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PubMed2001

Case-oriented computer-based-training in radiology: concept, implementation and evaluation.

BACKGROUND: Providing high-quality clinical cases is important for teaching radiology. We developed, implemented and evaluated a program for a university hospital to support this task. METHODS: The system was built with Intranet technology and connected to the Picture Archiving and Communications System (PACS). It contains cases for every user group from students to attendants and is structured according to the ACR-code (American College of Radiology) 2. Each department member was given an individual account, could gather his teaching cases and put the completed cases into the common database. RESULTS: During 18 months 583 cases containing 4136 images involving all radiological techniques were compiled and 350 cases put into the common case repository. Workflow integration as well as individual interest influenced the personal efforts to participate but an increasing number of cases and minor modifications of the program improved user acceptance continuously. 101 students went through an evaluation which showed a high level of acceptance and a special interest in elaborate documentation. CONCLUSION: Electronic access to reference cases for all department members anytime anywhere is feasible. Critical success factors are workflow integration, reliability, efficient retrieval strategies and incentives for case authoring.

Computer Communication Networks↗

PubMed2006

The caCORE Software Development Kit: streamlining construction of interoperable biomedical information services.

BACKGROUND: Robust, programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources are challenging to construct. Such systems require the adoption of common information models, data representations and terminology standards as well as documented application programming interfaces (APIs). The National Cancer Institute (NCI) developed the cancer common ontologic representation environment (caCORE) to provide the infrastructure necessary to achieve interoperability across the systems it develops or sponsors. The caCORE Software Development Kit (SDK) was designed to provide developers both within and outside the NCI with the tools needed to construct such interoperable software systems. RESULTS: The caCORE SDK requires a Unified Modeling Language (UML) tool to begin the development workflow with the construction of a domain information model in the form of a UML Class Diagram. Models are annotated with concepts and definitions from a description logic terminology source using the Semantic Connector component. The annotated model is registered in the Cancer Data Standards Repository (caDSR) using the UML Loader component. System software is automatically generated using the Codegen component, which produces middleware that runs on an application server. The caCORE SDK was initially tested and validated using a seven-class UML model, and has been used to generate the caCORE production system, which includes models with dozens of classes. The deployed system supports access through object-oriented APIs with consistent syntax for retrieval of any type of data object across all classes in the original UML model. The caCORE SDK is currently being used by several development teams, including by participants in the cancer biomedical informatics grid (caBIG) program, to create compatible data services. caBIG compatibility standards are based upon caCORE resources, and thus the caCORE SDK has emerged as a key enabling technology for caBIG. CONCLUSION: The caCORE SDK substantially lowers the barrier to implementing systems that are syntactically and semantically interoperable by providing workflow and automation tools that standardize and expedite modeling, development, and deployment. It has gained acceptance among developers in the caBIG program, and is expected to provide a common mechanism for creating data service nodes on the data grid that is under development.

Humans↗

PubMed2026

Targeted long-read genomic and epigenomic profiling enhances timely comprehensive variant discovery in hypotonia and muscle weakness.

BACKGROUND: Identifying the genetic basis of hypotonia and muscle weakness is critical for patient management and family counseling. However, diagnosis is often hindered by diverse genomic alterations, including repeat expansions, structural variants (SVs), and methylation defects. Standard-of-care testing, largely based on short-read sequencing, is limited in its ability to detect this heterogeneous variation landscape, leaving many patients undiagnosed or requiring lengthy sequential testing. Long-read sequencing represents a promising solution. However, its application as a first-tier diagnostic assay for hypotonia remains unexplored. METHODS: We retrospectively analyzed 227 patients with hypotonia to assess diagnostic yield, time-to-diagnosis, and costs associated with standard-of-care testing. A long-read whole-genome sequencing (LR-WGS) workflow with targeted analysis of hypotonia-associated genes was developed to detect and prioritize pathogenic SNVs, SVs, and CNVs, repeat expansions, and methylation changes at key disease loci. The workflow was validated in a reference-positive cohort with known diagnoses (n = 15) and applied to an unsolved cohort (n = 14). Variant interpretation followed ACMG guidelines and was confirmed with orthogonal methods. RESULTS: Standard-of-care testing achieved a diagnostic yield of 42% with an average time-to-diagnosis of 68.7 days; however, 30% of diagnosed patients experienced significant delays (average 169 days) due to sequential testing. The LR-WGS based approach identified all known pathogenic variants in the positive cohort, including SMN1 deletions, methylation defects at 15q11.2/Prader-Willi locus, FMR1 repeat expansions, and sequence and copy-number variants in > 100 genes underlying myopathies and muscular dystrophies. The targeted long-read pipeline reduced prioritized variant calls by 97.9-99.9% and, in the unsolved cohort, yielded one definitive diagnosis (de novo COL6A3 deletion) and one possible diagnosis (aberrant methylation and copy number at POMK), for an additional 14% yield. Among patients diagnosed after sequential testing (n = 29), LR-WGS is expected to reduce time-to-diagnosis by ~ 85% and decrease cumulative diagnostic delays, with projected healthcare cost savings of $396,000-439,000. Across the entire 227 patient cohort, LR-WGS is anticipated to reduce testing costs by 6.5%, yielding an average savings of $105 per patient. CONCLUSIONS: LR-WGS enables comprehensive discovery of genomic and epigenomic variants in hypotonia and muscle weakness, improving diagnostic yield, shortening diagnostic timelines, and reducing costs compared with current standard-of-care testing.

Humans↗

PubMed2026

Prospective clinical validation of targeted long-read sequencing for preimplantation genetic testing of α-thalassaemia.

BACKGROUND: Preimplantation genetic testing for monogenic disorders (PGT-M) can prevent transmission of severe α-thalassaemia, but conventional workflows remain limited by family-specific assay design for direct variant detection, dependence on additional family samples for haplotype construction, and labour-intensive multi-step procedures across several platforms. Targeted long-read sequencing-based PGT-M for α-thalassaemia (tlrPGT-α-thal) integrates direct variant detection and haplotype linkage analysis within a single assay, but prospective clinical validation is lacking. METHODS: This prospective clinical study enrolled 103 families at high risk of transmitting α-thalassaemia at a reproductive medicine centre between August 2024 and March 2025. All families underwent blinded parallel analysis using both conventional NGS-based PGT-M (comparator) and tlrPGT-α-thal. RESULTS: In the primary concordance analysis, tlrPGT-α-thal was fully concordant with conventional NGS-based PGT-M (507/507, 100.0%; exact 95% CI, 99.3-100.0). Direct variant detection was successful in 501/507 embryos (98.82%; 95% CI, 97.4-99.6), haplotype linkage was established in 505/507 embryos (99.61%; 95% CI, 98.6-100.0), and one meiotic recombination event was identified. Among 93 families proceeding to embryo transfer, 57 pregnancies underwent invasive prenatal diagnosis, and all were concordant with the corresponding tlrPGT-α-thal results. Of the 26 comparator-inconclusive embryos, tlrPGT-α-thal resolved 6 complex cases, including cases with incomplete pedigrees or insufficient informative SNPs. Among the remaining 20 embryos with HBA-region aneuploidies, genotype and parental origin could be determined in 12. CONCLUSIONS: The findings show that tlrPGT-α-thal enables direct detection of diverse α-thalassaemia-causing variants together with efficient haplotype linkage analysis within a single workflow, without requiring family-specific assay design or additional family samples. The method demonstrated high diagnostic accuracy while providing added value in complex scenarios. Taken together, tlrPGT-α-thal represents a simplified and broadly applicable strategy for α-thalassaemia PGT-M.

Humans↗

PubMed2026

An open-source clinical bioinformatics pipeline for real-world NGS implementation: translating genomic variants into actionable treatment strategies in oncology.

BACKGROUND: Next-Generation Sequencing (NGS) has become a cornerstone technology in clinical practice, yet its adoption presents significant challenges. Physicians and oncologists must manage vast amounts of genome-scale data and transform it into actionable insights for complex decision-making. While commercial systems exist to synthesize data from NGS experiments into clinical reports, many are hindered by limitations such as closed-source designs that restrict transparency and customization. Additionally, some fail to leverage publicly available genomic databases, missing opportunities to integrate valuable external data. Furthermore, the rigidity of many tools in accommodating diverse NGS panels limits their applicability across varied clinical scenarios. METHODS: To address these limitations, we developed OncoReport, an open-source tool that generates comprehensive reports from NGS analyses. By integrating publicly accessible databases, OncoReport provides a robust, user-friendly environment equipped with essential tools for NGS analysis. This design aims to enhance data interpretation and support informed clinical decision-making. RESULTS: Rigorous testing has demonstrated OncoReport’s effectiveness in producing detailed, actionable reports that are clear and easy to use. By automating key aspects of the workflow, the tool significantly reduces manual effort and expedites the synthesis and interpretation of NGS results, making genomic insights more accessible to clinicians. CONCLUSION: OncoReport offers a transparent, flexible, and efficient framework for clinicians to analyze and apply genomic data in patient care. By streamlining workflows and leveraging open-source principles, it empowers healthcare professionals to make informed, data-driven decisions. OncoReport is freely available at https://oncoreport.atlas.dmi.unict.it, with source code and issue tracking on GitHub: https://github.com/knowmics-lab/oncoreport .

Humans↗

PubMed2026

Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.

BACKGROUND: Cell type annotation is essential for gaining biological insight from single-cell RNA sequencing data, yet manual labeling remains time-consuming and difficult to reproduce. Various computational approaches have been developed to automate this process, and recent studies suggest that large language models can infer cell types with promising accuracy in single-cell analysis. However, most workflows still rely on cluster-specific markers derived from gene expression alone or manual curation. As a result, marker selection can be sensitive to statistical criteria and dataset-dependent bias, which may lead to the selection of less informative genes or missing important markers, while providing limited biological context. RESULTS: To address this limitation, we introduce CELLIA, an LLM-based workflow for automated and robust cell type annotation. CELLIA employs an integrative evidence-knowledge marker selection strategy that combines statistical differential expression criteria with curated tissue-specific marker resources to identify informative marker genes. In benchmarking analyses of 102 cell types, this approach improved agreement with manual annotations. In addition, CELLIA achieved higher agreement in subtype-level analyses of closely related immune populations and was further evaluated in a non-immune stromal subtype setting, covering 25 cell types in total. CONCLUSION: By integrating evidence-knowledge from gene expression with curated biological prior knowledge, CELLIA provides a more stable marker selection and improves the reliability of LLM-cell type annotation.

Cell type annotation↗

PubMed2026

Pangenomes aid accurate detection of large insertions and deletions from targeted sequencing: the case of cardiomyopathies.

BACKGROUND: Gene panels represent a widely used strategy for genetic testing in a vast range of Mendelian disorders. While this approach aids reliable bioinformatic detection of short coding variants, it often fails to detect many larger variants. Recent studies have recommended the adoption of pangenome references (as opposed to linear reference genomes like GRCh38) to augment detection of large variants from targeted sequencing, potentially providing diagnostic laboratories with the possibility to streamline diagnostic work-ups and reduce costs. METHODS: Here, we analyze 1969 cardiomyopathy cases and 1805 controls sequenced with the Illumina Trusight Cardio panel using a pangenome-based workflow (GRAF) and five conventional orthogonal methodologies (GATK HaplotypeCaller, GATK-gCNV, ExomeDepth, Manta and Lumpy-SV) to detect variants ≥ 20 bp in size. RESULTS: Following lab-based variant validation by means of PCR and Sanger sequencing, we show that GRAF conjugates higher precision and recall (F1 score 0.86) compared with other methods (F1 0-0.57) in detecting potentially pathogenic variants ≥ 20 bp from short-read panel data. Results were complemented by a comparison of the tools' performance in detecting ground truth variants on reference sample HG002 from Genome In A Bottle, which confirmed GRAF to outperform other tools also on exome sequencing (F1 0.97 vs. 0-0.94). Notably, in the HG002 benchmark dataset, GRAF also showed slightly improved performance compared to GATK HaplotypeCaller in the identification of small variants (1-19 bp; F1 0.975 vs. 0.968). CONCLUSIONS: Our results indicate that pangenome-based workflows aid improved detection of large variants from targeted sequencing data in the clinical context and suggest that they may contribute to more unified variant detection frameworks for all-size genetic variants in the future.

Humans↗

PubMed2026

Hide and seek: de novo identification in sugar beet reveals impact of non-autonomous LTR retrotransposons.

Plant genomes are filled with retrotransposons and their derivatives, constantly undergoing sequence diversification and structural rearrangement. Among them, short, non-autonomous retrotransposons lack full coding capacity and often form subfamilies. As a result, non-autonomous retrotransposons are incompletely identified in most to all genome assemblies.Here, we capitalize on our comprehensive understanding of the transposable element (TE) landscape in sugar beet (Beta vulgaris) to assess the extent of the blind spot for non-autonomous long terminal repeat (LTR) retrotransposons. This use case serves to answer if all of these sequences are derivatives of easier-to-identify full-length elements or if there is more variability that is currently overlooked.For this we applied a semi-automated structural discovery workflow followed by in-depth manual verification to characterize non-autonomous LTR retrotransposons in sugar beet. We retrieve more than 100 non-autonomous LTR retrotransposon families that lack complete autonomous coding capacity, including canonical terminal-repeat retrotransposons in miniature (TRIMs), elongated non-coding derivatives and families retaining fragmented coding remnants. The identified families span a broad range, including elements exceeding 15,000 bp in length and display evidence for reshuffling and modular evolution. Only a subset of families could be confidently linked to autonomous retrotransposons, showing sequence diversification within the non-autonomous LTR retrotransposon fraction beyond the autonomous genomic templates.We highlight that a large fraction of non-autonomous LTR retrotransposons is incompletely recovered with the current TE identification workflows, even if the output is well-curated and condensed into TE libraries and suggest procedures to remedy this gap. This study gives a genome-wide view into the non-autonomous LTR retrotransposon landscape of a single plant genome and highlights the importance of structure-based approaches for their identification and classification.

LTR retrotransposons↗

PubMedDate not supplied

Automating complex guidelines for chronic disease: lessons learned.

There is scant published experience with implementing complex, multistep computerized practice guidelines for the long-term management of chronic diseases. We have implemented a system for creating, maintaining, and navigating computer-based clinical algorithms integrated with our electronic medical record. This article describes our progress and reports on lessons learned that might guide future work in this field. We discuss issues and obstacles related to choosing and adapting a guideline for electronic implementation, representing and executing the guideline as a computerized algorithm, and integrating it into the clinical workflow of outpatient care. Although obstacles were encountered at each of these steps, the most difficult were related to workflow integration.

Algorithms↗

PubMed2005

Clinical research subject recruitment: the Volunteer for Vanderbilt Research Program www.volunteer.mc.vanderbilt.edu.

This article provides information concerning a novel research subject recruitment registry developed at Vanderbilt University. Project goals were (1) to provide a mechanism for lay individuals to self-enter information conveying interest in volunteering for clinical research and (2) provide tools for researchers to select and contact potential volunteers based on study-specific inclusion criteria. The registry was built and offered as an institutional resource to all university scientists conducting institutional review board-approved research. The authors present (1) a model for redesigning workflow associated with subject registration, volunteer retrieval, and subject contact; (2) details of a Web-based software application used as a focal point in designing workflow for our system; (3) descriptive statistics for volunteer and researcher use of the system during the first 32 months of operation; (4) cost estimates for the project; and (5) a set of recommendations for other medical centers wishing to adopt similar methodology.

Biomedical Research↗

PubMed2006

An XML-based system for synthesis of data from disparate databases.

Diverse data sets have become key building blocks of translational biomedical research. Data types captured and referenced by sophisticated research studies include high throughput genomic and proteomic data, laboratory data, data from imagery, and outcome data. In this paper, the authors present the application of an XML-based data management system to support integration of data from disparate data sources and large data sets. This system facilitates management of XML schemas and on-demand creation and management of XML databases that conform to these schemas. They illustrate the use of this system in an application for genotype-phenotype correlation analyses. This application implements a method of phenotype-genotype correlation based on phylogenetic optimization of large data sets of mouse SNPs and phenotypic data. The application workflow requires the management and integration of genomic information and phenotypic data from external data repositories and from the results of phenotype-genotype correlation analyses. Our implementation supports the process of carrying out a complex workflow that includes large-scale phylogenetic tree optimizations and application of Maddison's concentrated changes test to large phylogenetic tree data sets. The data management system also allows collaborators to share data in a uniform way and supports complex queries that target data sets.

Animals↗

PubMed2005

Improving acceptance of computerized prescribing alerts in ambulatory care.

Computerized drug prescribing alerts can improve patient safety, but are often overridden because of poor specificity and alert overload. Our objective was to improve clinician acceptance of drug alerts by designing a selective set of drug alerts for the ambulatory care setting and minimizing workflow disruptions by designating only critical to high-severity alerts to be interruptive to clinician workflow. The alerts were presented to clinicians using computerized prescribing within an electronic medical record in 31 Boston-area practices. There were 18,115 drug alerts generated during our six-month study period. Of these, 12,933 (71%) were noninterruptive and 5,182 (29%) interruptive. Of the 5,182 interruptive alerts, 67% were accepted. Reasons for overrides varied for each drug alert category and provided potentially useful information for future alert improvement. These data suggest that it is possible to design computerized prescribing decision support with high rates of alert recommendation acceptance by clinicians.

Adult↗

PubMed2026

Longitudinal Clinical, Physiological, and Molecular Profiling of Female Patients With Metastatic Cancer: Protocol and Feasibility of a Multicenter High-Definition Oncology Study.

PURPOSE: A substantial proportion of patients receiving genomically matched therapies do not achieve clinical benefit, underscoring the influence of nongenetic factors on cancer outcomes. High-Definition Oncology (HDO) proposes integrating longitudinal, multimodal patient data-spanning clinical, molecular, physiological, and behavioral domains-to enable truly individualized cancer care. This manuscript describes the HDO study design, framework, and feasibility results in women with metastatic cancer. METHODS: We initiated a prospective, multicenter observational study (HDO study; ClinicalTrials.gov identifier: NCT06590506) enrolling 300 female patients with newly diagnosed metastatic breast, lung, or colorectal cancer. Here, we report the study design, standardized workflows, prespecified feasibility criteria, and early internal pilot results. Eleven data modalities are collected longitudinally, including tumor and germline genomics, germline epigenomics, gut microbiome, blood and stool metabolomics and proteomics, exposome characterization, wearable-derived physiological monitoring, digital footprint assessment, medical imaging, and patient-reported outcomes. Standardized workflows govern clinical procedures, data acquisition, biospecimen processing, and quality control across all participating sites. RESULTS: Feasibility was evaluated in the first 30 participants (10% of planned accrual). Patients completed 100% of scheduled clinical visits, 97.4% of planned plasma collections, 80.7% of stool samples, and all tumor biopsies. Wearable devices captured activity, heart rate, sleep, and blood oxygen saturation data during 95.0%, 84.2%, 90.6%, and 70.7% of total patient-days, respectively. Biospecimens met predefined quality control metrics across all molecular modalities. Engagement with mobile applications for pain and emotion reporting exceeded 80%. CONCLUSION: The HDO study demonstrates the feasibility of comprehensive, longitudinal, multimodal data collection in women with metastatic cancer. This internal pilot establishes an integrated framework for future analyses aimed at characterizing disease trajectories, defining molecular and physiological determinants of outcomes, and developing patient-specific computational models.

Humans↗

PubMed2004

Intraoperative Iso-C C-arm navigation in craniospinal surgery: the first 60 cases.

OBJECTIVE: The intraoperative Iso-C C-arm (Siremobil Iso-C 3D; Siemens Medical Solutions, Erlangen, Germany) provides a unique ability to acquire and view multiplanar three-dimensional images of intraoperative anatomy. Registration for intraoperative surgical navigation may be automated, thus simplifying the operative workflow. METHODS: Iso-C C-arm intraoperative fluoroscopy acquires 100 images, each of which must be 1.8 degrees in a circumferential fashion about an "isocentric" point in space. The system generates a high-resolution isotropic three-dimensional data set that is available immediately after the 90-second C-arm rotation. The data set is ported to the image-guided workstation, registration is immediate and automated, and the surgeon can navigate with millimetric accuracy. The authors prospectively examined data from the initial 60 patients examined with the Iso-C, among whom were cases of anterior and posterior spinal instrumentation from the occiput to the sacrum. Percutaneous and minimally invasive spinal and cranial procedures were also included. RESULTS: Automated registration for image-guided navigation was attainable for anterior and posterior cases from the cranial base and entire spine. In most cases, intraoperative postprocedural imaging with the Iso-C mitigated the need for postoperative imaging. CONCLUSION: Intraoperative Iso-C three-dimensional scanning allows real-time feedback during cranial base and spinal surgery and during procedures involving instrumentation. In most cases, it obviates the need for postoperative computed tomography. Its usefulness is in its simplicity, and it can be easily adapted to the operating room workflow. When coupled with intraoperative navigation, this new technology facilitates complex neurosurgical procedures by improving the accuracy, safety, and time of surgery.

Adolescent↗

PubMed2026

Syndromic cholera diagnosis masks diverse causes of diarrhoeal disease in Burundi revealed by portable metagenomics.

BACKGROUND: Cholera outbreaks remain a major public-health challenge in sub-Saharan Africa, where diagnostic capacity is limited and clinical case definitions are non-specific and re ly heavily on syndromic diagnosis. Rapid identification of Vibrio cholerae is critical, yet cholera-suspected diarrhoea can have multiple infectious causes not captured by targeted diagnostics. METHODS: We evaluated a mobile, culture-independent metagenomic sequencing workflow for on-site detection of gastrointestinal pathogens directly from faecal samples in Burundi. The offline workflow combined long-read Oxford Nanopore Technologies (ONT) sequencing with rapid, laptop-based taxonomic and antimicrobial resistance (AMR) screening and was deployed across a health centre, a district hospital, and a refugee transit camp. The frontline and real-time results were verified using both conventional culturing and in-depth bioinformatic analyses. RESULTS: V. cholerae signals were only detected in a subset of suspected cholera cases, while many samples were dominated by alternative bacterial taxa, most frequently Escherichia coli. V. cholerae abundance correlated strongly with detection of the C holera T oxin P hage CTXφ, supporting differentiation between toxigenic signal and background exposure. AMR genes were detected across samples, providing early situational insight into resistance determinants among gastrointestinal bacteria. CONCLUSIONS: Mobile, offline metagenomic sequencing enables rapid frontline characterization of gastrointestinal disease, especially cholera-suspected, in resource-limited settings and complements existing diagnostics by improving etiological resolution and outbreak response.

Humans↗

PubMed2024

A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.

An arrayed CRISPR screen is a high-throughput functional genomic screening method, which typically uses 384 well plates and has different gene knockouts in different wells. Despite various computational workflows, there is currently no systematic way to find what is a good workflow for arrayed CRISPR screening data analysis. To guide this choice, we developed a statistical simulation model that mimics the data generating process of arrayed CRISPR screening experiments. Our model is flexible and can simulate effects on phenotypic readouts of various experimental factors, such as the effect size of gene editing, as well as biological and technical variations. With two examples, we showed that the simulation model can assist making principled choice of normalization and hit calling method for the arrayed CRISPR data analysis. This simulation model is implemented in an R package and can be downloaded from Github.

CRISPR-Cas Systems↗

PubMed2026

'PePApipe': A complete bioinformatics analysis pipeline for African Swine Fever Virus genome.

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. The viral genome is large and complex, and genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed 'PePApipe', a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, de novo genome assembly and variant calling. Programmed in Python, it can be executed locally through bash scripts, or using a Slurm protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structured folders and produces intermediate files that can be used as inputs for further or parallel analyses; users can also enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error, and enhances reproducibility and efficiency. This user-friendly pipeline facilitates the transition from sequencing to assembly and downstream analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance.

African Swine Fever Virus↗

PubMed2026

Plasma proteomics: considerations for preanalytical variability; a systematic review with narrative synthesis.

BACKGROUND: The plasma proteome (PP) is a dynamic system subject to pathology-associated changes and a focus for novel disease biomarker discovery. Disease-related PP research assumes protein concentrations in test specimens accurately reflect the in vivo milieu. However, measures to maintain the physicochemical integrity of the proteome before assay are often rudimentary, poorly described, or lacking standardisation in published studies. Contrastingly, in laboratory medicine, there is an expectation that errors in the so-called "preanalytical phase" (PAP) that impact patient results are understood, monitored, and mitigated against, while also being well described in research publications. There is therefore scope for good practice from laboratory medicine to inform PP research workflows. This review considers factors in the PAP which may impact the validity of PP results. CONTENT: A systematic review was conducted per PRISMA guidelines, limited to English-language peer-reviewed studies (2014-2024). Candidate studies were imported, screened, and managed using Covidence systematic review software. SUMMARY: 15 eligible studies were reviewed, covering many relevant processes. 11 studies reported statistically significant differences in PP due to factors in the PAP. Temperature and time-to-processing were the most commonly reported factors affecting the PP, with significant effects reported in 8 studies. OUTLOOK: PAP variability can significantly affect results in PP studies. Careful consideration of the effect of each stage of the PAP is needed when working with the PP. In multicenter studies, pre-defined and research question-specific sample processing workflows are essential for reducing PAP variability, which helps ensure the validity of PP studies.

Humans↗