Improvement of myocardial perfusion reserve detected by cardiovascular magnetic resonance after direct endomyocardial implantation of autologous bone marrow cells in patients with severe coronary artery disease

<p>Abstract</p> <p>Background</p> <p>Recent studies suggested that bone marrow (BM) cell implantation in patients with severe chronic coronary artery disease (CAD) resulted in modest improvement in symptoms and cardiac function. This study sought to investigate the functional changes that occur within the chronic human ischaemic myocardium after direct endomyocardial BM cells implantation by cardiovascular magnetic resonance (CMR).</p> <p>Methods and Results</p> <p>We compared the interval changes of left ventricular ejection fraction (LVEF), myocardial perfusion reserve and the extent of myocardial scar by using late gadolinium enhancement CMR in 12 patients with severe CAD. CMR was performed at baseline and at 6 months after catheter-based direct endomyocardial autologous BM cell (n = 12) injection to viable ischaemic myocardium as guided by electromechanical mapping. In patients randomized to receive BM cell injection, there was significant decrease in percentage area of peri-infarct regions (-23.6%, <it>P </it>= <it>0.04</it>) and increase in global LVEF (+9.0%, <it>P </it>= <it>0.02</it>), the percentage of regional wall thickening (+13.1%, <it>P= 0.04</it>) and MPR (+0.25%, <it>P </it>= <it>0.03</it>) over the target area at 6-months compared with baseline.</p> <p>Conclusions</p> <p>Direct endomyocardial implantation of autologous BM cells significantly improved global LVEF, regional wall thickening and myocardial perfusion reserve, and reduced percentage area of peri-infarct regions in patients with severe CAD.</p

Multi-objective Optimization of Wind Farm Layouts Under Energy Generation and Noise propagation

Wind farm design deals with the optimal placement of turbines in a wind farm. Past studies have focused on energymaximization, cost-minimization or revenue-maximization objectives. As land is more extensively exploited for onshore wind farms, wind farms are more likely to be in close proximity with human dwellings. Therefore governments, developers, and landowners have to be aware of wind farms’ environmental impacts. After considering land constraints due to environmental features, noise generation remains the main environmental/health concern for wind farm design. Therefore, noise generation is sometimes included in optimization models as a constraint. Here we present continuous-location models for layout optimization that take noise and energy as objective functions, in order to fully characterize the design and performance spaces of the optimal wind farm layout problem. Based on Jensen’s wake model and ISO-9613-2 noise calculations, we used single- and multiobjective genetic algorithms (NSGA-II) to solve the optimization problem. Preliminary results from the biobjective optimization model illustrate the trade-off between energy generation and noise production by identifying several key parts of Pareto frontiers. In addition, comparison of single-objective noise and energy optimization models show that the turbine layouts and the inter-turbine distance distributions are different when considering these objectives individually. The relevance of these results for wind farm layout designers is explored

Physiological responses of cultured bovine granulosa cells to elevated temperatures under low and high oxygen in the presence of different concentrations of melatonin

Our understanding of the effects of temperature on granulosa cell (GC) physiology is primarily limited to in vitro studies conducted under atmospheric (approx 20% O2) conditions. In the current series of factorial experiments we identify important effects of O2 level (i.e. 5% vs 20% O2) on GC viability and steroidogenesis, and go onto report effects of standard (37.5°C) vs high (40.0°C) temperatures under more physiologically representative (i.e. 5%) O2 levels in the presence of different levels of melatonin (0, 20, 200 and 2000 pg/mL); a potent free-radical scavenger and abundant molecule within the ovarian follicle. Cells aspirated from antral (4 to 6 mm) follicles were cultured in fibronectin-coated wells using serum-free M199 for up to 144 h. At 37.5 C viable cell number was enhanced and luteinization reduced under 5 vs 20% O2. Oxygen level interacted (P<0.001) with time in culture to affect aromatase activity and cell estradiol (E2) production (pg/mL/105 cells). These decreased between 48 and 96 h for both O2 levels but increased again by 144 h for cells cultured under 5% but not 20% O2. Progesterone (P4) concentration (ng/mL/105 cells) was greater (P<0.001) under 20 vs 5% O2 at 96 and 144 h. Cell number increased (P<0.01) with time in culture under 5% O2 irrespective of temperature. However, higher doses of melatonin increased viable cell number at 40.0°C but reduced viable cell number at 37.5°C (P=0.004). Melatonin also reduced (P<0.001) ROS generation at both O2 levels across all concentrations. E2 increased with time in culture at both temperatures under 5% O2, however P4 declined between 96 to 144 h at 40.0 but not 37.5°C. Furthermore, melatonin interacted (P<0.001) with temperature in a dose dependent manner to increase P4 at 37.5°C but to reduce P4 at 40.0°C. Transcript expression for HSD3B1 paralleled temporal changes in P4 production, and those for HBA were greater at 5% than 20% O2, suggesting that hemoglobin synthesis is responsive to changes in O2 level. In conclusion, 5% O2 enhances GC proliferation and reduces luteinization. Elevated temperatures under 5% O2 reduce GC proliferation and P4 production. Melatonin reduces ROS generation irrespective of O2 level and temperature, but interacts with temperature in a dose dependent manner to influence GC proliferation and luteinization

Return to work, work productivity loss and activity impairment in Chinese breast cancer survivors 12-month post-surgery: a longitudinal study

IntroductionExisting evidence of returning-to-work (RTW) after cancer comes predominately from Western settings, with none prospectively examined since the initial diagnostic phase. This study prospectively documents RTW-rate, time-to-RTW, work productivity loss, and activity impairment, within the first-year post-surgery among Chinese women with breast cancer (BCW) and identify potential causal co-variants.MethodsThis observational longitudinal study followed 371 Chinese BCW who were employed/self-employed at the time of diagnosis at 4-week post-surgery (baseline). RTW-status and time-to-RTW were assessed at baseline (T1), 4-month (T2), 6-month (T3), and 12-month (T4) post-baseline. WPAI work productivity loss and activity impairment were assessed at T4. Baseline covariates included demographics, medical-related factors, work satisfaction, perceived work demand, work condition, RTW self-efficacy, B-IPQ illness perception, COST financial well-being, EORTC QLQ-C30 and QLQ-BR23 physical and psychosocial functioning, and HADS psychological distress.ResultsA 68.2% RTW-rate (at 12-month post-surgery), prolonged delay in RTW (median = 183 days), and significant proportions of T4 work productivity loss (20%), and activity impairment (26%), were seen. BCW who were blue-collar workers with lower household income, poorer financial well-being, lower RTW self-efficacy, poorer job satisfaction, poorer illness perception, greater physical symptom distress, impaired physical functioning, and unfavorable work conditions were more likely to experience undesired work-related outcomes.DiscussionUsing a multifactorial approach, effective RTW interventions should focus on not only symptom management, but also to address psychosocial and work-environmental concerns. An organizational or policy level intervention involving a multidisciplinary team comprising nurses, psychologists, occupational health professionals, and relevant stakeholders in the workplace might be helpful in developing a tailored organizational policy promoting work-related outcomes in BCW

Maternal protein-energy malnutrition during early pregnancy in sheep impacts the fetal ornithine cycle to reduce fetal kidney microvascular development

This paper identifies a common nutritional pathway relating maternal through to fetal protein-energy malnutrition (PEM) and compromised fetal kidney development. Thirty-one twin-bearing sheep were fed either a control (n=15) or low-protein diet (n=16, 17 vs. 8.7 g crude protein/MJ metabolizable energy) from d 0 to 65 gestation (term, ∼ 145 d). Effects on the maternal and fetal nutritional environment were characterized by sampling blood and amniotic fluid. Kidney development was characterized by histology, immunohistochemistry, vascular corrosion casts, and molecular biology. PEM had little measureable effect on maternal and fetal macronutrient balance (glucose, total protein, total amino acids, and lactate were unaffected) or on fetal growth. PEM decreased maternal and fetal urea concentration, which blunted fetal ornithine availability and affected fetal hepatic polyamine production. For the first time in a large animal model, we associated these nutritional effects with reduced micro- but not macrovascular development in the fetal kidney. Maternal PEM specifically impacts the fetal ornithine cycle, affecting cellular polyamine metabolism and microvascular development of the fetal kidney, effects that likely underpin programming of kidney development and function by a maternal low protein diet

Factors Affecting Intention to Receive and Self-Reported Receipt of 2009 Pandemic (H1N1) Vaccine in Hong Kong: A Longitudinal Study

Background: Vaccination was a core component for mitigating the 2009 influenza pandemic (pH1N1). However, a vaccination program's efficacy largely depends on population compliance. We examined general population decision-making for pH1N1 vaccination using a modified Theory of Planned Behaviour (TBP). Methodology: We conducted a longitudinal study, collecting data before and after the introduction of pH1N1 vaccine in Hong Kong. Structural equation modeling (SEM) tested if a modified TPB had explanatory utility for vaccine uptake among adults. Principal Findings: Among 896 subjects who completed both the baseline and the follow-up surveys, 7% (67/896) reported being "likely/very likely/certain" to be vaccinated (intent) but two months later only 0.8% (7/896) reported having received pH1N1 vaccination. Perception of low risk from pH1N1 (60%) and concerns regarding adverse effects of the vaccine (37%) were primary justifications for avoiding pH1N1 vaccination. Greater perceived vaccine benefits (β = 0.15), less concerns regarding vaccine side-effects (β = -0.20), greater adherence to social norms of vaccination (β = 0.39), anticipated higher regret if not vaccinated (β = 0.47), perceived higher self-efficacy for vaccination (β = 0.12) and history of seasonal influenza vaccination (β = 0.12) were associated with higher intention to receive the pH1N1 vaccine, which in turn predicted self-reported vaccination uptake (β = 0.30). Social norm (β = 0.70), anticipated regret (β = 0.19) and vaccination intention (β = 0.31) were positively associated with, and accounted for 70% of variance in vaccination planning, which, in turn subsequently predicted self-reported vaccination uptake (β = 0.36) accounting for 36% of variance in reported vaccination behaviour. Conclusions/Significance: Perceived low risk from pH1N1 and perceived high risk from pH1N1 vaccine inhibited pH1N1 vaccine uptake. Both the TPB and the additional components contributed to intended vaccination uptake but social norms and anticipated regret predominantly associated with vaccination intention and planning. Vaccination planning is a more significant proximal determinant of uptake of pH1N1 vaccine than is intention. Intention alone is an unreliable predictor of future vaccine uptake. © 2011 Liao et al.published_or_final_versio

Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes. We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry. In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10 ) and AC058822.1 (P = 1.47 × 10 ), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C. Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10 ), demonstrating the importance of diversifying study cohorts. [Abstract copyright: © 2023. The Author(s).

LiSSA: Localized Stochastic Sensitive Autoencoders

The training of autoencoder (AE) focuses on the selection of connection weights via a minimization of both the training error and a regularized term. However, the ultimate goal of AE training is to autoencode future unseen samples correctly (i.e., good generalization). Minimizing the training error with different regularized terms only indirectly minimizes the generalization error. Moreover, the trained model may not be robust to small perturbations of inputs which may lead to a poor generalization capability. In this paper, we propose a localized stochastic sensitive AE (LiSSA) to enhance the robustness of AE with respect to input perturbations. With the local stochastic sensitivity regularization, LiSSA reduces sensitivity to unseen samples with small differences (perturbations) from training samples. Meanwhile, LiSSA preserves the local connectivity from the original input space to the representation space that learns a more robustness features (intermediate representation) for unseen samples. The classifier using these learned features yields a better generalization capability. Extensive experimental results on 36 benchmarking datasets indicate that LiSSA outperforms several classical and recent AE training methods significantly on classification tasks