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Conclusion EEBOV-specific CD8+ and CD4+ T cell responses were significantly higher in Ebola survivors with PES. These findings suggest that pathogenesis may occur as an immune mediated disease via virus-specific T cell immune response or that persistent antigen exposure leads to increased and sustained T cell responses.Study objectives Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea, OSA) but relies on labor-intensive EEG-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and has low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to automatically identify the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter. Methods PPG signals from the diagnostic polysomnographies of patients suspected of OSA (n=894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for 3-stage (wake/NREM/REM), 4-stage (wake/N1+N2/N3/REM), and 5-stage (wake/N1/N2/N3/REM) classification of sleep. Results The 3-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen's kappa (κ) of 0.65. The 4-stage and 5-stage models achieved 68.5% (κ=0.54), and 64.1% (κ=0.51) accuracies, respectively. [https://www.selleckchem.com/products/roc-325.html ROC-325] With the 5-stage model, the total sleep time was underestimated with mean (standard deviation) error of 7.5 (55.2) min. Conclusion The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitoring of sleep and help assess otherwise overlooked conditions such as REM-related OSA.Objectives Our goal was to assess the value of the Modified Arch Landing Areas Nomenclature (MALAN) for thoracic endovascular aortic repair (TEVAR), in which each landing area (LA) is identified by a proximal landing zone and the type of arch (e.g. 0/I), as predictors of postoperative proximal endograft performance. Methods A multicentre retrospective analysis was performed of patients treated with arch TEVAR (i.e. proximal landing zone 0-3) for various indications between 2007 and 2017. Patients were stratified by the MALAN classification into hostile LAs (i.e. 2/III and 3/III) and favourable LAs (i.e. 0/I-III, 1/I-III, 2/I-II and 3/I-II). Outcome criteria included composite proximal endograft failure (including type Ia endoleak, persistent false lumen perfusion at the level of the most proximal communication between the lumina in aortic dissections, endograft migration and retrograde dissection) and deaths from all causes. Competing risk analyses were performed. Results A total of 359 patients (hostile LAs 133; favourable LAs 226) were identified. The median age was 71.0 (62.0-77.0); 78.3% were men. Proximal endograft failure occurred in 28/133 patients (21.1%) in the hostile LA group and in 12/226 (5.3%) in the favourable LA group. On multivariate analysis, hostile LAs were independently associated with proximal endograft failure (P less then 0.0001). There was no other independent risk factor. Favourable LAs were associated with an increased mortality rate (P = 0.006), which could be attributed to the proximal LA subgroup (i.e. 0/I-III and 1/I-III) (P less then 0.0001), in addition to age (P less then 0.0001). Conclusions The MALAN classification identifies hostile proximal landing zones for TEVAR, namely 2/III and 3/III LAs, which are associated with dismal proximal endograft performance. The MALAN appears to be an intuitive and valuable tool to improve the preoperative decision-making process.Context Armadillo repeat containing 5 (ARMC5) on chromosome 16 is an adrenal gland tumor suppressor gene associated with primary aldosteronism, especially among African Americans (AAs). We examined the association of ARMC5 variants with aldosterone, plasma renin activity (PRA), blood pressure, glucose, and glycosylated hemoglobin A1c (HbA1c) in community-dwelling AAs. Methods The Jackson Heart Study is a prospective cardiovascular cohort study in AAs with baseline data collection from 2000 to 2004. Kernel machine method was used to perform a single joint test to analyze for an overall association between the phenotypes of interest (aldosterone, PRA, systolic and diastolic blood pressure [SBP, DBP], glucose, and HbA1c) and the ARMC5 single nucleotide variants (SNVs) adjusted for age, sex, BMI, and medications; followed by Baysian Lasso methodology to identify sets of SNVs in terms of associated haplotypes with specific phenotypes. Results Among 3223 participants (62% female; mean age 55.6 (SD ± 12.8) years), the average SBP and DBP were 127 and 76 mmHg, respectively. The average fasting plasma glucose and HbA1c were 101 mg/dL and 6.0%, respectively. ARMC5 variants were associated with all 6 phenotypes. Haplotype TCGCC (ch1631476015-31476093) was negatively associated, whereas haplotype CCCCTTGCG (ch1631477195-31477460) was positively associated with SBP, DBP, and glucose. Haplotypes GGACG (ch1631477790-31478013) and ACGCG (ch1631477834-31478113) were negatively associated with aldosterone and positively associated with HbA1c and glucose, respectively. Haplotype GCGCGAGC (ch1631471193-ch1631473597(rs114871627) was positively associated with PRA and negatively associated with HbA1c. Conclusions ARMC5 variants are associated with aldosterone, PRA, blood pressure, fasting glucose, and HbA1c in community-dwelling AAs, suggesting that germline mutations in ARMC5 may underlie cardiometabolic disease in AAs.Osteoblast cells are responsible for synthesizing new bone tissue, and determining how to control osteoblastic differentiation is vital to the treatment of osteoporosis. In the present study, we show that pentraxin 3 (PTX3) signaling is involved in the regulation of osteoblastic differentiation in MC3T3-E1 cells. Our data reveal that PTX3 is abundantly expressed in MC3T3-E1 cells and that its expression is inducible by the introduction of osteogenic induction medium (OIM). Overexpression of PTX3 was observed to significantly increase the expression of four osteoblast signature genes, including Runt-related transcription factor 2 (RUNX2), alkaline phosphatase (ALP), osteocalcin (OCN) and osterix (OSX), suggesting that the overexpression of PTX3 promotes osteoblastic differentiation. The relative level of gene expression between OIM and OIM plus overexpressed PTX3 was evaluated using the Affymetrix Gene Chip® mouse gene microarray. PTX3-related differentially expressed genes (DEGs) were screened. Gene ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes database (KEGG) pathway enrichment analyses were performed, and the PI3K/Akt signaling pathway was primarily involved in the osteogenic differentiation of PTX3.