Adjacent assembly of self-assembled monolayers for the construction of selective bio-platforms

Selective patterning of bio-substances onto solid platforms is of increasing importance in many areas and widely used for various applications ranging from bio-sensing to cell and tissue engineering. In this study, a new fabrication scheme for the construction of highly selective bio-platforms is presented. The method is based on a direct patterning of poly(ethyleneglycol) (PEG) bio-inert layers on a conducting indium tin oxide (ITO) substrate using electron beam lithography and subsequent assembly of modified amine reactive layers onto the exposed areas. The process is found to create very high “surface contrast” between adhesive and repulsive regions onto the substrate. The platforms are shown to be enable efficient for selective adsorption of a variety of bio-substances including protein arrays, latex beads, and single cells. The high resolution of the technique makes it also applicable for the construction and deposition of bio-structures at the sub-micron scale. The reported technique employs standard lithography and surface chemistry processes, which makes it useful and easy to adopt for a variety of applications and other conductive substrates.     

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques. A dataset acquired from the ‘Drugs.Com’ containing reviews of drug-related side effects and reactions, is used for experiments. A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text. Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level. Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods. Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy. We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.     

Terpenoids as Human Neutrophil Elastase (HNE) Inhibitors: A Comprehensive Review of Natural Anti-inflammatory Isoprenoids

Human neutrophil elastase (HNE) is an enzyme that plays a key role in the body‘s inflammatory response. It has been linked to several diseases such as chronic obstructive pulmonary disease (COPD), emphysema, and cystic fibrosis. As potential treatments for these diseases, HNE inhibitors are of great interest. Metabolites derived from plants, particularly terpenoids such as β-caryophyllene found in black pepper and other plants, and geraniol present in several essential oils, are recognized as significant sources of inhibitors for HNE. Because of their ability to inhibit HNE, terpenoids are considered promising candidates for developing novel therapies to treat inflammatory conditions such as COPD and emphysema. Furthermore, nature can serve as an excellent designer, and it may offer a safer drug candidate for inhibiting HNE production and activity in the future. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were searched to get relevant and up-to-date literature on terpenoids as human neutrophil elastase inhibitors. This review focuses on the isolation, chemical diversity, and inhibition of human neutrophil elastase (HNE) of various terpenoids reported from natural sources up to 2022. A total of 251 compounds from various terpenoids classes have been reported. Further, it also provides a summary of HNE inhibitors and includes a thorough discussion on the structure-activity relationship.     

Some Applications of Acoustic Emission in Particle Science and Technology

Acoustic emission (AE) has been used in many applications in the field of particle science and technology. AE sensors have been used in particle concentration measurements both in gas-continuous and oil-continuous flows in the oil and gas industry. To avoid formation sand flowing into pipelines, leading to erosion of valves and in many cases even to complete blockage of the flow of oil and gas, AE sensors are almost exclusively used in sand monitoring and control. These are very often among standard sensors stipulated by the operators of oil and gas production facilities in offshore, on shore, and subsea applications. Special types of sensor design have led to easy mounting of these AE sensors, which are very often clamp-on devices. This article presents a brief overview of AE-based particle monitoring in general and focuses on flange-mounted sensors in the monitoring of particle flow. By using two or more AE sensors located suitably in the process line, the particle velocity can also be evaluated, as is shown in examples using correlation in this article. The AE sensors can easily be adapted to detect malfunctioning of the process line, whether pneumatic lines or silos, just by analyzing the time series of signals from strategically based AE sensors along the process lines. Some examples are given based on recent measurement data. Finally, the article presents an overview of possibilities for improved particle flow monitoring using a multisensor suite incorporating AE sensors with other sensors/detectors such as those derived from capacitance, resistance, gamma ray, microwave, and optical devices. Artificial intelligence (AI) techniques, such as fuzzy logic and neural network algorithms, used in handling the data from these sensors lead to faster and more reliable control. Some of these topics are addressed also.     

Identification and characterization of novel α-amylase and α-glucosidase inhibitory peptides from camel whey proteins

This study explores the inhibitory properties of camel whey protein hydrolysates (CWPH) toward α-amylase (AAM) and α-glucosidase (AG). A general full factorial design (3 × 3) was applied to study the effect of temperature (30, 37, and 45°C), time (120, 240, and 360 min), and enzyme (pepsin) concentration (E%; 0.5, 1, and 2%). The results showed that maximum degree of hydrolysis was obtained when hydrolysis was carried out at higher temperature (45°C; P < 0.05), compared with lower temperatures of 30 and 37°C. Electrophoretic pattern displays degradation of all protein bands upon hydrolysis by pepsin at various hydrolysis conditions applied. All the 27 CWPH generated showed significant AAM and AG inhibitory potential as indicated by their lower IC₅₀ values (mg/mL) compared with intact whey proteins. In total 196 peptides were identified from selected hydrolysates and 15 potential peptides (PepSite score > 0.8; http://pepsite2.russelllab.org/) were explored via in silico approach. Novel peptides PAGNFLMNGLMHR, PAVACCLPPLPCHM, MLPLMLPFTMGY, and PAGNFLPPVAAAPVM were identified as potential inhibitors for both AAM and AG due to their high number of binding sites and highest binding probability toward the target enzymes. CCGM and MFE, as well as FCCLGPVPP were identified as AG and AAM inhibitory peptides, respectively. This is the first study that reports novel AG and AAM inhibitory peptides from camel whey proteins. The future direction for this research involves synthesis of these potential AG and AAM inhibitory peptides in a pure form and investigate their antidiabetic properties in the in vitro, as well as in vivo models. Thus, CWPH can be considered for potential applications in glycaemic regulation.     

Influence of compliant walls on peristaltic motion with heat/mass transfer and chemical reaction

In this work the fundamental problem of peristalsis with heat and mass transfer is investigated in the presence of a chemical reaction. An incompressible viscous fluid is considered in a channel with compliant walls. Mathematical modeling is based upon the laws of mass, linear momentum, energy and concentration. Analysis is presented not only for long wavelength and low Reynolds number but also for small Grashof number. The solutions are carried out for the stream function, temperature, concentration field and heat transfer coefficient. The results of various interesting parameters are plotted and discussed.