Finite element analysis of damage-healring behaviour in self-healing ceramic materials

In this study, we develop the constitutive model to analyse the self-healing ceramic materials within the framework of FEM. The self-hearing and isotropic damage constitutive model for ceramic materials can describe not only the damage process under a certain boundary condition, but also the self-healing process under a high-temperature condition. The damage process is formulated based on the fracture mechanics, and the self-healing process is formulated based on the kinetic model of self-healing time and velocity. Then, we apply the proposed model to analyses of homogeneous ceramic materials and unit cell model of fiberreinforced ceramic material

All-in-one platform for AI R&D in medical imaging, encompassing data collection, selection, annotation, and pre-processing

Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significant data imbalance, with most data from Europe/America and under 10% from Asia, despite its 60% global population share; and 2) hefty time and investment needed to curate proprietary datasets for commercial use. In response, we established the first commercial medical imaging platform, encompassing steps like: 1) data collection, 2) data selection, 3) annotation, and 4) pre-processing. Moreover, we focus on harnessing under-represented data from Japan and broader Asia, including Computed Tomography, Magnetic Resonance Imaging, and Whole Slide Imaging scans. Using the collected data, we are preparing/providing ready-to-use datasets for medical AI R&D by 1) offering these datasets to AI firms, biopharma, and medical device makers and 2) using them as training/test data to develop tailored AI solutions for such entities. We also aim to merge Blockchain for data security and plan to synthesize rare disease data via generative AI. DataHub Website: https://medical-datahub.ai/Comment: 5 pages, 3 figures, accepted to SPIE Medical Imaging 202

SBRT FOR CENTRAL LUNG TUMORS WITH 56 Gy/7 fr

Stereotactic body radiotherapy (SBRT) for centrally‑located lung tumors remains a challenge because of the increased risk of treatment‑related adverse events (AEs), and uncertainty around prescribing the optimal dose. The present study reported the results of central tumor SBRT with 56 Gy in 7 fractions (fr) at the University of Tokyo Hospital. A total of 35 cases that underwent SBRT with or without volumetric‑modulated arc therapy consisting of 56 Gy/7 fr for central lung lesions between 2010 and 2016 at the University of Tokyo Hospital were reveiwed. A central lesion was defined as a tumor within 2 cm of the proximal bronchial tree (RTOG 0236 definition) or within 2 cm in all directions of any critical mediastinal structure. Local control (LC), overall survival (OS), and AEs were investigated. The Kaplan‑Meier method was used to estimate LC and OS. AEs were scored per the Common Terminology Criteria for Adverse Events Version 4.0. Thirty‑five patients with 36 central lung lesions were included. Fifteen lesions were primary non‑small cell lung cancer (NSCLC), 13 were recurrences of NSCLC, and 8 had oligo‑recurrences from other primaries. Median tumor diameter was 29 mm. Eighteen patients had had prior surgery. At a median follow‑up of 13.1 months for all patients and 18.3 months in surviving patients, 22 patients had died, ten due to primary disease (4 NSCLC), while three were treatment‑related. The 1‑ and 2‑year OS were 57.3 and 40.4%, respectively, and median OS was 15.7 months. Local recurrence occurred in only two lesions. 1‑ and 2‑year LC rates were both 96%. Nine patients experienced grade ≥3 toxicity, representing 26% of the cohort. Two of these were grade 5, one pneumonitis and one hemoptysis. Considering the background of the subject, tumor control of our central SBRT is promising, especially in primary NSCLC. However, the safety of SBRT to central lung cancer remains controversial

Confluent hepatic fibrosis in liver cirrhosis: Possible relation with middle hepatic venous drainage

Purpose: Our aim was to retrospectively analyze the location of confluent hepatic fibrosis in relation to the portal and hepatic venous anatomy using multidetector computed tomography (CT) and to clarify the influence of the hepatic venous drainage on confluent fibrosis. Materials and methods: The study population consisted of 879 patients diagnosed with cirrhosis: 539 men and 340 women (65.9 ± 10.6 years) and 633 with Child-Pugh class A, 161 with class B, and 85 with class C. The cause of cirrhosis was hepatitis C (n = 528) and hepatitis B (n = 122) virus infection, alcoholism (n = 114), and others (n = 115). The confluent fibrosis was diagnosed using CT images according to previous reports and statistically analyzed (p < 0.05). Results: Thirty-five confluent fibrosis lesions in 30 patients (3.4 %) were identified. The predictive factors were alcoholic cirrhosis [odds ratio (OR), 7.25; p < 0.0001], Child-Pugh class C (OR, 6.95; p < 0.0001), and Child-Pugh class B (OR, 2.91; p < 0.0023). Confluent fibrosis was most frequently seen in the middle hepatic venous drainage area (n = 21) or at the boundary between the medial and anterior segments (n = 17), and each distribution of the location of confluent fibrosis was significantly unequal (p < 0.0001). Conclusion: Confluent fibrosis was most commonly located in the middle hepatic venous drainage area. © 2013 Japan Radiological Society

Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis

We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2-weighted magnetic resonance images (T2WIs). We proposed a framework and applied it to CE-T1WIs and T2WIs (with tumor region data) acquired preoperatively from 157 patients with malignant glioma (grade III: 55, grade IV: 102) as the primary dataset and 67 patients with malignant glioma (grade III: 22, grade IV: 45) as the validation dataset. Radiomic features such as size/shape, intensity, histogram, and texture features were extracted from the tumor regions on the CE-T1WIs and T2WIs. The Wilcoxon–Mann–Whitney (WMW) test and least absolute shrinkage and selection operator logistic regression (LASSO-LR) were employed to select the radiomic features. Various machine learning (ML) algorithms were used to construct prediction models for the malignant glioma grades using the selected radiomic features. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the prediction models in the primary dataset. The selected radiomic features for all folds in the LOOCV of the primary dataset were used to perform an independent validation. As evaluation indices, accuracies, sensitivities, specificities, and values for the area under receiver operating characteristic curve (or simply the area under the curve (AUC)) for all prediction models were calculated. The mean AUC value for all prediction models constructed by the ML algorithms in the LOOCV of the primary dataset was 0.902 ± 0.024 (95% CI (confidence interval), 0.873–0.932). In the independent validation, the mean AUC value for all prediction models was 0.747 ± 0.034 (95% CI, 0.705–0.790). The results of this study suggest that the malignant glioma grades could be sufficiently and easily predicted by preparing the CE-T1WIs, T2WIs, and tumor delineations for each patient. Our proposed framework may be an effective tool for preoperatively grading malignant gliomas

Possible cross-feeding pathway of facultative methylotroph Methyloceanibacter caenitepidi Gela4 on methanotroph Methylocaldum marinum S8

Non-methanotrophic bacteria such as methylotrophs often coexist with methane-oxidizing bacteria (methanotrophs) by cross-feeding on methane-derived carbon. Methanol has long been considered a major compound that mediates cross-feeding of methane-derived carbon. Despite the potential importance of cross-feeding in the global carbon cycle, only a few studies have actually explored metabolic responses of a bacteria when cross-feeding on a methanotroph. Recently, we isolated a novel facultative methylotroph, Methyloceanibacter caenitepidi Gela4, which grows syntrophically with the methanotroph, Methylocaldum marinum S8. To assess the potential metabolic pathways in M. caenitepidi Gela4 co-cultured with M. marinum S8, we conducted genomic analyses of the two strains, as well as RNA-Seq and chemical analyses of M. caenitepidi Gela4, both in pure culture with methanol and in co-culture with methanotrophs. Genes involved in the serine pathway were downregulated in M. caenitepidi Gela4 under co-culture conditions, and methanol was below the detection limit (< 310 nM) in both pure culture of M. marinum S8 and co-culture. In contrast, genes involved in the tricarboxylic acid cycle, as well as acetyl-CoA synthetase, were upregulated in M. caenitepidi Gela4 under co-culture conditions. Notably, a pure culture of M. marinum S8 produced acetate (< 16 μM) during growth. These results suggested that an organic compound other than methanol, possibly acetate, might be the major carbon source for M. caenitepidi Gela4 cross-fed by M. marinum S8. Co-culture of M. caenitepidi Gela4 and M. marinum S8 may represent a model system to further study methanol-independent cross-feeding from methanotrophs to non-methanotrophic bacteria