Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives.

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

The Use of Blockchain Technology in the Health Care Sector: Systematic Review.

BACKGROUND: Blockchain technology is a part of Industry 4.0's new Internet of Things applications: decentralized systems, distributed ledgers, and immutable and cryptographically secure technology. This technology entails a series of transaction lists with identical copies shared and retained by different groups or parties. One field where blockchain technology has tremendous potential is health care, due to the more patient-centric approach to the health care system as well as blockchain's ability to connect disparate systems and increase the accuracy of electronic health records. OBJECTIVE: The aim of this study was to systematically review studies on the use of blockchain technology in health care and to analyze the characteristics of the studies that have implemented blockchain technology. METHODS: This study used a systematic review methodology to find literature related to the implementation aspect of blockchain technology in health care. Relevant papers were searched for using PubMed, SpringerLink, IEEE Xplore, Embase, Scopus, and EBSCOhost. A quality assessment of literature was performed on the 22 selected papers by assessing their trustworthiness and relevance. RESULTS: After full screening, 22 papers were included. A table of evidence was constructed, and the results of the selected papers were interpreted. The results of scoring for measuring the quality of the publications were obtained and interpreted. Out of 22 papers, a total of 3 (14%) high-quality papers, 9 (41%) moderate-quality papers, and 10 (45%) low-quality papers were identified. CONCLUSIONS: Blockchain technology was found to be useful in real health care environments, including for the management of electronic medical records, biomedical research and education, remote patient monitoring, pharmaceutical supply chains, health insurance claims, health data analytics, and other potential areas. The main reasons for the implementation of blockchain technology in the health care sector were identified as data integrity, access control, data logging, data versioning, and nonrepudiation. The findings could help the scientific community to understand the implementation aspect of blockchain technology. The results from this study help in recognizing the accessibility and use of blockchain technology in the health care sector.

Improved mechanical strength, proton conductivity and power density in an 'all-protonic' ceramic fuel cell at intermediate temperature.

Protonic ceramic fuel cells (PCFCs) have become the most efficient, clean and cost-effective electrochemical energy conversion devices in recent years. While significant progress has been made in developing proton conducting electrolyte materials, mechanical strength and durability still need to be improved for efficient applications. We report that adding 5 mol% Zn to the Y-doped barium cerate-zirconate perovskite electrolyte material can significantly improve the sintering properties, mechanical strength, durability and performance. Using same proton conducting material in anodes, electrolytes and cathodes to make a strong structural backbone shows clear advantages in mechanical strength over other arrangements with different materials. Rietveld analysis of the X-ray and neutron diffraction data of BaCe0.7Zr0.1Y0.15Zn0.05O3-δ (BCZYZn05) revealed a pure orthorhombic structure belonging to the Pbnm space group. Structural and electrochemical analyses indicate highly dense and high proton conductivity at intermediate temperature (400-700 °C). The anode-supported single cell, NiO-BCZYZn05|BCZYZn05|BSCF-BCZYZn05, demonstrates a peak power density of 872 mW cm-2 at 700 °C which is one of the highest power density in an all-protonic solid oxide fuel cell. This observation represents an important step towards commercially viable SOFC technology.

Luminescent measurement systems for the investigation of a scramjet inlet-isolator.

Scramjets have become a main focus of study for many researchers, due to their application as propulsive devices in hypersonic flight. This entails a detailed understanding of the fluid mechanics involved to be able to design and operate these engines with maximum efficiency even at their off-design conditions. It is the objective of the present cold-flow investigation to study and analyse experimentally the mechanics of the fluid structures encountered within a generic scramjet inlet at M = 5. Traditionally, researchers have to rely on stream-thrust analysis, which requires the complex setup of a mass flow meter, a force balance and a heat transducer in order to measure inlet-isolator performance. Alternatively, the pitot rake could be positioned at inlet-isolator exit plane, but this method is intrusive to the flow, and the number of pitot tubes is limited by the model size constraint. Thus, this urgent need for a better flow diagnostics method is addressed in this paper. Pressure-sensitive paint (PSP) has been applied to investigate the flow characteristics on the compression ramp, isolator surface and isolator sidewall. Numerous shock-shock interactions, corner and shoulder separation regions, as well as shock trains were captured by the luminescent system. The performance of the scramjet inlet-isolator has been shown to improve when operated in a modest angle of attack.