Inhibitory effect of punicalagin on lipopolysaccharide-induced neuroinflammation, oxidative stress and memory impairment via inhibition of nuclear factor-kappaB.

Neuroinflammation is significant in the pathogenesis and development of Alzheimer's disease (AD). Previously, we showed lipopolysaccharide (LPS)-induced neuroinflammation caused memory impairment. We investigated the possible preventive effects of punicalagin (PUN), a component of pomegranate, on memory deficiency caused by LPS, along with the fundamental mechanisms. LPS-treated cultured astrocytes and microglial BV-2 cells were investigated for anti-neuroinflammatory effects of PUN. PUN (1.5 mg/kg) ameliorates LPS (250 µg/kg daily 7 times)-induced memory impairment as well as prevents the LPS-induced expression of inflammatory proteins. In in vitro study, we also found that PUN (1 µg/ml) inhibited the LPS-(10, 20 and 50 µM) induced expression of iNOS and Cox-2 as well as the production of ROS, NO, TNF-α and IL-1ß. PUN also suppress activation of NF-κB via inhibition of IκB degradation as well as p50 and p65 translocation into the nucleus in LPS treated mouse brain and cultured astrocytes and microglial BV-2 cells. Consistent with the inhibitory effect on neuro inflammation, PUN inhibited LPS-induced Aß1-42 generation through down-regulation of APP and BACE1 expression in in vivo and in vitro study. Moreover, PUN directly binds to NF-κB subunit p50 evidenced by a docking model and pull down assay. These results suggest that PUN inhibits LPS-induced memory impairment via anti-inflammatory and anti-amylogenic mechanisms through inhibition of NF-κB activation.

Comparison of three actigraphic algorithms used to evaluate sleep in patients with obstructive sleep apnea.

PURPOSE: Actigraphy is a non-invasive and valid method to detect sleep/wake status. However, the technique lacks reliability in patients with sleep-disordered breathing and its results may depend on the algorithm employed. METHODS: We compared three currently used algorithms (the Cole-Kripke, Sadeh, and University of California San Diego [UCSD]) and determined which is the most reliable in patients with obstructive sleep apnea (OSA) assessing total sleep time. After identification of the most reliable algorithm, we compared total sleep time with the severity of obstructive sleep apnea. RESULTS: The mean total sleep time was not significantly different from that yielded by polysomnography when the UCSD algorithm was employed (p = 0.798) and UCSD algorithm was associated with the smallest bias. The correlation levels (with polysomnographic data) were mild-to-modest when the results yielded by all algorithms were evaluated, but were highest when the UCSD algorithm was employed (UCSD, r = 0.498, p < 0.001; Cole-Kripke, r = 0.389, p < 0.01; Sadeh, r = 0.272, p = 0.057). Actigraphic measures of mean total sleep time underestimated sleep in patients with severe obstructive sleep apnea (apnea-hypopnea index [AHI] ≥30), and the correlation was low (r = 0.317, p = 0.116), but overestimated sleep, with high correlations, in patients with mild (5 ≤ AHI < 15) and moderate OSA (15 ≤ AHI < 30; r = 0.859, p < 0.001; r = 0.842, p < 0.001, respectively). CONCLUSIONS: Among the three actigraphic algorithms tested in this study, sleep duration estimated by the UCSD algorithm was the most correlated with polysomnography data in an OSA population. However, none of them was reliable enough for estimating sleep time in patients with sleep-disordered breathing, especially in patients with severe OSA.