Thrombocytopenia, anasarca, fever, renal insufficiency, and organomegaly constitute TAFRO syndrome, a variant of Castleman disease. We describe a patient with TAFRO syndrome who underwent renal biopsy. A 79-year-old woman was referred to us with fever and leg edema. She also had thrombocytopenia, pleural effusion, ascites, and acute kidney injury, and was admitted to our hospital. Her response to initial therapy with corticosteroid and cyclosporine was poor. Therefore, she received 4 doses of rituximab per week, which resulted in clinical improvement, including recovery of thrombocytopenia. A kidney biopsy thereafter showed diffuse, global glomerular endothelial injury indicating thrombotic microangiopathy (TMA). These findings suggested that TMA is associated with the thrombocytopenia and renal insufficiency of TAFRO syndrome.

The proportion of end-stage CKD patients with low blood thiamine concentration is high. Low physical function (low BI score) is an independent risk factor of thiamine deficiency. Clinicians should be aware of thiamine deficiency in end-stage CKD patients, especially those with low physical function.

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PD is a desirable home care therapy for elderly patients, but the burden on caregivers should be considered.

Neural architecture search (NAS) aims to automate architecture design processes and improve the performance of deep neural networks. Platform-aware NAS methods consider both performance and complexity and can find well-performing architectures with low computational resources. Although ordinary NAS methods result in tremendous computational costs owing to the repetition of model training, one-shot NAS, which trains the weights of a supernetwork containing all candidate architectures only once during the search process, has been reported to result in a lower search cost. This study focuses on the architecture complexity-aware one-shot NAS that optimizes the objective function composed of the weighted sum of two metrics, such as the predictive performance and number of parameters. In existing methods, the architecture search process must be run multiple times with different coefficients of the weighted sum to obtain multiple architectures with different complexities. This study aims at reducing the search cost associated with finding multiple architectures. The proposed method uses multiple distributions to generate architectures with different complexities and updates each distribution using the samples obtained from multiple distributions based on importance sampling. The proposed method allows us to obtain multiple architectures with different complexities in a single architecture search, resulting in reducing the search cost. The proposed method is applied to the architecture search of convolutional neural networks on the CIAFR-10 and ImageNet datasets. Consequently, compared with baseline methods, the proposed method finds multiple architectures with varying complexities while requiring less computational effort.