This work aims to investigate the environmentally sustainable technique to synthesize the copper nanoparticles using bougainvillea flower ethanolic extract at ambient temperature. Copper nanoparticles have considerable potential for reducing the environment’s harmful pigments and nitrogen contaminants. The oxidized copper nanoscale catalysts are enclosed inside nanomaterial, which work as a benign and sustainable resource for capping agents. Ultraviolet spectroscopic, transmission electron microscopy (TEM), and X-ray crystallography (XRD) techniques were used to evaluate the produced oxidized copper nanocrystals. The particles produced have been very robust, are cylindrical in form, and have an outer diameter of 12 nm. Furthermore, under normal conditions, copper oxide (CuO) nanomaterials demonstrated strong photocatalytic efficiency in liquid media for the oxidation of Congo red, bromothymol blue, and 4-nitrophenol in an acidic solution acetic anhydride. Moreover, the CuO nanocrystalline enzyme could be readily vortexed or used for five cycles with an exchange rate of even over 90%. The evaporation process caused around 18% of the loss of weight between 25°C and 190°C, while soil organic breakdown caused almost 31% of the loss of weight around 700°C. As a result, the little reduction in enzymatic effectiveness of the recoverable multilayer CuO substrate might be attributed to catalytic degradation throughout spinning and processing.

Both fossil and renewable fuel sources are used widely to produce electricity around the globe. The dependency on fossil fuels for energy leads to the depletion of reserves and various forms of pollution. Coal fly ash (CFA) is one of the most burning issues in the whole world due to its large amount of production in thermal power plants. Every year a million tons (MTs) of CFA are generated globally of which almost half is utilized in various forms, while the remaining half remains unused, leading to various types of pollution. Hence, there is an immediate requirement for CFA management approaches for the efficient and sustainable use of fly ash. In the present review, the authors emphasize the status of energy and its supply and demand. A detailed description of coal fly ash-based thermal power plants, fly ash production, and utilization is provided. Moreover, the current and emerging applications of CFA are also provided.

One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparameters achieved the highest testing accuracy of 87.91% for dataset-I and the extreme gradient boosting classifier with tuned hyperparameters achieved the highest testing accuracy of 99.03% for the comprehensive dataset-II. The novelty of this work was the use of grid search cross-validation to enhance the performance in the form of training and testing. The ideal parameters for predicting heart disease were identified through experimental results. Comparative studies were also carried out with the existing studies focusing on the prediction of heart disease, where the approach used in this work significantly outperformed their results.

There have been magnificent advancements in the understanding of molecular mechanisms of chronic diseases over the past several years, but these diseases continue to be a considerable cause of death worldwide. Most of the approved medications available for the prevention and treatment of these diseases target only a single gene/protein/pathway and are known to cause severe side effects and are less effective than they are anticipated. Consequently, the development of finer therapeutics that outshine the existing ones is far-reaching. Natural compounds have enormous applications in curbing several disastrous and fatal diseases. Oroxylin A (OA) is a flavonoid obtained from the plants Oroxylum indicum, Scutellaria baicalensis, and S. lateriflora, which have distinctive pharmacological properties. OA modulates the important signaling pathways, including NF-κB, MAPK, ERK1/2, Wnt/β-catenin, PTEN/PI3K/Akt, and signaling molecules, such as TNF-α, TGF-β, MMPs, VEGF, interleukins, Bcl-2, caspases, HIF-1α, EMT proteins, Nrf-2, etc., which play a pivotal role in the molecular mechanism of chronic diseases. Overwhelming pieces of evidence expound on the anti-inflammatory, anti-bacterial, anti-viral, and anti-cancer potentials of this flavonoid, which makes it an engrossing compound for research. Numerous preclinical and clinical studies also displayed the promising potential of OA against cancer, cardiovascular diseases, inflammation, neurological disorders, rheumatoid arthritis, osteoarthritis, etc. Therefore, the current review focuses on delineating the role of OA in combating different chronic diseases and highlighting the intrinsic molecular mechanisms of its action.

The disorder of Alzheimer’s is marked by progressive pathophysiological neurodegeneration. The amino acid peptides in the amyloid plaques found in the brains of people with Alzheimer’s disease (AD) are known as amyloid-beta (Aβ). Current treatments are not curative, and the effects associated with AD are reduced. Improving treatment results involved the targeting of drugs at optimum therapeutic concentration. Nanotechnology is seen as an unconventional, modern technology that plays a key role in the treatment of Alzheimer’s disease. Using nanoparticles, molecular detection, effective drug targeting, and their combination offer high sensitivity. The aim of this review is to shed light on the function and successful role of nanoparticles to resolve Aβ aggregation and thus to help cure Alzheimer’s disease. The analysis divides these nanoparticles into three categories: polymer, lipid, and gold nanoparticles. A thorough comparison was then made between the nanoparticles, which are used according to their role, properties, and size in the procedure. The nanoparticles can prevent the accumulation of Aβ during the efficient delivery of the drug to the cells to treat Alzheimer’s disease. Furthermore, this comparison demonstrated the ability of these nanoparticles to deal efficiently with Alzheimer’s disease. The role of these nanoparticles varied from delivering the drug to brain cells to dealing with the disease-causing peptide.