Despite the ever-increasing number of patients with dementia worldwide, fundamental therapeutic approaches to this condition have not been established. Epidemiological studies suggest that intake of fermented dairy products prevents cognitive decline in the elderly. However, the active compounds responsible for the effect remain to be elucidated. The present study aims to elucidate the preventive effects of dairy products on Alzheimer’s disease and to identify the responsible component. Here, in a mouse model of Alzheimer’s disease (5xFAD), intake of a dairy product fermented with Penicillium candidum had preventive effects on the disease by reducing the accumulation of amyloid β (Aβ) and hippocampal inflammation (TNF-α and MIP-1α production), and enhancing hippocampal neurotrophic factors (BDNF and GDNF). A search for preventive substances in the fermented dairy product identified oleamide as a novel dual-active component that enhanced microglial Aβ phagocytosis and anti-inflammatory activity towards LPS stimulation in vitro and in vivo. During the fermentation, oleamide was synthesized from oleic acid, which is an abundant component of general dairy products owing to lipase enzymatic amidation. The present study has demonstrated the preventive effect of dairy products on Alzheimer’s disease, which was previously reported only epidemiologically. Moreover, oleamide has been identified as an active component of dairy products that is considered to reduce Aβ accumulation via enhanced microglial phagocytosis, and to suppress microglial inflammation after Aβ deposition. Because fermented dairy products such as camembert cheese are easy to ingest safely as a daily meal, their consumption might represent a preventive strategy for dementia.

The BW GM antigen detection test is thought to be a promising test for the diagnosis of pulmonary aspergillosis other than IPA.

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lung cancer radiotherapy. Baseline weight of 388 patients who underwent intensity modulated radiation therapy (IMRT) was measured between one month prior to and one week after the start of IMRT. Weight change between one week and two months after the commencement of IMRT was analyzed, and dichotomized at 5% WL. Each patient had a planning CT and contours of gross tumor volume (GTV) and esophagus (ESO). A total of 355 features including clinical parameter (CP), GTV and ESO (GTV&ESO) dose-volume histogram (DVH), GTV radiomics, and GTV&ESO dosiomics features were extracted. R&D features were categorized as first- (L1), second- (L2), higher-order (L3) statistics, and three combined groups, L1 + L2, L2 + L3 and L1 + L2 + L3. Multi-view texture analysis was performed to identify optimal R&D input features. In the training set (194 earlier patients), feature selection was performed using Boruta algorithm followed by collinearity removal based on variance inflation factor. Machine-learning models were developed using Laplacian kernel support vector machine (lpSVM), deep neural network (DNN) and their averaged ensemble classifiers. Prediction performance was tested on an independent test set (194 more recent patients), and compared among seven different input conditions: CP-only, DVH-only, R&D-only, DVH + CP, R&D + CP, R&D + DVH and R&D + DVH + CP. Combined GTV L1 + L2 + L3 radiomics and GTV&ESO L3 dosiomics were identified as optimal input features, which achieved the best performance with an ensemble classifier (AUC = 0.710), having statistically significantly higher predictability compared with DVH and/or CP features (p < 0.05). When this performance was compared to that with full R&D-only features which reflect traditional single-view data, there was a statistically significant difference (p < 0.05). Using optimized multi-view R&D input features is beneficial for predicting early WL in lung cancer radiotherapy, leading to improved performance compared to using conventional DVH and/or CP features.

The pressure-temperature phase diagram of SnI 4 was investigated to examine the inherent instability of crystalline SnI 4 in terms of undergoing pressure-induced solid state amorphization, by conducting molecular dynamics simulations prior to studies involving laboratory experiments. The SnI 4 molecules are regarded as rigid tetrahedra interacting with one another via van der Waals forces. In order for the isothermal-isobaric ensemble to be achieved, the well-established Nosé -Klein scheme combined with the momentum scaling method was adopted when carrying out the simulations. The system was carefully heated up under fixed hydrostatic pressure from the low-pressure crystalline state across the melting point, which was determined by monitoring the time-dependence of the mean square displacement of the molecules, whereas, on cooling, the liquid state remained supercooled down to room temperature. The slope of the liquidus in the phase diagram between the low-pressure crystalline phase and the liquid phase was found to be positive, implying that the low-pressure crystalline phase has little connection with solid state amorphization. An expected overall pressure-temperature phase diagram of SnI 4 is discussed.

We have studied light-induced degradation in hydrogenated and deuterated amorphous silicon alloy solar cells. Replacing hydrogen with deuterium in the intrinsic layer of the cell improves stability against light exposure. Possible explanations for the improved stability are discussed.