Background
Various bioactive aromatic compounds containing the indole nucleus showed clinical and biological applications. Indole scaffold has been found in many of the important synthetic drug ...molecules which gave a valuable idea for treatment and binds with high affinity to the multiple receptors helpful in developing new useful derivatives.
Main text
Indole derivatives possess various biological activities, i.e., antiviral, anti-inflammatory, anticancer, anti-HIV, antioxidant, antimicrobial, antitubercular, antidiabetic, antimalarial, anticholinesterase activities, etc. which created interest among researchers to synthesize a variety of indole derivatives.
Conclusion
From the literature, it is revealed that indole derivatives have diverse biological activities and also have an immeasurable potential to be explored for newer therapeutic possibilities.
•Reduction of IFT to ultra-low value by ionic and non-ionic surfactants in presence of alkalis and salt.•Wettability alteration of oil-wet rock surface by surfactants in presence of alkalis and ...salt.•Phase stability of oil-water emulsion by surfactants and alkalis.
Surfactant flooding is one of the most promising method of enhanced oil recovery (EOR) used after the conventional water flooding. The addition of alkali improves the performance of surfactant flooding due to synergistic effect between alkali and surfactant on reduction of interfacial tension (IFT), wettability alteration and emulsification. In the present study the interfacial tension, contact angle, emulsification and emulsion properties of cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS) and polysorbate 80 (Tween 80) surfactants against crude oil have been investigated in presence of sodium chloride (NaCl) and alkalis viz. sodium hydroxide (NaOH), sodium carbonate (Na2CO3), ammonium hydroxide (NH4OH), sodium metaborate (SMB) and diethanolamine (DEA). All three surfactants significantly reduce the IFT values, which are further reduced to ultra-low value (∼10−4mN/m) by addition of alkalis and salt. It has been found experimentally that alkali-surfactant systems change the wettability of an intermediate-wet quartz rock to water-wet. Emulsification of crude oil by surfactant and alkali has also been investigated in terms of the phase volume and stability of emulsion. A comparative FTIR analysis of crude oil and different emulsions were performed to investigate the interactions between crude oil and displacing water in presence of surfactant and alkali.
Pancreatic cancer (PC) is a very lethal disease with a low survival rate, making timely and accurate diagnoses critical for successful treatment. PC classification in computed tomography (CT) scans ...is a vital task that aims to accurately discriminate between tumorous and non-tumorous pancreatic tissues. CT images provide detailed cross-sectional images of the pancreas, which allows oncologists and radiologists to analyse the characteristics and morphology of the tissue. Machine learning (ML) approaches, together with deep learning (DL) algorithms, are commonly explored to improve and automate the performance of PC classification in CT scans. DL algorithms, particularly convolutional neural networks (CNNs), are broadly utilized for medical image analysis tasks, involving segmentation and classification. This study explores the design of a tunicate swarm algorithm with deep learning-based pancreatic cancer segmentation and classification (TSADL-PCSC) technique on CT scans. The purpose of the TSADL-PCSC technique is to design an effectual and accurate model to improve the diagnostic performance of PC. To accomplish this, the TSADL-PCSC technique employs a W-Net segmentation approach to define the affected region on the CT scans. In addition, the TSADL-PCSC technique utilizes the GhostNet feature extractor to create a group of feature vectors. For PC classification, the deep echo state network (DESN) model is applied in this study. Finally, the hyperparameter tuning of the DESN approach occurs utilizing the TSA which assists in attaining improved classification performance. The experimental outcome of the TSADL-PCSC method was tested on a benchmark CT scan database. The obtained outcomes highlighted the significance of the TSADL-PCSC technique over other approaches to PC classification.
Background Cadmium (Cd) is a non-essential toxic heavy metal, an environmental toxicant, and toxic at a low concentration, and it has no known beneficial role in the human body. Its exposure induces ...various health impairments including hostile reproductive health. Objective The present review discusses the information on exposure to Cd and human reproductive health impairments including pregnancy or its outcome with respect to environmental and occupational exposure. Methods The present review provides current information on the reproductive toxic potential of Cd in humans. The data were collected using various websites and consulting books, reports, etc. We have included recent data which were published from 2000 onward in this review. Results Cd exposure affects human male reproductive organs/system and deteriorates spermatogenesis, semen quality especially sperm motility and hormonal synthesis/release. Based on experimental and human studies, it also impairs female reproduction and reproductive hormonal balance and affects menstrual cycles. Based on the literature, it might be concluded that exposure to Cd at low doses has adverse effects on both human male and female reproduction and affects pregnancy or its outcome. Further, maternal prenatal Cd exposure might have a differential effect on male and female offspring especially affecting more female offspring. Hence, efforts must be made to prevent exposure to Cd. Conclusion Cd affects both male and female reproduction, impairs hormone synthesis/regulation and deteriorates pregnancy rate or its outcome even at lower doses.
Biopsy is one of the most commonly used modality to identify breast cancer in women, where tissue is removed and studied by the pathologist under the microscope to look for abnormalities in tissue. ...This technique can be time‐consuming, error‐prone, and provides variable results depending on the expertise level of the pathologist. An automated and efficient approach not only aids in the diagnosis of breast cancer but also reduces human effort. In this paper, we develop an automated approach for the diagnosis of breast cancer tumors using histopathological images. In the proposed approach, we design a residual learning‐based 152‐layered convolutional neural network, named as ResHist for breast cancer histopathological image classification. ResHist model learns rich and discriminative features from the histopathological images and classifies histopathological images into benign and malignant classes. In addition, to enhance the performance of the developed model, we design a data augmentation technique, which is based on stain normalization, image patches generation, and affine transformation. The performance of the proposed approach is evaluated on publicly available BreaKHis dataset. The proposed ResHist model achieves an accuracy of 84.34% and an F1‐score of 90.49% for the classification of histopathological images. Also, this approach achieves an accuracy of 92.52% and F1‐score of 93.45% when data augmentation is employed. The proposed approach outperforms the existing methodologies in the classification of benign and malignant histopathological images. Furthermore, our experimental results demonstrate the superiority of our approach over the pre‐trained networks, namely AlexNet, VGG16, VGG19, GoogleNet, Inception‐v3, ResNet50, and ResNet152 for the classification of histopathological images.
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The simultaneous administration of multiple drugs increases the probability of interaction among them, as one drug may affect the activities of others. This interaction among drugs ...may have a positive or negative impact on the therapeutic outcomes. Thus, identification of unknown drug-drug interactions (DDIs) is of significant concern for improving the safety and efficacy of drug consumption. Although multiple DDI resources exist, it is becoming infeasible to maintain these up-to-date manually with the number of biomedical texts growing at a fast pace. Most existing methods model DDI extraction as a classification problem and rely mainly on handcrafted features, and certain features further depend on domain-specific tools. Recently, neural network models using latent features have been demonstrated to yield similar or superior performance compared to existing models. In this study, we present three long short-term memory (LSTM) network models, namely B-LSTM, AB-LSTM, and Joint AB-LSTM. All three models use word and position embedding as latent features; thus, they do not rely on explicit feature engineering. Furthermore, the use of a bidirectional LSTM (Bi-LSTM) network allows for extraction of implicit features from an entire sentence. The two models AB-LSTM and Joint AB-LSTM also apply attentive pooling in the Bi-LSTM layer output in order to assign weights to features. Our experimental results on the SemEval-2013 DDI extraction dataset indicate that the Joint AB-LSTM model produces reasonable performance (F-score: 69.39%) even with the simple architecture.
•Cellulase was immobilized on carbodiimide-activated surface of MWCNTs.•The bionanoconjugates were thermally more stable.•The t1/2 of bionanoconjugates was increased by four fold as compared to free ...cellulase.•The preparation could be reused ten times without significant loss in enzyme activity.
In present study, Aspergillus niger cellulase was immobilized onto functionalized multiwalled carbon nanotubes (MWCNTs) via carbodiimide coupling. MWCNTs offer unique advantages including enhanced electronics properties, a large edge to basal plane ratio, rapid electrode kinetics and it’s possess higher tensile strength properties due to their structural arrangements. The immobilization was confirmed by FTIR (Fourier transform infrared spectroscopy) and SEM (scanning electron microscope). The bionanoconjugates prepared under optimized condition retained 85% activity with improved pH and thermal stability. The t1/2 of immobilized cellulase at 70 °C was four fold higher than free enzyme. The Km value indicates that affinity of bionanoconjugates towards substrate has increased by two times. The preparation could be reused ten times without much loss in enzyme activity. The enhanced catalytic efficiency, stability and reusability makes it useful for efficient cellulose hydrolysis.
Biological membranes constitute boundaries of cells and cell organelles. These membranes are soft fluid interfaces whose thermodynamic states are dictated by bending moduli, induced curvature fields, ...and thermal fluctuations. Recently, there has been a flood of experimental evidence highlighting active roles for these structures in many cellular processes ranging from trafficking of cargo to cell motility. It is believed that the local membrane curvature, which is continuously altered due to its interactions with myriad proteins and other macromolecules attached to its surface, holds the key to the emergent functionality in these cellular processes. Mechanisms at the atomic scale are dictated by protein–lipid interaction strength, lipid composition, lipid distribution in the vicinity of the protein, shape and amino acid composition of the protein, and its amino acid contents. The specificity of molecular interactions together with the cooperativity of multiple proteins induce and stabilize complex membrane shapes at the mesoscale. These shapes span a wide spectrum ranging from the spherical plasma membrane to the complex cisternae of the Golgi apparatus. Mapping the relation between the protein-induced deformations at the molecular scale and the resulting mesoscale morphologies is key to bridging cellular experiments across various length scales. In this review, we focus on the theoretical and computational methods used to understand the phenomenology underlying protein-driven membrane remodeling. Interactions at the molecular scale can be computationally probed by all atom and coarse grained molecular dynamics (MD, CGMD), as well as dissipative particle dynamics (DPD) simulations, which we only describe in passing. We choose to focus on several continuum approaches extending the Canham–Helfrich elastic energy model for membranes to include the effect of curvature-inducing proteins and explore the conformational phase space of such systems. In this description, the protein is expressed in the form of a spontaneous curvature field. The approaches include field theoretical methods limited to the small deformation regime, triangulated surfaces and particle-based computational models to investigate the large-deformation regimes observed in the natural state of many biological membranes. Applications of these methods to understand the properties of biological membranes in homogeneous and inhomogeneous environments of proteins, whose underlying curvature fields are either isotropic or anisotropic, are discussed. The diversity in the curvature fields elicits a rich variety of morphological states, including tubes, discs, branched tubes, and caveola. Mapping the thermodynamic stability of these states as a function of tuning parameters such as concentration and strength of curvature induction of the proteins is discussed. The relative stabilities of these self-organized shapes are examined through free-energy calculations. The suite of methods discussed here can be tailored to applications in specific cellular settings such as endocytosis during cargo trafficking and tubulation of filopodial structures in migrating cells, which makes these methods a powerful complement to experimental studies.
The shapes of cell membranes are largely regulated by membrane-associated, curvature-active proteins. Herein, we use a numerical model of the membrane, recently developed by us, with elongated ...membrane inclusions possessing spontaneous directional curvatures that could be different along, and perpendicular to, the membrane’s long axis. We show that, due to membrane-mediated interactions, these curvature-inducing membrane-nematogens can aggregate spontaneously, even at low concentrations, and change the local shape of the membrane. We demonstrate that for a large group of such inclusions, where the two spontaneous curvatures have equal sign, the tubular conformation and sometimes the sheet conformation of the membrane are the common equilibrium shapes. We elucidate the factors necessary for the formation of these protein lattices. Furthermore, the elastic properties of the tubes, such as their compressional stiffness and persistence length, are calculated. Finally, we discuss the possible role of nematic disclination in capping and branching of the tubular membranes.
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Vernal keratoconjunctivitis (VKC) is a chronic, bilateral, at times asymmetrical, seasonally exacerbated, allergic inflammation of the ocular surface, involving tarsal and/or bulbar conjunctiva. ...Though the allergic nature of this entity has been accepted for a long time, the accumulation of a large amount of immunological data has proved that the pathogenesis of VKC is much more complex than a mere type 1 hypersensitivity reaction. In the past several years, many clinical and experimental studies about the cells and mediators involved in initiating and perpetuating the ocular allergic inflammation have shown that T helper type 2 cells and their cytokines, corneal fibroblasts and epithelium along with various growth factors play an important role in the pathogenesis of VKC. Based on this information about the pathogenesis of VKC newer, more selective drugs like anti‐chemokine receptor antibodies and leukotriene receptor antagonists are under evaluation. Cyclosporine has been shown to be effective in the treatment of VKC but further randomized control trials are required to establish the minimum effective concentration.
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