Predicting invasion in mammographically detected microcalcifcation: a preliminary report

BACKGROUND: With the increased use of mammography for breast cancer screening, the diagnosis of ductal carcinoma in situ (DCIS) too has increased. This study was carried out to identify clinical and radiological factors that may predict the presence of invasive disease within mammographically detected microcalcifcation. MATERIALS AND METHODS: A retrospective analysis of 13 vacuum-assisted breast biopsies (Mammotome(®)) of mammographic calcification, which were reported to be either DCIS or invasive disease on final histopathology, was carried out. Final surgical pathology was correlated with pre-operative features (clinical, radiological and core histology) to predict the presence of an invasive component. RESULTS: The overall sensitivity of Mammotome(® )was 81.8%, while for invasion it was 50%. Small size, granular morphology, increased number and area of calcification cluster may help in predicting invasion on mammography. CONCLUSIONS: Mammotome(® )biopsy fails to detect invasion correctly in half the cases despite ascertaining correctness of biopsy with post biopsy x-ray

Cell population‐based framework of genetic epidemiology in the single‐cell omics era

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology

Data-driven comparison of multiple high-dimensional single-cell expression profiles

Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets

Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging

オミックスデータから非線形な加齢変化の全体像を取得する解析手法を開発. 京都大学プレスリリース. 2023-02-13.[Background] Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown. [Result] We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age. [Conclusion] DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process

Modeling of the Fukushima Daiichi Nuclear Power Plant Derived Radioactive Cesium Dynamics in Grazing Grassland

The damage to the Fukushima Daiichi Nuclear Power Plant incurred following the Great East Japan Earthquake and tsunami on March 11, 2011 resulted in serious radioactive pollution of Eastern Japan. In some grasslands of this area, radioactive cesium (Cs) content of grasses exceeded the provisional safety standard for use as feed for dairy and beef cattle of 100 Bq kg–1 fresh weight, and the livestock industry has been seriously affected in numerous ways: needing to dispose of polluted forage, grazing prohibitions, declines in beef prices, suspensions of shipping beef to market, and blanket testing of beef cattle (Manabe et al., 2013). The spatial distribution of radioactive Cs in grasslands was complex in various scales (Tsuiki and Maeda, 2012a; 2012b). So it is difficult to estimate actual pollution level in grassland ecosystems. The transfer of radioactive Cs from soil to plant is affected by soil soluble potassium (K) concentration, pH, clay and organic matter contents (Absalom et al., 2001; Tsuiki et al., 2013). The radioactive Cs dynamics in soil-plantanimal system is complex and modeling is necessary to clarify the relationships. In this study, a model of radioactive Cs dynamics in Zoysia japonica Steud. dominated grazing grassland was developed to predict radioactive Cs concentration of grass and grazing cattle

Genome-wide association study of individual differences of human lymphocyte profiles using large-scale cytometry data

Human immune systems are very complex, and the basis for individual differences in immune phenotypes is largely unclear. One reason is that the phenotype of the immune system is so complex that it is very difficult to describe its features and quantify differences between samples. To identify the genetic factors that cause individual differences in whole lymphocyte profiles and their changes after vaccination without having to rely on biological assumptions, we performed a genome-wide association study (GWAS), using cytometry data. Here, we applied computational analysis to the cytometry data of 301 people before receiving an influenza vaccine, and 1, 7, and 90 days after the vaccination to extract the feature statistics of the lymphocyte profiles in a nonparametric and data-driven manner. We analyzed two types of cytometry data: measurements of six markers for B cell classification and seven markers for T cell classification. The coordinate values calculated by this method can be treated as feature statistics of the lymphocyte profile. Next, we examined the genetic basis of individual differences in human immune phenotypes with a GWAS for the feature statistics, and we newly identified seven significant and 36 suggestive single-nucleotide polymorphisms associated with the individual differences in lymphocyte profiles and their change after vaccination. This study provides a new workflow for performing combined analyses of cytometry data and other types of genomics data

Polyneuropathy caused by cobalt-chromium metallosis after total hip replacement

金沢大学附属病院神経内科Although metal intoxication after arthroplasty causes various symptoms, polyneuropathy has never been the focus of clinical investigation. We report the case of a 56-year-old woman with metal neuropathy. She had metallosis after hip arthroplasty with a cobalt-chromium alloy prosthesis. She developed progressive sensory disturbance, hearing loss, and hypothyroidism. Sural nerve biopsy indicated axonopathy. After exchange arthroplasty, blood levels of cobalt and chromium decreased, and her symptoms improved. Cobalt or chromium can cause axonopathy. © 2010 Wiley Periodicals, Inc