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At least 19 recordsLinked to original sources
PubMed1992

Tentative reference values for gold, silver and platinum: literature data analysis.

Data available on biologic fluid content of therapeutic metals are uneven for silver, gold and platinum respectively. Tentative reference values may be proposed on the basis of the most representative studies for silver and gold. For silver it is suggested that the variability in normal subjects could range up to 10 micrograms/l in whole blood and up to 1 microgram/l in urine. For gold 0.5 microgram/l can be considered the upper limit for both whole blood and urine. For platinum there is no indication that concentration in either blood or urine could reach detectable amounts in normal subjects.

Body Fluids↗
PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗
PubMed1977

The molecular function of hemoglobin as reflected in ligand binding data: analysis of data on erythrocytes.

Hemoglobin oxygen binding data on erythrocytes at diffrent pH, PCO2 and bisphosphoglycerate concentrations have been analyzed in terms of an extended version of the Herzfeld-Stanley model of 1972. The binding of oxygen to subunits when the tetramer is in the quaternary oxy conformation was found to be insensitive to moderate changes in pH and pCO2 (0.71 +/- 0.05 mm Hg-1). Utilizing this circumstance it has been possible to obtain, for the first time, unique estimates of energy parameters related to hemoglobin cooperativity and effector action. At 37 degrees C, pH 7.2 and pCO2 22 mm Hg the following parameter values were obtained: The allosteric constant: (1.5 + 0.4)-10(4); the oxygen binding constant of the deoxy state: (5.4 +/- 0.3).10(-3) mm Hg-1; the 2,3-bisphosphoglycerate binding constants: (3.3 +/- 1.3).10(3)1. mol-1(deoxy), (1.3 +/- 0.5).10(2)1. mol-1 (oxy). Quarternary transition most likely takes place after binding of the second O2 molecule. Following the concepts of Perutz the results suggest that (1) protons and carbon dioxide act as constraint effectors and/or as quaternary effectors; (2) the difference in total conformational energy between the two quaternary ligand-free states is almost exclusively confined to molecular constraints and very little to the difference in quaternary conformational energy. The consistency of the results indicate that the model may be regarded as a useful tool for the description of the functional interrelations in the hemoglobin oxygenation process as reflected in oxygen binding data.

Binding Sites↗
PubMed1991

Multivariate data analysis of NMR data.

Multivariate methods based on principal components (PCA and PLS) have been used to reduce NMR spectral information, to predict NMR parameters of complicated structures, and to relate shift data sets to dependent descriptors of biological significance. Noise reduction and elimination of instrumental artifacts are easily performed on 2D NMR data. Configurational classification of triterpenes and shift predictions in disubstituted benzenes can be obtained using PCA and PLS analysis. Finally, the shift predictions of tripeptides from descriptors of amino acids open the possibility of automatic analysis of multidimensional data of complex structures.

Amino Acid Sequence↗
PubMed1977

[Graphical methods in data analysis (author's transl)].

Data analysis is concerned with attentive description and communication of the information contents of a body of data. Background information, conceptual insight and especially graphical methods play a key role in data analysis for developing a feeling for the data both by formal procedures to be applied in the light of specified models and even more by informal inference or methods that are suggestive and conctructive. This paper reviews graphical methods useful for description, screening, analysis, cross-examining, selection, reduction, presentation and summary of data: for uncovering distributional peculiarities and understanding the structure underlying experimental and survey data. Moreover scatter plots, probability plots and residual plots provide insight into the possible inappropriateness of certain assumptions of the statistical model. Some techniques are illustrated by examples: four-dimensional data may be reprented as scatter plot on ordinary graph paper by using a combination of 2 different sets of symbols for at most 7 different levels of the third variable (formula: see text) and of the fourth variable (formula: see text). Comments on the use of tables and graphical methods, a small overview of the latter and of the scope of applications endeavour to pave the way such that structures may be better understandable and unanticipated characteristics may be spotted.

Factor Analysis, Statistical↗
PubMedDate not supplied

A computerized data analysis system for electrogastrogram.

A comprehensive computerized data analysis system for the electrogastrogram is presented in this paper. The electrogastrogram (EGG) is a cutaneous measurement of electrical activity of the stomach by positioning electrodes on the abdominal skin. Since the signal-to-noise ratio of the EGG is very low, visual analysis is impossible. The data analysis system presented in this paper contains a series of PC programs to perform: (a) data acquisition and real-time A/D conversion; (b) digital filter design and digital filtering; (c) adaptive cancellation of respiratory artifact; (d) smoothed power spectral analysis; (e) adaptive running spectral analysis; (f) two- or three-dimensional display of the EGG and analysis results. The basic principles of the system and sample results are presented.

Data Display↗
PubMed2026

An introductory practical guide to secondary data analysis in pediatric urology.

INTRODUCTION: Secondary data analysis (SDA) has become an increasingly important approach in pediatric urology, enabling the study of long-term outcomes, care variation, and disparities in populations with chronic or congenital urologic conditions. With the growing availability of large datasets, a structured approach to designing and conducting SDA studies is increasingly relevant. OBJECTIVES: To provide an introductory, practical guide to SDA in pediatric urology by (1) summarizing commonly used data sources with representative studies, (2) outlining a stepwise approach to designing and executing SDA studies, and (3) highlighting key methodological considerations, limitations, and opportunities for future work. STUDY DESIGN: Narrative review of existing literature and commonly used datasets relevant to pediatric urology, including administrative claims, hospital encounter databases, clinical registries, electronic health record networks, and population-based surveys. RESULTS: Data sources differ in scope, clinical granularity, longitudinal follow-up, and representativeness, and each is suited to specific research questions. We present a practical workflow for SDA, including dataset selection, cohort definition, and analytic planning. Linkage across datasets can provide a more comprehensive view of care patterns and outcomes, although feasibility is influenced by legal, technical, and data-quality constraints. DISCUSSION: SDA enables population-level analyses and the study of rare conditions that are challenging to evaluate through single-center or prospective designs. However, careful cohort definition, feasibility assessment, and awareness of data limitations are essential to ensure validity and interpretability. CONCLUSION: SDA provides a scalable, cost-efficient framework for generating meaningful evidence in pediatric urology. Continued efforts to harmonize data elements, improve linkage infrastructure, and support cross-institution collaboration will enhance the quality and impact of future research. This article provides a practical framework and examples to support the design and execution of SDA studies.

Humans↗
PubMed1992

[Data analysis by statistical models].

The basic idea for the realization of effective statistical data analysis is illustrated with an example. The use of statistical models is explained and the feasibility of objective comparison of the models by an information criterion AIC is demonstrated. Further, the possibility of practical use of Bayesian models for complex data analysis is explained. Finally, the necessity of cooperation between the experts of respective fields and statisticians for further development of statistical data analysis is mentioned.

Adult↗
PubMed1979

A data analysis microcomputer package (DAMP) for biomedical signals.

The advent of cheap, powerful microcomputer systems makes the analysis of data via sophisticated techniques available to the personnel who are non-specialists in computing systems. The DAMP package described here is intended for use on personal computers and has therefore been written in BASIC for portability. The analysis techniques are powerful, comprising algorithms to perform sample-data generation, plotting displays, digital data filtering, auto-correlation functions, fast Fourier transforms and autoregressive modelling. The last technique contains a number of options including the display of z-plane plots, frequency response of the model, residual plotting and auto-correlation of the residuals. Illustrative results are shown from psychological mood data and rat locomotor activity. The package is designed both to instruct a user in the techniques of spectral analysis, and also to provide a range of methods for investigating time and frequency behaviour of biomedical data.

Biomedical Engineering↗
PubMed1992

Determination of the rate of cerebral oxygen consumption and regional cerebral blood flow by non-invasive 17O in vivo NMR spectroscopy and magnetic resonance imaging: Part 1. Theory and data analysis methods.

Theory and novel data analysis methods of 17O inhalation measurements are presented for the calculation of CMRO2, regional cerebral blood flow (rCBF), the reflow (R), the arterial venous difference (AVD) and the partition coefficient (lambda). Several of the methods proposed for the determination of CMRO2 do not require measurements of regional cerebral blood flow and H2(17)O arterial concentration. All methods of analysis are based on the Kety-Schmidt approach.

Animals↗
PubMed1991

Discussion of PET workshop reports, including recommendations of PET Data Analysis Working Group.

On May 1-2, 1989, a PET Data Analysis Working Group convened to consider positron emission tomography (PET) methodology and data analysis. The papers presented and the recommendations of the Group are reviewed. The Group recommended that a standard phantom of the human brain be used by different institutions to examine machine and data reconstruction PET variables. Interinstitutional comparisons could be aided by using a standard three-dimensional coordinate system. Deformations within individual diseased or atypical brains would require nonlinear as well as linear transformations to the standard space, using magnetic resonance images in register with the PET images. Methods for intersubject averaging of pixel-by-pixel or region-of-interest data, as well as appropriate statistical methods, need to be developed. PET data may first be exploratory and hypothesis-generating (with less stringent statistical theory), then later used to test hypotheses (with more stringent statistical criteria). Common databases, obtained by computer simulation models with known inherent structure, or directly by PET measurements on different groups, could be used to compare analytical and statistical methods among institutions.

Brain↗
PubMed2025

SpectroPipeR-a streamlining post Spectronaut® DIA-MS data analysis R package.

SUMMARY: Proteome studies frequently encounter challenges in down-stream data analysis due to limited bioinformatics resources, rapid data generation, and variations in analytical methods. To address these issues, we developed SpectroPipeR, an R package designed to streamline data analysis tasks and provide a comprehensive, standardized pipeline for Spectronaut® DIA-MS data. This novel package automates various analytical processes, including XIC plots, ID rate summary, normalization, batch and covariate adjustment, relative protein quantification, multivariate analysis, and statistical analysis, while generating interactive HTML reports for e.g. ELN systems. AVAILABILITY AND IMPLEMENTATION: The SpectroPipeR package (manual: https://stemicha.github.io/SpectroPipeR/) was written in R and is freely available on GitHub (https://github.com/stemicha/SpectroPipeR).

Software↗
PubMed1992

Evaluation of Bayesian estimation in comparison to NONMEM for population pharmacokinetic data analysis: application to pefloxacin in intensive care unit patients.

The pharmacokinetics of pefloxacin (PF) were investigated in a population of 74 intensive care unit patients receiving 400 mg bid as 1-hr infusion using (i) Bayesian estimation (BE) of individual patient parameters followed by multiple linear regression (MLR) analysis and (ii) NONMEM analysis. The data consisted of 3 to 9 PF plasma levels per patient measured over 1 to 3 dosage intervals (total 113) according to four different limited (suboptimal) sampling 3-point protocols. Twenty-nine covariates (including 15 comedications) were considered to explain the interpatient variability. Predicted PF CL for a patient with median covariates values was similar in both BE/MLR and NONMEM analysis (4.02 and 3.92 L/hr, respectively). Bilirubin level and age were identified as the major determinants of PF CL by both approaches with similar predicted magnitude of effects (about 40 and 30% decrease of median CL, respectively). Confounding effects were observed between creatinine clearance (26% decrease of PF CL in the BE/MLR model), simplified acute physiology score (a global score based on 14 biological and clinical variables) (18% decrease of median CL in the NONMEM model) and age (entered in both models) which were highly correlated in our data base. However, both models predicted similar PF CL for actual subpopulations by using actual covariate values. Finally, the NONMEM analysis allowed identification of an effect of weight on CL (decrease of CL for weight < 65 kg) whereas the BE/MLR analysis predicted an increase of CL in patients treated with phenobarbital. In conclusion, both approaches allowed identification of the major risk factors of PF pharmacokinetics in ICU patients. Their potential use at different stages of drug development is discussed.

Adolescent↗
PubMed1992

Distributed data analysis in a multicenter study: the CARDIA Study.

Unlike distributed data entry, which is used in many large epidemiologic studies and multicenter clinical trials, distributed data analysis is a relatively new concept. This paper reports on the usefulness of such a system in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. CARDIA distributes the entire examination dataset to participating centers soon after completion of each round of data collection. The process was designed to encourage more numerous, diverse, and rapid publications, and to allow for more efficient use of the manpower and expertise in centers. Responsibilities of the coordinating center have changed from a conventional coordinating center but remain substantial due to the need for collating, monitoring, verifying, and documenting the distributed data analysis (DDA) system. DDA is successful from the standpoint of implementation and operation--21 manuscripts representing work analyzed at six participating centers had been submitted for publication within 3.5 years of the completion of the baseline examination.

Adolescent↗
PubMed2025

UALCAN Mobile, an app for cancer proteogenomic data analysis.

Cancer is a complex disease affecting various organs and is a major cause of death worldwide. During cancer initiation, disease progression, and tumor metastasis, various genomic and proteomic alterations are observed. Recent technological advances have led to the generation of large amounts of molecular data, including genomics and transcriptomics. These large-scale datasets can be utilized to analyze and identify sub-class-specific cancer biomarkers and targets. However, there is a need for the development of user-friendly tools for large-scale data analysis, disseminating the analyzed data in a visualizable format to cancer researchers with no programming skills. We developed UALCAN, a comprehensive platform that allows users to integrate disparate data to better understand the genes, proteins, and pathways perturbed in cancer and make discoveries of potential biomarkers and targets. In the current study, we describe the development of the UALCAN Mobile application (app) that will provide cancer transcriptomic data obtained from The Cancer Genome Atlas (TCGA) project to evaluate protein-coding gene expression based on various stratifications, including stage, grade, race, gender, and molecular-subtypes across over 30 types of cancers. In addition, the UALCAN mobile provides data analysis options for epigenetic changes due to DNA promoter methylation and Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer proteomic data. The app provides access to large cancer molecular datasets on the go. To find changes in the expression of causative genes and proteins and to identify biomarkers and therapeutic targets, UALCAN mobile app will be extremely valuable. The "UALCAN Mobile" app is free to use and can be downloaded from both the iOS/Apple and the Android Play Store and has been downloaded over 100 times in each of iOS and android app stores.

app↗
PubMed1991

Uncovering psychiatric test information with graphical techniques of Exploratory Data Analysis.

This article illustrates how Exploratory Data Analysis (EDA) can complement conventional statistical methods in evaluating psychiatric tests. Using one recent EDA computer program, we evaluated the ability of repeated psychiatric screening tests (the General Health Questionnaire [GHQ]) to predict medical and psychiatric service use in a Health Maintenance Organization (HMO), the Harvard Community Health Plan (HCHP). Using a stratified random sample of 244 new HCHP enrollees and viewing three-dimensional graphs of their data from multiple perspectives, we found two subpopulations: low GHQ scorers, for whom the tests did not predict service use; and high scorers, for whom they did. Surprisingly, improving scores forecast increased use and chronically high scores predicted diminished use. Using another stratified random sample of 213 new HCHP enrollees, and with scatterplot matrices from another interactive computer program, we found that high and unchanging GHQ scores forecast HMO dropout. We examine possible interpretations--for example, that chronically distressed patients may become immobilized, diminish service use, and ultimately leave the HMO. We also explain how EDA methods may help uncover elusive results in other data (e.g., mental health outcomes).

Adult↗
PubMed2026

BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool.

SUMMARY: Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular features (e.g., genes, proteins, metabolites). While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine. AVAILABILITY AND IMPLEMENTATION: The BioNeuralNet library is available via The Python Package Index (PyPI). Source code, documentation, tutorials, and workflows are hosted at https://bioneuralnet.readthedocs.io. Code archived at https://doi.org/10.5281/zenodo.17503083.

Graph Neural Networks↗