Evidence of causality of low body mass index on risk of adolescent idiopathic scoliosis: a Mendelian randomization study

IntroductionAdolescent idiopathic scoliosis (AIS) is a disorder with a three-dimensional spinal deformity and is a common disease affecting 1-5% of adolescents. AIS is also known as a complex disease involved in environmental and genetic factors. A relation between AIS and body mass index (BMI) has been epidemiologically and genetically suggested. However, the causal relationship between AIS and BMI remains to be elucidated.Material and methodsMendelian randomization (MR) analysis was performed using summary statistics from genome-wide association studies (GWASs) of AIS (Japanese cohort, 5,327 cases, 73,884 controls; US cohort: 1,468 cases, 20,158 controls) and BMI (Biobank Japan: 173430 individual; meta-analysis of genetic investigation of anthropometric traits and UK Biobank: 806334 individuals; European Children cohort: 39620 individuals; Population Architecture using Genomics and Epidemiology: 49335 individuals). In MR analyses evaluating the effect of BMI on AIS, the association between BMI and AIS summary statistics was evaluated using the inverse-variance weighted (IVW) method, weighted median method, and Egger regression (MR-Egger) methods in Japanese.ResultsSignificant causality of genetically decreased BMI on risk of AIS was estimated: IVW method (Estimate (beta) [SE] = -0.56 [0.16], p = 1.8 × 10-3), weighted median method (beta = -0.56 [0.18], p = 8.5 × 10-3) and MR-Egger method (beta = -1.50 [0.43], p = 4.7 × 10-3), respectively. Consistent results were also observed when using the US AIS summary statistic in three MR methods; however, no significant causality was observed when evaluating the effect of AIS on BMI.ConclusionsOur Mendelian randomization analysis using large studies of AIS and GWAS for BMI summary statistics revealed that genetic variants contributing to low BMI have a causal effect on the onset of AIS. This result was consistent with those of epidemiological studies and would contribute to the early detection of AIS

Diagnosis of desmoplastic small-round-cell tumor by cytogenetic analysis: a case report

markdownabstract__Abstract__ The increased complexity of the business environment, such as globalization of the market, faster introduction of new products, more interdependencies among firms and financial crises, has reduced the forecasting accuracy of conventional prediction methods based on historical data or experts. How can we predict the future? Where can we find information about the future? Over the past decade, some in the business world have come to believe that the best forecasts emerge from neither past behavior patterns nor far-removed experts who analyze markets, but rather crowds; the front-line employees who are working directly with new products and services and interacting daily with buyers, sellers and customers in the field, as they have the most relevant and updated information and knowledge required for forecasting. A prediction market, an elegant and well-designed method for capturing the wisdom of crowds and predicting the outcome of a future event, has been, therefore, introduced. Its promising forecasting results have inspired much enthusiasm among both researchers and practitioners in recent years. This dissertation adopts the information-based view to investigate the effect of information transparency on traders’ behavior and prediction market performance. The research consists of three empirical studies. The case study investigates the activity of and dynamic interactions between traders in an internal prediction market. The subsequent laboratory experiment examines the effect of price information transparency on market performance via traders’ behavior. The final field experiment further investigates different levels of price information transparency in an internal prediction market in a real business environment. The dissertation distinguishes clearly between information aggregation efficiency and market predictive accuracy for the analysis of prediction market performance by defining and developing a measurement of information aggregation efficiency. This research, as a whole, contributes to the academic literature on information transparency and prediction markets, and also demonstrates the considerable potential of prediction markets in managerial decision-making

Validation of therapeutic response assessment by bone scintigraphy in patients with bone-only metastatic breast cancers during zoledronic acid treatment: comparison with computed tomography assessment

Purpose: To validate the use of bone scintigraphy (BS) versus computed tomography (CT) for therapeutic monitoring in patients during treatment with zoledronic acid. Materials and Methods: Eleven patients with bone-only metastatic disease and being treated with zoledronic acid were included. The effects of therapies including chemotherapy and hormone therapy were evaluated in 25 separate examinations in total as follows: complete response (CR), when no bone metastasis was visible; partial response (PR), when a decrease in the lesion area was detected; stable disease (SD), when no or slight change was observed; and progressive disease (PD), when new or enlarged lesion areas were observed. Results: The accuracies of examination by Readers 1, 2, and 3 respectively were 76%, 80% and 76% for BS, 52%, 48%, and 40% for CT, and 64%, 52% and 60% for BS and CT combined with Readers 2 and 3 observing significant differences between CT and BS results. The rates of interobserver agreement between Readers 1 and 2, between Readers 1 and 3, and between Reader 2 and 3 respectively, were 84%, 80% and 88% (κ = 0.648, 0.561 and 0.766) for BS, 52%, 56%, and 60% (κ = 0.180, 0.278 and 0.282) for CT, and 52%, 60%, and 56% (κ = 0.215, 0.282 and 0.232) for CT and BS combined. Conclusion: BS is effective for assessing the response of bone metastasis to therapy in patients during zoledronic acid treatment

Intelligent Image-Activated Cell Sorting

世界初のIntelligent Image-Activated Cell Sorterを開発 --細胞画像の深層学習により高速細胞選抜を実現--. 京都大学プレスリリース. 2018-09-05.A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences