Informatics in Medicine Unlocked最新文献

{"title":"Evaluating usability of computerized physician order entry systems: Insights from a developing nation","authors":"Zahra Mohammadzadeh ,&nbsp;Ali Mohammad Nickfarjam ,&nbsp;Fatemeh Atoof ,&nbsp;Ali Akbar Shakeri ,&nbsp;Fatemeh Aghasizadeh ,&nbsp;Zahra Rasooli ,&nbsp;Yalda Miranzadeh","doi":"10.1016/j.imu.2024.101487","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101487","url":null,"abstract":"<div><h3>Background</h3><p>Electronic prescribing is vital in healthcare systems, providing an efficient alternative to manual prescriptions and addressing issues like errors in writing. This study evaluates Iran's Computerized Physician Order Entry (CPOE) system due to its significant role in the health system.</p></div><div><h3>Method</h3><p>Conducted as a cross-sectional case study in 2023, this research targeted physicians and outpatient unit users in three hospitals affiliated with Kashan University of Medical Sciences. User satisfaction was assessed using the QUIS Questionnaire for user interaction satisfaction and the System Usability Scale (SUS) for overall usability. Statistical analysis included descriptive statistics, independent-sample t-tests, one-way ANOVA, and SUS questionnaire calculation via SPSS software.</p></div><div><h3>Result</h3><p>The QUIS and SUS questionnaires revealed an overall user satisfaction range of 4.65 out of 9 for physicians and 5.73 out of 9 for outpatient unit users. The SUS questionnaire scored the CPOE system at 72 out of 100 for physicians and 76 out of 100 for outpatient unit users, indicating good usability.</p></div><div><h3>Conclusion</h3><p>Iran's CPOE system received positive feedback, emphasizing ease of use, learnability, control, stimulation, and flexibility to user needs. While the evaluation was generally positive, there are areas for improvement. Future versions should address user demands, incorporate human-computer interaction principles, and rectify identified shortcomings for enhanced competency. Authorities should prioritize user-centric updates in the continuous development of the Iranian CPOE system.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101487"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000431/pdfft?md5=df8231a9d7741fe72c03fe75fc16f361&pid=1-s2.0-S2352914824000431-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140540425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based sperm motility and morphology estimation on stacked color-coded MotionFlow","authors":"Sigit Adinugroho ,&nbsp;Atsushi Nakazawa","doi":"10.1016/j.imu.2024.101459","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101459","url":null,"abstract":"<div><p>Motility and morphology are crucial factors in determining male fertility. The current gold standard defined by the World Health Organization (WHO) mandates that semen analysis be performed by trained technicians. Despite strict standardization and technical guidelines set by the WHO, variability in semen analysis results remains prevalent owing to human subjectivity. Computer-Aided Sperm Analysis presents a further challenge because of its poor agreement with human analysis. This study aimed to enhance the accuracy of automated semen analysis by introducing a new method for expressing sperm motion and investigating advanced deep neural network architectures to estimate motility and morphology. Initially, we extracted motion information from the VISEM dataset using our novel motion representation technique called MotionFlow, along with shape information. Consequently, motility and morphology neural networks were constructed to exploit transfer learning in other fields to improve performance. These networks ingested motion and shape features and made separate predictions for motility and morphology. The evaluation process utilized a K-Fold cross-validation scheme to determine the mean absolute error (MAE) and maintain objectivity throughout the analysis. The proposed method achieved a greater level of performance than the current methods by attaining MAE of 6.842% and 4.148% for motility and morphology estimation, respectively. The improvement accomplished by this research may pave the way toward a fully automated human sperm quality assessment.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101459"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000157/pdfft?md5=f0fe6cdeef00ee82aa620cda44f80d3f&pid=1-s2.0-S2352914824000157-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139709078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards revolutionizing precision healthcare: A systematic literature review of artificial intelligence methods in precision medicine","authors":"Wafae Abbaoui ,&nbsp;Sara Retal ,&nbsp;Brahim El Bhiri ,&nbsp;Nassim Kharmoum ,&nbsp;Soumia Ziti","doi":"10.1016/j.imu.2024.101475","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101475","url":null,"abstract":"<div><p>In the realm of medicine, artificial intelligence (AI) has emerged as a transformative force, harnessing the power to convert raw data into meaningful insights. Rather than supplanting the discernment of physicians, AI serves as an unprecedented enabler, equipping them with unimaginable tools. Its far-reaching applications encompass drug discovery, disease diagnosis, prognosis, treatment optimization, and outcome prediction. This technological revolution owes much to the prowess of machine learning algorithms, which adeptly process multifaceted data. Consequently, AI is poised to become an integral pillar of digital health systems, shaping and bolstering the realm of personalized medicine. The current landscape is abuzz with AI’s exponential growth, fueling a surge of research ventures aimed at enhancing medical practices. By delving into the realm of precision medicine, this paper endeavors to scrutinize and evaluate recent advancements in healthcare pertaining to the utilization of machine learning (ML) and deep learning (DL) algorithms. This systematic review comprehensively encompasses previously published works, dissecting key concepts, innovations, significant contributions, and pivotal enabling techniques. Aspiring to equip readers with a profound understanding and invaluable insights, this paper proves indispensable to those dedicated to exploring the state-of-the-art and contributing to future literature in this domain.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101475"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000315/pdfft?md5=28b55ce3ae11e376f833b6eb1a872020&pid=1-s2.0-S2352914824000315-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140134041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient ECG classification based on the probabilistic Kullback-Leibler divergence","authors":"Dhiah Al-Shammary ,&nbsp;Mohammed Radhi ,&nbsp;Ali Hakem AlSaeedi ,&nbsp;Ahmed M. Mahdi ,&nbsp;Ayman Ibaida ,&nbsp;Khandakar Ahmed","doi":"10.1016/j.imu.2024.101510","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101510","url":null,"abstract":"<div><p>Diagnostic systems of cardiac arrhythmias face early and accurate detection challenges due to the overlap of electrocardiogram (ECG) patterns. Additionally, these systems must manage a huge number of features. This paper proposes a new classifier Kullback-Leibler classifier (KLC) that combines feature optimization and probabilistic Kullback-Leibler (KL) divergence. Particle swarm optimization (PSO) is used for optimizing the features of ECG data, while KL divergence counts the variance between training and testing probability distributions. The proposed framework led the new classifier to distinguish normal and abnormal rhythms accurately. MIT-BIH Standard Arrhythmia Dataset (MIT-BIH) is used to test the validity of the proposed model. The experimental results show the proposed classifier achieves results in precision (86.67%), recall (86.67%), and F1_Score (86.5%).</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101510"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000662/pdfft?md5=bd106f50aaf88b9f4241ed8fb538665e&pid=1-s2.0-S2352914824000662-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors influencing nurses’ acceptance of patient safety reporting systems based on the unified theory of acceptance and use of technology (UTAUT)","authors":"Abbas Sheikhtaheri ,&nbsp;Sharare Taheri Moghadam ,&nbsp;Afsaneh Dehnad ,&nbsp;Parvin Tatarpoor","doi":"10.1016/j.imu.2024.101554","DOIUrl":"10.1016/j.imu.2024.101554","url":null,"abstract":"<div><h3>Introduction</h3><p>Patient safety reporting systems (PSRS) play a crucial role in hospitals by collecting patient safety data, primarily from nurses. Identifying the factors influencing nurses' safety reporting behaviors provides safety managers with insights to encourage reporting. This study aims to identify the key factors impacting nurses’ acceptance of PSRS.</p></div><div><h3>Methods</h3><p>This cross-sectional study, conducted in 2022, enrolled 249 nurses from 14 teaching and non-teaching hospitals in Tehran, Iran. A questionnaire was developed on the basis of the unified theory of acceptance and use of technology (UTAUT) constructs, encompassing actual use, behavioral intention to use, facilitation conditions, effort expectancy, performance expectancy, and social influence. Additional constructs such as perceived positive outcomes, perceived negative outcomes, management support, and trust were also included. Data analysis comprised linear regression and Partial Least Squares-Structural Equation Modeling (PLS-SEM). The reliability and validity of the measurement model were assessed by using metrics like Cronbach's alpha, composite reliability, Rho_A, average variance extracted, and Heterotrait-Monotrait Ratio of Correlations before calculating path coefficients, the coefficient of determination, effect size, and predictive relevance of influencing factors.</p></div><div><h3>Results</h3><p>The study indicated favorable attitudes among nurses toward PSRS. Significant relationships were observed between behavioral intention (β = 0.379) and facilitation conditions with actual use (β = 0.228). Additionally, effort expectancy (β = 0.101), management support (β = 0.268), and performance expectancy (β = 0.180) demonstrated significant associations with behavioral intention. The R² values for behavioral intention and actual use were 0.198 and 0.246, respectively.</p></div><div><h3>Conclusion</h3><p>Simplifying reporting systems to reduce nurses’ reporting burden, providing effective facilitation within hospitals, enhancing perceived benefits associated with reporting systems for nurses, and ensuring robust managerial support are pivotal strategies that can significantly boost the acceptance of PSRS among nursing staff.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101554"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001102/pdfft?md5=817b28c52a53b137bdc358150b350b23&pid=1-s2.0-S2352914824001102-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration technologies in laboratory information systems: A systematic review","authors":"Jay Mark Edayan ,&nbsp;Arthon Jon Gallemit ,&nbsp;Niña Eunice Sacala ,&nbsp;Xavier-Lewis Palmer ,&nbsp;Lucas Potter ,&nbsp;Junil Rarugal ,&nbsp;Lemuel Clark Velasco","doi":"10.1016/j.imu.2024.101566","DOIUrl":"10.1016/j.imu.2024.101566","url":null,"abstract":"<div><p>Clinical laboratories have evolved with technological advancements through integrating various subsystems into Health Information Systems (HIS), particularly the Laboratory Information System (LIS). The LIS automates processes, manages results, and interfaces with healthcare information sources. Challenges include workflow inefficiencies and data interpretation issues. Despite increased data accessibility, managing clinical data across systems remains complex. Integrating laboratory machines into LIS is essential for optimizing healthcare delivery, requiring effective integration technologies. This study aims to synthesize the existing empirical studies on the utilization of integration technologies for Software-to-Software (S2S) communication in automating clinical laboratory processes. This study systematically examined integration technologies in LIS using PubMed and following PRISMA 2020 guidelines. The three-phase methodology included a scoping analysis, methodological analysis, and a gap analysis, focusing on S2S communication, interoperability frameworks, data standards, communication protocols, and challenges in LIS integration technologies. Analysis of 28 sample studies revealed a complex landscape in LIS integration shaped by end-users, care providers, and researchers. Clinical laboratories prioritize integration, focusing on patient data and sustainability. Standards like HL7 and FHIR ensure interoperability. Eleven methodologies highlight system development in Health Information Systems (HIS). Interoperability is a common objective, with 22 out of 28 studies achieving success. Challenges include limited generalizability, poor validation, and post-implementation modifications. Issues like security, data incompatibility, and evolving standards persist.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101566"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001229/pdfft?md5=8401cd00049dc965a7b59f8427a928c0&pid=1-s2.0-S2352914824001229-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative assessment of projection and clustering method combinations in the analysis of biomedical data","authors":"Jörn Lötsch ,&nbsp;Alfred Ultsch","doi":"10.1016/j.imu.2024.101573","DOIUrl":"10.1016/j.imu.2024.101573","url":null,"abstract":"<div><h3>Background</h3><p>Clustering on projected data is common in biomedical research analysis. Principal component analysis (PCA) is widely used for projection, focusing on data dispersion (variance), while clustering identifies data concentrations (neighborhood). These are conflicting aims. This study re-evaluates combinations of PCA and other projection methods with common clustering algorithms.</p></div><div><h3>Methods</h3><p>Six projection methods (PCA, ICA, isomap, MDS, t-SNE, UMAP) were combined with five clustering algorithms (k-means, k-medoids, single link, Ward's method, average link). Projections and clusterings were evaluated using a numerical criterion for evaluating clustering performance and a visual criterion based on plotting the projected data on a Voronoi tessellation plane with class-wise coloring. Nine artificial and five real biomedical datasets were analyzed.</p></div><div><h3>Results</h3><p>No combination consistently captured prior classifications in projections and clusters. Visual inspection proved essential. PCA was often but not always outperformed or equaled by neighborhood-based methods (UMAP, t-SNE) and manifold learning techniques (isomap).</p></div><div><h3>Conclusions</h3><p>The results dissaprove PCA as a standard projection method prior to clustering. Therefore, method selection should be data specific as a tailored approach to data projection and clustering in biomedical analysis. To aid this process, we propose a novel visualization technique that combines Voronoi tessellation with color coding.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101573"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001291/pdfft?md5=94cb1089dab67b47fecf55f0a7d21d34&pid=1-s2.0-S2352914824001291-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TrajectoryViz: Interactive visualization of treatment trajectories","authors":"Maarja Pajusalu,&nbsp;Kerli Mooses,&nbsp;Marek Oja,&nbsp;Sirli Tamm,&nbsp;Markus Haug,&nbsp;Raivo Kolde","doi":"10.1016/j.imu.2024.101558","DOIUrl":"10.1016/j.imu.2024.101558","url":null,"abstract":"<div><h3>Background and objectives</h3><p>With the proliferation of real-world or observational health data, there is increasing interest in studying treatment trajectories. The real-life treatment trajectories can be complex, and one has to simplify the patterns to draw any conclusions; however, oversimplification will cause the loss of essential details. Thus, the visualization challenge is to strike a balance between the two extremes.</p></div><div><h3>Methods</h3><p>We have implemented the observation of treatment trajectories starting from cohort definitions in cooperation with medical specialists, data processing, and then generating the interactive visualizations and detailed data tables derived from input data within an open-source R package as a Shiny dashboard. The created R package called TrajectoryViz (<span><span>https://github.com/HealthInformaticsUT/TrajectoryViz</span><svg><path></path></svg></span>) enables reproducible visual analysis and visual content generation for various data investigations and explanations.</p></div><div><h3>Results</h3><p>We illustrate the use of the tool by assessing the sequence of events present within the data of cervical cancer prevention pathways, as well as the proportions of timely follow-up procedure events.</p></div><div><h3>Conclusion</h3><p>Building a toolset to access, manage, and analyze observational health data enables more accessible visual analysis of complicated data, adding time dimension to otherwise simplified event sequences that make up trajectories.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101558"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235291482400114X/pdfft?md5=0894191c372d2fc40671b6cd74491d0e&pid=1-s2.0-S235291482400114X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic tricuspid valve annulus extraction and measurement from computed tomography images","authors":"Gakuto Aoyama ,&nbsp;Zhexin Zhou ,&nbsp;Longfei Zhao ,&nbsp;Shun Zhao ,&nbsp;Keitaro Kawashima ,&nbsp;James V. Chapman ,&nbsp;Masahiko Asami ,&nbsp;Yui Nozaki ,&nbsp;Shinichiro Fujimoto ,&nbsp;Takuya Sakaguchi","doi":"10.1016/j.imu.2024.101577","DOIUrl":"10.1016/j.imu.2024.101577","url":null,"abstract":"<div><h3>Background and objective</h3><p>Tricuspid regurgitation (TR) is one of the most common forms of valvular heart diseases. The morphological information of the tricuspid valve annulus (TVA) is critical in treatment planning for TR. It is necessary to extract the TVA from medical images to obtain that information, however this task is difficult and time-consuming to perform manually. In this paper, we propose a method to automatically extract and measure the TVA from computed tomography (CT) images.</p></div><div><h3>Methods</h3><p>Our proposed method coarsely crops CT images to the region surrounding the tricuspid valve based on the right atrium and the right ventricle regions. The cropped CT images are input to a stacked hourglass network with loss function integrating the mean squared error loss, the focal loss and the shape-aware weighted Hausdorff distance loss to extract 36 landmarks on the TVA. The extraction accuracy of TVA landmarks was evaluated by five-fold cross validation using 120 CT images with manually annotated TVA landmarks. In addition, measurements of TVA morphology based on automatically extracted TVA and those based on manually annotated TVA were calculated and compared using the same measurement algorithm which provides a means to automatically generate seven measurements based on TVA landmarks.</p></div><div><h3>Results</h3><p>Our proposed method extracted TVA inside the right heart in all CT images without any processing interruption. The mean processing time was 27.09 ± 8.65 s, and the Chamfer distance and Hausdorff distance were 2.07 ± 0.53 and 4.09 ± 1.29, respectively. The mean absolute error between the measurements based on automatically extracted TVA and those based on manually annotated TVA was less than 4 mm, which is less than the typical device size interval for surgical prosthetic valve rings in current use, for measurement items related to distance. For all seven measurement items, significant correlations (<em>r</em> = 0.51–0.99, <em>p</em> &lt; 0.0071) were shown between the measurements based on automatically extracted TVA and those based on manually annotated TVA.</p></div><div><h3>Conclusions</h3><p>Our proposed method was able to automatically extract and measure the TVA. This method is expected to reduce the time and effort required by physicians in treatment planning for TR.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101577"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001333/pdfft?md5=b7839b2fa5e3a7bce93db530a02e4724&pid=1-s2.0-S2352914824001333-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and design of CRISPR-based diagnostic for Acinetobacter baumannii by employing off-target gene editing of sgRNA","authors":"Zulqarnain Baqar ,&nbsp;Sk Injamamul Islam ,&nbsp;Gunjan Das ,&nbsp;Sarower Mahfuj ,&nbsp;Foysal Ahammad","doi":"10.1016/j.imu.2024.101462","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101462","url":null,"abstract":"<div><p><em>Acinetobacter baumannii</em> is widely recognized as a human opportunistic pathogen in nosocomial infections. The proliferation of multidrug-resistant strains of <em>A. baumannii</em> has presented an array of difficulties for clinical anti-infective therapies and diagnostic procedures, owing to the existence of numerous variations. The development of therapy utilizing CRISPR/Cas9 for treatment and diagnosis necessitates an in-depth study of potential off-target consequences. The objective of this work is to assess potential off-target effects associated with a single guide RNA (sgRNA) designed to identify several variants present in <em>A. baumannii</em>. The current investigation involved the identification of Cas12 nuclease-specific protospacer adjacent motif (PAM) and downstream target sequences. This was achieved by utilizing computational tools and software to analyze conserved sections of the <em>A. baumannii</em> siderophore protein gene. Further, the <em>in-silico</em> expression vector was created with the SnapGene software. A total of 24 potential off-target sequences were identified in these sequences with 100% query identity with 96 different <em>A. baumannii</em> strains. In addition, a target-specific oligonucleotide single-guide RNA (sgRNA) template was synthesized by appending an additional nucleotide 'G' to the 5′ end. This research uses <em>A. baumannii</em> as an example of a problem that affects all treatments and diagnosis procedures to illustrate the significance of screening off-targets in different variants of a pathogen. Our findings may impact the safety and effectiveness of CRISPR/Cas9, which may have wider implications for additional targets that are currently being used therapeutically.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101462"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000182/pdfft?md5=eb4876167a9018484ca7e27ab10f59bc&pid=1-s2.0-S2352914824000182-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139985242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}