Software Tools Research Articles

Insilico Simulation Studies for Tuberculosis Drug Discovery and its Response

From high treatment and with such precaution measures TB (Tuberculosis) came with leading cause of deaths in the world. For Tuberculosis it increasing drug resistance for different kind of TB like Multidrug-resistant TB and Extensively drug-resistant TB, increasing relapse and in efficiency of drug for TB pathogen.it take longer duration, it affect on human body with infection from HIV and other autoimmune disorder have pushed it to a lethal level and for this we need more effective drugs. With the help of CADD technology we can findout more viable drug molecules for the better treatment of Mycobacterium tuberculosis .Insilico drug design is a more efficient ,time saving and cost effective process. With the help of inslico drug design we can identify/Generate lead molecules that can inhibit the activity of TB. In inslico process we took 4GZR protein which is Crystal structure of the Mycobacterium tuberculosis and lead(ligand) with the support of computer aided drug design (CADD) pipeline and bioinformatics software tools. Key words: Tuberculosis, Insilico Drug Design, Computer Aided Drug Design (CADD), Bioinformatics Tool and Technology.

Innovative Technology Integration in Teaching Practicum: Pre-Service Teachers' Mastery of the TPACK Framework at Universitas Widya Gama Mahakam Samarinda

Consider how Technology has been used in the teaching and learning process. This study aimed to explore PSTs' final practice in practicum integrated into Technology and how they solve the problem if they have it. This research was conducted at Widya Gama Mahakam University. This study focused on PSTs' competence when designing and using Technology in teaching final practice in practicum programs through the TPACK framework. The design of this study was a qualitative study that illustrates the PSTs' competence in integrating Technology in the final practice teaching in the practicum program through TPACK Framework. There were 22 PSTs of seven semesters as the participants. There were three items of instruments. Documents, interviews, Observation, and three PSTs were willing to be interviewees. The study result showed they plan the lesson well before teaching using Technology even though there are slight differences in their performance. The Technology they used, combined with pedagogy and content, varied depending on the state of equipment, facilities, PSTs' abilities, and student levels. PSTs also stated the involvement of supervising teachers was beneficial when they faced difficulties in teaching practice. The Researcher suggested that PSTs design their lesson plan effectively. The teacher preparation program prepares PSTs with technological education skills, and the school provides nondigital or digital technology support, student-centered software, and collaboration media tools.

A Critical Review on the Advancements in Exploration and Production of Shale Gas

The exploration and production of shale gas have seen significant advancements in recent years, driven by the increasing global demand for clean energy sources. These advancements encompass the understanding of a wide range of areas, including the formation theory of shale gas reservoirs, evaluation methods, well drilling, completion, and simulation techniques. Innovative design, modelling, and field practices have been developed for shale gas well drilling and completion, as well as its production techniques. Furthermore, new simulation techniques, software, algorithms, and other tools have been introduced to improve the modelling and simulation of shale gas production. Experimental and simulation research in shale gas exploration, development, and production using hydraulic fracturing has also seen considerable progress. These advancements are contributing significantly to the global demand for clean energy sources with a promise to achieve sustainable development goals. This review work explores the exploration and exploitation of shale gas with a detailed description of multidisciplinary research efforts, providing valuable insights into shale reservoir/rock characterization and geo-mechanics, fluid-surface interactions, gas adsorption behaviours and single- and multi-phase flow mechanics. These advancements are not only increasing the efficiency and productivity of shale gas extraction but also paving the way to meet the future global energy demand while also contributing to the goal of achieving net-zero carbon emissions. The novelty of the review work is that it exhibits a combined qualitative and quantitative study of recent advancements in the sphere of exploration and production of shale gas reserves and its discoveries around the globe.

Toward integration of glycan chemical databases: an algorithm and software tool for extracting sugars from chemical structures.

Integration of glycan-related databases between different research fields is essential in glycoscience. It requires knowledge across the breadth of science because most glycans exist as glycoconjugates. On the other hand, especially between chemistry and biology, glycan data has not been easy to integrate due to the huge variety of glycan structure representations. We have developed WURCS (Web 3.0 Unique Representation of Carbohydrate Structures) as a notation for representing all glycan structures uniquely for the purpose of integrating data across scientific data resources. While the integration of glycan data in the field of biology has been greatly advanced, in the field of chemistry, progress has been hampered due to the lack of appropriate rules to extract sugars from chemical structures. Thus, we developed a unique algorithm to determine the range of structures allowed to be considered as sugars from the structural formulae of compounds, and we developed software to extract sugars in WURCS format according to this algorithm. In this manuscript, we show that our algorithm can extract sugars from glycoconjugate molecules represented at the molecular level and can distinguish them from other biomolecules, such as amino acids, nucleic acids, and lipids. Available as software, MolWURCS is freely available and downloadable ( https://gitlab.com/glycoinfo/molwurcs ).

GeoPick ‐ A web application for georeferencing natural history collections following best practices

Georeferencing is a key process in the digitization of natural history collections as it assigns spatial coordinates to preserved specimen collecting locations, facilitating their use in ecological, evolutionary and conservation research. Georeference data in public repositories such as GBIF is often missing or incomplete, jeopardising their use in research and limiting the return on investment made by public institutions. Despite the existence of guidelines for best practices for georeferencing and widely accepted standards for biodiversity data, there is a lack of a simple yet effective software tool that offers the implementation of both concepts. We present GeoPick with the aim to offer the collections community a standards‐compliant tool that eases the georeferencing process, making it more cost‐effective, and which, by applying best practices, contributes to the betterment of the occurrence data in public repositories. GeoPick also offers the possibility of collaboration between users and institutions through the sharing of georeferences. The tool is hosted by GBIF at https://geopick.gbif.org, and is open source. Its code is available at a public GitHub repository (https://github.com/rtdeb/GeoPick).Keywords: Darwin Core, georeferencing, natural history collections, point‐radius method, web application, Well Known Text Format, WKT

A Novel Technique for Repairing Alveolar Clefts: Computer-Assisted Design With Posterior of Mandible Block Bone Grafts

Objective: Iliac cancellous bone graft is the gold standard for the treatment of alveolar clefts. However, the resorption rate of iliac cancellous bone is generally high. In this study, the authors propose a new method for repairing alveolar clefts using block bone grafts in the posterior region of the mandibular molar and the anterior margin of the ascending branch. Methods: Ten patients with a diagnosis of unilateral or bilateral alveolar cleft were collected. Block bone grafting between the posterior mandibular molar region and the inner and outer oblique lines of the anterior margin of the ascending branch was used to treat the alveolar synostosis. Preoperative, 1-week postoperative, and 6-month postoperative multislice spiral computed tomography scans of the patients were collected and analyzed for preoperative design and postoperative graft bone morphology using computer-assisted software tools, which were also required to measure the changes in the bone tissue density in the posterior mandibular bone extraction area. Results: At 6 months postoperatively, the mean bone resorption rate of the posterior mandibular bone grafts for cleft alveolar processes was 33.96% ± 11.23%. Bone tissue density in the posterior mandibular bone grafting area recovered to near preoperative levels at 6 months postoperatively. Conclusion: The novel method of bone grafting for the cleft alveolar process used in this paper is really effective. Postoperative posterior mandibular bone extraction did not cause sequelae such as mandibular bone defects or poor morphology in the patients. Computer-assisted design and three-dimensional printing technology helped to improve intraoperative bone grafting accuracy and postoperative assessment.

Digital marketing and small and medium-sized enterprises’ business performance in emerging markets

Purpose This study aims to investigate the comprehensive conceptualization how digital marketing and its adoption increase the productivity of small and medium-sized enterprises (SMEs), as well as the impact of technological orientation, customer relationship management and digital marketing on SMEs’ business performance in emerging markets (EMs). Design/methodology/approach Anchored by social network theory, 178 SMEs were conveniently drawn from the National Board for Small Scale Industries now Ghana enterprise agencies in Ghana. The hypotheses were analysed using the SPSS package tool, and structural equation modelling was used, as well. Findings This study reveals that the conceptualization and adoption of digital marketing positively affect the performance of SMEs’ business management in EMs. In addition, a positive moderating effect of dynamism is observed in the relationship between digital marketing and SMEs’ business performance. Research limitations/implications This study focuses only on SMEs from the Greater Accra and Ashanti regions of Ghana as an EM, and it proposes priorities for future research streams in both developed and emerging countries. Practical implications The findings encourage SMEs to develop quality digital marketing technologies, good customer relationships and technological orientation and customer relationship management competencies in EMs to maximise profit. Originality/value This paper fills considerable knowledge, evidence and contextual gaps in the literature on SMEs, entrepreneurship and marketing in EMs by presenting a comprehensive overview of how SMEs can use digital marketing in an EM context to achieve the following: customer acquisitions, satisfaction and retention; increased profitability; quality service delivery; value creation; and cost reductions.

Pediatric Sickle Cell Disease Patients' Hematological Changes during Vaso-Occlusive Crises (VOCS) Versus Steady State

Background: The primary acute outcome of sickle cell disease (SCD) is a vaso-occlusive crisis (VOC) with extreme pain. More than three vaso-occlusive crisis (VOC) hospitalizations in a year increase the risk of early death. The objective of the study was to evaluate the hematological alterations in complete blood count (hemoglobin, red blood cell indices, white blood cell count, and platelets) and C-reactive protein levels during vaso-occlusive crises in patients with sickle cell anemia (HbSS) compared to a stable symptomatic state. Methodology: This study is a prospective descriptive study conducted in a hospital setting. During the period from February to June 2015, a Sudan Sickle Cell Anemia Center (SSCAC) in North Kordofan State, Sudan, treated a total of 100 patients with sickle cell anemia. Out of these patients, 74 had a vaso-occlusive crisis and 26 had a steady-state condition. All patients had measurements of their complete blood count (CBC) and C-reactive protein (CRP) levels. We calculated the average values of CBC and CRP level counts during vaso-occlusive crises and compared them to the values during steady state using independent t-tests. We analyzed the collected data using the Statistical Tool for Social Sciences (SPSS) software tool, specifically version 13. Results: The statistical analysis of the patient sample during vaso-occlusive crisis and steady state revealed that the concentration of hemoglobin (Hb) was 6.55 g/dl ± 1.3 and 9.8 g/dl ± 2.4, respectively, with a p-value of 0.000. The packed cell volume (PCV) was 20.4% ± 3.9 and 30.7% ± 7.1, respectively, with a p-value of 0.000. The red blood cell (RBC) count was 2.5×106/µl ± 0.57 and 3.9×106/µl ± 1.1, respectively, with a p-value of 0.000. A lot more white blood cells (WBC) were present in vasoocclusive than in steady state, with numbers 19.36 x 109/l ± 10.645 and 76.76 x 109/l ± 20.19, respectively, and a P-value of ≤0.000. There was a notable statistical disparity in the platelet count, with values of (357.108109/l ± 162.838) and (290.4610×9/l ± 124.40), with a P value of 0.05. In vaso-occlusive crises, the amounts of hemoglobin, packed cell volume (PCV), and red blood cells (RBCs) are lower than they are in the steady-state phase. Conversely, white blood cell (WBC) and platelet levels are much greater in patients experiencing vaso-occlusive crises compared to those in a steady state. The levels of C-reactive protein (CRP) were significantly different between patients experiencing vaso-occlusive crises and those in a stable condition.

GP-Marker facilitates the analysis of intact glycopeptide quantitative data at different levels.

Protein glycosylation is a highly heterogeneous post-translational modification that has been demonstrated to exhibit significant variations in various diseases. Due to the differential patterns observed in disease and healthy populations, the glycosylated proteins hold promise as early indicators for multiple diseases. With the continuous development of liquid chromatography-mass spectrometry (LC-MS) technology and spectrum analysis software, the sensitivity for the decipher of the tandem mass spectra of the glycopeptides carrying intact glycans, i.e., intact glycopeptides, enzymatic hydrolyzed from glycoproteins has been significantly improved. From quantified intact glycopeptides, the difference of protein glycosylation at multiple levels, e.g., glycoprotein, glycan, glycosite, and site-specific glycans, could be obtained for different samples. However, the manual analysis of the intact glycopeptide quantitative data at multiple levels is tedious and time consuming. In this study, we have developed a software tool named "GP-Marker" to facilitate large-scale data mining of spectra dataset of intact N-glycopeptide at multiple levels. This software provides a user-friendly and interactive interface, offering operational tools for machine learning to researchers without programming backgrounds. It includes a range of visualization plots displaying differential glycosylation and provides the ability to extract multi-level data analysis from intact glycopeptide data quantified by Glyco-Decipher.

Musketeer: a software tool for the analysis of titration data.

Musketeer is a powerful open-source software tool for the analysis of titration data, featuring a simple cross-platform graphical interface for importing data directly from UV-vis, fluorescence and NMR spectrometers, or from spreadsheets. The fast data analysis algorithm can be used to obtain equilibrium constants for simple binding isotherms, as well as for more complicated systems with multiple competing equilibria. Applications of Musketeer for the analysis of a range of different supramolecular and biomolecular systems are illustrated, including titrations with multiple spectroscopically active species, competitive binding assays, denaturation experiments, optimisation of concentrations as variables. The software also includes a number of tools that can be used to select the binding isotherm that represents the best model to describe a dataset.

Blockchain-Based Strategy to Optimize Certified Notifications from Government Entities

Currently, 4.0 technologies have generated important changes in government entities, promoting digital government transformation (DGT). This article addresses the challenge of improving the security, reliability and traceability of communications and certified notifications of state entities, particularly in the case of the Colombian government entity called the Comisión de Regulación de Comunicaciones (CRC). Four important points are presented in this report: (a) Research interest in blockchain and smart contracts has been greatly increasing, evidenced in a literature review conducted in the SCOPUS index; (b) a global reference framework on the use of emerging technologies, as is the case of blockchain, has been developed, as well as the legal and juridical framework in Colombia as a baseline; (c) a multi-criteria decision making framework has been used, resulting in the choice of the Zilliqa platform; and (d) The Tecnotifica™ V1.0 software tool is developed to automate notifications with stakeholders using blockchain. Its effectiveness and acceptance is validated with a technology acceptance level test (TAM). It is concluded that the incorporation of emerging technologies such as blockchain is of a very high interest and relevance in the digital transformation of the state sector in the world.

Glypred: Lysine Glycation Site Prediction via CCU-LightGBM-BiLSTM Framework with Multi-Head Attention Mechanism.

Glycation, a type of posttranslational modification, preferentially occurs on lysine and arginine residues, impairing protein functionality and altering characteristics. This process is linked to diseases such as Alzheimer's, diabetes, and atherosclerosis. Traditional wet lab experiments are time-consuming, whereas machine learning has significantly streamlined the prediction of protein glycation sites. Despite promising results, challenges remain, including data imbalance, feature redundancy, and suboptimal classifier performance. This research introduces Glypred, a lysine glycation site prediction model combining ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional long short-term memory network (BiLSTM) methodologies, with an additional multihead attention mechanism integrated into the BiLSTM. To achieve this, the study undertakes several key steps: selecting diverse feature types to capture comprehensive protein information, employing a cluster-based undersampling strategy to balance the data set, using LightGBM for feature selection to enhance model performance, and implementing a bidirectional LSTM network for accurate classification. Together, these approaches ensure that Glypred effectively identifies glycation sites with high accuracy and robustness. For feature encoding, five distinct feature types─AAC, KMER, DR, PWAA, and EBGW─were selected to capture a broad spectrum of protein sequence and biological information. These encoded features were integrated and validated to ensure comprehensive protein information acquisition. To address the issue of highly imbalanced positive and negative samples, various undersampling algorithms, including random undersampling, NearMiss, edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately chosen to remove redundant nonglycated training data, establishing a balanced data set that enhances the model's accuracy and robustness. For feature selection, the LightGBM ensemble learning algorithm was employed to reduce feature dimensionality by identifying the most significant features. This approach accelerates model training, enhances generalization capabilities, and ensures good transferability of the model. Finally, a bidirectional long short-term memory network was used as the classifier, with a network structure designed to capture glycation modification site features from both forward and backward directions. To prevent overfitting, appropriate regularization parameters and dropout rates were introduced, achieving efficient classification. Experimental results show that Glypred achieved optimal performance. This model provides new insights for bioinformatics and encourages the application of similar strategies in other fields. A lysine glycation site prediction software tool was also developed using the PyQt5 library, offering researchers an auxiliary screening tool to reduce workload and improve efficiency. The software and data sets are available on GitHub: https://github.com/ZBYnb/Glypred.

BuildAMol: a versatile Python toolkit for fragment-based molecular design

In recent years computational methods for molecular modeling have become a prime focus of computational biology and cheminformatics. Many dedicated systems exist for modeling specific classes of molecules such as proteins or small drug-like ligands. These are often heavily tailored toward the automated generation of molecular structures based on some meta-input by the user and are not intended for expert-driven structure assembly. Dedicated manual or semi-automated assembly software tools exist for a variety of molecule classes but are limited in the scope of structures they can produce. In this work we present BuildAMol, a highly flexible and extendable, general-purpose fragment-based molecular assembly toolkit. Written in Python and featuring a well-documented, user-friendly API, BuildAMol empowers researchers with a framework for detailed manual or semi-automated construction of diverse molecular models. Unlike specialized software, BuildAMol caters to a broad range of applications. We demonstrate its versatility across various use cases, encompassing generating metal complexes or the modeling of dendrimers or integrated into a drug discovery pipeline. By providing a robust foundation for expert-driven model building, BuildAMol holds promise as a valuable tool for the continuous integration and advancement of powerful deep learning techniques.Scientific contributionBuildAMol introduces a cutting-edge framework for molecular modeling that seamlessly blends versatility with user-friendly accessibility. This innovative toolkit integrates modeling, modification, optimization, and visualization functions within a unified API, and facilitates collaboration with other cheminformatics libraries. BuildAMol, with its shallow learning curve, serves as a versatile tool for various molecular applications while also laying the groundwork for the development of specialized software tools, contributing to the progress of molecular research and innovation.

Numerical calculation of groundwater geofiltration processes in multilayer porous media

In this The article presents the modeling of multilayer hydrogeological processes in porous media, the results of fundamental and applied research, numerical modeling of hydrogeological processes, computational mathematics and methods of finite-difference schemes for solving simple differential equations, the development and improvement of mathematical models, and considers computational algorithms and software tools for solving problems analysis and forecasting of processes.

Investigating the impact of a window air conditioner with H-14 HEPA filter on lessening SARS-COV-2 aerosols

This research breaks new ground by proposing a unique solution to combat SARS-COV-2 aerosols: modifying a readily available window air conditioner’s indoor blower to accommodate a HEPA (high-efficiency particulate air) filter. While traditional public health measures like lockdowns and mask-wearing remain crucial, this study explores an innovative engineering approach to air purification within homes and offices. The widespread impact of COVID-19 across various sectors—agriculture, manufacturing, finance, and more—necessitates exploring diverse solutions. Current efforts to utilize HEPA filters in HVAC systems face limitations. These filters, while highly effective at capturing airborne particles, increasingly impede airflow and require substantial pressure, posing challenges for standard HVAC systems to maintain efficient operation. This study addresses this critical gap by proposing a targeted modification to a specific window air conditioner (AC) model (GJC07AF-K3RNB9D) to incorporate an H-14 HEPA filter. Utilizing cutting-edge design tool (CF-TURBO) and advanced simulation software (STARCCM +), the research will provide a new blower specifically optimized for this application. This virtual testing will meticulously evaluate the modified system’s performance, ensure optimal airflow, predict noise levels, and identify any potential design flaws before implementing the modification in a physical prototype. The success of this study could pave the way for the development of increasingly efficient and accessible HEPA-based air purification solutions for everyday use, particularly in areas with limited resources. Furthermore, this research can be a valuable foundation for future work aimed to increasingly improve indoor air quality (IAQ).

Research trends of bone tumor treatment with 3D printing technology from 2013 to 2022: a bibliometric analysis

ObjectiveBibliometrics was employed in this study to determine the research trends in the worldwide application of 3D printing technology to treat bone tumors over the previous 10 years.MethodsPublished from 2013 to 2022, the papers related to bone tumors treated with 3D printing were located in Web of Science Core Collection (WoSCC), PubMed, and Scopus. The screened articles were included in this bibliometric study. From these papers in WoSCC, information on annual publications, journals, keywords, countries, authors, institutions, and cited references were extracted and visualized with CiteSpace (version 6.1.R6) software to investigate the state of bone tumor treatment using 3D printing as well as research hotspots. The Carrot2 online visualization tool and Vosviewer software (version 1.6.20) were employed to visualize the publications from PubMed and Scopus, respectively, in order to ascertain the most popular research topics from both databases.ResultsA total of 606, 233, and 364 publications were obtained from WoSCC, PubMed, and Scopus, respectively, between the years 2013 and 2022. In WoSCC, the peak number of publications was found in 2021, with 145 publications published. Acta Biomaterialia (11 publications) and World Neurosurgery (10 publications) were the most prolific journals, and Biomaterials was the journal cited the most (244 times). Yong Zhou was the most productive author with 14 publications, while Kwok-Chuen Wong (69 citations) and William F Enneking, (69 citations) possessed the most citations. The country with the largest quantity of publications was China (207). Among all institutions, Shanghai Jiao Tong University produced the most publications (29). Rapid prototyping was the keyword with the strongest citation burst (4.73). ‘Reconstruction’, ‘surgery’, ‘resection’, and ‘design’ caught the significant attention of researchers. 3D-printed materials, pelvic reconstruction, mandibular reconstruction, computer-assisted surgical techniques, photothermal therapy, and in vitro experiments were recognized as hot subjects and trends in current research. The most frequently occurring topics in Scopus are not significantly different from those found in WoSCC. The most prevalent research areas in PubMed encompass implant, patient-specific, bioceramic, models, and pelvic.

Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials Design.

When Olga Kennard founded the Cambridge Crystallographic Data Centre in 1965, the Cambridge Structural Database was a pioneering attempt to collect scientific data in a standard format. Since then, it has evolved into an indispensable resource in contemporary molecular materials science, with over 1.25 million structures and comprehensive software tools for searching, visualizing and analyzing the data. In this perspective, we discuss the use of the CSD and CCDC tools to address the multiscale challenge of predictive materials design. We provide an overview of the core capabilities of the CSD and CCDC software and demonstrate their application to a range of materials design problems with recent case studies drawn from topical research areas, focusing in particular on the use of data mining and machine learning techniques. We also identify several challenges that can be addressed with existing capabilities or through new capabilities with varying levels of development effort.

How Leadership and Behavior Impacts and Improves the Decision-Making in Organizations

The purpose of this research article is to analyze the influence of leadership style and behavior on decision-making. In more detail, the impact of leadership style and decision-making skills and competencies on decision-making, respectively on decision-making effectiveness. For the purpose of research, both, quantitative and qualitative methods are applied in the paper. As tool for primary data collection the questionnaire was used and all the data collected were analyzed through SPSS software tool. The research sample consisted from eighty-two (82) respondents-leaders of various businesses in Kosovo. The collected data were analyzed through Statistical Package for the Social Sciences software, respectively through linear regression analysis and other necessary complementary analysis. The findings from the conducted research show that the different leadership styles applied by business leaders have a positive impact on decision-making, namely on the effectiveness of decision-making. According to results obtained, both supportive behaviors and directive behaviors of leaders towards their employees, influence positively the decision-making, depending on the situation with which they face during their work. Meanwhile leaders who possess skills related to decision-making, such as: identifying and analyzing problems, gathering the information necessary to solve problems, generating possible alternatives/solutions, making decisions, the implementation and evaluation of the decisions made, have a higher tendency to be involved in effective decision-making and foster the effectiveness of organizations in general.

Electronic data capture in resource-limited settings using the lightweight clinical data acquisition and recording system

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software’s capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

Knowledge domain and emerging trends in the rupture risk of intracranial aneurysms research from 2004 to 2023.

Intracranial aneurysms (IAs) pose significant health risks, attributable to their potential for sudden rupture, which can result in severe outcomes such as stroke and death. Despite extensive research, the variability of aneurysm behavior, with some remaining stable for years while others rupture unexpectedly, remains poorly understood. To employ bibliometric analysis to map the research landscape concerning risk factors associated with IAs rupture. A systematic literature review of publications from 2004 to 2023 was conducted, analyzing 3804 documents from the Web of Science Core Collection database, with a focus on full-text articles and reviews in English. The analysis encompassed citation and co-citation networks, keyword bursts, and temporal trends to delineate the evolution of research themes and collaboration patterns. Advanced software tools, CiteSpace and VOSviewer, were utilized for comprehensive data visualization and trend analysis. Analysis uncovered a total of 3804 publications on IA rupture risk factors between 2006 and 2023. Research interest surged after 2013, peaking in 2023. The United States led with 28.97% of publications, garnering 37706 citations. Notable United States-China collaborations were observed. Capital Medical University produced 184 publications, while Utrecht University boasted a citation average of 69.62 per publication. "World Neurosurgery" published the most papers, contrasting with "Stroke", the most cited journal. The PHASES score from "Lancet Neurology" emerged as a vital rupture risk prediction tool. Early research favored endovascular therapy, transitioning to magnetic resonance imaging and flow diverters. "Subarachnoid hemorrhage" stood out as a recurrent keyword. This study assesses global IA research trends and highlights crucial gaps, guiding future investigations to improve preventive and therapeutic approaches.