Search Tool Research Articles

Chaos-infused wind power integration in the grey wolf optimal paradigm for combine thermal-wind power plant systems

This research presents a novel methodology for tackling the combined thermal-wind economic load dispatch (ELD) issue in contemporary power system. The proposed approach involves hybridizing active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) into grey wolf optimization (GWO) algorithm, while effectively incorporating the intricacies associated with renewable energy sources (RES). A more accurate model is made possible by hybridization for complex systems with memory and hereditary characteristics. The GWO is used as a tool for global search while ASA, IPA and SQP methods are used for rapid local optimization mechanism. The performance evaluation of the design heuristics is carried out on 37 thermal and 3 wind power generating units and outcomes endorse the effectiveness of the proposed scheme over state-of-the-art counterparts. The worthy performance is further validated on statistical assessments in case of thermal-wind integrated ELD problem in terms of measure of central tendency and variation on cost and complexity indices.

Navigating Knowledge

In this workshop, we will demonstrate four research tools: 1) card sorting, 2) upward laddering, 3) think-aloud, and 4) a search visualizer. Card sorting can be useful as an exploratory knowledge modelling method to gain insight into people’s understanding of their surrounding world. Derived from Kelly’s Personal Construct Theory (PCT) (Kelly, 1955, Herd, 2001), card sorting can help to explore peoples’ constructs and their perceptions of how the constructs relate to each other; in other words, card sorting can help to elucidate mental frameworks. Although there are many kinds of card sorting, we will guide participants through a hands-on exercise using single-criterion card sorting (using both digital and in person techniques). Participants will learn about text-based, image-based, and object-based options, when to use card sorting, how to collect data, and how to analyse card sorting data through visual-numeric ‘heat maps’ (co-occurrence matrices). The card sorting exercise will lead into upward laddering, a technique that is often used to complement card sorting. While card sorting provides evidence of how constructs are related, upward laddering allows exploration into goals and values (Rugg & Gerrard, 2023). Workshop participants will have an opportunity to access a simple, robust, newly created online tool for upward laddering. Also used alongside card sorting is the think-aloud method which involves both observation to see how people perform a task and think-aloud to hear what people are thinking and noticing while they perform the task. This inexpensive, easy-to-use method allows researchers to tap into reasons and tacit knowledge. The fourth tool we will demonstrate is a newly launched search visualiser (SV). Using keywords, this search tool allows researchers to comb through specific databases and/or to access Google results. Rather than simply return a list of links to articles, the SV returns a visual depiction of the keywords within each text. Each key word is represented as a coloured square. A user can hover their mouse pointer over a given square to see the phrase within which the word appears. Using this tool, researchers can get a better, visual sense of whether the article is likely to offer useful content. There is now a version with audio for visually impaired users as well as a version that can explore synonyms to support textual-literary analysis.

Discrimination of three commercial tuna species through species-specific peptides: From high-resolution mass spectrometry discovery to MRM validation

The risk of tuna adulteration is high driven by economic benefits. The authenticity of tuna is required to protect both consumers and tuna stocks. Given this, the study is designed to identify species-specific peptides for distinguishing three commercial tropical tuna species. The peptides derived from trypsin digestion were separated and detected using ultrahigh-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS) in data-dependent acquisition (DDA) mode. Venn analysis showed that there were differences in peptide composition among the three tested tuna species. The biological specificity screening through the National Center for Biotechnology Information’s Basic Local Alignment Search Tool (NCBI BLAST) revealed that 93 peptides could serve as potential species-specific peptides. Finally, the detection specificity of species-specific peptides of raw meats and processed products was carried out by multiple reaction monitoring (MRM) mode based on a Q-Trap mass spectrometer. The results showed that three, one and two peptides of Katsuwonus pelamis, Thunnus obesus and Thunnus albacores, respectively could serve as species-specific peptides.

Phenotyping Wheat Kernel Symmetry as a Consequence of Different Agronomic Practices

The use of instrumental methods of analysis in the assessment of indices that record changes in symmetry in the structure of grains to evaluate the quality of durum and soft wheat grain is currently considered a search tool that will allow us to obtain previously unavailable data by finding correlations associated with differences in the shape and ratio of starch granules in conditionally symmetrical and asymmetrical wheat fruits (kernels) formed in different field conditions and with different genotypes. Indicators that had previously shown their effectiveness were used to analyze the obviously complex unique material obtained as a result of growing under critically unique sowing conditions in 2022, which affected the stability of grain development and filling. For the evaluation, a typical agronomic comparative experiment was chosen, which was used to evaluate the soil tillage practices (fallow, non-moldboard loosening, and plowing) and sowing dates (early and after excessive rainfalls), which made it possible to analyze a wider range of factors influencing the studied indices. The soil tillage methods were found to affect the uniformity of kernel fullness and their symmetry, and the sowing dates did not lead to significant differences. This study presents detailed changes in the shape of the middle cut of a wheat kernel, associated with assessing the efficiency of kernel filling and the symmetrical distribution of storage substances under the influence of external and internal physical factors that affect the formation of the wheat kernel. The data obtained may be of interest to breeders and developers of predictive phenotyping programs for cereal grain and seeds of other crops, as well as plant physiologists.

Describing diversity of real world data sources in pharmacoepidemiologic studies: The DIVERSE scoping review.

Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence. In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes. Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization. The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

TPMGD: A genomic database for the traditional medicines in Pakistan

ObjectiveIn Pakistan, traditional medicines are an important component of the medical system, with numerous varieties and great demands. However, due to the scattered resources and the lack of systematic collection and collation, adulteration of traditional Pakistani medicine (TPM) is common, which severely affects the safety of their medicinal use and the import and export trades. Therefore, it is urgent to systematically organize and unify the management of TPM and establish a set of standards and operable methods for the identification of TPM. MethodsWe collected and organized the information on 128 TPMs with regard to their medicinal parts, efficacy, usage, and genetic material, based on Pakistan Hamdard Pharmacopoeia of Eastern Medicine: Pharmaceutical Codex. The genetic information of TPM is summarized from national center for biotechnology information (NCBI) and global pharmacopoeia genome database (GPGD). Furthermore, we utilized bioinformatics technology to supplement the chloroplast genome (cp-genome) data of 12 TPMs. To build the web server, we used the Linux + Apache + MySQL + PHP (LAMP) system and constructed the webpage on a PHP: Hypertext Preprocessor (PHP) model view controller (MVC) framework. ResultsWe constructed a new genomic database, the traditional Pakistani medicine genomic database (TPMGD). This database comprises five entries, namely homepage, medicinal species, species identification, basic local alignment search tool (BLAST), and download. Currently, TPMGD contains basic profiles of 128 TPMs and genetic information of 102 TPMs, including 140 cytochrome c oxidase subunit I (COI) sequences and 119 mitochondrial genome sequences from Bombyx mori, 1 396 internal transcribed spacer 2 (ITS2) sequences and 1 074 intergenic region (psbA-trnH) sequences specific to 92 and 83 plant species, respectively. Additionally, TPMGD includes 199 cp-genome sequences of 82 TPMs. ConclusionTPMGD is a multifunctional database that integrates species description, functional information inquiry, genetic information storage, molecular identification of TPM, etc. The database not only provides convenience for TPM information queries but also establishes the scientific basis for the medication safety, species identification, and resource protection of TPM.

Diagnosis in Bytes: Comparing the Diagnostic Accuracy of Google and ChatGPT 3.5 as an Educational Support Tool.

Adopting advanced digital technologies as diagnostic support tools in healthcare is an unquestionable trend accelerated by the COVID-19 pandemic. However, their accuracy in suggesting diagnoses remains controversial and needs to be explored. We aimed to evaluate and compare the diagnostic accuracy of two free accessible internet search tools: Google and ChatGPT 3.5. To assess the effectiveness of both medical platforms, we conducted evaluations using a sample of 60 clinical cases related to urological pathologies. We organized the urological cases into two distinct categories for our analysis: (i) prevalent conditions, which were compiled using the most common symptoms, as outlined by EAU and UpToDate guidelines, and (ii) unusual disorders, identified through case reports published in the 'Urology Case Reports' journal from 2022 to 2023. The outcomes were meticulously classified into three categories to determine the accuracy of each platform: "correct diagnosis", "likely differential diagnosis", and "incorrect diagnosis". A group of experts evaluated the responses blindly and randomly. For commonly encountered urological conditions, Google's accuracy was 53.3%, with an additional 23.3% of its results falling within a plausible range of differential diagnoses, and the remaining outcomes were incorrect. ChatGPT 3.5 outperformed Google with an accuracy of 86.6%, provided a likely differential diagnosis in 13.3% of cases, and made no unsuitable diagnosis. In evaluating unusual disorders, Google failed to deliver any correct diagnoses but proposed a likely differential diagnosis in 20% of cases. ChatGPT 3.5 identified the proper diagnosis in 16.6% of rare cases and offered a reasonable differential diagnosis in half of the cases. ChatGPT 3.5 demonstrated higher diagnostic accuracy than Google in both contexts. The platform showed satisfactory accuracy when diagnosing common cases, yet its performance in identifying rare conditions remains limited.

Early-stage biomarkers identification by integrated genomic analysis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a type of liver cancer, ranks as the third-leading cause of death due to the lack of definite biomarkers for early-stage detection. HCC progression occurs by the dysregulation of several genes. Though several studies focus on biomarkers for HCC diagnosis, stage-specific marker identification remains elusive. Hence, the present study aims to identify early-stage biomarkers for the detection of HCC through integrated in silico analysis. The differential gene expression was performed using GEO2R for the datasets (GSE14520, GSE63898, GSE121248, GSE124535, GSE94660, and GSE6764) retrieved from Gene Expression Omnibus (GEO) of patients with cirrhotic liver or HCC. The common differentially expressed gene enrichment analysis was performed using Funrich for Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomics (KEGG) gene mapping. The Protein-Protein Interaction (PPI) network was performed using the Search Tool for the Retrieval of Interacting Genes (STRING). The hub genes were identified using the CytoHubba plug-in of Cytoscape software. The identified genes were verified for their prognostic value using the Kaplan-Meier plotter and Immunohistochemistry micrographs obtained from the Human Protein Atlas database. An overall of 243 common differentially expressed genes (DEGs) were identified containing 171 upregulated and 72 downregulated genes. With the help of PPI network construction, ten hub genes were identified as CDK1, AURKA, CCNB1, CCNB2, CENPF, CDC20, TOP2A, BUB1, RRM2, and HMMR, which are dysregulated owing to HCC proliferation, tumorigenesis and poor prognosis in patients. These hub genes are suitable waypoints for the diagnosis and targeted therapy against early-stage HCC.

Identification of Potential Breast Cancer Stem Cell Biomarkers in the Secretome Using a Network Interaction Approach Analysis.

Breast cancer stem cells (BCSCs) play a role in the high rates of resistance, recurrence, and metastasis. The precise biomarkers of BCSCs can assist effectively in identifying cancer, assessing prognosis, diagnosing, and monitoring therapy. The aim of this study was to give a complete analysis for predicting specific biomarkers of BCSCs. We aggregated profile datasets in this work to shed light on the underlying critical genes and pathways of BCSCs. We obtained two expression profiling by array datasets (GSE7513 and GSE7515) from the Gene Expression Omnibus (GEO) database to identify biomarkers in BCSCs. Enrichr was used to do functional analysis, including gene ontology (GO) and reactome pathway. Furthermore, the protein-protein interaction (PPI) of these differential expression genes (DEGs) was visualized using Cytoscape with the search tool for the retrieval of interacting genes (STRING). The hub genes in the PPI network were chosen for further investigation. We identified 65 up-regulated and 190 down- regulated DEGs and the GO enrichment analysis revealed that these DEGs were enriched in biological process related to tumorigenesis and stemness, including alter the extracellular matrix's physicochemical properties, cytoskeletal reorganisation, adhesion, motility, migration, growth, and survival. The Reactome analysis indicated that these DEGs were also involved in modulating function of ECM, regulation cancer metabolism and angiogenesis, tumor growth, proliferation, and metastasis. Our bioinformatic study revealed that FYN, INADL, OCLN, F11R, and TOP2A were potential biomarker panel of BCSCs from secretome.

Nasal Lavage Fluid Proteomics Reveals Potential Biomarkers of Asthma Associated with Disease Control.

Little research has explored the proteomic characteristics of nasal lavage fluid from asthmatic patients. This study aims to investigate whether differentially expressed proteins (DEPs) in nasal lavage fluid can serve as a biomarker to differentiate asthma patients from healthy controls (HCs) and to discern between individuals with well controlled and poorly controlled asthma. We enrolled patients with allergic rhinitis (AR), asthma, or both conditions, and HCs in this study. We recorded patients' demographic and medical history data and administered asthma quality of life questionnaire (AQLQ) and asthma control questionnaire (ACQ). Nasal fluid samples were collected, followed by protein measurements, and proteomic analysis utilizing the data-independent acquisition (DIA) method. Twenty-four with asthma, 27 with combined asthma+ AR, 25 with AR, and 12 HCs were enrolled. Four proteins, superoxide dismutase 2 (SOD2), serpin B7 (SERPINB7), kallikrein-13 (KLK13), and bleomycin hydrolase (BLMH) were significantly upregulated in nasal lavage fluid samples of asthma without AR, compared to HCs (Fold change ≥2.0, false-discovery rate [FDR] <0.05). Conversely, 56 proteins including secretoglobin family 2A member 1 (SCGB2A1) were significantly downregulated (fold change ≥2.0, FDR <0.05). Furthermore, 96.49% of DEPs including peptidase inhibitor 3 (PI3) and C-X-C motif chemokine 17 (CXCL17) were upregulated in poorly controlled asthma patients without AR relative those with well- or partly controlled asthma (fold change ≥1.5, FDR <0.05). Search tool for the retrieval of interacting genes/proteins (STRING) analysis showed that PI3, with 18 connections, may be pivotal in asthma control. The study revealed significant alteration in the nasal lavage proteome in asthma without AR patients. Moreover, our results indicated a potential association between the expression of proteome in the upper airway and the level of asthma control. Specifically, PI3 appears to be a key role in the regulation of asthma without AR.

Changes in Tryptophan Hydroxylase 2 and 5HT1A Receptor Density May Contribute to Developmental Variation in Response to Serotonin in the in vitro Bullfrog Brainstem

Bullfrog, Lithobates catesbeiana, is a vertebrate animal model that can be used to study toxicology and developmental physiology. Our research is focused on better understanding vulnerability to brainstem respiratory centers in the pathology of sudden infant death syndrome (SIDS). We propose that a link between nicotine exposure during development leads to changes in the serotonergic signaling cascades of respiratory coordinating neurons. Caudal raphe neurons send serotonergic fibers that arborize and terminate directly on or near respiratory neurons in the brainstem. In bullfrogs, serotonin elicits a developmentally dependent effect on respiratory motor output. Late metamorphic tadpoles demonstrate an increase in lung burst activity with exposure to low concentrations of serotonin (≤ 0.5μM 5-hydroxytryptamine; 5-HT) mediated in part by 5-HT1a receptor (5HT1aR) activation. This developmental change in activity may be due to changes in the number of serotonergic fibers and/or 5-HT1a receptor density. Using the NIH Basic Local Alignment Search Tool (BLAST) we compared human and bullfrog 5HT1aR and tryptophan hydroxylase 2 (TH2) protein sequences and found a 34 and 65 percent identity match respectively. Quantitative western blot analysis of both proteins indicates developmental shifts in the serotonergic network during metamorphosis. These results were supported by immunohistochemical staining of 5HT1aR and TH2 in caudal raphe containing brainstem sections from pre-metamorphic tadpoles and post-metamorphic frogs. Impairments in the maturation of this serotonergic network may underlie the toxidrome associated with developmental nicotine exposure and further support the increased risk of SIDS following that exposure. This research was supported by the University of Wisconsin- La Crosse (UWL) through support from the UWL College of Science and Health, two UWL Undergraduate Research Creative Grants and a UWL FY23/24 Faculty Research Grant. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry.

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.'s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

Media and representation of others. Case of Iran in the 2020 Tokyo Olympics

Media and representation of others. Case of Iran in the 2020 Tokyo Olympics

Molecular identification of the bacterium acute conjunctivitis by the method of sequensing gen 16S rRNA

Conjunctivitis is the most common eye disease, characterized by contextual inflammation, which can be caused by bacteria. The diagnosis of conjunctivitis is established based various factors, including the patients’s medical history, onset of eye symptoms, non-ocular symptoms, previous disease history, family medical history, allergies, and physical examination of the eyes, which may include assessments of visual acuity and vision field. Some cases of conjunctivitis require laboratory examinations to confirm the diagnosis. These may include cytological examination with Giemsa staining, Chlamydia Diagnostic Test, Nucleic acid amplification tests (NAATs) or Polymerase chain reaction (PCR), and microbiological tests. Microbiological examination helps identify the bacteria responsible for conjunctivitis and aids in treatment by prescribing antibiotics to suppress the growth of the infecting bacteria. While identification tests are not routinely performed, researchers often seek to determine the specific type of bacteria causing conjunctivitis infections, which may require several laboratory tests. Bacterial virulence plays a significant role, with genetic mutations potentially leading to severe infections of varying severity. Virulence genes encode proteins that express pathogenic properties. The species responsible for conjunctivitis can be definitively identified definitively through microbiological examination, utilizing methods such as the 16S Ribosomal RNA Sequencing (rRNA) technique, known for its accuracy and speed. This study aims to analyze the results of rRNA sequencing in cases of acute bacterial conjunctivitis caused by 16S rRNA genotyping. The research employed an exploratory metholodolot, with the results analyzed using the The Basic Local Alignment Search Tool (BLAST) tracking program database on the National Center for Biotechnology Information (NCBI) website. The findings revealed that Sphingomonas paucimobilis encoded the 16S rRNA using Universal Primary 27 F and 1492 R, obtained in a sequence size of 1351bp. The isolate demonstrated similarities to Sphingomonas paucimobilis.

Assessment of chemical characteristics of bottom sediments of Zobe Dam Reservoir, Katsina State, Nigeria

Zobe Dam is one of the silted dams in Nigeria. It is located in the Dustin-ma area in Katsina State northwestern Nigeria. The bottom reservoir sediments of the dam were sampled during the bathymetric studies of the reservoir. The objectives are to: determine the status of mineral elements and their distribution; measure the anomalous elemental concentration, as a tool in search for solid mineral deposits; and determine the usefulness of such sediments when dredged out. A total of 15 samples were obtained and analysed at Federal Steel Council laboratory in Kaduna using a Scanning Electron Microscope which was then transferred electronically to EDX that revealed the elemental constituents of the samples in mg/kg. A total of 27 elements were examined, the order of enrichment is Si &gt; Fe &gt; Al &gt; Hg&gt; K &gt; Pb &gt; Ti &gt; Ca &gt; Cd….All the elements were several times greater than the world's mean crustal abundance. The preponderance of these elements could be attributed to the contamination from artisanal mining of polymetallic mineral associations in Jibia, Dandire, Mallam Tanko Kafur, Dutsin-Ma and other areas within the State; as well as the anthropogenic factors like organic fertilizer application by farmers. It is recommended that the erosion of sand into the dam be checked through the planting of trees and other environmental conservation practices.

Hierarchical Co‐generation of Parcels and Streets in Urban Modeling

AbstractWe present a computational framework for modeling land parcels and streets. In the real world, parcels and streets are highly coupled with each other since a street network connects all the parcels in a certain area. However, existing works model parcels and streets separately to simplify the problem, resulting in urban layouts with irregular parcels and/or suboptimal streets. In this paper, we propose a hierarchical approach to co‐generate parcels and streets from a user‐specified polygonal land shape, guided by a set of fundamental urban design requirements. At each hierarchical level, new parcels are generated based on binary splitting of existing parcels, and new streets are subsequently generated by leveraging efficient graph search tools to ensure that each new parcel has a street access. At the end, we optimize the geometry of the generated parcels and streets to further improve their geometric quality. Our computational framework outputs an urban layout with a desired number of regular parcels that are reachable via a connected street network, for which users are allowed to control the modeling process both locally and globally. Quantitative comparisons with state‐of‐the‐art approaches show that our framework is able to generate parcels and streets that are superior in some aspects.

MiR-153 promotes neural differentiation by activating the cell adhesion/Ca2+ signaling pathway and targeting ion channel activity in HT-22 cells by bioinformatic analysis

MicroRNAs have been studied extensively in neurodegenerative diseases. In a previous study, miR-153 promoted neural differentiation and projection formation in mouse hippocampal HT-22 cells. However, the pathways and molecular mechanism underlying miR-153-induced neural differentiation remain unclear. To explore the molecular mechanism of miR-153 on neural differentiation, we performed RNA sequencing on miR-153-overexpressed HT-22 cells. Based on RNA sequencing, differentially expressed genes (DEGs) and pathways in miR-153-overexpressed cells were identified. The Database for Annotation, Visualization and Integrated Discovery and Gene Set Enrichment Analysis were used to perform functional annotation and enrichment analysis of DEGs. Targetscan predicted the targets of miR-153. The Search Tool for the Retrieval of Interacting Genes and Cytoscape, were used to construct protein-protein interaction networks and identify hub genes. Q-PCR was used to detect mRNA expression of the identified genes. The expression profiles of the identified genes were compared between embryonic days 9.5 (E9.5) and E11.5 in the embryotic mouse brain of the GDS3442 dataset. Cell Counting Kit-8 assay was used to determine cell proliferation and cellular susceptibility to amyloid β-protein (Aβ) toxicity in miR-153-overexpressed cells. The results indicated that miR-153 increased cell adhesion/Ca2+ (Cdh5, Nrcam, and P2rx4) and Bdnf/Ntrk2 neurotrophic signaling pathway, and decreased ion channel activity (Kcnc3, Kcna4, Clcn5, and Scn5a). The changes in the expression of the identified genes in miR-153-overexpressed cells were consistent with the expression profile of GDS3442 during neural differentiation. In addition, miR-153 overexpression decreased cellular susceptibility to Aβ toxicity in HT-22 cells. In conclusion, miR-153 overexpression may promote neural differentiation by inducing cell adhesion and the Bdnf/Ntrk2 pathway, and regulating electrophysiological maturity by targeting ion channels. MiR-153 may play an important role in neural differentiation; the findings provide a useful therapeutic direction for neurodegenerative diseases.

Bioinformatics analysis and identification of potential key genes and pathways in the pathogenesis of nonischemic cardiomyopathy.

Nonischemic cardiomyopathy (NICM) is a major cause of advanced heart failure, and the morbidity and mortality associated with NICM are serious medical problems. However, the etiology of NICM is complex and the related mechanisms involved in its pathogenesis remain unclear. The microarray datasets GSE1869 and GSE9128 retrieved from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs) between NICM and normal samples. The co-expressed genes were identified using Venn diagrams. Kyoto Encyclopedia of Genes and Genomes pathway analyses and gene ontology enrichment were used to clarify biological functions and signaling pathways. Analysis of protein-protein interaction networks using Search Tool for the Retrieval of Interacting Genes/Proteins online to define the hub genes associated with NICM pathogenesis. A total of 297 DEGs were identified from GSE1869, 261 of which were upregulated genes and 36 were downregulated genes. A total of 360 DEGs were identified from GSE9128, 243 of which were upregulated genes and 117 were downregulated genes. In the 2 datasets, the screening identified 36 co-expressed DEGs. Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology analysis showed that DEGs were mainly enriched in pantothenate and CoA biosynthesis, beta-alanine metabolism, kinetochore, G-protein beta/gamma-subunit complex, and other related pathways. The PPI network analysis revealed that DUSP6, EGR1, ZEB2, and XPO1 are the 4 hub genes of interest in the 2 datasets. Bioinformatics analysis of hub genes and key signaling pathways is an effective way to elucidate the mechanisms involved in the development of NICM. The results will facilitate further studies on the pathogenesis and therapeutic targets of NICM.

Anti-Obesity Therapeutic Targets Studied In Silico and In Vivo: A Systematic Review.

In the age of information technology and the additional computational search tools and software available, this systematic review aimed to identify potential therapeutic targets for obesity, evaluated in silico and subsequently validated in vivo. The systematic review was initially guided by the research question "What therapeutic targets have been used in in silico analysis for the treatment of obesity?" and structured based on the acronym PECo (P, problem; E, exposure; Co, context). The systematic review protocol was formulated and registered in PROSPERO (CRD42022353808) in accordance with the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis Protocols (PRISMA-P), and the PRISMA was followed for the systematic review. The studies were selected according to the eligibility criteria, aligned with PECo, in the following databases: PubMed, ScienceDirect, Scopus, Web of Science, BVS, and EMBASE. The search strategy yielded 1142 articles, from which, based on the evaluation criteria, 12 were included in the systematic review. Only seven these articles allowed the identification of both in silico and in vivo reassessed therapeutic targets. Among these targets, five were exclusively experimental, one was exclusively theoretical, and one of the targets presented an experimental portion and a portion obtained by modeling. The predominant methodology used was molecular docking and the most studied target was Human Pancreatic Lipase (HPL) (n = 4). The lack of methodological details resulted in more than 50% of the papers being categorized with an "unclear risk of bias" across eight out of the eleven evaluated criteria. From the current systematic review, it seems evident that integrating in silico methodologies into studies of potential drug targets for the exploration of new therapeutic agents provides an important tool, given the ongoing challenges in controlling obesity.

Accelerating research processes with Scopus AI: A place branding case study

Academic databases play a crucial role in advancing science by hosting a vast array of peer-reviewed literature. However, academic database search tools involve a relatively slow and rather unintuitive process of searching and evaluating content. To address these challenges, in January 2024, Elsevier introduced Scopus AI, a generative artificial intelligence that synthesises evidence indexed in Scopus based on prompts. This study assesses the utility of Scopus AI (in its beta version at the time of the research), within the context of a doctoral thesis through a specific case study. By employing a relational prompt and three follow-up questions, the study aims to pinpoint intersections between different topics within the realm of Social Sciences and, more specifically, Communication, with a case on place branding. The consolidated result provides an initial list of references, offers a comprehensive overview, and allows to generate a meta-synthesis based on the summaries provided by each prompt. Scopus AI (beta) presents features that enable researchers to identify influential authors and works, explore relevant keywords, review recent literature, and identify potential research gaps. Although Scopus AI has some limitations, such as the dependence on the abstracts of documents indexed in Scopus, the simplification of concepts, or the relative disconnection between arguments, the results demonstrate the value of this tool in accelerating research processes, as it synthesises research in a given area, maps its main characteristics and allows for information discovery.