Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohort

The impact of crystal phase transition on the hardness and structure of kidney stones

The version of record of this article, first published in Urolithiasis, is available online at Publisher’s website: https://doi.org/10.1007/s00240-024-01556-5.Calcium oxalate kidney stones, the most prevalent type of kidney stones, undergo a multi-step process of crystal nucleation, growth, aggregation, and secondary transition. The secondary transition has been rather overlooked, and thus, the effects on the disease and the underlying mechanism remain unclear. Here, we show, by periodic micro-CT images of human kidney stones in an ex vivo incubation experiment, that the growth of porous aggregates of calcium oxalate dihydrate (COD) crystals triggers the hardening of the kidney stones that causes difficulty in lithotripsy of kidney stone disease in the secondary transition. This hardening was caused by the internal nucleation and growth of precise calcium oxalate monohydrate (COM) crystals from isolated urine in which the calcium oxalate concentrations decreased by the growth of COD in closed grain boundaries of COD aggregate kidney stones. Reducing the calcium oxalate concentrations in urine is regarded as a typical approach for avoiding the recurrence. However, our results revealed that the decrease of the concentrations in closed microenvironments conversely promotes the transition of the COD aggregates into hard COM aggregates. We anticipate that the suppression of the secondary transition has the potential to manage the deterioration of kidney stone disease

Evidence for Solution-Mediated Phase Transitions in Kidney Stones: Phase Transition Exacerbates Kidney Stone Disease

Maruyama M., Tanaka Y., Momma K., et al. Evidence for Solution-Mediated Phase Transitions in Kidney Stones: Phase Transition Exacerbates Kidney Stone Disease. Crystal Growth and Design 23, 4285 (2023); https://doi.org/10.1021/acs.cgd.3c00108.In this study, we investigated calcium oxalate (CaOx) kidney stones and showed direct evidence of the solution-mediated phase transition of calcium oxalate dihydrate (COD; the metastable phase) to calcium oxalate monohydrate (COM; the stable phase). We examined the crystal phases, crystal textures, and protein distributions within thin sections of calcium oxalate kidney stones. Observation with a polarized-light microscope showed that the outline of the mosaic texture, in which COM crystals are assembled in a mosaic pattern, roughly coincides with COD’s crystallographically stable face angles. Microfocus X-ray CT measurement captured the intermediate process of the phase transition, starting inside the COD single crystal and gradually transforming to COM crystals. In addition, the distribution of osteopontin and prothrombin fragment-1, common proteins contained in urine and visualized by multicolor fluorescence immunostaining, showed no apparent striations inside the COM single crystals with the mosaic texture, although the striation is apparent inside the COD single crystals. This is probably because the phase transition of mosaic-like COM occurred in a semiclosed system inside the COD single crystal, so the effect of periodic (day-night, seasonal, etc.) urinary protein concentration changes was small. On the other hand, striations were visible in concentrically laminated COM. This indicated that concentrically laminated COM formed in response to the changes in urinary protein concentrations. From the above, we conclude that the COD single crystals and the concentrically laminated COM seen in CaOx stones are primary structures, and the mosaic COM is a secondary structure that is a pseudomorph formed by the solution-mediated phase transition from COD single crystals

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction  = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Simple physical model with empirical formulas for solid-state sintering of CaCO3 for estimation of porosity

The porosity after solid-state sintering is theoretically estimated by using a simple physical model with empirical formulas as a function of applied pressure and initial particle size. The comparison with the experimental data has revealed that tight aggregation of CaCO3 nanoparticles strongly increases porosity in solid-state sintering compared to that predicted for isolated nanoparticles