Multi-satellite altimetry and GOCE geoid based surface and subsurface currents in the Mediterranean Sea

Peer ReviewedPostprint (published version

NEUROPROTECTIVE EFFECT OF TERMINALIA CHEBULA EXTRACTS AND ELLAGIC ACID IN PC12 CELLS

Alzheimer’s disease (AD) is one of the most prevalent severe neurological disorders afflicting our aged population. The study was to determine neuroprotective effects of the Terminalia chebula extracts and ellagic acid by using beta-amyloid25-35 (Ab25-35)-induced cell cytotoxicity in undifferentiated rat pheochromocytoma (PC12) cellular model. The methanolic and water extracts of T. chebula and ellagic acid exhibited the strongest neuroprotective activity against Ab25-35-induced undifferentiated PC12 cell deaths at 0.5–5.0 ug/ml. The ellagic acid also exhibited the partial neuroprotective activity against H2O2-induced undifferentiated PC12 cell deaths at 0.5–5.0 ug/ml. The methanolic and water extracts of T. chebula and ellagic acid protected undifferentiated PC12 cells from the damaging effects of Ab25-35 in several ways: (1) by securing cell viability; (2) by suppressing reactive oxygen species production; and (3) by eliminating calcium ion influx. The T. chebula extracts maybe represent a promising plant-source for medicine in the application of the treatment of AD. Further investigation of the ellagic acid is necessary to verify the neuroprotective efficacy and mechanisms in vivo

A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer’s disease

AimsOur aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms.MethodsA total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms.Results(a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier.ConclusionIntegrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage

The Role of Pulmonary Veins in Cancer Progression from a Computed Tomography Viewpoint

Background. We studied the role of pulmonary veins in cancer progression using computed tomography (CT) scans. Methods. We obtained data from 260 patients with pulmonary vein obstruction syndrome (PVOS). We used CT scans to investigate pulmonary lesions in relation to pulmonary veins. We divided the lesions into central and peripheral lesions by their anatomical location: in the lung parenchymal tissue or pulmonary vein; in the superior or inferior pulmonary vein; and by unilateral or bilateral presence in the lungs. Results. Of the 260 PVOS patients, 226 (87%) had central lesions, 231 (89%) had peripheral lesions, and 190 (75%) had mixed central and peripheral lesions. Among the 226 central lesions, 93% had lesions within the superior pulmonary vein, either bilaterally or unilaterally. Among the 231 peripheral lesions, 65% involved bilateral lungs, 70% involved lesions within the inferior pulmonary veins, and 23% had obvious metastatic extensions into the left atrium. All patients exhibited nodules within their pulmonary veins. The predeath status included respiratory failure (40%) and loss of consciousness (60%). Conclusion. CT scans play an important role in following tumor progression within pulmonary veins. Besides respiratory distress, PVOS cancer cells entering centrally can result in cardiac and cerebral events and loss of consciousness or can metastasize peripherally from the pulmonary veins to the lungs

iPSCORE: A Resource of 222 iPSC Lines Enabling Functional Characterization of Genetic Variation across a Variety of Cell Types.

Large-scale collections of induced pluripotent stem cells (iPSCs) could serve as powerful model systems for examining how genetic variation affects biology and disease. Here we describe the iPSCORE resource: a collection of systematically derived and characterized iPSC lines from 222 ethnically diverse individuals that allows for both familial and association-based genetic studies. iPSCORE lines are pluripotent with high genomic integrity (no or low numbers of somatic copy-number variants) as determined using high-throughput RNA-sequencing and genotyping arrays, respectively. Using iPSCs from a family of individuals, we show that iPSC-derived cardiomyocytes demonstrate gene expression patterns that cluster by genetic background, and can be used to examine variants associated with physiological and disease phenotypes. The iPSCORE collection contains representative individuals for risk and non-risk alleles for 95% of SNPs associated with human phenotypes through genome-wide association studies. Our study demonstrates the utility of iPSCORE for examining how genetic variants influence molecular and physiological traits in iPSCs and derived cell lines

Global, regional, and national burden of chronic kidney disease, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI