Hasil untuk "Computer applications to medicine. Medical informatics"

Xiangjun Qu, Kai Qin, Yaping Li et al.

In domains such as autonomous driving, 3D object detection is a key technology for environmental perception. By integrating multimodal information from sensors such as LiDAR and cameras, the detection accuracy can be significantly improved. However, the current multimodal fusion perception framework still suffers from two problems: first, due to the inherent physical limitations of LiDAR detection, the number of point clouds of distant objects is sparse, resulting in small target objects being easily overwhelmed by the background; second, the cross-modal information interaction is insufficient, and the complementarity and correlation between the LiDAR point cloud and the camera image are not fully exploited and utilized. Therefore, we propose a new multimodal detection strategy, Semantic-Enhanced and Temporally Refined Bidirectional BEV Fusion (SETR-Fusion). This method integrates three key components: the Discriminative Semantic Saliency Activation (DSSA) module, the Temporally Consistent Semantic Point Fusion (TCSP) module, and the Bilateral Cross-Attention Fusion (BCAF) module. The DSSA module fully utilizes image semantic features to capture more discriminative foreground and background cues; the TCSP module generates semantic LiDAR points and, after noise filtering, produces a more accurate semantic LiDAR point cloud; and the BCAF module’s cross-attention to camera and LiDAR BEV features in both directions enables strong interaction between the two types of modal information. SETR-Fusion achieves 71.2% mAP and 73.3% NDS values on the nuScenes test set, outperforming several state-of-the-art methods.

Gabriel D. Parker, Andrew Plymale, Luke Hanley et al.

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology. Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution and high mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis. This dataset covers ToF-SIMS analysis of Paenibacillus sp. 300A (300A) isolated from the Hanford site in Richland, WA. The strain is known to have metal and sulfur reducing properties and can be used for bioremediation, wastewater treatment, bioengineering and technology development. There is a current need to identify small molecules and fragments produced from bacterial biofilms. Static ToF-SIMS spectra of 300A were obtained using an IONTOF TOF-SIMS V instrument equipped with a 25 keV Bi3+ metal ion gun. Identified molecules and molecular fragments are compared against known biological databases and the reported peaks have at least 65 ppm mass accuracy. These molecules range from lipids and fatty acids to flavonoids, quinolones, and other naturally occurring organic compounds. It is anticipated that the spectral identification of key peaks will assist detection of metabolites, extracellular polymeric substance molecules like polysaccharides, and biologically relevant small molecules using ToF-SIMS in future surface and interface research of bacterial biofilms.

Zarif Bin Akhtar

The field of molecular engineering in medicine has witnessed remarkable progress in recent years, revolutionizing healthcare, diagnostics, and therapy development. However, the pandemic showcased there is still more requirement for progress along with further detailed investigation which is paramount and also a necessity moving forward. This research investigation delves into the interdisciplinary realm of molecular engineering, exploring its impact on regenerative medicine, biomaterials, tissue engineering, and the innovation from various advanced biotechnologies which has accelerated health science. The main objective for this research aims at providing an in depth investigative exploration of biomaterial applications with their respective roles within regenerative medicine and its associated advancements along with, tissue engineering, organ-on-a-chip device peripheral mechanics functionality and how bioprinting is paving the way for the creation of functional tissues and organs with a case study analysis on drug discovery, immune engineering, to the field of precision medicine, gene editing with the insight towards drug discovery processing, design and screening pipelined for biologics and the how therapeutics and drugs will play out in future healthcare. This exploration also provides many meaningful and remarkable conclusions on the advanced technologies which are explored and investigated throughout the step-by-step systematic technical computing methods approached for the research.

Ludmila Oliveira Ruela, Caroline de Castro Moura, Bianca Shieu et al.

BackgroundThe increased incidence of breast cancer implies the appearance of frequent symptoms associated with disease and treatments, such as pain. For the management of this issue, auricular therapy has been used in a complementary manner, especially for its safety and analgesic action. ObjectiveThis systematic review aims to summarize available evidence on the effects of auricular therapy on pain in women undergoing breast cancer treatment. MethodsThis is a systematic review that includes randomized controlled trials that evaluated the effects of auricular therapy on pain in women with breast cancer, as compared with other interventions (sham or placebo auricular therapy, other nonpharmacological interventions, and routine pain treatments) during the treatment of the disease. Pain, whether induced or not by cancer treatments, is the main outcome to be evaluated. The search for the studies was performed in the following databases: MEDLINE through PubMed, CINAHL, CENTRAL, Embase, Web of Science, Scopus, VHL, TCIM Americas Network, CNKI, and Wanfang Data. The reviewers have independently evaluated the full texts, and in the near future, they will extract the data and assess the risk of bias in the included studies. The certainty of the evidence will be assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE), and a meta-analysis will be carried out to evaluate the intervention, considering the homogeneity of the results, using the Cochran Q test and quantified by the Higgins inconsistency index. The guidelines of the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) have been respected in the elaboration of this protocol. ResultsThe records screening stage has been completed, and the synthesis and meta-analysis were conducted in February 2024. We hope to have finished the preparation of the paper for publication by September 2024. Review reporting will follow standard guidelines for reporting systematic reviews. The results will be published in peer-reviewed scientific journals. ConclusionsThis review will compile the strength of evidence for the use of auricular therapy in the management of pain in women with breast cancer during the treatment of the disease, identifying gaps in the available evidence as well as assisting health professionals in indicating the intervention for clinical practice. Trial RegistrationPROSPERO CRD42022382433; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=382433 International Registered Report Identifier (IRRID)DERR1-10.2196/55792

Fatma S. Ahmed, Saleh Aly, Xiangrong Liu

Abstract Background Recognition of enhancer–promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of effort, time, and resources. Therefore, more and more studies are being done on developing computational techniques, particularly using deep learning and other machine learning techniques, to address such problems. Unfortunately, the majority of current computational methods are based on convolutional neural networks, recurrent neural networks, or a combination of them, which don’t take into consideration contextual details and the long-range interactions between the enhancer and promoter sequences. A new transformer-based model called EPI-Trans is presented in this study to overcome the aforementioned limitations. The multi-head attention mechanism in the transformer model automatically learns features that represent the long interrelationships between enhancer and promoter sequences. Furthermore, a generic model is created with transferability that can be utilized as a pre-trained model for various cell lines. Moreover, the parameters of the generic model are fine-tuned using a particular cell line dataset to improve performance. Results Based on the results obtained from six benchmark cell lines, the average AUROC for the specific, generic, and best models is 94.2%, 95%, and 95.7%, while the average AUPR is 80.5%, 66.1%, and 79.6% respectively. Conclusions This study proposed a transformer-based deep learning model for EPI prediction. The comparative results on certain cell lines show that EPI-Trans outperforms other cutting-edge techniques and can provide superior performance on the challenge of recognizing EPI.

Stephen A. Johnson, Marta Karas, Katherine M. Burke et al.

Abstract Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer the self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2–4 weeks. Each participant used a wrist-worn activity monitor (ActiGraph Insight Watch) or an ankle-worn activity monitor (Modus StepWatch) continuously. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcome measure development.

Wan-Xia Yang, Hong-Wei Gao, Jia-Bo Cui et al.

Abstract Background Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated. Methods Firstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan–Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment. Results We identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group. Conclusions The CRRS model has reliable predictive value for the prognosis of HCC patients.

Omneya Attallah

With the current health crisis caused by the COVID-19 pandemic, patients have become more anxious about infection, so they prefer not to have direct contact with doctors or clinicians. Lately, medical scientists have confirmed that several diseases exhibit corresponding specific features on the face the face. Recent studies have indicated that computer-aided facial diagnosis can be a promising tool for the automatic diagnosis and screening of diseases from facial images. However, few of these studies used deep learning (DL) techniques. Most of them focused on detecting a single disease, using handcrafted feature extraction methods and conventional machine learning techniques based on individual classifiers trained on small and private datasets using images taken from a controlled environment. This study proposes a novel computer-aided facial diagnosis system called FaceDisNet that uses a new public dataset based on images taken from an unconstrained environment and could be employed for forthcoming comparisons. It detects single and multiple diseases. FaceDisNet is constructed by integrating several spatial deep features from convolutional neural networks of various architectures. It does not depend only on spatial features but also extracts spatial-spectral features. FaceDisNet searches for the fused spatial-spectral feature set that has the greatest impact on the classification. It employs two feature selection techniques to reduce the large dimension of features resulting from feature fusion. Finally, it builds an ensemble classifier based on stacking to perform classification. The performance of FaceDisNet verifies its ability to diagnose single and multiple diseases. FaceDisNet achieved a maximum accuracy of 98.57% and 98% after the ensemble classification and feature selection steps for binary and multiclass classification categories. These results prove that FaceDisNet is a reliable tool and could be employed to avoid the difficulties and complications of manual diagnosis. Also, it can help physicians achieve accurate diagnoses without the need for physical contact with the patients.

Davide Chicco, Giuseppe Jurman

Functional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics annotated databases such as The Gene Ontology or KEGG; the more abundant pathways are identified through statistical techniques such as Fisher’s exact test. All PEA tools require a list of genes as input. A few tools, however, read lists of genomic regions as input rather than lists of genes, and first associate these chromosome regions with their corresponding genes. These tools perform a procedure called genomic regions enrichment analysis, which can be useful for detecting the biological pathways related to a set of chromosome regions. In this brief survey, we analyze six tools for genomic regions enrichment analysis (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, and ReactomePA), outlining and comparing their main features. Our comparison results indicate that the inclusion of data for regulatory elements, such as ChIP-seq, is common among these tools and could therefore improve the enrichment analysis results.

Hossam Faris, Maria Habib, Mohammad Faris et al.

Jeroen Van Schependom, Diego Vidaurre, Lars Costers et al.

In multiple sclerosis, the interplay of neurodegeneration, demyelination and inflammation leads to changes in neurophysiological functioning. This study aims to characterize the relation between reduced brain volumes and spectral power in multiple sclerosis patients and matched healthy subjects.During resting-state eyes closed, we collected magnetoencephalographic data in 67 multiple sclerosis patients and 47 healthy subjects, matched for age and gender. Additionally, we quantified different brain volumes through magnetic resonance imaging (MRI).First, a principal component analysis of MRI-derived brain volumes demonstrates that atrophy can be largely described by two components: one overall degenerative component that correlates strongly with different cognitive tests, and one component that mainly captures degeneration of the cortical grey matter that strongly correlates with age. A multimodal correlation analysis indicates that increased brain atrophy and lesion load is accompanied by increased spectral power in the lower alpha (8–10 Hz) in the temporoparietal junction (TPJ). Increased lower alpha power in the TPJ was further associated with worse results on verbal and spatial working memory tests, whereas an increased lower/upper alpha power ratio was associated with slower information processing speed.In conclusion, multiple sclerosis patients with increased brain atrophy, lesion and thalamic volumes demonstrated increased lower alpha power in the TPJ and reduced cognitive abilities.

Carolyn H. Still, Seunghee P. Margevicius, Jackson T. Wright et al.

Behavioral interventions consolidating technology are underutilized and do not reach diverse populations such as African Americans with hypertension. This pilot study aimed to evaluate the effects of a theoretically derived, technology-based intervention in African Americans with hypertension. African Americans with hypertension (N = 18; age range 25-85; 72.22% females) were randomized to the technology-based plus positive psychological training (PPT) experimental group (n = 10) or the comparison group (n = 8) for 12 weeks. The technology-based intervention included analytic components (web-based education, self-monitoring of blood pressure [BP], and medication management using a commercially free app-Medisafe) and an emotional component (comprised of skills and behaviors directed at engaging 1 in positive activities to help build increasing healthy behaviors). The comparison group received the technology-based intervention alone. Demographic information, self-management cognitive processes, self-management behaviors, and health status outcomes were assessed. After completing the 12-week intervention, the groups did not significantly differ in health outcomes, health behavior outcomes, and technology utilization outcomes. Mean systolic BP decrease 6.02 mmHg (standard deviation [SD] = 22.75) in the comparison group and 1.1 mmHg (SD = 20.64; P  = .439) in the experimental group. Diastolic BP decreased 0.1 mmHg (SD = 11.78) in the comparison group and 1.5 mmHg (SD = 12.7; P  = .757) in the experimental group. Our findings suggest that behavioral interventions using technology have the potential to improve self-management outcomes among African American populations. Further research is warranted in a larger sample size and a longer time frame to identify the intervention’s effectiveness.

O. Panagiotou, Lori Hoffman Högg, H. Hricak et al.

Importance There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems. Objective To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. Evidence Review The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology. Findings Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics. Conclusions and Relevance Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.

L. Williams, R. Grainger

Over the last 30 years, there have been repeated calls to integrate health informatics into undergraduate health professional curricula, in recognition of the integral role computing plays in medicine. The rise of big data sets in health, and the application of advanced computer algorithms to interrogate these, is yet another call for health professionals to receive appropriate training in these technologies.  Machine learning (ML) algorithms can learn tasks or make decisions without a requirement for specific behaviours to be pre-programmed. High-impact literature has described ML approaches to clinical problems such as achieving more accurate and timely diagnoses, increasing precision of prognosis and guiding treatment. Despite the promise of ML in healthcare, there are risks of adverse outcomes, unanticipated consequences, misuse and even abuse of ML technologies. For health professionals to advocate for patients and hold those developing ML algorithms in healthcare accountable, they must feel comfortable discussing the fundamental concepts and limitations of ML in healthcare.  Healthcare professionals are uniquely positioned to identify problems that could be solved by ML and related technologies. Yet, there is inadequate coverage of ML, or of the wider field of health informatics, in most medical curricula. To create future health professionals who can advocate for positive change and ensure that patients remain at the centre of ML applications in healthcare, we must provide future health professionals with an understanding of how ML will change healthcare delivery and the doctor–patient dynamic, as well as new ethical challenges that arise with the digital healthcare revolution.

Lu Wang, Zhaoyu Liu, J. Xie et al.

Purpose To summarize the data of previously reported medical imaging features on human malignancies to provide a scientific basis for more credible imaging feature selection for future studies. Materials and Methods A search was performed in PubMed from database inception through March 23, 2018, for studies clearly stating the decoding of medical imaging features for malignancy-related objectives and/or hypotheses. The Newcastle-Ottawa scale was used for quality assessment of the included studies. Unsupervised hierarchical clustering was performed on the manually extracted features from each included study to identify the application rules of medical imaging features across human malignancies. CT images of 1000 retrospective patients with non–small cell lung cancer were used to reveal a pattern for the value distribution of complex texture features. Results A total of 5026 imaging features of malignancies affecting 20 parts of the human body from 930 original articles were collated and assessed in this study. A meta-feature construct was proposed to facilitate the investigation of details of any high-dimensional complex imaging features of malignancy. A correlation atlas was constructed to clarify the general rules of applying medical imaging features to the analysis of human malignancy. Assessment of this data revealed a pattern of value distributions of the most commonly reported texture features across human malignancies. Furthermore, the significant expression of the gene mutational signature 1B across human cancer was highly consistent with the presence of the run length imaging feature across different human malignancy types. Conclusion The results of this study may facilitate more credible imaging feature selection in all oncology tasks across a wide spectrum of human malignancies and help to reduce bias and redundancies in future medical imaging studies. Keywords: Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Evidence Based Medicine, Informatics, Research Design, Statistics, Technology Assessment Supplemental material is available for this article. Published under a CC BY 4.0 license.

Susan Rauch

ObjectiveTo track and visually assess how automated attention structures within the electronic health record (EHR) compete for clinicians attention during computer physician order entry that could potentially lead to transactions hazards in the clinical narrative.IntroductionIn recent years, studies in health and medicine have shifted toward eHealth communication and the relationships among human interaction, computer literacy, and digital text content in medical discourses (1-6). Clinicians, however, continue to struggle with EHR usability, including how to effectively capture patient data without error (7-9). Usability is especially problematic for clinicians, who must now acquire new skills in electronic documentation (10). Challenges with the EHR occur because of clinicians’ struggle with attention to the non-linear format of clinical content and automated technologies (11). It is therefore important to understand how attention structures are visually situated within the EHR’s narrative architecture and audience for whom electronic text is written. It is equally important to visualize and track how automated language and design in health information technology (HIT) affect users’ attention when documenting clinical narratives (12). In the study of health information technology, researchers of eHealth platforms need to recognize how the construction of human communication lies within the metaphoric expression, design, and delivery of the EHR’s information architecture (13). Many studies of electronic health records (EHR) examine the design and usability in the development stages. Some studies focus on the economic value of the EHR Medicare incentive program, which affects providers’ return on investment (ROI). Few studies, however, identify the communicative value of how attention structures within the EHR’s information architecture compete for users’ attention during the clinical documentation process (9, 14).MethodsThis paper highlights methods from an observed EHR pre-launch testing event that analyzes the visual effects of attention structures within the EHR’s information landscape. The observation was completed in two separate stages, each with one IT facilitator and two participant demographics: Stage 1. On-site HIT clinical application staff testing and, Stage 2. Twenty-five participants (RN and non-RN clinical staff). During the second stage of the event, one participant’s task performance was screencast-recorded. The length of the testing for the one participant totaled 37 minutes. Because the EHR domain is propelled by both the Internet and Intranet, a contextual-rhetorical analysis of the data collected was performed which incorporated Nielsen's 10 Usability Heuristics for Interaction Design (15, 16) and Stuart Blythe’s methodological approach to analyzing digital writing and technology to defining rhetorical units of analysis in digital Web research (17).ResultsThe UX observation and contextual-rhetorical analysis of EHR design supports a 4-year qualitative study consisting of hospital interviews at two acute-care facilities and an online, national survey of revenue integrity and clinical documentation improvement specialists. The testing event served as an opportunity to observe how a healthcare organization user-experience tests the functionality of the EHR’s design build before launching it live. The testing event also provides an understanding of clinicians’ organizational needs and challenges during the clinical documentation process. The contextual-rhetorical analysis identified how the structure of narrative in the EHR represents rhetorical units of value that might influence how clinicians make decisions about narrative construction.ConclusionsThis UX case study analysis of an EHR testing event identifies how scarcity of attention and clinicians’ reliance on technology affect clinical documentation best practices leading to potential transaction hazards in the clinical narrative.The study is relevant in eHealth data surveillance because it shows how visual cues within the design of the EHR's technological landscape affect clinicians’ decision-making processes while documenting the EHR-generated clinical narrativeReferences1. Black A, Car J, Majeed A, Sheikh A. Strategic considerations for improving the quality of eHealth research: we need to improve the quality and capacity of academia to undertake informatics research. Journal of Innovation in Health Informatics. 2008;16(3):175-7.2. Meeks DW, Smith MW, Taylor L, Sittig DF, Scott JM, Singh H. An analysis of electronic health record-related patient safety concerns. Journal of the American Medical Informatics Association. 2014;21(6):1053-9.3. Owens KH. Rhetorics of e-Health and information age medicine: A risk-benefit analysis. JAC. 2011:225-35.4. Petersson J. Geographies of eHealth: Studies of Healthcare at a Distance2014.5. Solomon S. How we can end the disconnect in health. Health Voices. 2014(15):23.6. Subbiah NK. Improving Usability and Adoption of Tablet-based Electronic Health Record (EHR) Applications: Arizona State University; 2018.7. Khairat S, Burke G, Archambault H, Schwartz T, Larson J, Ratwani RM. Perceived Burden of EHRs on Physicians at Different Stages of Their Career. Applied clinical informatics. 2018;9(02):336-47.8. Staggers N, Elias BL, Makar E, Alexander GL. The Imperative of Solving Nurses’ Usability Problems With Health Information Technology. Journal of Nursing Administration. 2018;48(4):191-6.9. Yackel TR, Embi PJ. Unintended errors with EHR-based result management: a case series. Journal of the American Medical Informatics Association. 2010;17(1):104-7.10. Stewart WF, Shah NR, Selna MJ, Paulus RA, Walker JM. Bridging the inferential gap: the electronic health record and clinical evidence. Health Affairs. 2007;26(2):w181-w91.11. Johnson SB, Bakken S, Dine D, Hyun S, Mendonça E, Morrison F, et al. An electronic health record based on structured narrative. Journal of the American Medical Informatics Association. 2008;15(1):54-64.12. Lanham RA. The economics of attention: Style and substance in the age of information: University of Chicago Press; 2006.13. Salvo MJ. Rhetorical action in professional space: Information architecture as critical practice. Journal of Business and Technical Communication. 2004;18(1):39-66.14. Sittig DF, Singh H. A new socio-technical model for studying health information technology in complex adaptive healthcare systems. Cognitive Informatics for Biomedicine: Springer; 2015. p. 59-80.15. Nielsen J. 10 usability heuristics for user interface design. Nielsen Norman Group. 1995;1(1).16. Nielsen J, Molich R, editors. Heuristic evaluation of user interfaces. Proceedings of the SIGCHI conference on Human factors in computing systems; 1990: ACM.17. Blythe S. Digital Writing Research. In: McKee HA, DeVoss D, editors. Digital Writing Research: Technologies, Methodologies and Ethical Issues (New Dimensions in Computers and Composition)Cresskill, NJ: Hampton Press; 2007. p. 203-28.

Raghavendra Tejo Karthik Poluri, Charles Joly Beauparlant, Arnaud Droit et al.

Prostate cancer (PCa) is the most frequent cancer in North American men and PCa cells rely on the androgen receptor (AR) for growth and survival. To understand the effect of AR in cancer cells, we have treated LNCaP and LAPC4 cells, two immortalized human PCa cells in vitro, with the synthetic androgen R1881 and then performed RNA-seq analyses. High quality sequencing data have been analyzed using our bioinformatic pipeline which consists of FastQC for quality controls, Trimmomatic for trimming, and Kallisto for pseudoalignment to the transcriptome. Differentially expressed genes were identified using DESeq2 after adjustment for false-discovery rate (FDR q values < 0.05) and Relative Log Expression (RLE) normalization. Gene Set Enrichment Analysis (GSEA) was also performed to identify biological pathways significantly modulated by androgens. GSEA analyses identified the androgen signaling pathway, as well as several metabolic pathways, as significantly enriched following androgen stimulation. These analyses highlight the most significant metabolic pathways up-regulated following AR activation. Raw and processed RNA-seq data were deposited and made publicly available on the Gene Expression Omnibus (GEO; GSE128749). These data have been incorporated in a recent article describing the functions of AR as a master regulator of PCa cell metabolism. For more details about interpretation of these results, please refer to “Functional genomics studies reveal the androgen receptor as a master regulator of cellular energy metabolism in prostate cancer” by Gonthier et al. (doi: 10.1016/j.jsbmb.2019.04.016). Keywords: Steroid, Nuclear receptor, Hormone receptor, Metabolism, Metabolic reprogramming, Castration-resistance, Mitochondria, Glycolysis, Fatty acid metabolism

Dennis Grishin, Kamal Obbad, Preston Estep et al.

In the years since the first human genome was sequenced at a cost of over $3 billion, technological advancements have driven the price below $1,000, making personal genome sequencing affordable to many people. Personal genome sequencing has the potential to enable better disease prevention, more accurate diagnoses, and personalized therapies. Furthermore, sharing genomic data with researchers promises identification of the causes of many diseases and the development of new therapies. However, sequencing costs, data privacy concerns, regulatory restrictions, and technical challenges impede the growth of genomic data and hinder data sharing. In this article, we propose that these challenges can be addressed by combining decentralized system design, privacy-preserving technologies, and an equitable compensation model in a platform that vests control over data with individual owners; ensures transparency and privacy; facilitates regulatory compliance; minimizes expensive data transfers; and shifts the sequencing costs from consumers, patients, and biobanks to researchers in industry and academia. We exemplify this by describing the implementation of Nebula, a distributed genomic data generation, sharing, and analysis platform.

Zahra Mostafavian, Zahra Abbasi Shaye, Arezou Faraj Pour et al.

Locus of control is a concept defined based on social learning theory, and focuses on individuals' beliefs regarding factors that influence their health status. Health Locus of Control (HLC) and its relationship with Quality of Life (QOL) in HIV positive patients in local population were studied. This was a cross-sectional study on 80 HIV-positive patients. Multidimensional Health Locus of Control (MHLC) Scale and Medical Outcome Study Short-Form Health Survey (MOS-SF-36) used to measure patients' HLC and QOL, respectively. Internal, external, and chance HLC mean ± SD scores were 30.31±3.87, 24.17±5.03, and 32.01±4.49, respectively. Positive correlation was found between internal HLC scores and both physical (p <0.001, r = 0.53) and mental quality of life (p <0.001, r = 0.48). Multiple regression analysis showed that internal HLC was the only significant predictor of quality of life. HIV-positive patients who believe their health is mostly influenced by individual's actions and behaviors (internal HLC) showed a higher quality of life. These findings suggest that modifying health locus of control beliefs, hypothetically could influence patients' quality of life. Keywords: HIV, Health locus of control, Quality of life, Medicine

Tuyet Dao Van, T. T. Cong, H. Hoang et al.

Medical Imaging Informatics has begun over two or more decades. Currently, the share of X-ray, CT images used in medical diagnostics, or share medical images in collaborative research purposes and other public administrative report is one of the challenging issues for medicine and computer science. In the developing country, almost all the general hospital has been equipped with a number of modality for medical examination including an X-ray machine, CT scanner, MRI machine, Ultrasound machine. Initially, these devices were developed as a separated system and unconnected so that their digital images cannot be stored in a server, can not be shared and supported by specialized departments, and especially for Teleradiology activity. Our research proposes a topology for integrating Picture Archiving and Communication System (PACS) and Teleradiology. Design the model and deploy the local DICOM PACS and some application of Teleradiology on the system in the General Hospital. In this paper, we also propose some novel workflow and substitute for the current process which has been used by physicians at the General Hospital so far.