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PubMed1975

A profile for managing sensory integrative test data.

A concise method for compiling a data profile from a general sensory integrative test battery has been presented. Subtests from each test used were categorized according to the sensory integrative and motor functions being tested. These categories have been defined and include: tactile-kinesthetic perception, visual perception-figure ground, visual perception-constancy, ocular control, gross motor control, fine motor control, integration of function-two sides of the body, orientation in space, body awareness, and auditory discrimination. A method for converting the various scores into descriptive terminology is provided in which the test results are reported as above age expectancy, appropriate for age, somewhat deficient for age, and markedly deficient for age. The clinical implications of the technique are discussed.

Auditory Perception↗
PubMed1986

Integration of data obtained at fixed intervals.

This article discusses the design and implementation of a program well suited to integrating experimental or simulated data obtained at fixed intervals. The program uses Simpson's method and produces substantially better accuracy than trapezoidal rule integration at little extra computational cost. It accepts command line specification of integration parameters (step size and/or number) and source files. Multiple source files and integration parameters can be specified at runtime. Output can be displayed on the console or redirected to an ASCII file.

Computers↗
PubMed2025

RNAcare: integrating clinical data with transcriptomic evidence using rheumatoid arthritis as a case study.

BACKGROUND: Gene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. RESULTS: Our RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing transcriptomic data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings. CONCLUSION: We present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/ , and its source code is https://github.com/sii-scRNA-Seq/RNAcare/ .

Humans↗
PubMed1982

Cytophotometric DNA determinations and autoradiographic studies in salivary gland nuclei from larvae with different karyotypes in Drosophila melanogaster.

Cytophotometric DNA determinations in Feulgen stained mitotic diploid chromosome sets of neuroblasts from larvae of Drosophila melanogaster stocks, which possess different karyotypes, show significant differences between the 4C values, caused by an additional or deficient X- and Y-chromosome depending on the karyotype. The ranges of polytenic DNA size classes are theoretically expected to be doublings of the corresponding 4C mean value of each karyotype. The extinction integral data of nuclei with completely duplicated 4C quantities exclusively fall into the range of the expected size classes. Not all data falling into the range of a size class necessarily originate from duplicated nuclei, because the limits of the DNA size classes cannot be determined by measurements, but must be estimated from the confidence limits of the corresponding 4C mean value. The validity of the mitotic 4C values of the karyotypes X/X and X/Y is tested using data from non-labeled interphase nuclei, where extinction integral data accumulate in two groups. The larger values (= G2-nuclei) confirm the 4C values of mitotic chromosome sets, and the lower values (= G1-nuclei) are just half of these. Extinction integrals from individual, 3H-thymidine non-incorporating polytene salivary gland nuclei accumulate in distinct, non-overlapping groups which are always complete doublings of the preceding smaller group. In each karyotype, the most frequent data of each group are in accord with the 4C doublings. The data from labeled nuclei alternate with those from unlabeled nuclei. The measured DNA values of individual polytene nuclei that did not incorporate any 3H-thymidine, demonstrate that all chromosomal DNA replicates completely during polytenization of the chromosomes in the larval salivary gland nuclei of Drosophila melanogaster. Specifically, this would mean that the heterochromatic Y-chromosome replicates as well as the partially heterochromatic X-chromosome along with the autosomes. There is no indication of underreplicating heterochromatin.

Animals↗
PubMed1989

Effect of uncertainty and diagnosticity on classification of multidimensional data with integral and separable displays of system status.

Integrative, objectlike displays have been advocated for presenting multidimensional system data. In this research two experiments assess the effect of uncertainty on the processing of integral and separable displays. In each experiment 30 subjects were trained to classify instances of system state into one of four state categories using a configural display, a bar graph display, or a digital display. In Experiment 1 the range of instances from the state categories was uniform; in Experiment 2 the distribution was biased toward those instances of highly uncertain state category membership. After training, subjects received extended practice classifying system data. In both experiments uncertainty was found to have the greatest effect on classification performance. In Experiment 1 the bar graph display was consistently superior; the configural display was superior to the digital display only under conditions of low uncertainty. In Experiment 2 the superiority of the bar graph display diminished, producing results equivalent to those of the digital display, with the configural display producing the worst performance. The effect of uncertainty on classification performance is discussed, with specific attention paid to the apparent configural and separable properties of the bar graph display.

Adolescent↗
PubMed2026

Toward large-scale mass spectrometry-based omics for clinical applications.

INTRODUCTION: As healthcare advances toward personalized medicine, mass spectrometry-based research is advancing our understanding of cellular biology and disease states, and translating these findings into clinical applications. This review highlights recent advances in methodology and technology that demonstrate the capabilities of mass spectrometry-based proteomics, lipidomics, and metabolomics in clinical practice. AREAS COVERED: The ability to directly analyze functional molecules with mass spectrometry uncovers crucial clinical information. Each data modality (proteins, lipids, and metabolites) provides essential insight into healthy and disease states. As technology advances, integrating data from different modalities unlocks new possibilities for clinical research. To gain the most from this multi-omic data, unsupervised integration methods can provide detailed insights into complex biological processes. As the field applies this knowledge, healthcare could experience significant leaps in the near future. This review examines recent advancements in mass spectrometry-based proteomics, lipidomics, and metabolomics, focusing on how improvements in sample preparation, automation, and multi-omics data integration are making large-scale clinical studies more accessible. EXPERT OPINION: Recent technical and methodological advancements in mass spectrometry analysis have propelled healthcare toward a tipping point, shifting from traditional RNA- and DNA-based research to downstream analysis of protein, lipid, and metabolite effectors.

Humans↗
PubMed1990

A standardized data collection tool.

To successfully integrate data collection into the staff's responsibilities, the process must be simple, concise, and easy to use. The data collection tool described in this article includes all the important information at a glance, permits easy comparison with projected and actual thresholds, and analysis of data with follow-up action. It reduces the volumes of paperwork required in many systems. Since the indicators are written on only one paper, there is a reduction in transcription time. Additionally, one paper that contains a sample of twenty should be adequate for a unit-based indicator. Use of this tool reduces the number of papers that must be handled by the QA coordinator as well. Finally, the tool is flexible enough to use in a variety of settings.

Data Collection↗
PubMed2026

VIJB: a companion of the JBROWSE genome browser for the visually impaired people.

MOTIVATION: The availability of touch-sensitive and haptic devices has been a keystone development for the inclusion of visually impaired people (VIPs) in modern, highly digitized work environments. Braille displays have proven efficient and versatile enough to parse large and complex text files, making bioinformatics and text-heavy programming accessible to VIPs. However, the complex graphical objects -combining numerous datasets- typically generated during data integration remain challenging, even with the aid of descriptive AI. This is particularly true in functional genomics. Here, we present VIJB, a simple application that displays the multilayered output of the JBROWSE genome browser on a Braille reader, enabling VIPs to fully participate in data integration in functional genomics. AVAILABILITY AND IMPLEMENTATION: VIJB is programmed in Python and relies on the scientific library NumPy, the braillegraph and pyBigWig libraries, and the TABIX software. The architecture is summarized in Supplementary Material 1, available as supplementary data at Bioinformatics online. VIJB is available for download at the GitHub repository https://GitHub.com/NiBuMNHN/VIJB and is licenced under the GPL 3.0.

Persons with Visual Disabilities↗
PubMed2026

DIVAS: an R package for identifying shared and individual variations of multiomics data.

MOTIVATION: Multiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. RESULTS: We present an open-source R package implementing data integration via analysis of subspaces (DIVAS), a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas existing methods did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. AVAILABILITY AND IMPLEMENTATION: DIVAS is available at https://github.com/ByronSyun/DIVAS, with documentation and vignettes. The COVID-19 case study vignette is available at https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/.

Multiomics↗
PubMedDate not supplied

Integrating safety data: the expert report.

Complete evaluation of the toxicity of a new chemical entity requires critical analysis of the pattern of positive and negative findings in all types of toxicity tests, pharmacokinetics and metabolism in the species examined, and correlation of the effects with information about its pharmacodynamic and pharmacological properties. The goal is to obtain sufficient understanding of the mechanisms underlying the therapeutic and toxic effects of the compound to permit a well-supported extrapolation from the test to the target species. The Expert Report system in the European Community is based on comprehensive 25-page reviews of information about a compound arranged under 3 headings: Chemistry and Pharmacy, Pharmacology and Toxicology, and Clinical Studies. Each section requires a searching review of the experimental work and the relevant literature, integration of the findings, and then careful correlation among these 3 main areas of knowledge to indicate the circumstances of safe and effective use of the drug.

Documentation↗
PubMed1995

Canadian anaesthesia physician resource planning--is it possible?

This study was undertaken with the objective of assessing current sources of information for anaesthesia Physician Resource Planning (PRP). Four major data bases, the annual reports of Health and Welfare Canada (H&W), the education statistics from the Canadian Post-M.D. Education Registry (CAPER), the Royal College of Physicians and Surgeons of Canada (RCPSC) and the Physician Resource Data System of the Canadian Medical Association (PRDS), were examined for the period 1982 to 1991. The ratio of the number of surgical (S) to anaesthesia (A) clinicians decreased over this period despite an increase in the S:A ratios for trainees and certificants. The number of female anaesthetists has progressively increased. A steady decline in the number of rural anaesthetists has occurred. Age distribution of active certified anaesthetists revealed marked inter-regional differences. Little change was noted in the total mean hours worked per week. Each database provided valuable, but limited, data. The PRDS data is useful in assessing trends (age, sex and practice activity). Information provided by H&W tends to underestimate anaesthesia resource information by at least 10%. While information obtained from RCPSC and CAPER is accurate, the current mode of presentation of data limits their usefulness. Integrating data from all the databases appears to provide a meaningful assessment for PRP rather than assessing each database in isolation. Interpretation of the information and its value must take into account the limitations of the data being provided. Assessing present and planning future needs based on the current information structure will prove extremely difficult.

Adult↗
PubMed2026

Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.

BACKGROUND: With growing interest in ART, fertility preservation, and postmenopausal health of women, reproductive medicine is increasingly focused on characterizing oocytes and ovarian tissue composition, as well as understanding the molecular mechanisms that guide ovarian function throughout its lifecycle. High-throughput omics technologies have enabled the characterization of different molecular layers, leading to substantial advances in our understanding of their complex dynamics. However, not all molecular aspects are studied equally, and studies examining the same modalities often show inconsistencies, underscoring the need for data standardization and highlighting the potential for using transformative artificial intelligence and machine-learning (AI/ML) methods for ovary studies. OBJECTIVE AND RATIONALE: This study aims to evaluate how multi-omic studies have advanced our understanding of the ovarian lifecycle from fetal development to postmenopause. We systematically reviewed published studies that have investigated molecular/omic layers, including the genome, methylome, transcriptome, and proteome throughout ovarian development and aging. Our analysis identified key molecular and cellular patterns, highlighted inconsistencies across studies and addressed gaps in data analysis, interpretation, and reproducibility to guide future research. SEARCH METHODS: We conducted a systematic literature search of Medline (PubMed), Embase (Ovid), and Web of Science Core Collection (Clarivate) using a combination of controlled and free text terms for human ovary, oogenesis, folliculogenesis, ovary development and (epi)genome, transcriptome, proteome, and multi-omic mechanisms to find relevant articles published before August 2025. To focus the scope of the current review, studies of domesticated and farm animals, rodents and other model organisms, non-human primates, as well as those examining various human ovarian pathologies were excluded. OUTCOMES: The search identified 23 546 studies for screening, of which 637 full-text studies were assessed for eligibility. Subsequently, we extracted data from 121 studies. Most studies analyzed the transcriptome of oocytes, granulosa cells, and ovarian tissue from reproductive-age individuals (n = 91), with fewer studies examining samples from individuals of advanced reproductive age (n = 45) and fetal (n = 16) samples. Transcriptome analyses were most common (n = 103, 85%), followed by proteome (n = 19, 16%) and epigenome (n = 14, 12%) studies. We found substantial variation in how studies defined and reported participants' groups as well as in their sequencing technologies and data analysis methods, with a lack of standardized reporting of background clinical information, data analysis methods, and pipeline details. The key findings underscore the prevailing consensus on genes defining major ovarian cell types and their roles throughout the ovarian lifespan, from prenatal development to postmenopausal transformation. This review highlighted the underrepresentation of certain patient groups, particularly prepubertal and peri-/postmenopausal individuals, among researched populations, due to obvious clinical and ethical reasons. WIDER IMPLICATIONS: This scoping review offers a comprehensive overview and benchmark of the current state of high-throughput omics-based research on ovarian cellular composition and molecular dynamics. To address these shortcomings, we propose general recommendations for multi-omics ovary studies and emphasize the necessity for more thorough multi-omic data integration by effectively applying novel AI/ML approaches. They can potentially improve the quality of multi-omics analyses at both single-cell and tissue levels despite limited sample sizes and enable integration of molecular profiling data with clinical and radiology datasets, enabling a more comprehensive understanding of ovarian biology. Such advancements can enhance reproducibility of research findings and guide future research to deepen our understanding of ovarian biology and ultimately support the development of medical technologies for better preserving fertility and alleviating infertility. REGISTRATION NUMBER: A protocol was published a priori on the Open Science Framework (https://osf.io/z38gb/).

Female↗
PubMed1989

Discrimination of mode of action of anxiolytics using an integrated computer data bank and Dynamic Brain Mapping (CNS effects of diazepam and lorazepam).

In a double-blind, placebo-controlled, crossover study, the CNS effects of intravenously administered diazepam and lorazepam were investigated in anxious subjects through the quantitative pharmaco-EEG (QPEEG) method. For up to 4 1/2 hours following administration the effects of each substance on brain function were measured using computer analyzed EEG recordings (CEEG) and a new technique called Dynamic Brain Mapping. The following observations were made: 1. Both active drugs produce statistically significant CNS effects as measured by CEEG changes. These changes were observed earlier with diazepam than with lorazepam. 2. Although both compounds are classified as anxiolytic by the routine computer EEG data base, the detailed brain mapping technology indicated that the CNS effects of diazepam and lorazepam were quantitatively and qualitatively different. 3. Clinical CNS side-effects (sedation) were seen more frequently with lorazepam than with diazepam. This was consistent with the EEG slowing producing properties of lorazepam. The EEG fast activity which is characteristic for all anxiolytics was established more with diazepam than lorazepam.

Adult↗
PubMed2025

A comprehensive integration of data on the association of ITPKC polymorphisms with susceptibility to Kawasaki disease: a meta-analysis.

BACKGROUND: This study aims to conduct a comprehensive meta-analysis of existing research to define clear associations between variations in the ITPKC gene and the risk of developing Kawasaki disease (KD). METHODS: A comprehensive search was conducted across multiple databases, including but not limited to PubMed, Scopus, EMBASE, and CNKI, up to June 1, 2024, to gather relevant information. This search utilized keywords and MeSH terms related to hyperbilirubinemia and genetic factors. The inclusion criteria encompassed original case-control, longitudinal, or cohort studies. Correlations were analyzed as odds ratios (ORs) with 95% confidence intervals (CIs) using Comprehensive Meta-Analysis software. RESULTS: Eighteen case-control studies with 5,434 KD cases and 9,419 controls were analyzed. Of these, ten studies assessed 3,129 KD cases and 6,172 controls for the rs28493229 variant, four examined 1,039 cases and 1,688 controls for the rs2290692 variant, two focused on 595 cases and 820 controls for the rs7251246 variant, and two investigated 671 cases and 739 controls for the rs10420685 variant. Results showed a significant association between the rs28493229 polymorphism and increased KD risk across all five genetic models. Subgroup analysis indicated this polymorphism correlates with KD susceptibility in Asians but not in the Chinese population. In contrast, no associations were found between the rs2290692, rs7251246, and rs10420685 polymorphisms and KD risk. CONCLUSIONS: Our pooled data indicate a significant association between the ITPKC rs28493229 polymorphism's minor allele and an increased risk of developing KD, suggesting this variant may enhance susceptibility. Conversely, SNPs rs2290692, rs7251246, and rs10420685 do not demonstrate a statistically significant relationship with KD.

Humans↗