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PubMedDate not supplied

Genomic messaging system and DNA mark-up language for information-based personalized medicine with clinical and proteome research applications.

The convergence of clinical medicine and the Life Sciences, commencing with opportunities in clinical trials and clinically linked medical research, presents many novel challenges. The Genomic Messaging System (GMS) described here was originally developed as a tool for assembling clinical genomic records of individual and collective patients, and was then generalized to become a flexible workflow component that will link clinical records to a variety of computational biology research tools, for research and ultimately for a more personalized, focused, and preventative healthcare system. Prominent among the applications linked are protein science applications, including the rapid automated modeling of patient proteins with their individual structural polymorphisms. In an initial study, GMS formed the basis of a fully automated system for modeling patient proteins with structural polymorphisms as a basis for drug selection and ultimately design on an individual patient basis.

Clinical Medicine↗

PubMed2004

Transforming nursing practice: clinical systems and the nursing unit of the future.

The healthcare industry has historically focused information system expenditures on financial systems. Finally, enabling the practice of clinicians through information systems is beginning to be viewed as a strategic initiative for hospitals and health systems. Vendor applications are now better able to support clinician workflow. Infrastructure is better able to support mobile devices, which bring information to the bedside. Implementation of nursing applications provides a unique opportunity to redesign inefficient and redundant documentation, thereby enabling greater efficiency. Healthcare organizations can jump-start nursing automation by setting a finite and clear tactical plan for initiatives to be rolled out within a year in a single location. The single location enables the organization to experience how well the initiatives integrate to support optimal delivery of care. This article explores nursing information technology initiatives planned to be deployed at a 1,000-bed teaching facility through the use of a Nursing Unit of the Future.

Hospitals, Teaching↗

PubMed1992

Washer-sterilizers vs. indexed washer-decontaminators: a cost comparison.

Washer-sterilizers are the most common type of automated decontamination equipment in hospitals today, but from a cost standpoint, installing an indexed washer-decontaminator makes more sense for hospitals with active decontamination areas. An indexed washer-decontaminator is a highly productive and efficient machine that results in streamlined workflow in decontamination. Even in hospitals that own washer-sterilizers, the economics can be so compelling that they might want to consider replacing them with indexed washer-decontaminators.

Costs and Cost Analysis↗

PubMed2006

[Clinical laboratory system in a complete electronic hospital].

The use of a Laboratory Information System (LIS) and Laboratory Automation System (LAS) are clearly superior to manual results in terms of completeness and legibility. As the current clinical information system (CIS) in Gifu University Hospital consists of a complete electronic medical record system, the clinical laboratory was re-engineered to simplify workflow, pay attention to quality assurance, and decrease various costs by an intelligent laboratory management system. We named the new system Gifu Premiotic Intelligent Laboratory System (GPILS). Further advances will concentrate on the cost-effective use of LIS, and the potential use of GPILS in our laboratories will be discussed. In addition, because one of our clinical laboratories currently provides various useful clinical laboratory-oriented information to physicians and patients, we have developed new tools named the "Reference Data Base (RefDB)". In the near future, several different types of computer technology for various clinical laboratory information will lead to the development of highly specific platforms for LIS corresponding to a complete electronic medical record system.

Clinical Laboratory Information Systems↗

PubMed2006

Gemvid, an open source, modular, automated activity recording system for rats using digital video.

BACKGROUND: Measurement of locomotor activity is a valuable tool for analysing factors influencing behaviour and for investigating brain function. Several methods have been described in the literature for measuring the amount of animal movement but most are flawed or expensive. Here, we describe an open source, modular, low-cost, user-friendly, highly sensitive, non-invasive system that records all the movements of a rat in its cage. METHODS: Our activity monitoring system quantifies overall free movements of rodents without any markers, using a commercially available CCTV and a newly designed motion detection software developed on a GNU/Linux-operating computer. The operating principle is that the amount of overall movement of an object can be expressed by the difference in total area occupied by the object in two consecutive picture frames. The application is based on software modules that allow the system to be used in a high-throughput workflow. Documentation, example files, source code and binary files can be freely downloaded from the project website at http://bioinformatics.org/gemvid/. RESULTS: In a series of experiments with objects of pre-defined oscillation frequencies and movements, we documented the sensitivity, reproducibility and stability of our system. We also compared data obtained with our system and data obtained with an Actiwatch device. Finally, to validate the system, results obtained from the automated observation of 6 rats during 7 days in a regular light cycle are presented and are accompanied by a stability test. The validity of this system is further demonstrated through the observation of 2 rats in constant dark conditions that displayed the expected free running of their circadian rhythm. CONCLUSION: The present study describes a system that relies on video frame differences to automatically quantify overall free movements of a rodent without any markers. It allows the monitoring of rats in their own environment for an extended period of time. By using a low-cost, open source hardware/software solution, laboratories can greatly simplify their data acquisition and analysis pipelines and improve their workload.

Journal Article↗

PubMed2000

Computer decision support systems.

Computer decision support systems are computer applications designed to aid clinicians in making diagnostic and therapeutic decisions in patient care. They can simplify access to data needed to make decisions, provide reminders and prompts at the time of a patient encounter, assist in establishing a diagnosis and in entering appropriate orders, and alert clinicians when new patterns in patient data are recognized. Decision support systems that present patient-specific recommendations in a form that can save clinicians time have been shown to be highly effective, sustainable tools for changing clinician behavior. Designing and implementing such systems is challenging because of the computing infrastructure required, the need for patient data in a machine-processible form, and the changes to existing workflow that may result. Despite these difficulties, there is substantial evidence from trials in a wide range of clinical settings that computer decision support systems help clinicians do a better job caring for patients. As computer-based records and order-entry systems become more common, automated decision support systems will be used more broadly.

Computer Systems↗

PubMedDate not supplied

From telepathology to virtual pathology institution: the new world of digital pathology.

Telepathology has left its childhood. Its technical development is mature, and its use for primary (frozen section) and secondary (expert consultation) diagnosis has been expanded to a great amount. This is in contrast to a virtual pathology laboratory, which is still under technical constraints. Similar to telepathology, which can also be used for e-learning and e-training in pathology, as exemplarily is demonstrated on Digital Lung Pathology (Klaus.Kayser@charite.de) at least two kinds of virtual pathology laboratories will be implemented in the near future: a) those with distributed pathologists and distributed (> or = 1) laboratories associated to individual biopsy stations/surgical theatres, and b) distributed pathologists (usually situated in one institution) and a centralized laboratory, which digitizes complete histological slides. Both scenarios are under intensive technical investigations. The features of virtual pathology comprise a virtual pathology institution (mode a) that accepts a complete case with the patient's history, clinical findings, and (pre-selected) images for first diagnosis. The diagnostic responsibility is that of a conventional institution. The Internet serves as platform for information transfer, and an open server such as the iPATH (http://telepath.patho.unibas.ch) for coordination and performance of the diagnostic procedure. The size and number of transferred images have to be limited, and usual different magnifications have to be used. The sender needs to possess experiences in image sampling techniques, which present with the most significant information. A group of pathologists is "on duty", or selects one member for a predefined duty period. The diagnostic statement of the pathologist(s) on duty is retransmitted to the sender with full responsibility. The first experiences of a virtual pathology institution group working with the iPATH server working with a small hospital of the Salomon islands are promising. A centralized virtual pathology institution (mode b) depends upon the digitalization of a complete slide, and the transfer of large sized images to different pathologists working in one institution. The technical performance of complete slide digitalization is still under development. Virtual pathology can be combined with e-learning and e-training, that will serve for a powerful daily-work-integrated pathology system. At present, e-learning systems are "stand-alone" solutions distributed on CD or via Internet. A characteristic example is the Digital Lung Pathology CD, which includes about 60 different rare and common lung diseases with some features of electronic communication. These features include access to scientific library systems (PubMed), distant measurement servers (EuroQuant), automated immunohisto-chemistry measurements, or electronic journals (Elec J Pathol Histol, www.pathology-online.org). It combines e-learning and e-training with some acoustic support. A new and complete database based upon this CD will combine e-learning and e-teaching with the actual workflow in a virtual pathology institution (mode a). The technological problems are solved and do not depend upon technical constraints such as slide scanning systems. At present, telepathology serves as promoter for a complete new landscape in diagnostic pathology, the so-called virtual pathology institution. Industrial and scientific efforts will probably allow an implementation of this technique within the next two years with exciting diagnostic and scientific perspectives.

Computer Communication Networks↗

PubMed2026

ALPAR: automated learning pipeline for antimicrobial resistance.

SUMMARY: The field of machine learning in antimicrobial resistance (AMR) research has experienced rapid growth, fueled by advancements in high-throughput genome sequencing and the growing capacity of computational resources. However, the complexity and lack of standardized data preparation and bioinformatic analyses present significant challenges, especially for newcomers to the domain. In response to these challenges, we introduce ALPAR (Automated Learning Pipeline for Antimicrobial Resistance), a comprehensive AMR data analysis tool covering the entire process from processing of raw genomic data to training machine learning models to interpretation of results. Our method relies on a reproducible pipeline that integrates widely used bioinformatics tools, presenting a simplified, automatic workflow specifically tailored for single-reference AMR analysis. Accepting genomic data in the form of FASTA files as input, ALPAR facilitates the generation of machine learning-ready data tables and both the training of machine learning and the execution of genome-wide association studies (GWAS) experiments. Additionally, our tool offers supplementary functionalities such as phylogeny-based analysis of the distribution of mutations, enhancing its utility for researchers. The tool has also proven its performance in competitive benchmarks, winning the 2024 CAMDA Anti-Microbial Resistance Prediction Challenge and placing third in the 2025 edition. AVAILABILITY AND IMPLEMENTATION: ALPAR is open-source and freely accessible via GitHub (https://github.com/kalininalab/ALPAR). The pipeline is fully reproducible and can be easily installed as a Conda package (https://anaconda.org/kalininalab/ALPAR).

Machine Learning↗

PubMed2026

OmicsQ: a user-friendly platform for interactive quantitative omics data analysis.

MOTIVATION: High-throughput omics technologies generate complex datasets with thousands of features that are quantified across multiple experimental conditions, but often suffer from incomplete measurements, missing values, and individually fluctuating variances. This requires analytical tools for accurate, deep and insightful biological interpretation, capable of dealing with a large variety of data properties and different amounts of completeness. Software capable of handling such data complexity and integrating with external applications for downstream analysis remains rare and mostly relies on programming-based environments, limiting accessibility for researchers without computational expertise. RESULTS: We present OmicsQ, an interactive, web-based platform designed to streamline quantitative omics data analysis. OmicsQ provides an intuitive, browser-based visualization interface that integrates established statistical processing tools. Those include robust batch correction, automated experimental design annotation, and handling of missing data without imputation, which maintains data integrity and avoids artifacts from a priori assumptions. OmicsQ seamlessly interacts with external applications (e.g. PolySTest, VSClust, ComplexBrowser) for statistical testing, clustering, analysis of protein complex behavior, and pathway enrichment, offering a comprehensive and flexible workflow from data import to biological interpretation that is broadly applicable across domains. AVAILABILITY AND IMPLEMENTATION: OmicsQ is implemented in R and Shiny and is available at https://computproteomics.bmb.sdu.dk/app_direct/OmicsQ. Source code and installation instructions: https://github.com/computproteomics/OmicsQ, DOI: 10.5281/zenodo.17778420.

Software↗

PubMed2024

Development of a clinical metagenomics workflow for the diagnosis of wound infections.

BACKGROUND: Wound infections are a common complication of injuries negatively impacting the patient's recovery, causing tissue damage, delaying wound healing, and possibly leading to the spread of the infection beyond the wound site. The current gold-standard diagnostic methods based on microbiological testing are not optimal for use in austere medical treatment facilities due to the need for large equipment and the turnaround time. Clinical metagenomics (CMg) has the potential to provide an alternative to current diagnostic tests enabling rapid, untargeted identification of the causative pathogen and the provision of additional clinically relevant information using equipment with a reduced logistical and operative burden. METHODS: This study presents the development and demonstration of a CMg workflow for wound swab samples. This workflow was applied to samples prospectively collected from patients with a suspected wound infection and the results were compared to routine microbiology and real-time quantitative polymerase chain reaction (qPCR). RESULTS: Wound swab samples were prepared for nanopore-based DNA sequencing in approximately 4 h and achieved sensitivity and specificity values of 83.82% and 66.64% respectively, when compared to routine microbiology testing and species-specific qPCR. CMg also enabled the provision of additional information including the identification of fungal species, anaerobic bacteria, antimicrobial resistance (AMR) genes and microbial species diversity. CONCLUSIONS: This study demonstrates that CMg has the potential to provide an alternative diagnostic method for wound infections suitable for use in austere medical treatment facilities. Future optimisation should focus on increased method automation and an improved understanding of the interpretation of CMg outputs, including robust reporting thresholds to confirm the presence of pathogen species and AMR gene identifications.

Humans↗

PubMed1998

Clinical application of the UMLS in a computerized order entry and decision-support system.

Vanderbilt University Medical Center uses the UMLS as a dictionary, an interlingua, and a knowledge source within the WizOrder system. WizOrder provides direct care-provider order entry and integrated clinical decision-support capabilities. Linking the two functions enables efficient decision-support during the "normal" workflow of care providers, at the point where decisions are made. WizOrder uses the UMLS as a dictionary to encode free-text entries into controlled vocabularies such as ICD9. As an interlingua, the UMLS provides mapping between vocabularies, allowing to translate patient-specific information to MeSH terms and perform automated literature retrieval. WizOrder uses the tables of co-occurring concepts and the Semantic Network to provide sensible lists of potential drug interactions and adverse drug reactions, and generate fully-formed MEDLINE queries for PubMed.

Decision Support Systems, Clinical↗

PubMed2026

scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.

Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. One key analytical task is to determine cell type identity based on epigenetic data. Here, we introduce scATAnno, a Python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates reference atlases from publicly available datasets, enabling accurate cell type annotation by integrating query data with reference atlases without the use of single-cell RNA sequencing (scRNA-seq) data. To enhance annotation accuracy, we incorporated k-nearest neighbors (KNN)-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compared and benchmarked scATAnno against five other published cell annotation approaches, demonstrating its superior performance across multiple datasets and metrics. We further showcased the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cells (PBMCs), triple-negative breast cancer (TNBC), and basal cell carcinoma (BCC), and demonstrated that scATAnno accurately annotates cell types across diverse biological conditions. Overall, scATAnno is a useful tool for scATAC-seq reference atlas construction and cell type annotation and can facilitate the interpretation of new scATAC-seq datasets in complex biological systems. scATAnno is publicly available at https://scatanno-main.readthedocs.io/.

Single-Cell Analysis↗

PubMed2005

GNARE: automated system for high-throughput genome analysis with grid computational backend.

Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The Grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics analysis. To leverage this we have developed the Genome Analysis Research Environment (GNARE)--a scalable computational system for the high-throughput analysis of genomes, which provides an integrated database and computational backend for data-driven bioinformatics applications. GNARE efficiently automates the major steps of genome analysis including acquisition of data from multiple genomic databases; data analysis by a diverse set of bioinformatics tools; and storage of results and annotations. High-throughput computations in GNARE are performed using distributed heterogeneous Grid computing resources such as Grid2003, TeraGrid, and the DOE Science Grid. Multi-step genome analysis workflows involving massive data processing, the use of application-specific tools and algorithms and updating of an integrated database to provide interactive web access to results are all expressed and controlled by a "virtual data" model which transparently maps computational workflows to distributed Grid resources. This paper describes how Grid technologies such as Globus, Condor, and the Gryphyn Virtual Data System were applied in the development of GNARE. It focuses on our approach to Grid resource allocation and to the use of GNARE as a computational framework for the development of bioinformatics applications.

Computational Biology↗

PubMed2006

Automatic image hanging protocol for chest radiographs in PACS.

Chest radiography is one of the most widely used techniques in diagnostic imaging. It comprises at least one-third of all diagnostic radiographic procedures in hospitals. However, in the picture archive and communication system, images are often stored with the projection and orientation unknown or mislabeled, which causes inefficiency for radiologists' interpretation. To address this problem, an automatic hanging protocol for chest radiographs is presented. The method targets the most effective region in a chest radiograph, and extracts a set of size-, rotation-, and translation-invariant features from it. Then, a well-trained classifier is used to recognize the projection. The orientation of the radiograph is later identified by locating the neck, heart, and abdomen positions in the radiographs. Initial experiments are performed on the radiographs collected from daily routine chest exams in hospitals and show promising results. Using the presented protocol, 98.2% of all cases could be hung correctly on projection view (without protocol, 62%), and 96.1% had correct orientation (without protocol, 75%). A workflow study on the protocol also demonstrates a significant improvement in efficiency for image display.

Algorithms↗

PubMed2005

High performance workflow implementation for protein surface characterization using grid technology.

BACKGROUND: This study concerns the development of a high performance workflow that, using grid technology, correlates different kinds of Bioinformatics data, starting from the base pairs of the nucleotide sequence to the exposed residues of the protein surface. The implementation of this workflow is based on the Italian Grid.it project infrastructure, that is a network of several computational resources and storage facilities distributed at different grid sites. METHODS: Workflows are very common in Bioinformatics because they allow to process large quantities of data by delegating the management of resources to the information streaming. Grid technology optimizes the computational load during the different workflow steps, dividing the more expensive tasks into a set of small jobs. RESULTS: Grid technology allows efficient database management, a crucial problem for obtaining good results in Bioinformatics applications. The proposed workflow is implemented to integrate huge amounts of data and the results themselves must be stored into a relational database, which results as the added value to the global knowledge. CONCLUSION: A web interface has been developed to make this technology accessible to grid users. Once the workflow has started, by means of the simplified interface, it is possible to follow all the different steps throughout the data processing. Eventually, when the workflow has been terminated, the different features of the protein, like the amino acids exposed on the protein surface, can be compared with the data present in the output database.

Automation↗

PubMed2026

Quantitative Fluorescence Imaging of Alphavirus Infection for Antiviral Screenings.

Fluorescence microscopy offers a highly sensitive and versatile approach for investigating alphavirus infection at the cellular level. By combining fluorescently labeled viruses with quantitative image analysis, this method enables detailed spatial and temporal characterization of infection dynamics, including the detection of subtle differences in replication kinetics and cell-to-cell spread. A central aim of this protocol is its application in antiviral screening assays. Image-based quantification of fluorescence intensity provides a robust and reproducible means to assess the efficacy of antiviral compounds, allowing early and sensitive detection of inhibitory effects in infected cells. This facilitates the identification of promising antiviral hits and supports the evaluation of dose-dependent responses. The approach is also well-suited for comparative studies of different alphavirus strains or mutants, as variations in replication behavior and dissemination patterns become readily apparent. Its flexibility, compatibility with multiple cell lines, and straightforward integration into automated imaging platforms makes the method scalable and suitable for high-throughput screening campaigns. Overall, this protocol advances the discovery and evaluation of antiviral strategies. Given that several alphaviruses cause significant human and veterinary diseases, lack approved antiviral therapies, and continue to expand geographically with emerging outbreaks, the identification of novel antivirals remains an urgent priority. Therefore, this fluorescence-based workflow represents a valuable and timely contribution to modern alphavirus research.

Antiviral Agents↗

PubMed2002

Three-year experience in using total laboratory automation system.

We implemented a total laboratory automation (TLA) system, currently used in Chungnam National University Hospital (CNUH), designed around the A&T Corporation to perform the general and specific laboratory testing throughout the system in a timely and cost-effective manner. The system consists of two major lines: chemistry and hematology. The analyzers attached to the hematology line include one SE 9000 hematology analyzer (Sysmex Corporation, Kobe, Japan), one SP-100 slide maker (Sysmex), and one R9000 reticulocyte counter (Sysmex). The chemistry line consists of preanalytic system and linked analyzers. The analyzers attached to chemistry line are one Hitachi 747 chemistry analyzer (Hitachi, Ltd., Tokyo, Japan), one A&T 502X analyzer (A&T Corporation, Tokyo, Japan) and one Architect i2000 immunochemistry analyzer (Abbott Diagnostics Division, Santa Clara, USA). The preanaytic system including start stocker, centrifuge, de-cap unit and aliquoting unit were connected with analyzers. The turn around time (TAT) of all tests was shortened in comparison to manual systems. Consequently it makes feasible one-day care clinic and makes reduced inherent errors in sample identification, dispensing, and reporting. In addition, real-time re-run can make test quality more reliable. As many as 12 departments can be reduced to 7 departments. The subsidiary advantages of smaller size were incorporated into reduced working steps facilitating workflow. Overall, better qualified and quicker results can be provided to the clinicians, and proper cost-effectiveness and better quality services can be anticipated as a result of much easier system management and higher reliability. In conclusion, the TLA has been successful within our laboratory.

Automation↗

PubMed2026

BioEMMA: Automated Generation of Model-Specific Escher-Compatible Maps from KEGG Pathways.

Genome-scale metabolic models are widely used to investigate cellular metabolism, but their interpretation and comparison are limited by the lack of reproducible pathway-level visualizations with a common spatial organization. This study presents BioEMMA, a Python-based tool for the automated generation of model-specific metabolic pathway maps in the Escher JSON format using coordinate information from curated KEGG pathway maps. BioEMMA parses KGML files, map reaction and metabolite identifiers to model database namespaces, filters pathway elements according to an input SBML model, adds non-primary metabolites, reconstructs Escher-compatible layouts, and supports flux visualization. The tool was integrated into a reproducible BioUML workflow for metabolic model reconstruction. BioEMMA was evaluated using the e_coli_core model and the KEGG glycolysis/gluconeogenesis pathway while generating a model-specific map with overlaid FBA fluxes. It was then applied to compare E. coli reconstructions generated by gapseq, ModelSEEDpy, and Reconstructor across three central carbon metabolism pathways. To broaden the evaluation, BioEMMA was applied using 87 prokaryotic BiGG models and three eukaryotic models. The analysis revealed pathway-specific differences in reaction coverage, shared and model-specific reactions, and predicted flux activity. BioEMMA therefore provides a reproducible framework for pathway-level visualization and comparison of genome-scale metabolic reconstructions within a common spatial coordinate system.

Escher maps↗