Development of Tibiofemoral Angle in Korean Children

This study was performed to identify the chronological changes of the knee angle or the tibiofemoral angles in normal healthy Korean children. Full-length anteroposterior view standing radiographs of 818 limbs of 452 Korean children were analyzed. The overall patterns of the chronological changes in the knee angle were similar to those described previously in western or Asian children, but the knee angle development was delayed, i.e., genu varum before 1 yr, neutral at 1.5 yr, increasing genu valgum with maximum a value of 7.8° at 4 yr, followed by a gradual decrease to approximately 5-6° of genu valgum of the adult level at 7 to 8 yr of age. These normative data on chronological changes of knee angles should be taken into consideration when evaluating lower limb alignment in children

Inhibitory effect of 4-O-methylhonokiol on lipopolysaccharide-induced neuroinflammation, amyloidogenesis and memory impairment via inhibition of nuclear factor-kappaB in vitro and in vivo models

<p>Abstract</p> <p>Background</p> <p>Neuroinflammation is important in the pathogenesis and progression of Alzheimer disease (AD). Previously, we demonstrated that lipopolysaccharide (LPS)-induced neuroinflammation caused memory impairments. In the present study, we investigated the possible preventive effects of 4-<it>O</it>-methylhonokiol, a constituent of <it>Magnolia officinalis</it>, on memory deficiency caused by LPS, along with the underlying mechanisms.</p> <p>Methods</p> <p>We investigated whether 4-<it>O</it>-methylhonokiol (0.5 and 1 mg/kg in 0.05% ethanol) prevents memory dysfunction and amyloidogenesis on AD model mice by intraperitoneal LPS (250 μg/kg daily 7 times) injection. In addition, LPS-treated cultured astrocytes and microglial BV-2 cells were investigated for anti-neuroinflammatory and anti-amyloidogenic effect of 4-<it>O</it>-methylhonkiol (0.5, 1 and 2 μM).</p> <p>Results</p> <p>Oral administration of 4-<it>O</it>-methylhonokiol ameliorated LPS-induced memory impairment in a dose-dependent manner. In addition, 4-<it>O</it>-methylhonokiol prevented the LPS-induced expression of inflammatory proteins; inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) as well as activation of astrocytes (expression of glial fibrillary acidic protein; GFAP) in the brain. In <it>in vitro </it>study, we also found that 4-<it>O</it>-methylhonokiol suppressed the expression of iNOS and COX-2 as well as the production of reactive oxygen species, nitric oxide, prostaglandin E₂, tumor necrosis factor-α, and interleukin-1β in the LPS-stimulated cultured astrocytes. 4-<it>O</it>-methylhonokiol also inhibited transcriptional and DNA binding activity of NF-κB via inhibition of IκB degradation as well as p50 and p65 translocation into nucleus of the brain and cultured astrocytes. Consistent with the inhibitory effect on neuroinflammation, 4-<it>O</it>-methylhonokiol inhibited LPS-induced Aβ_1-42generation, β- and γ-secretase activities, and expression of amyloid precursor protein (APP), BACE1 and C99 as well as activation of astrocytes and neuronal cell death in the brain, in cultured astrocytes and in microglial BV-2 cells.</p> <p>Conclusion</p> <p>These results suggest that 4-<it>O</it>-methylhonokiol inhibits LPS-induced amyloidogenesis via anti-inflammatory mechanisms. Thus, 4-<it>O</it>-methylhonokiol can be a useful agent against neuroinflammation-associated development or the progression of AD.</p

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages