Medical Corporation River Mizuma hospital

Late-onset psychosis refers to the development of psychotic symptoms after the age of 40 and can encompass various conditions like schizophrenia, mood disorders with psychotic features, and delusional disorder. Non-pharmacological interventions are critically important in older adults with psychosis, especially considering the lack of evidence for the efficacy of antipsychotics and the high risk of side effects. Social isolation is recognized as one of the risks of late-onset psychosis, and interventions to eliminate this risk are becoming increasingly important in Japan, where social isolation among older people is widespread as society ages. We present a case of late-onset delusional disorder in which multidimensional interventions for social isolation and loneliness, which have significant impacts on symptoms, were effective in achieving persistent remission. The case involved a woman in her mid-70s who began to complain of paranoia that taking a painkiller recommended by her husband caused persistent pain. Her husband's hospitalization and the deterioration of her relationship with her children resulted in her becoming socially isolated, which intensified her delusions and led to aggressive behavior and depression. Although antipsychotic medications had a limited effect, remission was achieved through supportive psychotherapy, cognitive therapy, family education, participation in group occupational therapy, and the introduction of daycare services, which were implemented to reduce loneliness and social isolation. Specifically, supportive psychotherapy and family education for her loneliness, awareness of another possible cause of pain that she developed through Socratic questioning, and environmental adjustments played important roles in preparing her to accept cognitive therapy, achieve delusional remission, and maintain her state of remission, respectively. The interventions implemented in this case provide insights for addressing social isolation and loneliness in late-onset psychosis.

Sprint competition often comes down to a difference of 0.01 second. Developing good sprint spikes has the potential to improve sprint performance, and it has been reported that bending stiffness is...

Capturing human movement has become available in detail due to the advancement of motion sensor technology integrated by micro-machine and also due to the one of optical recording by high speed and high resolution image sensors. Therefore, we can easily record the human activity as the body movement BigData and analyze it to quest skill to become an expert of a target body movement. Especially, in the sports activity, the quest for becoming an expert athlete has been tried by using a mathematical model of an ideal body movement experienced from the biomechanics approach. The skill is discussed by comparing the differences from the predicted coordinates of body parts captured during the target performance. However, the approach potentially includes difficulties such as modeling the body control from the dynamics system for all human movements. And also the approach needs for adjusting jitters of the individual characteristics. Therefore, when applying the conventional approach, we must discuss a huge number of combinations of mathematical models and then we would find a model for the ideal body movement. To overcome the difficulty, this paper proposes an approach to visualize skill differences among experts and beginners from the BigData called the skill grouping method. It exploits the skill groups clustered by machine learning approach based on a kernel method. This paper shows applications of the skill grouping method from sports activities. Those show validities for finding the skill differences comparing to the BigData of skillful athletes, and also the one for managing skill transition of an athlete in a timeline.