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PubMed1992

Lifetime data analysis of disease and aging by the Weibull probability distribution.

We applied the Weibull distribution to the life-table and age-patterns of diseases in Japan. The life-table follows a composite Weibull distribution composed of initial failure and two stage wear-out failure periods. The extension of lifespan during the past century is manifested as increases in the scale parameters in all three periods and the shape parameters in the wear-out periods with female predominancy. The shape parameters of diseases show time-independent sex-dependent specific values. When consistent changes are observed, such as increases in the shape parameters of tuberculosis and pneumonia, legitimate causes such as prevention and repair at the societal and medical level are present. Cancer and arteriosclerosis share a common range of shape parameters suggesting analogous underlying biological processes. An analysis of the epidemiology of human aging and disease by the Weibull distribution discloses intrinsic properties of man as a biological organization as well as a societal presence.

Adolescent↗
PubMed1992

[Estimation of the incidence of diabetes in the Federal Republic of Germany based on insurance data. A secondary data analysis of a representative random sample of locally insured persons in the city of Dortmund].

All records relating to medical prescriptions and services relating to diabetes for a representative sample (n = 6478) of inhabitants of Dortmund in 1988, who were members of the Local Health Insurance in Dortmund, were analysed. 264 patients (4.08%) were receiving antidiabetic drugs during that year, 198 (3.06%) oral ones, 42 (0.65%) insulin and 24 (0.37%) both. 12.41% of all those insured over the age of 60 were on antidiabetic drugs, more women than men (5.08 vs 2.94%; P less than 0.001). The most frequently used oral antidiabetics were sulphonylurea ones (95.1%), while the proportion of biguanides was only 4.4%. On the basis of established diagnosis, regular blood sugar and HbA1 tests, as well as optic fundi examination, a further 104 persons (1.60%) are assumed to have diabetes but not treated by drugs. This sample indicates an overall prevalence of diabetes of 5.68% among the population. Relating it to the age and sex pattern of the population of the Federal Republic of Germany this corresponds to a prevalence of diagnosed diabetes of 4.82%. Thus there are probably a little less than 4 million diabetic citizens of the Federal Republic of Germany.

Age Factors↗
PubMed2025

NOODAI: a webserver for network-oriented multi-omics data analysis and integration pipeline.

SUMMARY: Omics profiling has proven of great use for unbiased and comprehensive identification of key features that define biological phenotypes and underlie medical conditions. While each omics profile assists characterization of specific molecular components relevant for the studied phenotype, their joint evaluation can offer deeper insights into the overall mechanistic functioning of biological systems. Here, we introduce an approach where, starting from representative traits (e.g. differentially expressed elements) obtained for each omics profile, we construct and analyze joint interaction networks. The resulting networks rely on the existing knowledge of confident interactions among biological entities. We use these maps to identify and describe central elements, which connect multiple entities characteristic of the studied phenotypes and we leverage MONET network decomposition tool in order to highlight functionally connected network modules. In order to enable broad usage of this approach, we developed the NOODAI software platform, which enables integrative omics analysis through a user-friendly interface. The analysis outcomes are presented both as raw output tables as well as informative summary plots and written reports. Since the MONET tool enables the use of algorithms with strong performance in identifying disease-relevant modules, NOODAI software platform can be of a high value for analyzing clinical multi-omics datasets. AVAILABILITY AND IMPLEMENTATION: NOODAI is freely accessible at https://omics-oracle.com. Source code is available under GPL3 at: https://github.com/TotuTiberiu/NOODAI with the DOI: 10.5281/zenodo.17203984.

Software↗
PubMed2026

BRIDGE: an interactive application for multi-omics data analysis, visualization and integration.

SUMMARY: BRIDGE is a Shiny-based application that provides an accessible, modular platform for individual and integrative multi-omics analysis. Using an independent SQLite database backend, it offers a local, private, and user-friendly environment that requires no prior computational expertise. The application supports proteomics, phospho-proteomics, and RNA-seq analyses through a comprehensive suite of visualization and analytical modules, together with an integrated multi-omics analysis pipeline. Built-in caching and asynchronous processing improve responsiveness, enabling efficient exploration, analysis, and visualization of multi-omics datasets on moderate hardware. AVAILABILITY AND IMPLEMENTATION: BRIDGE is implemented in R using Shiny and is freely available as a Docker container at https://ghcr.io/paulilab/bridge. A public demonstration server with example datasets is available at https://bridge.imp.ac.at. Code and datasets are also available at https://github.com/paulilab/BRIDGE and under DOI: https://doi.org/10.5281/zenodo.20215824.

Multiomics↗
PubMed1992

Crystallization and preliminary data analysis of Flock House virus.

Flock House virus, purified from infected cultured Drosophila cells, crystallizes into three different forms under identical growth conditions. Two crystal forms grow in the trigonal space group R3, both with equivalent cell constants a = 323.6 A, alpha = 61.7 degrees. The difference between the two trigonal crystal forms is 1.1 degrees in the orientation of the virus particle as determined from the rotation function. Early crystal setups grew in one form, while recent crystals grew in the other form. The third space group, which accounts for 5% of the observed crystals and grows with both trigonal forms, is orthorhombic I222 with cell parameters a = 416.7, b = 332.1, c = 351.2 A. The trigonal crystal forms contain one virion per unit cell and the orthorhombic form contains two particles per cell. All three crystal forms diffract X-rays to 2.8 A resolution.

Animals↗
PubMedDate not supplied

Some interrelationships between plasma levels of LH, FSH, oestradiol 17beta, androgens and semen analysis data in male infertility patients.

Serum LH, FSH and immunoreactive testosterone-like substances (TLS) have been measured by radioimmunoassay in 130 male infertility patients and oestradiol 17beta in 26 cases. A weak but significant negative correlation was found between FSH and sperm count (rs = -0.19, p less than 0.05) but not LH and sperm count. However, LH and FSH were strongly correlated in the azoospermic (rs = 0.71, p less than 0.01) and oligozoospermic (rs = 0.53, p less than 0.01) groups and levels of both gonadotrophins were significantly elevated in the azoospermic and oligozoospermic as compared to the normozoospermic group. The elevated LH levels in the oligozoo- and azoospermic groups could not be explained by reduced negative feedback of testosterone or oestradiol 17beta since firstly, TLS and oestradiol 17beta levels were similar in all three groups and, secondly, within-group correlations between LH and TLS were either non-significant or positive (azoospermic group r = 0.37, p = 0.06). It is suggested that spermatogenesis-related feedback factor(s) may inhibit LH as well as FSH secretion. No role for oestradiol 17beta as a selective inhibitor of FSH secretion seemed likely as oestradiol 17beta levels were similar in the three groups and were correlated (r = 0.51, p less than 0.01) to TLS levels i.e. to leydig cell rather than spermatogenic function. Seminal fructose was negatively correlated (r = -0.26, p less than 0.01) with sperm count but not significantly with plasma TLS. It would thus seem unlikely that the tendency for seminal fructose levels to increase as sperm count decreases is due to increased androgen production or that seminal fructose can be used as an index of a patient's androgenic status. Patients with varicoceles had hormone levels similar to other patients of similar sperm count. Both sperm morphology and motility were strongly correlated to log sperm count (r = 0.84 and 0.55 respectively) and semen volume was significantly greater in oligozoospermic than normozoospermic patients (p less than 0.01).

Androgens↗