Search Tool Research Articles

Tech Showdown – AI Search Tools Special Issue

New digital tools for literature searching are emerging almost weekly, making it challenging for librarians to stay updated. Imagine how overwhelming it must be for our patrons, who balance clinical responsibilities, research, and professional development alongside this constant influx of tools. How can we keep up with these tools? How can we identify and meet user needs? How can we recommend digital tools effectively?

Identification of potential biomarkers associated with oxidative stress in the pathogenesis of pre-eclampsia.

Pre-eclampsia (PE) is a multisystem pregnancy disorder characterized by placental and maternal endothelial dysfunction, and affects approximately 5% to 7% of pregnancies worldwide, leading to significant maternal and neonatal morbidity and mortality. Mounting evidence indicates that placental oxidative stress (OS) plays a critical role in the pathogenesis of PE. However, the specific mechanisms associated with OS during the occurrence and progression of PE remain largely unknown. Thus, we aimed to identify the key molecules associated with OS and explore their potential mechanisms in PE. Transcriptome data were downloaded from the Gene Expression Omnibus database, including 80 PE and 77 normal placental tissues. OS-related genes were identified using the Gene Ontology database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to analyze the functions and pathways of the OS-related differentially expressed genes (OS-DEGs). Protein-protein interaction networks were constructed using the Search Tool for the Retrieval of Interacting Genes database, and hub genes were screened using molecular complex detection and CytoHubba. Finally, the diagnostic value and drug-gene interactions of the hub genes were evaluated. We identified 470 differentially expressed genes and 43 OS-DEGs. These genes were mainly enriched in OS-related biological processes, the HIF-1 and MAPK signaling pathways. Furthermore, 5 hub genes were identified: VEGFA, CCL2, mitogen-activated protein kinase 8 (MAPK8), HMOX1, and Cytochrome B-245 Beta Chain (CYBB). CYBB and MAPK8 had the highest diagnostic accuracies, with area under the curve values of 0.767 and 0.764, respectively. We predicted 43 potentially targeted drugs for PE treatment. CYBB and MAPK8 may be valuable biomarkers that mediate OS through multiple pathways to promote the occurrence and development of PE. We conclude from our study that OS has involvement in PE, and improved our understanding of OS-related molecular pathways in the pathogenesis of PE.

Diagnostic, prognostic, and immunological roles of NCAPG in pan-cancer: A bioinformatics analysis.

Growing studies have shown that non-SMC condensin I complex subunit G (NCAPG) was highly expressed in a variety of tumors and was involved in the progression of multitumors, but the role of NCAPG in tumorigenesis is not fully understood. Our study purposed to systematically investigate the role of NCAPG across cancer types. Interacting molecules with NCAPG were analyzed using searching bioinformatics websites including Search Tool for the Retrieval of Interacting Genes/Proteins, GeneMANIA, and Global Positioning System-Prot. NCAPG-related diseases were acquired using the Open Targets Platform. The interaction of NCAPG and 14 cancer functional states was achieved using the CancerSEA website. The databases including the University of California Santa Cruz Xena, Genotype-Tissue Expression, The Cancer Genome Atlas Program, Human Protein Atlas, and XIANTAO Academic were used to interpret the expression of NCAPG. Correlations between NCAPG expression and immune infiltration and immune-related molecules were analyzed by using Tumor Immune Estimation Resource Version 2 and Tumor and Immune System Interaction Database databases. NCAPG expression was significantly upregulated in most cancer types. NCAPG was identified as a marker of diagnostic value and prognostic significance in most cancer types. NCAPG expression was related to immune cell infiltration and immune-related molecules across various cancers, especially kidney renal clear cell carcinoma and thyroid carcinoma. Furthermore, NCAPG expression could affect the enrichment and decrease immune cell infiltration to influence prognosis in kidney renal clear cell carcinoma but was devoid of evidence in thyroid carcinoma. NCAPG was a prospective marker for the diagnosis and prognosis of pan-cancer. Our results suggested that NCAPG was a potential cancer biomarker for the diagnosis and prognosis of pan-cancer. NCAPG might affect the immune microenvironment, which could be applied in the development of new-targeted drugs for immunotherapy.

An Automated Repository for the Efficient Management of Complex Documentation

The accelerating digitalization of the public and private sectors has made information technologies (IT) indispensable in modern life. As services shift to digital platforms and technologies expand across industries, the complexity of legal, regulatory, and technical requirement documentation is growing rapidly. This increase presents significant challenges in managing, gathering, and analyzing documents, as their dispersion across various repositories and formats hinders accessibility and efficient processing. This paper presents the development of an automated repository designed to streamline the collection, classification, and analysis of cybersecurity-related documents. By harnessing the capabilities of natural language processing (NLP) models—specifically Generative Pre-Trained Transformer (GPT) technologies—the system automates text ingestion, extraction, and summarization, providing users with visual tools and organized insights into large volumes of data. The repository facilitates the efficient management of evolving cybersecurity documentation, addressing issues of accessibility, complexity, and time constraints. This paper explores the potential applications of NLP in cybersecurity documentation management and highlights the advantages of integrating automated repositories equipped with visualization and search tools. By focusing on legal documents and technical guidelines from Portugal and the European Union (EU), this applied research seeks to enhance cybersecurity governance, streamline document retrieval, and deliver actionable insights to professionals. Ultimately, the goal is to develop a scalable, adaptable platform capable of extending beyond cybersecurity to serve other industries that rely on the effective management of complex documentation.

Exploring the Role of BCL2 Interactome in Cancer: A Protein/Residue Interaction Network Analysis

BCL2 is a critical regulator of intrinsic and extrinsic pathways of apoptosis that have been implicated in cancer progression and therapeutic resistance. In this study, the protein–protein interactions (PPIs) of BCL2 with potential binding partners and their role in cancer was investigated. A comprehensive PPI network for BCL2 has been generated by using the Protein Interactions Network Analysis (PINA) platform to identify key interactors. To further investigate the network, Molecular Operating Environment (MOE), Search Tool for the Retrieval of Interacting Genes (STRING), Residue Interaction Network Generation (RING), and the gProfiler server were used. Docking and Molecular Dynamics (MD) simulations were performed by using HDOCK and Gromacs to analyze the binding dynamics and stability of protein complexes. The BCL2 interactome revealed that three key interactors (p53, RAF1, and MAPK1) are involved in cancer-related processes. Docking studies highlighted BCL2 residues such as ASP111, ASP140, ARG107, and ARG146 that were predominantly involved in multiple hydrogen bonds, ionic interactions, and van der Waals contacts, highlighting conserved binding sites that play critical roles in the stability and specificity of protein–protein interactions. MD simulations (200 ns) of the BCL2-p53 complex showed that the RMSD was increased, suggesting the suppression of BCL2’s anti-apoptotic activity by p53. The RMSD for BCL2-RAF1 was also increased, showing protein domain structural rearrangements that enhance BCL2 anti-apoptotic activity. The BCL2-MAPK1 complex revealed structural, distinct flexibility patterns and dynamic hydrogen bonding interactions. These findings provide valuable insights into the molecular dynamics by which BCL2 modulates apoptosis and its potential as a promising therapeutic in cancer and apoptosis-related diseases.

MS-RT: A Method for Evaluating MS/MS Clustering Performance for Metabolomics Data.

The clustering of tandem mass spectra (MS/MS) is a crucial computational step to deduplicate repeated acquisitions in data-dependent experiments. This technique is essential in untargeted metabolomics, particularly with high-throughput mass spectrometers capable of generating hundreds of MS/MS spectra per second. Despite advancements in MS/MS clustering algorithms in proteomics, their performance in metabolomics has not been extensively evaluated due to the lack of database search tools with false discovery rate control for molecule identification. To bridge this gap, this study introduces the MS1-retention time (MS-RT) method to assess MS/MS clustering performance in metabolomics data sets. Here, we validate MS-RT by comparing MS-RT to established proteomics clustering evaluation approaches that utilize database search identifications. Additionally, we evaluate the performance of several MS/MS clustering tools on metabolomics data sets, highlighting their advantages and drawbacks. This MS-RT method and the MS/MS clustering tool benchmarking will provide valuable real world practical recommendations for tools and set the stage for future advancements in metabolomics MS/MS clustering.

Limelight: An Open, Web-Based Tool for Visualizing, Sharing, and Analyzing Mass Spectrometry Data from DDA Pipelines.

Liquid chromatography-tandem mass spectrometry employing data-dependent acquisition (DDA) is a mature, widely used proteomics technique routinely applied to proteome profiling, protein-protein interaction studies, biomarker discovery, and protein modification analysis. Numerous tools exist for searching DDA data and myriad file formats are output as results. While some search and post processing tools include data visualization features to aid biological interpretation, they are often limited or tied to specific software pipelines. This restricts the accessibility, sharing and interpretation of data, and hinders comparison of results between different software pipelines. We developed Limelight, an easy-to-use, open-source, freely available tool that provides data sharing, analysis and visualization and is not tied to any specific software pipeline. Limelight is a data visualization tool specifically designed to provide access to the whole "data stack", from raw and annotated scan data to peptide-spectrum matches, quality control, peptides, proteins, and modifications. Limelight is designed from the ground up for sharing and collaboration and to support data from any DDA workflow. We provide tools to import data from many widely used open-mass and closed-mass search software workflows. Limelight helps maximize the utility of data by providing an easy-to-use interface for finding and interpreting data, all using the native scores from respective workflows.

Holocaust remembrance in the digital age: The transformative influence of technology, digital archives, and connective memory

The digital age has profoundly transformed Holocaust remembrance through the influence of digital archives, connectivity, and emerging technologies. This research investigates the transformation of personal memories into connective memory shaped by online social platforms, Internet search tools, and artificial intelligence. It employs an analysis of digital memory platforms and conducts interviews centered on a specific case study examining the memory patterns of a Holocaust survivor. The increasing reliance on algorithmic mediation raises concerns about the potential distortion and manipulation of historical narratives. This study highlights the need for human agency in memory construction and the challenges of technologically mediated memory. It suggests that collaborative efforts involving scholars, survivors, and community members should continue to play a central role in developing technological tools for remembrance. The implications extend beyond Holocaust memory, informing discussions on the digitization, preservation, and ethical dissemination of technologically mediated historical knowledge in the twenty-first century.

ViroNia: LSTM based proteomics model for precise prediction of HCV.

ViroNia: LSTM based proteomics model for precise prediction of HCV.

Point Mutation Analysis of the Drug Efflux Pump MexB in Clinical Isolates of Pseudomonas aeruginosa.

The rapid emergence of drug-resistant microbes has recently become a major concern in the medical field. In Pseudomonas aeruginosa, one of the most important mechanisms underlying antibiotic resistance is MexAB-OprM system, increases in this efflux system result in greater resistance to a wide range of drugs, and genetic mutations have been identified as a contributing factor. Thus, this study characterized point mutations in the mexB gene that are common to 39 clinical P. aeruginosa isolates obtained from The Pseudomonas Genome Database. Basic Local Alignment Search Tool (BLAST) was used to compare the mexB gene sequences of those 39 strains with PAO1. The majority of these point mutations were silent mutations without amino acid mutations. Mutations 2730, 495, and 2280, which were abundant in the strains examined, were characterized by greater codon usage after the mutation. A positive correlation has been reported between tRNA levels and codon usage in Escherichia coli, and the same relationship may be present in P. aeruginosa. In this study, the silent mutations observed in many strains mainly involved the substitution of C or G, which resulted in a higher codon usage and stronger binding power after than before the mutation. This change is considered advantageous for survival in the human body by increasing the translation efficiency of the MexB protein. Thus, combining the silent mutation identified in this study with information on the expression level of mexB is expected to be used as an indicator to identify multidrug-resistant P. aeruginosa.

Development of a loop-mediated isothermal amplification assay for detecting Porphyromonas endodontalis

Context: Loop-mediated isothermal amplification (LAMP) may be used in the future to detect infecting microorganisms. LAMP assays exist for the endodontic pathogens Enterococcus faecalis and Fusobacterium nucleatum, but not yet for Porphyromonas endodontalis. Aim: To develop a LAMP assay for detecting P. endodontalis. Settings and Design: It was an in vitro benchtop study. Subjects and Methods: The National Center for Biotechnology Information GenBank Basic Local Alignment Search Tool was used to identify a segment of the dipeptidyl peptidase 11 (DPP11) gene unique to P. endodontalis. A primer design tool was used to generate six primers required for developing the LAMP assay. WarmStart Colorimetric LAMP 2X Master Mix was used to evaluate the LAMP assay, using purified P. endodontalis DNA as a control. Statistical Analysis Used: Statistical parameters for sensitivity and specificity. Results: The assay was performed in triplicate on pure DNA from P. endodontalis and P. gingivalis and on the DNA that was extracted from P. endodontalis, P. gingivalis, F. nucleatum, and E. faecalis cells and diluted two-fold from 1/2 to 1/256. Assays for the diluted samples were performed in triplicate, and the contingency tables indicated the LAMP assay to be 82% sensitive and 90% specific for P. endodontalis. Conclusions: LAMP assay could be a highly sensitive and specific chairside detection method for P. endodontalis.

Towards eco-friendly pharmaceuticals: Regulatory and policy approaches for sustainable medicines use.

Towards eco-friendly pharmaceuticals: Regulatory and policy approaches for sustainable medicines use.

Exploring the therapeutic potential of Rhododendron anthopogon D.Don essential oil constituents against lung cancer: A network pharmacology-based analysis with molecular docking and experimental studies.

Exploring the therapeutic potential of Rhododendron anthopogon D.Don essential oil constituents against lung cancer: A network pharmacology-based analysis with molecular docking and experimental studies.

A guide to reverse metabolomics-a framework for big data discovery strategy.

Untargeted metabolomics is evolving into a field of big data science. There is a growing interest within the metabolomics community in mining tandem mass spectrometry (MS/MS)-based data from public repositories. In traditional untargeted metabolomics, samples to address a predefined question are collected and liquid chromatography with MS/MS data are generated. We then identify metabolites associated with a phenotype (for example, disease versus healthy) and elucidate or validate their structural details (for example, molecular formula, structural classification, substructure or complete structural annotation or identification). In reverse metabolomics, we start with MS/MS spectra for known or unknown molecules. These spectra are used as search terms to search public data repositories to discover phenotype-relevant information such as organ/biofluid distribution, disease condition, intervention status (for example, pre- and postintervention), organisms (for example, mammals versus others), geography and any other biologically relevant associations. Here we guide the reader through a four-part process: (1) obtaining the MS/MS spectra of interest (Universal Spectrum Identifier) and (2) Mass Spectrometry Search Tool searches to find the files associated with the MS/MS that are in available databases, (3) using the Reanalysis Data User Interface framework to link the files with their metadata and (4) validating the observations. Parts 1-3 could take from hours to days depending on the method used for collecting MS/MS spectra. For example, we use MS/MS spectra from three small molecules: phenylalanine-cholic acid (a microbially conjugated bile acid), phenylalanine-C4:0 and histidine-C4:0 (two N-acyl amides). We leverage the Global Natural Products Social Molecular Networking-based framework to explore the microbial producers of these molecules and their associations with health conditions and organ distributions in humans and rodents.

The impact of benzodiazepine use on treatment retention in opioid agonist treatment: A literature review

It is well established that there is an elevated overdose risk with benzodiazepine (BZD) use during opioid agonist treatment (OAT). However, further studies regarding other aspects of how BZDs influence OAT are necessary. This review summarizes the literature on concurrent BZD use with medications for opioid use disorder (MOUD) and how they affect treatment retention in OAT. EMBASE (Ovid), PubMed, and Google Scholar database search tools were used to search for studies that examined the effect of concurrent BZD and MOUD on treatment retention in OAT. Studies published up to January 30th, 2024, were included with no restriction applied other than English language. The criteria for included literature were the presence of both BZD and at least one MOUD as a variable and treatment retention or MOUD adherence as an outcome. Fourteen articles met the criteria for review: eleven retrospective studies and three observational studies. Methadone was utilized in seven studies, buprenorphine in five, naltrexone in one, and suboxone (buprenorphine + naloxone) in one study. The included studies indicated that when BZDs are taken as prescribed and for shorter periods in conjunction with OAT, subjects are retained in their MOUD programs just as well as patients who do not take BZDs. Any potential benefits of increased treatment retention must be balanced against potential harmful effects of BZD use, such as drug overdose and addiction. Further studies must be performed to validate the results of treatment retention and to evaluate other factors that might affect OAT.

Factors Influencing the Adoption of Green Libraries for Environmental Sustainability: A Systematic Literature Review

Abstract This study was conducted to determine the factors influencing the adoption of green libraries for environmental sustainability, to find out the impacts and challenges faced by the librarians to adopt green libraries. For the systematic literature review, the study adopted the “Preferred Reporting Items for the Systematic Review and Meta-analysis” (PRISMA) as the methodology. Different search tools and techniques to access the relevant but extensive literature for the study were applied. Taylor and Francis, Emerald, Sage, IEEE, Elsevier, Wiley, EBSCO, Scopus, and Google Scholar were used to search the required literature. Initially, 844 documents were retrieved and after applying inclusion/exclusion criteria, finally, 33 papers were finalized to conduct the systemic literature review. The findings revealed that external, internal, and institutional goals, technological advancements, Green Library (GL) initiatives, and sustainable development goals (SDGs) are the major factors in adopting GL. Funding issues and the lack of technology are the major challenges faced by librarians. This study is beneficial for the administration, policymakers, LIS schools, and library professionals to gain awareness, planning, and execution to establish green libraries.

Systematic Analysis of Stay-Green Genes in Six Ipomoea Species Reveals the Evolutionary Dynamics, Carotenoid and Anthocyanin Accumulation, and Stress Responses of Sweet Potato

Background/Objectives: Stay-green proteins (SGRs) play a vital role in regulating plant chlorophyll degradation and senescence. However, this gene family has not been explored in Ipomoea species and sweet potato. Methods: A total of 19 SGR family genes (SGRs) were identified using Basic Local Alignment Search Tool (BLAST) methods. The proteins’ physiological properties, evolutionary and phylogenetic relationships, conserved domain and motifs, gene structures, collinearity, and promoter cis-elements were systematically analyzed. Moreover, expression patterns and protein interaction network analyses were performed for sweet potato. Results: In this study, we identified 19 SGRs in six Ipomoea species. These SGRs were divided into four subgroups according to their phylogenetic relationships. Domian analysis revealed that SGRs had the conserved “stay-green” domain. Gene structure analysis showed that SGRs had similar structures. The collinearity analysis revealed that the SGRs originated from two genes, with one gene undergoing duplication during evolution history; moreover, the SGRs experienced rearrangement throughout the evolutionary process in the Ipomoea species. Cis-elements related to pigment biosynthesis and hormone and stress responses were found. In addition, expression pattern analysis showed that IbSGRs, especially IbSGR1, IbSGR2, and IbSGR3, might play an important role in pigment accumulation. The SGRs could also respond to stress responses (i.e., cold, drought, and salt) and take part in hormone crosstalk (i.e., abscisic acid (ABA), methyl jasmonate (MeJA), salicylic acid (SA)). Conclusions: Taken together, the findings of this study provide new insights for further understanding the functions of SGRs and candidate genes for pigment accumulation and stress tolerance in sweet potatoes.

Machine learning-based interactive dynamic resilience assessment for complex hydropower systems

Complex hydropower systems are vital infrastructure in modern societies. Because of the risks associated with the operation of hydropower plants and dam structures, it is necessary to assess the dynamic resilience of these systems by considering the effects of faulty equipment and/or extreme environmental events. Common approach is to evaluate the performance of system components when exposed to a set of scenarios, combining various equipment faults in adverse conditions (e.g. flooding, earthquake). The evaluation is usually carried out with specific needs: (1) establishing design criteria; (2) providing real-time operation of the hydropower system. However, these specific needs may limit the application in complex circumstances, as they require repetition of evaluation system performance with a widespread of input datasets. The authors propose a novel methodology for interactive dynamic resilience assessment based on machine learning techniques and big data. The methodology is based on producing an extensive computational dataset for various scenario assessment and providing a machine learning-based tool for rapid search within the dataset previously simulated. These scenarios are then evaluated interactively, without subsequent simulations, against user-defined resilience metrics. This way, an operation manager may evaluate the dynamic resilience of the hydropower system for various operational constraints in timely manner. The Pirot hydropower system located in Serbia is analysed and findings are presented. The results indicate that the proposed search methodology enhances the traditional simulation model for dynamic resilience assessment by reducing the time required for series of simulation runs, thereby enabling faster improvements in system resilience.

Flood-Resilient Housing: State-of-The-Art In Patents

Objective: Technological prospecting in patents on flood-resilient housing, with the aim of analyzing the state-of-the-art and existing solutions to mitigate the impacts of floods in vulnerable areas. Theoretical Framework: Concepts and theories that support patent research as a source of technological information. Highlighted are the importance of patents as a reflection of the dynamism of knowledge production, the use of the International Patent Classification (IPC) as an organization and search tool, and the analysis of legal status to determine their validity and scope of protection. Method: Technological prospection in the Espacenet and Google Patents databases, using IPC E04H9/14 and the keyword "flood" as search strategies, as well as applying year filters, title reading, and drawing analysis to select and analyze relevant patents. Results and Discussion: Revealed a growth in the number of patents related to flood-resilient housing from the 2000s onwards, with peaks in 2007, 2015, and 2022. The analysis of legal status indicates a predominance of granted patents, evidencing the consolidation of technologies and solutions for the problem. Research Implications: The practical and theoretical implications provide insights into the development of technologies for flood-resilient housing, with potential application in areas vulnerable to extreme weather events, especially in Brazil, where patent protection is still incipient. Originality/Value: The study contributes to the literature by highlighting the importance of patent prospecting in identifying technological advances, offering important insights for public policies and innovation strategies in the field of flood-resilient housing.

Investigating the general effects of different types of toluene exposure on the health of workers: an integrative review of the literature

ObjectivesThis integrative review aims to evaluate the available empirical and theoretical literature on the effects caused by different types of occupational toluene exposure on the health of workers. We aim...