Search results

Chemical databases have had a significant impact on the way scientists search for and use information. The purpose of this paper is to spark informed discussion and fuel debate on the issue of citations to chemical databases.

A citation analysis to four major chemical databases was undertaken to examine resource coverage and impact in the scientific literature. Two commercial databases (SciFinder and Reaxys) and two public databases (PubChem and ChemSpider) were analyzed using the “Cited Reference Search” in the Science Citation Index Expanded from the Web of Science (WoS) database. Citations to these databases between 2000 and 2016 (inclusive) were evaluated by document types and publication growth curves. A review of the distribution trends of chemical databases in peer-reviewed articles was conducted through a citation count analysis by country, organization, journal and WoS category.

In total, 862 scholarly articles containing a citation to one or more of the four databases were identified as only steadily increasing since 2000. The study determined that authors at academic institutions worldwide reference chemical databases in high-impact journals from notable publishers and mainly in the field of chemistry.

The research is a first attempt to evaluate the practice of citation to major chemical databases in the scientific literature. This paper proposes that citing chemical databases gives merit and recognition to the resources as well as credibility and validity to the scholarly communication process and also further discusses recommendations for citing and referencing databases.

Taurine (2-aminoethane sulfonic acid; C2H7NO3S) is a nonprotein sulfur-containing β-amino acid present in nearly all mammalian tissues and the most ubiquitous free endogenous biomolecule in human cells. Taurine is commonly known as a conditionally essential amino acid because taurine is one of the few amino acids that are not incorporated in protein synthesis. The purpose of this study is to review the existing articles related to taurine and to give an account how useful is taurine to the different body systems. In this thorough overview, taurine is covered in terms of its essentiality, sources, advantages for neonates and the elderly, the effects of taurine deficiency, and the safety and toxicity of taurine supplements.

This is a narrative review into the subject matter. Published articles were searched on different portals like PubMed, EMBASE, Scopus, Google Scholar, PubChem etc. The authors also evaluated the availability of taurine in commercially available energy drinks.

This comprehensive review, presents the potential clinical benefits and functional properties of taurine as a conditionally essential amino acid. Energy drinks containing taurine (and their concentration) are also reported in this review.

This is the first data that the authors are aware of that shows taurine content in a variety of energy drinks on the market.

The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.

Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.

This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.

This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.

With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels.

This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation.

Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper.

The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

The purpose of this paper is to evaluate local biodegradable oils with long chain fatty acids namely: castor (Ricinus communis L.), jojoba (Simmondsia chinensis L.), olive (Oleo europaea L.), and sunflower (Helianthus annuus L.) oils for lubrication candidacy as a substitute to petroleum mineral oils.

Evaluation criteria includes antiwear, lubricity, and extreme pressure (load carrying capacity) using the four‐ball configuration, oxidation induction by pressure differential scanning calorimetry, thermal stability by thermo‐gravimetric analyses, and viscometry using relevant American Society of Testing and Materials (ASTM) standards.

The results show that the lubrication films at the interface failed by the decomposition of the metallic soaps formed by the chemical reaction of the constituent fatty acid molecules and the rubbing surfaces. The biodegradable oils show superior lubricant performance compared to the paraffin‐based mineral oil, despite their poor oxidation stability. Oxidation induction and thermo‐gravimetric characteristics of the biodegradable oils are closely related to their polyunsaturated and monounsaturated fatty acid composition.

The paper shows how these biodegradable oils could be used as good substitute for petroleum mineral oils in as‐received state or little antioxidant additives.

Alzheimer’s disease (AD), the most common cause of dementia, is a neurodegenerative disorder caused by the aggregation of amyloid-beta (Aβ) at outside of neuron cells and also due to tau aggregation inside the cell. Corosolic acid is aimed to be selected as a main active constituent of Lagerstroemia speciosa for the study.

In the present study, molecular docking of corosolic acid and tau protein was examined using PyRx-v.0.8 software. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties were described and a molecular dynamics study of the bound complex was performed using Desmond.

The docking score and interactions suggested that the corosolic acid (CID:6918774) could bind to tau protein to prevent the fibrillar network, to prevent AD. During simulation corosolic acid-bound protein root mean square deviation (RMSD) values showed more stability when compared to the Apo form of protein. Molecular dynamics study of tau protein and corosolic acid complex gave the insights to develop a drug-like candidate against AD.

The use of corosolic acid of Lagerstroemia speciosa to prevent AD is supported by preliminary analysis on a computational basis. This compound should explore in terms of experimental strategies for the further drug development process. However, in vitro and in vivo evaluation studies are required to suggest the use of corosolic acid against AD.

The aim of this study is to provide a review of the research studies that took place in the previous years regarding Mangiferin and its potential use in the treatment of various disorders such as diabetes, cardiovascular diseases, cancer and human immunodeficiency virus.

Mangiferin can be easily extracted from the plant at a very low cost to treat some common as well as deadly diseases, which will be a miracle in herbal treatment. In support of this, the recent related articles were considered to understand the bioactive compound “Mangiferin” to treat lifestyle diseases. Various review articles and research papers were collected from international and national journals, and internet sources were also being used to compile the current manuscript.

Mangiferin has been shown to have the potential to mitigate many life style diseases such as diabetes, obesity, hypertension, fatty liver, atherosclerosis, dyslipidemia and diabetes-related end-organ complications such as retinopathy, neuropathy and nephropathy.

In this comprehensive review, this study evaluates Mangiferin and its lifestyle disease-modifying properties for nutraceuticals purposes.

The purpose of this paper is to propose a tool that generates authority files to be integrated with linked data by means of learning rules. AUTHORIS is software developed to enhance authority control and information exchange among bibliographic and non-bibliographic entities.

The article analyzes different methods previously developed for authority control as well as IFLA and ALA standards for managing bibliographic records. Semantic Web technologies are also evaluated. AUTHORIS relies on Drupal and incorporates the protocols of Dublin Core, SIOC, SKOS and FOAF. The tool has also taken into account the obsolescence of MARC and its substitution by FRBR and RDA. Its effectiveness was evaluated applying a learning test proposed by RDA. Over 80 percent of the actions were carried out correctly.

The use of learning rules and the facilities of linked data make it easier for information organizations to reutilize products for authority control and distribute them in a fair and efficient manner.

The ISAD-G records were the ones presenting most errors. EAD was found to be second in the number of errors produced. The rest of the formats – MARC 21, Dublin Core, FRAD, RDF, OWL, XBRL and FOAF – showed fewer than 20 errors in total.

AUTHORIS offers institutions the means of sharing data with a high level of stability, helping to detect records that are duplicated and contributing to lexical disambiguation and data enrichment.

The software combines the facilities of linked data, the potency of the algorithms for converting bibliographic data, and the precision of learning rules.