Impacts of Age and Gender on Brain Edema in a Mouse Water Intoxication Model

Brain edema causes abnormal fluid retention and can be fatal in severe cases. Although it develops in various diseases, most treatments for brain edema are classical. We analyzed the impacts of age and gender on the characteristics of a water intoxication model that induces pure brain edema in mice and examined the model’s usefulness for research regarding new treatments for brain edema. C57BL/6J mice received an intraperitoneal administration of 10% body weight distilled water, and we calculated the brain water content by measuring the brain-tissue weight immediately after dissection and after drying. We analyzed 8-OHdG and caspase-3 values to investigate the brain damage. We also applied this model in aquaporin 4 knockout (AQP4−) mice and compared these mice with wild-type mice. The changes in water content differed by age and gender, and the 8-OHdG and caspase-3 values differed by age. Suppression of brain edema by AQP4− was also confirmed. These results clarified the differences in the onset of brain edema by age and gender, highlighting the importance of considering the age and gender of model animals. Similar studies using genetically modified mice are also possible. Our findings indicate that this water intoxication model is effective for explorations of new brain edema treatments

高齢者への健康相談・保健指導を行う保健室による地元への貢献

　研究の目的は，地域において高齢者を対象にした先駆的な取り組みを行う看護職による健康相談・保健指導を行う窓口である保健室の特徴と，地元への貢献を明らかにすることである．高齢者を対象にした先駆的な取り組みを行う保健室8 か所の実施窓口担当者，保健室運営に関係が深い自治体の担当者1 名にインタビューを実施した．8 か所はその特徴から，経験豊かなプラチナナースが主体となり運営されている保健室，地域に開かれた事業所と共に運営されている保健室，そして震災復興の生活再建支援から継続する保健室という3 つに分類された．地元への貢献は，地元住民同士による生活・療養支援の自主活動の支援，地元の特性に融合した健康支援，地域包括ケアシステムの活用とならないはざまの支援，地元ボランティアから地元住民への効果の波及といったことがあった．3 つのタイプの保健室がもたらした地元への貢献の共通点として，地元住民の健康への意識を高める支援や地元住民との心理的な距離を縮める取り組みがあった

Mutations and Deregulation of Ras/Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR Cascades Which Alter Therapy Response

The Ras/Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR cascades are often activated by genetic alterations in upstream signaling molecules such as receptor tyrosine kinases (RTK). Certain components of these pathways, RAS, NF1, BRAF, MEK1, DUSP5, PP2A, PIK3CA, PIK3R1, PIK3R4, PIK3R5, IRS4, AKT, NFKB1, MTOR, PTEN, TSC1, and TSC2 may also be activated/inactivated by mutations or epigenetic silencing. Upstream mutations in one signaling pathway or even in downstream components of the same pathway can alter the sensitivity of the cells to certain small molecule inhibitors. These pathways have profound effects on proliferative, apoptotic and differentiation pathways. Dysregulation of components of these cascades can contribute to: resistance to other pathway inhibitors, chemotherapeutic drug resistance, premature aging as well as other diseases. This review will first describe these pathways and discuss how genetic mutations and epigenetic alterations can result in resistance to various inhibitors

Machine-learning assisted steady-state profile predictions using global optimization techniques

Predicting plasma profiles with a stiff turbulent transport model is important for experimental analysis and development of operation scenarios. Due to the sensitivity of turbulent fluxes to profile gradients, robust predictions are still arduous with a stiff model incorporated in a conventional transport code. With global optimization techniques employed, the new steady-state transport code, global optimization version of the transport equation stable solver, has been developed to overcome these difficulties. It enables us to attain smooth profiles of diffusivity and temperature even though jagged profiles thereof are inclined to emerge in simulations with a stiff model. A neural-network-based surrogate model of a transport model is developed to compensate slow computation inherent to global optimization. Hyperparameter optimization realizes the surrogate model with very good accuracy

Development of a Surrogate Turbulent Transport Model and Its Usefulness in Transport Simulations

For accelerating a transport simulation with an advanced physics turbulent transport model like TGLF, we have been developing a surrogate model that mimics the behavior of the model based on a neural network model. With a steady-state transport solver GOTRESS used, the surrogate model has shown its ability to successfully predict temperature profiles almost equivalent to those by TGLF. The performance of the surrogate model is improved by optimizing hyperparameters and eliminating outliers from training data. Extrapolability of the optimized model is examined by changing the normalized temperature gradient. The objective is to better investigate the nature of the model in addition to measuring its utility in transport simulations. The versatile model, which has been trained with data of multiple cases, is developed applicable to many situations. It shows the same reproducibility as the model specific to each individual case, a fact which unveils great potential of the surrogate model in transport simulations