Search

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. Graphical abstract

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.

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 ...

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.

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.

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., Highlights Structural analysis of SARS‐CoVs aids in understanding its nature, activity, and pathophysiologyRevealing the surface morphology of SARS‐CoVs using scanning electron microscope and atomic force microscopyComputational methods help to understand the structure of SARS‐CoVs and their interactions with various inhibitors

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.

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., Graphical abstract Unlabelled Image

This study aimed to screen putative drug targets associated with biofilm formation of multidrug-resistant

SARS-CoV-2 is the pathogen accountable for the recent COVID-19 outbreak that originated from China in December 2019. There are about a million confirmed cases of the infection, and thousands of deaths have been witnessed across 216 countries worldwide as of first week of July 2020. As no approved drugs are currently available for treating COVID-19, it is an alarming situation calling the need to develop alternative therapeutic agents. In order to address the aforementioned need, the underlying mechanism and structural information of SARS-CoV-2 at the molecular level should be understood. The computational biology approaches such as computer-aided virtual screening and molecular modelling provide a significant breakthrough in understanding the structural aspects of various molecular targets of coronavirus and identification novel lead molecules. Natural molecules are one of the probable alternatives as many of these compounds possess ideal drug likeliness and pharmacokinetic features and might probably be used as lead molecules against various targets of SARS-CoV-2. The current chapter provides an overview of different types of coronaviruses, SARS-CoV-2 and its genetic and structural information, the impact of COVID-19 on various sectors such as health and economy, conventional drugs currently used and their shortcomings, various anti-viral compounds present in nature, the importance of natural lead molecules, computational approaches for molecular modelling of the target proteins, major drug targets that are identified, and virtual screening of herbal-based molecules using molecular docking and molecular dynamic simulation studies. This chapter is thus focused on portraying the relevance of utilizing natural lead molecules by virtual screening and pharmacokinetics prediction for the development of effective lead molecules against SARS-CoV-2.

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