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At least 55 records · Page 3Linked to original sources
PubMed2026

Vancomycin Effectiveness in Reducing Surgical Site Infection in Posterior Spinal Fusion Surgery: A Retrospective Data Analysis of the STRIVE Trial.

STUDY DESIGN: Retrospective analysis of prospectively collected data. OBJECTIVE: To re-evaluate vancomycin as a preventive measure for surgical site infection (SSI). SUMMARY OF BACKGROUND DATA: Intrawound vancomycin powder is used to prevent SSIs in spinal surgery. Prior studies, often limited to single institutions or small samples, have shown mixed efficacy and potential increases in non- S. aureus and Gram-negative infections. We hypothesized that SSIs rates would be similar with and without intrawound vancomycin in posterior spinal fusion (PSF) surgery. METHODS: Prospectively collected data from the 3595 patients in the STaphylococcus aureus suRgical Inpatient Vaccine Efficacy (STRIVE) trial were stratified by intrawound antibiotic usage. Multivariate logistic regression assessed the effect of vancomycin use on SSI, adjusting for patient demographics and SSI-associated risk factors. Secondary outcomes included critical care stay, reoperation, sepsis, and hospital readmission. RESULTS: Of 3311 patients who underwent surgery, 847 (26%) received only intrawound vancomycin and 1534 (46%) received no intrawound antibiotics. Sixty (8%) patients developed postoperative SSI, of whom 20 (33%) had received intrawound vancomycin. Receiving intrawound vancomycin was not associated with SSI incidence versus no intrawound antibiotics [odds ratio (OR): 0.77; 95% CI: 0.42-1.42], critical care stay (OR: 0.94; 95% CI: 0.78-1.12), or sepsis (OR: 2.04; 95% CI: 0.62-6.73). However, intrawound vancomycin was associated with increased odds of hospital readmission (OR: 1.82; 95% CI: 1.28-2.6; P < 0.001) and reoperation (OR: 1.75; 95% CI: 1.18-2.6; P = 0.005). Factors significantly associated with intrawound vancomycin use included intraoperative antibiotic readministration (OR: 2.97; 95% CI: 1.36-6.5; P =0.006) and hospital location, lower odds in Europe (OR: 0.13; 95% CI: 0.06-0.29; P < 0.001) or Asia (OR: 0.02; 95% CI: 0-0.08; P < 0.001) versus North America. CONCLUSIONS: Intraoperative vancomycin use was not associated with reduced SSI incidence compared with no intrawound antibiotics after PSF surgery. LEVEL OF EVIDENCE: Level II.

Humans↗
PubMed1991

Results of the central data analysis.

This chapter presents the results of blind serological studies carried out by workshop participants on 87 monoclonal antibodies (mAbs) supplied to them as a coded panel. Twenty six mAbs had been studied in the first workshop. Participants were asked to carry out immunohistochemical, immunocytological or flow cytometric analysis on a mandatory panel of target tissues or cells. Central computer analysis and other supporting data allowed the assignment of 33 mAbs to seven clusters. Two of the antigens identified have been cloned while two more have been defined as carbohydrate epitopes. The results allow comparison of new mAbs against lung cancer with existing ones and are beginning to provide a description of the antigenic structure of the SCLC cell surface.

Antibodies, Monoclonal↗
PubMed1991

Data analysis of the Second International Workshop on Small Cell Lung Cancer Antigens.

Methods of data collection for the 2nd Small Cell Lung Cancer Workshop are described, and data reliability is reviewed. The method of cluster analysis of the workshop antibodies is described and discussed. Of the 27,111 results submitted 20,705 were judged to be reliable for analysis and 13,802 of these came from immunohistology experiments. Data derived from immunocytochemistry experiments were somewhat less reproducible than flow cytometry, immunohistology and ELISA experiments. The cluster analysis was developed from methods employed in the leucocyte antigens workshops. Several checks on the methods of cluster analysis and the transformation of data did not substantially alter the final groupings. The workshop confirms that, although there are some methodological difficulties, the cluster analysis can successfully be applied to data derived largely from immunohistology, and thus has applicability to other tumour types.

Antibodies, Monoclonal↗
PubMed1979

Some applications of categorical data analysis to epidemiological studies.

Several examples of categorized data from epidemiological studies are analyzed to illustrate that more informative analysis than tests of independence can be performed by fitting models. All of the analyses fit into a unified conceptual framework that can be performed by weighted least squares. The methods presented show how to calculate point estimate of parameters, asymptotic variances, and asymptotically valid chi 2 tests. The examples presented are analysis of relative risks estimated from several 2 x 2 tables, analysis of selected features of life tables, construction of synthetic life tables from cross-sectional studies, and analysis of dose-response curves.

Actuarial Analysis↗
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↗
PubMed2025

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

Characterizing cancer presents a delicate challenge as it involves deciphering complex biological interactions within the tumor's microenvironment. Clinical trials often provide histology images and molecular profiling of tumors, which can help understand these interactions. Despite recent advances in representing multimodal data for weakly supervised tasks in the medical domain, achieving a coherent and interpretable fusion of whole slide images and multi-omics data is still a challenge. Each modality operates at distinct biological levels, introducing substantial correlations between and within data sources. In response to these challenges, we propose a novel deep-learning-based approach designed to represent multi-omics & histopathology data for precision medicine in a readily interpretable manner. While our approach demonstrates superior performance compared to state-of-the-art methods across multiple test cases, it also deals with incomplete and missing data in a robust manner. It extracts various scores characterizing the activity of each modality and their interactions at the pathway and gene levels. The strength of our method lies in its capacity to unravel pathway activation through multimodal relationships and to extend enrichment analysis to spatial data for supervised tasks. We showcase its predictive capacity and interpretation scores by extensively exploring multiple TCGA datasets and validation cohorts. The method opens new perspectives in understanding the complex relationships between multimodal pathological genomic data in different cancer types and is publicly available on Github.

Deep Learning↗
PubMed1979

A versatile physiological data analysis system using an Intel 8080 microprocessor.

A microprocessor based physiological data processor has been realised. The system controls the data flow from physiological experiments and performs on-line mean and variance calculations with an output in graphical form. The analyser accepts one data point every 0.5 ms and has a capacity of 128 records each containing 800 data points. Post stimulus histogram and interval histogram analysis programs have also been written and implemented.

Computers↗
PubMed2025

AmpSeqR: an R package for amplicon deep sequencing&#xa0;data&#xa0;analysis.

Amplicon sequencing (AmpSeq) is a methodology that targets specific genomic regions of interest for polymerase chain reaction (PCR) amplification so that they can be sequenced to a high depth of coverage. Amplicons are typically chosen to be highly polymorphic, usually with several highly informative, high frequency single nucleotide polymorphisms (SNPs) segregating in an amplicon of 100-200 base pair (bp). This allows high sensitivity detection and quantification of the frequency of each sequence within each sample making it suitable for applications such as low frequency somatic mosaicism detection or minor clone detection in mixed samples. AmpSeq is being increasingly applied to both biological and medical studies, in applications such as cancer, infectious diseases and brain mosaicism studies. Current bioinformatics pipelines for AmpSeq data processing lack downstream analysis, have difficulty distinguishing between true sequences and PCR sequencing errors and artifacts, and often require bioinformatic expertise. We present a new R package: AmpSeqR, designed for the processing of deep short-read amplicon sequencing data, with a focus on infectious diseases. The pipeline integrates several existing R packages combining them with newly developed functions to perform optimal filtering of reads to remove noise and improve the accuracy of the detected sequences data, permitting detection of very low frequency clones in mixed samples. The package provides useful functions including data pre-processing, amplicon sequence variants (ASVs) estimation, data post-processing, data visualization, and automatically generates a comprehensive Rmarkdown report that contains all essential results facilitating easy inclusion into reports and publications. AmpSeqR is publicly available at https://github.com/bahlolab/AmpSeqR.

High-Throughput Nucleotide Sequencing↗
PubMed2026

SimpleMicrobiome: An integrated web-based platform for streamlined microbiome data analysis and visualization.

Microbiome studies require multiple analytical steps after initial sequence processing. These steps commonly include data harmonization, preprocessing, taxonomic profiling, diversity analysis, differential abundance testing, predictive modeling, network inference, and preparation of publication-ready outputs. Although robust packages are available for many of these tasks, routine use often depends on command-line workflows, repeated data reformatting, and method-specific scripting. These requirements can limit accessibility for experimental researchers and complicate consistent analysis across interdisciplinary teams. We developed SimpleMicrobiome, a web-based R Shiny platform that integrates established microbiome analysis methods into a single interactive downstream workflow. The application accepts standard abundance, taxonomy, and metadata tables, supports interactive preprocessing and sample filtering, and provides modules for taxa profile visualization, alpha and beta diversity analysis, ANCOM-BC2 and MaAsLin2 differential abundance testing, Random Forest modeling with SHAP-based interpretation, microbial association network inference using SparCC and SPIEC-EASI through NetCoMi, correlation heatmaps, and dbRDA/CAP-style association biplots. The platform is implemented as a modular Shiny application so that preprocessing choices are propagated across downstream analyses, results can be exported as figures and tables, and the same application can be run through the public server, source-code installation, or a Docker image. SimpleMicrobiome consolidates major downstream microbiome analysis tasks in an accessible browser-based environment while retaining links to established analytical frameworks. The platform may reduce technical barriers for non-programming users, improve consistency across exploratory and reporting-oriented analyses, and support collaborative microbiome research. The public application is available at https://simplemicrobiome.mglab.org, the source code is available at https://github.com/yjcho2252/SimpleMicrobiome, and a Docker image for local deployment is available at https://hub.docker.com/r/mglab2252/simplemicrobiome.

differential abundance↗
PubMed1975

Multivariate data analysis based on a computerized patient monitoring system.

Multivariate time series data in post-operative patients (respiratory and cardiovascular) are compared to reference groups. Using this technique under the program control of a computerized patient monitoring system (IBM 1800) various classes in the respiratory and cardiovascular spectrum can define the co-ordinate system in hyperspace. The patient in crisis is recognised by his deviation from normal rates of change of the variable set, as well as by the time trajectories of recovery in the hyperspace.

Computers↗
PubMed2026

Identification of Critical Genes for Recurrent Aphthous Ulcer by Transcriptome Data Analysis and Mendelian Randomization.

PURPOSE: Recurrent aphthous ulcer (RAU) is a common oral mucosal disorder with a poorly understood etiology, significantly affecting patients' quality of life. This study aims to investigate critical genes linked to RAU and explore their biological mechanisms using transcriptomic data and Mendelian randomization (MR) analysis. MATERIALS AND METHODS: RAU-related gene expression data from the GEO database (GSE37265) were analyzed to identify differentially expressed genes (DEGs). A two-sample MR approach was used to assess the causal impact of expression quantitative trait loci (eQTL) on RAU. Critical genes were identified by intersecting DEGs with significant MR findings. GO and KEGG pathway enrichment analyses were performed, along with GSEA and immune cell infiltration analysis, to investigate the functions and mechanisms of these genes in RAU. RESULTS: A total of 184 differentially expressed genes (DEGs) were identified, while 339 RAU-associated genes were screened through MR analysis. Cross-validation further identified 7 critical genes. Among these, CCR1, ERP27, HCK, MICB, and SLC2A3 showed protective associations with RAU risk, whereas CD177 and IFITM1 were positively associated with increased risk. Enrichment analysis revealed that these genes are involved in specific biological processes, including cell migration, immune response, and metabolic regulation, which are closely linked to RAU pathogenesis. CONCLUSION: This systematic study comprehensively investigates the critical causative genes underlying RAU, emphasizing the intricate relationships between immune regulation and metabolic disturbances in its pathology. These findings lay a solid foundation for the development of novel biomarkers and may inform future research on targeted therapeutic strategies for RAU.

Stomatitis, Aphthous↗
PubMedDate not supplied

Multidimensional statistical data analysis of viral hepatitis report records.

The internal connections between the variables defined by the data obtained from viral hepatitis (VH) report records completed ih a Romanian county durihg 1973 are analysed. The mathematical model for linear factorial analysis allowed a simultaneous processing of all variables and the distinction of representative factors. The conclusions of the study suggest new possible approaches by the introduction of supplementary questions in the VH report records.

Factor Analysis, Statistical↗
PubMed2025

NanoASV: a snakemake workflow for reproducible field-based Nanopore full-length 16S metabarcoding amplicon data analysis.

SUMMARY: NanoASV is a conda environment and snakemake-based workflow using state-of-the-art bioinformatics software to process full-length SSU rRNA (16S/18S) amplicons acquired with Oxford Nanopore Sequencing technology. Its strength lies in reproducibility, portability, and the possibility to run offline, allowing in-field analysis. It can be installed on the Nanopore MK1C sequencing device and process data locally. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are freely available at https://github.com/ImagoXV/NanoASV and Zenodo archive at https://doi.org/10.5281/zenodo.14730742.

Software↗
PubMed2025

PEELing: an integrated and user-centric platform for spatially resolved proteomics data analysis.

SUMMARY: Molecular compartmentalization is vital for cellular physiology. Spatially resolved proteomics allows biologists to survey protein composition and dynamics with subcellular resolution. Here, we present PEELing, an integrated package and user-friendly web service for analyzing spatially resolved proteomics data. PEELing assesses data quality using curated or user-defined references, performs cutoff analysis to remove contaminants, connects to databases for functional annotation, and generates data visualizations-providing a streamlined and reproducible workflow to explore spatially resolved proteomics data. AVAILABILITY AND IMPLEMENTATION: PEELing and its tutorial are publicly available at https://peeling.janelia.org/ (Zenodo DOI: 10.5281/zenodo.15692517). A Python package of PEELing is available at https://github.com/JaneliaSciComp/peeling/ (Zenodo DOI: 10.5281/zenodo.15692434).

Proteomics↗
PubMed2026

TaxTriage: an open-source metagenomic sequencing data analysis pipeline enabling putative pathogen detection.

MOTIVATION: TaxTriage is a comprehensive pathogen identification workflow designed for both short- and long-read untargeted DNA and RNA sequencing data. Combining read classification, mapping, and de novo assembly approaches, putative pathogens are identified through comparisons to curated pathogens and abundance expectations from healthy cohort data. Flexible installation options are enabled using Nextflow&#x2122; (NF), including cloud deployment via NF Tower (Seqera Platform) and local installation on a variety of systems, including standalone installations without external internet access. Final analysis summaries are compiled into an Organism Discovery Report, which lists likely pathogens and supporting data, including a custom confidence score. RESULTS: Evaluation of published in silico, clinical, and outbreak datasets identified performance comparable to alternative cloud-based processing pipelines for expected pathogen and co-infection detection with similar sensitivity and increased specificity. To support both public health and veterinary diagnostics communities, customization options have been incorporated to enable improved performance for host species of interest. AVAILABILITY AND IMPLEMENTATION: Source code for TaxTriage is freely available at https://github.com/jhuapl-bio/taxtriage. TaxTriage v2.1.1 has been archived on Zenodo at https://zenodo.org/records/17081354 to permit reproducible analysis as described in this manuscript.

Software↗