ClinicalTrials.gov (NCT01406379).

Angiosarcoma is a rare tumor that account for less than 1% of all sarcomas. Although hepatic angiosarcoma usually presents with unspecific symptoms, it rapidly progresses and has a high mortality. We report a rare case of primary hepatic angiosarcoma manifested as recurrent hemoperitoneum.

Background Limited data are available on the clinical impact of healthy lifestyle behaviors on the risk of dementia in patients with new‐onset atrial fibrillation (AF). Here, we aimed to evaluate the association between a combination of healthy lifestyle behaviors and the risk of incident dementia in patients with AF. Methods and Results Using the Korean National Health Insurance database between 2009 and 2016, we identified 199 952 adult patients who were newly diagnosed as AF without dementia. Patients were categorized into 4 groups by healthy lifestyle behavior score (HLS) with 1 point each being assigned for no current smoking, alcohol abstinence, and regular exercise. The HLS 0, 1, 2, and 3 groups included 4.4%, 17.4%, 53.4%, and 24.8% of the patients, respectively. We performed an inverse probability of treatment weighting to balance covariates between HLS groups. The HLS 1, 2, and 3 groups were associated with a lower risk of dementia compared with the HLS 0 group (hazard ratio [HR], 0.769; 95% CI, 0.704–0.842 for HLS 1; HR, 0.770; 95% CI, 0.709–0.836 for HLS 2; and HR, 0.622; 95% CI, 0.569–0.679 for HLS 3). The risk of dementia showed a tendency to decrease with an increase in HLS ( P ‐for‐trend <0.001). Conclusions A clustering of healthy lifestyle behaviors was associated with a significantly lower risk of dementia in patients with new‐onset AF. These findings support the promotion of a healthy lifestyle within an integrated care approach to AF patient management.

Abstract. In wireless sensor networks, each sensor node has severe resource constraints in terms of energy, computing device, and memory space. Especially, the memory space of the platform hardware is much smaller than that of the other computing systems. In this paper, we propose a OTL, which is an on-demand thread stack allocation scheme for MMU-less real-time sensor operating systems. The OTL enables to adaptively adjust the stack size by allocating stack frame based on the amount of each function's stack usage. The amount of the function's stack usage is checked at compile-time, and the adaptive adjustment of the stack occurs at run-time. Our experimental results show that the OTL significantly minimizes the spatial overhead of the threads' stacks with tolerable time overhead compared with fixed stack allocation mechanism of the existing sensor operating systems.