations thereof.

Types of spectroscopy and microscopy techniques for cancer diagnosis: a review.

Cancer is a life-threatening disease that has claimed the lives of many people worldwide. With the current diagnostic methods, it is hard to determine cancer at an early stage, due to its versatile nature and lack of genomic biomarkers. The rapid development of biophotonics has emerged as a potential tool in cancer detection and diagnosis. Using the fluorescence, scattering, and absorption characteristics of cells and tissues, it is possible to detect cancer at an early stage. The diagnostic techniques addressed in this review are highly sensitive to the chemical and morphological changes in the cell and tissue during disease progression. These changes alter the fluorescence signal of the cell/tissue and are detected using spectroscopy and microscopy techniques including confocal and two-photon fluorescence (TPF). Further, second harmonic generation (SHG) microscopy reveals the morphological changes that occurred in non-centrosymmetric structures in the tissue, such as collagen. Again, Raman spectroscopy is a non-destructive method that provides a fingerprinting technique to differentiate benign and malignant tissue based on Raman signal. Photoacoustic microscopy and spectroscopy of tissue allow molecule-specific detection with high spatial resolution and penetration depth. In addition, terahertz spectroscopic studies reveal the variation of tissue water content during disease progression. In this review, we address the applications of spectroscopic and microscopic techniques for cancer detection based on the optical properties of the tissue. The discussed state-of-the-art techniques successfully determines malignancy to its rapid diagnosis.

Deep learning-based image processing in optical microscopy.

Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places.

Carbon fullerene and nanotube are probable binders to multiple targets of SARS-CoV-2: Insights from computational modeling and molecular dynamic simulation studies.

The present study aimed to predict the binding potential of carbon nanotube and nano fullerene towards multiple targets of SARS-CoV-2. Based on the virulent functions, the spike glycoprotein, RNA-dependent RNA polymerase, main protease, papain-like protease, and RNA binding domain of the nucleocapsid proteins of SARS-CoV-2 were prioritized as the molecular targets and their three-dimensional (3D) structures were retrieved from the Protein Data Bank. The 3D structures of carbon nanotubes and nano-fullerene were computationally modeled, and the binding potential of these nanoparticles to the selected molecular targets was predicted by molecular docking and molecular dynamic (MD) simulations. The drug-likeness and pharmacokinetic features of the lead molecules were computationally predicted. The current study suggested that carbon fullerene and nanotube demonstrated significant binding towards the prioritized multi-targets of SARS-CoV-2. Interestingly, carbon nanotube showed better interaction with these targets when compared to carbon fullerene. MD simulation studies clearly showed that the interaction of nanoparticles and selected targets possessed stability and conformational changes. This study revealed that carbon nanotubes and fullerene are probably used as effectual binders to multiple targets of SARS-CoV-2, and the study offers insights into the experimental validation and highlights the relevance of utilizing carbon nanomaterials as a therapeutic remedy against COVID-19.

Structural insights on the interaction potential of natural leads against major protein targets of SARS-CoV-2: Molecular modelling, docking and dynamic simulation studies.

Though significant efforts are in progress for developing drugs and vaccines against COVID-19, limited therapeutic agents are available currently. Thus, it is essential to undertake COVID-19 research and to identify therapeutic interventions in which computational modeling and virtual screening of lead molecules provide significant insights. The present study aimed to predict the interaction potential of natural lead molecules against prospective protein targets of SARS-CoV-2 by molecular modeling, docking, and dynamic simulation. Based on the literature survey and database search, fourteen molecular targets were selected and the three targets which lack the native structures were computationally modeled. The drug-likeliness and pharmacokinetic features of ninety-two natural molecules were predicted. Four lead molecules with ideal drug-likeliness and pharmacokinetic properties were selected and docked against fourteen targets, and their binding energies were compared with the binding energy of the interaction between Chloroquine and Hydroxychloroquine to their usual targets. The stabilities of selected docked complexes were confirmed by MD simulation and energy calculations. Four natural molecules demonstrated profound binding to most of the prioritized targets, especially, Hyoscyamine and Tamaridone to spike glycoprotein and Rotiorinol-C and Scutifoliamide-A to replicase polyprotein-1ab main protease of SARS-CoV-2 showed better binding energy, conformational and dynamic stabilities compared to the binding energy of Chloroquine and its usual target glutathione-S-transferase. The aforementioned lead molecules can be used to develop novel therapeutic agents towards the protein targets of SARS-CoV-2, and the study provides significant insight for structure-based drug development against COVID-19.

Elucidating Methods for Isolation and Quantification of Exosomes: A Review.

Exosomes are the smallest extracellular vesicles present in most of the biological fluids. They are found to play an important role in cell signaling, immune response, tumor metastasis, etc. Studies have shown that these vesicles also have diagnostic and therapeutic roles for which their accurate detection and quantification is essential. Due to the complexity in size and structure of exosomes, even the gold standard methods face challenges. This comprehensive review discusses the various standard methods such as ultracentrifugation, ultrafiltration, size-exclusion chromatography, precipitation, immunoaffinity, and microfluidic technologies for the isolation of exosomes. The principle of isolation of each method is described, as well as their specific advantages and disadvantages. Quantification of exosomes by nanoparticle tracking analysis, flow cytometry, tunable resistive pulse sensing, electron microscopy, dynamic light scattering, and microfluidic devices are also described, along with the applications of exosomes in various biomedical domains.

Carbon fullerene acts as potential lead molecule against prospective molecular targets of biofilm-producing multidrug-resistant Acinetobacter baumanni and Pseudomonas aerugenosa: computational modeling and MD simulation studies.

This study aimed to screen putative drug targets associated with biofilm formation of multidrug-resistant Acinetobacter baumannii and Pseudomonas areugenosa and prioritize carbon nano-fullerene as potential lead molecule by structure-based virtual screening. Based on the functional role, 36 and 83 genes that are involved in biofilm formation of A. baumannii and P. areugenosa respectively were selected and metabolic network was computationally constructed. The genes that lack three-dimensional structures were predicted and validated. Carbon nano-fullerene selected as lead molecule and their drug-likeliness and pharmacokinetics properties were computationally predicted. The binding potential of carbon nano-fullerene toward selected drug targets was modeled and compared with the binding of conventional drugs, doripenem, and polymyxin-B with their usual targets. The stabilities of four best-docked complexes were confirmed by molecular dynamic (MD) simulation. This study suggested that selected genes demonstrated relevant interactions in the constructed metabolic pathways. Carbon fullerene exhibited significant binding abilities to most of the prioritized targets in comparison with the binding of last-resort antibiotics and their usual target. The four best ligand-receptor interactions predicted by molecular docking revealed that stability throughout MD simulation. Notably, carbon fullerene exhibited profound binding with outer membrane protein (OmpA) and ribonuclease-HII (rnhB) of A. baumannii and 2-heptyl-4(1H)-quinolone synthase (pqsBC) and chemotaxis protein (wspA) of P. aeruginosa. Thus, the current study suggested that carbon fullerene was probably used as potential lead molecules toward selected targets of A. baumannii and P. aeruginosa and the applied aspects probably scaled up to design promising lead molecules toward these pathogens. Communicated by Ramaswamy H. Sarma.

Structural and molecular basis of the interaction mechanism of selected drugs towards multiple targets of SARS-CoV-2 by molecular docking and dynamic simulation studies- deciphering the scope of repurposed drugs.

The repurposing of FDA approved drugs is presently receiving attention for COVID-19 drug discovery. Previous studies revealed the binding potential of several FDA-approved drugs towards specific targets of SARS-CoV-2; however, limited studies are focused on the structural and molecular basis of interaction of these drugs towards multiple targets of SARS-CoV-2. The present study aimed to predict the binding potential of six FDA drugs towards fifteen protein targets of SARS-CoV-2 and propose the structural and molecular basis of the interaction by molecular docking and dynamic simulation. Based on the literature survey, fifteen potential targets of SARS-CoV-2, and six FDA drugs (Chloroquine, Hydroxychloroquine, Favipiravir, Lopinavir, Remdesivir, and Ritonavir) were selected. The binding potential of individual drug towards the selected targets was predicted by molecular docking in comparison with the binding of the same drugs with their usual targets. The stabilities of the best-docked conformations were confirmed by molecular dynamic simulation and energy calculations. Among the selected drugs, Ritonavir and Lopinavir showed better binding towards the prioritized targets with minimum binding energy (kcal/mol), cluster-RMS, number of interacting residues, and stabilizing forces when compared with the binding of Chloroquine, Favipiravir, and Hydroxychloroquine, later drugs demonstrated better binding when compared to the binding with their usual targets. Remdesvir showed better binding to the prioritized targets in comparison with the binding of Chloroquine, Favipiravir, and Hydroxychloroquine, but showed lesser binding potential when compared to the interaction between Ritonavir and Lopinavir and the prioritized targets. The structural and molecular basis of interactions suggest that the FDA drugs can be repurposed towards multiple targets of SARS-CoV-2, and the present computational models provide insights on the scope of repurposed drugs against COVID-19.

Elucidating the microscopic and computational techniques to study the structure and pathology of SARS-CoVs.

Severe Acute Respiratory Syndrome Coronaviruses (SARS-CoVs), causative of major outbreaks in the past two decades, has claimed many lives all over the world. The virus effectively spreads through saliva aerosols or nasal discharge from an infected person. Currently, no specific vaccines or treatments exist for coronavirus; however, several attempts are being made to develop possible treatments. Hence, it is important to study the viral structure and life cycle to understand its functionality, activity, and infectious nature. Further, such studies can aid in the development of vaccinations against this virus. Microscopy plays an important role in examining the structure and topology of the virus as well as pathogenesis in infected host cells. This review deals with different microscopy techniques including electron microscopy, atomic force microscopy, fluorescence microscopy as well as computational methods to elucidate various prospects of this life-threatening virus.

Response regulator GacA and transcriptional activator RhlR proteins involved in biofilm formation of Pseudomonas aeruginosa are prospective targets for natural lead molecules: Computational modelling, molecular docking and dynamic simulation studies.

Pseudomonas aeruginosa has become a global concern due to its extreme resistance to most of the last resort antibiotics. Present study focuses on the screening of potential molecular targets involved in regulation of biofilm formation in P. aeruginosa and identification of potential natural lead molecules against these targets by molecular modelling, docking and simulation studies. Response regulator (GacA) and transcriptional activator (RhlR) involved in biofilm formation in P. aeruginosa were identified as molecular targets by metabolic pathway analysis and the three dimensional structures of these proteins were predicted by homology modelling and validated. By thorough literature survey, 78 lead molecules were screened and their pharmacokinetic profiles were determined and best two of them selected. The binding potential of selected lead molecules against GacA and RhlR were predicted by molecular docking and their binding energy was compared with the interaction of meropenem and its usual target penicillin binding protein-3. The stabilities of best docked complex were studied by molecular dynamic (MD) simulation. This study showed that Celastrol present in Celastrus paniculatus and Rotiorinol present in Chaetomium cupreum showed better binding affinities with GacA (binding energy -7.2 kcal/mol) and RhlR (binding energy -8.0 kcal/mol) respectively in comparison with the binding of Meropenem and its target (binding energy -6.2 kcal/mol). MD simulation studies showed that GacA-Celastrol and RhlR-Rotiorinol complexes demonstrated conformational stability throughout the simulation. This study highlights the application of GacA and RhlR as prospective targets and Celastrol and Rotiorinol are the potential lead molecules towards biofilm producing drug resistant P. aeruginosa.

Mitogen activated protein kinase-1 and cell division control protein-42 are putative targets for the binding of novel natural lead molecules: a therapeutic intervention against Candida albicans.

Candida albicans, fungal yeast causes several lethal infections in immune-suppressed patients and recently emerged as drug-resistant pathogens worldwide. The present study aimed to screen putative drug targets of Candia albicans and to study the binding potential of novel natural lead compounds towards these targets by computational virtual screening and molecular dynamic (MD) simulation. Through extensive analysis of mitogen-activated protein kinase (MAPK) signalling pathways, mitogen-activated protein kinase-1 (HOG1) and cell division control protein-42 (CDC42) genes were prioritized as putative targets based on their virulent functions. The three-dimensional structures of these genes, not available in their native forms, were computationally modeled and validated. 76 lead molecules from various natural sources were screened and their drug likeliness and pharmacokinetic features were predicted. Among these ligands, two lead molecules that demonstrated ideal drug-likeliness and pharmacokinetic features were docked against HOG1 and CDC42 and their binding potential was compared with the binding of conventional drug Fluconazole with their usual target. The prediction was computationally validated by MD simulation. The current study revealed that Cudraxanthone-S present in Cudrania cochinchinensis and Scutifoliamide-B present in Piper scutifolium exhibited ideal drug likeliness, pharmacokinetics and binding potential to the prioritized targets in comparison with the binding of Fluconazole and their usual target. MD simulation showed that CDC42-Cudraxanthone-S and HOG1-Scutifoliamide-B complexes were exhibited stability throughout MD simulation. Thus, the study provides significant insight into employing HOG1 and CDC42 of MAPK as putative drug targets of C. albicans and Cudraxanthone-S and Scutifoliamide-B as potential inhibitors for drug discovery.Communicated by Ramaswamy H. Sarma.