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.     

Natural fibers and zinc hydroxystannate 3D microspheres based composite paper sheets for modern bendable energy storage application

For modern high-tech flexible energy storage devices, it becomes important to synthesize micro-/nanostructures as per the required shape and morphology with superior physical and electro-active characteristics. This work shares the fabrication and characterization of ZnSn(OH)₆ (Zinc hydroxystannate [ZHS]) prepared by facile microwave-assisted technique and furthermore converted into flexible sheets by employing lignocelluloses (LC) known as natural fibers, collected from Carica papaya leaf petiole as a substrate to provide the flexible matrix. X-ray diffraction measurements confirm the successful crystalline structure of ZHS. Scanning electron microscopy and transmission electron microscopy showed the solid spherical structure of ZHS microspheres. Fourier transform infrared spectrometry and Raman spectroscopy confirmed the composite formation of ZHS and LC-based composite sheets (ZHS/LC sheets). Electrochemical measurements that is, cyclic voltammetry (CV), Galvanostatic charge/discharge, and electrochemical impedance (EIS) spectroscopy revealed the electroactive behavior of ZHS/LC paper sheets as working electrode for energy storage applications. CV measurements revealed the specific capacitance of 100 F/g and EIS measurements confirmed the decrease in the resistance of LC fiber after the growth of ZHS microspheres. Presented flexible ZHS based paper sheets will be highly feasible for the modern bendable/flexible/disposable energy storage applications.     

Synergistic Functionality of Dopants and Defects in Co-Phthalocyanine/B-CN Z-Scheme Photocatalysts for Promoting Photocatalytic CO2 Reduction Reactions

The realization of solar-light-driven CO₂ reduction reactions (CO₂ RR) is essential for the commercial development of renewable energy modules and the reduction of global CO₂ emissions. Combining experimental measurements and theoretical calculations, to introduce boron dopants and nitrogen defects in graphitic carbon nitride (g-C₃N₄), sodium borohydride is simply calcined with the mixture of g-C₃N₄ (CN), followed by the introduction of ultrathin Co phthalocyanine through phosphate groups. By strengthening H-bonding interactions, the resultant CoPc/P-BNDCN nanocomposite showed excellent photocatalytic CO₂ reduction activity, releasing 197.76 and 130.32 µmol h⁻¹ g⁻¹ CO and CH₄, respectively, and conveying an unprecedented 10-26-time improvement under visible-light irradiation. The substantial tuning is performed towards the conduction and valance band locations by B-dopants and N-defects to modulate the band structure for significantly accelerated CO₂ RR. Through the use of ultrathin metal phthalocyanine assemblies that have a lot of single-atom sites, this work demonstrates a sustainable approach for achieving effective photocatalytic CO₂ activation. More importantly, the excellent photoactivity is attributed to the fast charge separation via Z-scheme transfer mechanism formed by the universally facile strategy of dimension-matched ultrathin (≈4 nm) metal phthalocyanine-assisted nanocomposites.     

Indium containing sillenite semiconductor: Synthesis,structural, spectroscopic,and thermogravimetric analysis

Sillenite-type ceramics are non-centrosymmetric phases of ongoing research interest because of their structural defects and optoelectronic properties. We report a series of sillenite compounds with a general composition Bi₁₂(Bi³⁺_4/5−3xIn³⁺_5x□_1/5−2x)O_19.2+3x□_0.8−3x for x = 0.03–0.27 to understand how the crystal–physicochemical properties change with a successive filling of empty Bi³⁺ positions in the tetrahedral site by In³⁺ cation. Conventional solid-state synthesis method is used to prepare the microcrystalline samples. Each sample is characterized by X-ray diffraction, Raman, UV/Vis diffuse reflectance spectroscopy, and thermogravimetry (TG/DSC). X-ray powder data Rietveld refinement reveals that phase-pure samples can be obtained for x = 0.03–0.08 in the space group I23. Appearance of starting In₂O₃ as minor phases with the final products for 0.10 < x < 0.27 suggests for x_max = 0.08. The successive decrease of the lattice parameter indicates the incorporation of smaller In³⁺ cations in the structure. The effect of the lone electron pairs of Bi³⁺ and the structural cation vacancies lead to the modification of the interatomic bond lengths. At least one Raman active phonon mode shows hardening for decreasing cation vacancy concentration in the system. The bandgap energy increases with increasing indium content. An additional absorption band at lower energy for x = 0.03–0.08 complements the theoretical study, which completely disappears for x > 0.08. The stronger In–O bonds play pivotal roles in the thermal stability of the phases studied by TGA/DSC analysis.     

Advances in riboswitch-based biosensor as food samples detection tool

Food safety has always been a hot issue of social concern, and biosensing has been widely used in the field of food safety detection. Compared with traditional aptamer-based biosensors, aptamer-based riboswitch biosensing represents higher precision and programmability. A riboswitch is an elegant example of controlling gene expression, where the target is coupled to the aptamer domain, resulting in a conformational change in the downstream expression domain and determining the signal output. Riboswitch-based biosensing can be extensively applied to the portable real-time detection of food samples. The numerous key features of riboswitch-based biosensing emphasize their sustainability, renewable, and testing, which promises to transform engineering applications in the field of food safety. This review covers recent developments in riboswitch-based biosensors. The brief history, definition, and modular design (regulatory mode, reporter, and expression platform) of riboswitch-based biosensors are explained for better insight into the design and construction. We summarize recent advances in various riboswitch-based biosensors involving theophylline, malachite green, tetracycline, neomycin, fluoride, thrombin, naringenin, ciprofloxacin, and paromomycin, aiming to provide general guidance for the design of riboswitch-based biosensors. Finally, the challenges and prospects are also summarized as a way forward stratagem and signs of progress.     

Zero-Dimensional Gua3SbCl6 Crystals as Intrinsically Reabsorption-Free Scintillators for Radiation Detection

The search for efficient, re-absorption-free scintillators has recently focused the attention on antimony-based halides, which exhibit largely Stokes shifted luminescence due to radiative recombination of excitons self-trapped (STE) in strongly Jahn–Teller distorted Sb³⁺ color centers. Here, the synthesis of a hybrid structure is reported with chemical formula (C₁₃H₁₄N₃)₃SbCl₆ consisting of spatially isolated [SbCl₆]³⁻ octahedra separated by organic N,N'-diphenylguanidinium (Gua) molecules. The optical properties of this material are mainly determined by the inorganic component and are characterized by a pronounced Stokes shift of ≈1.3 eV and a room-temperature photoluminescence (PL) efficiency of up to 85%. Remarkably, highly efficient radioluminescence (RL) is observed with scintillation light yields of ≈2000 ph MeV⁻¹ using both soft X-rays and a 124 keV gamma source. Temperature-dependent PL and RL measurements confirm the minor role of non-radiative channels, which are completely suppressed below 100 K. Thermally stimulated luminescence measurements suggest that the traps in Gua₃SbCl₆ crystals have a significantly large energy depth distribution below the absorbing state.     

Inhibitor and Activator: Dual Role of Subsurface Sulfide Enables Selective and Efficient Electro-Oxidation of Methanol to Formate on CuS@CuO Core-Shell Nanosheet Arrays

Selective electro-oxidation of aliphatic alcohols into value-added carboxylates at lower potentials than that of the oxygen evolution reaction (OER) is an environmentally and economically desirable anode reaction for clean energy storage and conversion technologies. However, it is challenging to achieve both high selectivity and high activity of the catalysts for the electro-oxidation of alcohols, such as the methanol oxidation reaction (MOR). Herein, a monolithic CuS@CuO/copper-foam electrode for the MOR with superior catalytic activity and almost 100% selectivity for formate is reported. In the core-shell CuS@CuO nanosheet arrays, the surface CuO directly catalyzes MOR, while the subsurface sulfide not only serves as an inhibitor to attenuate the oxidative power of the surface CuO to achieve selective oxidation of methanol to formate and prevent over-oxidation of formate to CO₂ but also serves as an activator to form more surface O defects as active sites and enhances the methanol adsorption and charge transfer to achieve superior catalytic activity. CuS@CuO/copper-foam electrodes can be prepared on a large scale by electro-oxidation of copper-foam at ambient conditions and can be readily utilized in clean energy technologies.     

Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Forecasting on success or failure of software has become an interesting and, in fact, an essential task in the software development industry. In order to explore the latest data on successes and failures, this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework? What human factors contribute to success or failure of a software? What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work? In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors, misinterpretation, and miscommunication of requirements and decision-making processes play their roles in software success forecasting. We discussed a potential relationship between forecasting of software success or failure and the development processes. We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study, and they were more likely to rate software success forecasting linking to the development processes. Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate. The results of this empirical study also validated that requirements’ misinterpretation and miscommunication were the main causes behind software systems’ failure. It has been seen that reliable, relevant, and trustworthy sources of information help in decision-making to predict software systems’ success in the software industry. This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects. Future investigation can be performed to identify the other human factors that may impact software systems’ success.     

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed. In this technique, the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data. Next is the training of a balanced dataset using SVM. Since this algorithm requires an iterative process to search for the best penalty parameter during training, an improved SA algorithm was proposed for this task. In this proposed improvement, a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process. Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM. Registering at an average of 89.65% of accuracy for the binary class classification has demonstrated the good performance of the proposed works.