Cells as delivery vehicles for cancer therapeutics

Cell-based therapeutics have advanced significantly over the past decade and are poised to become a major pillar of modern medicine. Three cell types in particular have been studied in detail for their ability to home to tumors and to deliver a variety of different payloads. Neural stem cells, mesenchymal stem cells and monocytes have each been shown to have great potential as future delivery systems for cancer therapy. A variety of other cell types have also been studied. These results demonstrate that the field of cell-based therapeutics will only continue to grow

Energy-efficient hardware implementation of LR-aided K-Best MIMO decoder for 5G networks

Energy efficiency is a primary design goal for future green wireless communication technologies. Multiple-input multiple-output (MIMO) schemes have been proposed in literature to improve the throughput of communication systems, they are expected to play a prominent role in the upcoming 5th generation (5G) standard. This paper presents a novel high efficiency MIMO decoder based on the K-Best algorithm with lattice reduction. We have designed a novel hardware architecture for this decoder, which was implemented using 32nm standard CMOS technology. Our results show that the proposed decoder can achieve on average a four-fold reduction in the power costs compared to recently published designs for 5G networks. The throughput of the design is 506 Mbits/sec, which is comparable to existing designs

Thermal-Hydraulic performance in a microchannel heat sink equipped with longitudinal vortex generators (LVGs) and nanofluid

In this study, the numerical conjugate heat transfer and hydraulic performance of nanofluids flow in a rectangular microchannel heat sink (RMCHS) with longitudinal vortex generators (LVGs) was investigated at different Reynolds numbers (200-1200). Three-dimensional simulations are performed on a microchannel heated by a constant temperature with five different configurations with different angles of attack for the LVGs under laminar flow conditions. The study uses five different nanofluid combinations of Al2O3 or CuO, containing low volume fractions in the range of 0.5% to 3.0% with various nanoparticle sizes that are dispersed in pure water, PAO (Polyalphaolefin) or ethylene glycol. The results show that for Reynolds number ranging from 100 to 1100, Al2O3-water has the best performance compared with CuO nanofluid with Nusselt number values between 7.67 and 14.7, with an associated increase in Fanning friction factor by values of 0.0219-0.095. For the case of different base fluids, the results show that CuO-PAO has the best performance with Nusselt number values between 9.57 and 15.88, with an associated increase in Fanning friction factor by 0.022-0.096. The overall performance of all configurations of microchannels equipped with LVGs and nanofluid showed higher values than the ones without LVG and water as a working fluid

Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%

Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study

This study focuses on improving the sustainability of electrical supply in the healthcare system in the UK, to contribute to current efforts made towards the 2050 net-zero carbon target. As a case study, we propose a grid-connected hybrid renewable energy system (HRES) for a hospital in the south-east of England. Electrical consumption data were gathered from five wards in the hospital for a period of one year. PV-battery-grid system architecture was selected to ensure practical execution through the installation of PV arrays on the roof of the facility. Selection of the optimal system was conducted through a novel methodology combining multi-objective optimisation and data forecasting. The optimisation was conducted using a genetic algorithm with two objectives (1) minimisation of the levelised cost of energy and (2) CO2 emissions. Advanced data forecasting was used to forecast grid emissions and other cost parameters at two year intervals (2023 and 2025). Several optimisation simulations were carried out using the actual and forecasted parameters to improve decision making. The results show that incorporating forecasted parameters into the optimisation allows to identify the subset of optimal solutions that will become sub-optimal in the future and, therefore, should be avoided. Finally, a framework for choosing the most suitable subset of optimal solutions was presented

Robust Estimators in Generalized Pareto Models

This paper deals with optimally-robust parameter estimation in generalized Pareto distributions (GPDs). These arise naturally in many situations where one is interested in the behavior of extreme events as motivated by the Pickands-Balkema-de Haan extreme value theorem (PBHT). The application we have in mind is calculation of the regulatory capital required by Basel II for a bank to cover operational risk. In this context the tail behavior of the underlying distribution is crucial. This is where extreme value theory enters, suggesting to estimate these high quantiles parameterically using, e.g. GPDs. Robust statistics in this context offers procedures bounding the influence of single observations, so provides reliable inference in the presence of moderate deviations from the distributional model assumptions, respectively from the mechanisms underlying the PBHT.Comment: 26pages, 6 figure

Early breast cancer screening using iron/iron oxide-based nanoplatforms with sub-femtomolar limits of detection

Citation: Udukala, D. N., Wang, H. W., Wendel, S. O., Malalasekera, A. P., Samarakoon, T. N., Yapa, A. S., . . . Bossmann, S. H. (2016). Early breast cancer screening using iron/iron oxide-based nanoplatforms with sub-femtomolar limits of detection. Beilstein Journal of Nanotechnology, 7, 364-373. doi:10.3762/bjnano.7.33Additional Authors: Ortega, R.;Toledo, Y.;Bossmann, L.;Robinson, C.;Janik, K. E.;Koper, O. B.;Motamedi, M.;Zhu, G. H.Proteases, including matrix metalloproteinases (MMPs), tissue serine proteases, and cathepsins (CTS) exhibit numerous functions in tumor biology. Solid tumors are characterized by changes in protease expression levels by tumor and surrounding tissue. Therefore, monitoring protease levels in tissue samples and liquid biopsies is a vital strategy for early cancer detection. Water-dispersable Fe/Fe3O4-core/shell based nanoplatforms for protease detection are capable of detecting protease activity down to sub-femtomolar limits of detection. They feature one dye (tetrakis(carboxyphenyl) porphyrin (TCPP)) that is tethered to the central nanoparticle by means of a protease-cleavable consensus sequence and a second dye (Cy 5.5) that is directly linked. Based on the protease activities of urokinase plasminogen activator (uPA), MMPs 1, 2, 3, 7, 9, and 13, as well as CTS B and L, human breast cancer can be detected at stage I by means of a simple serum test. By monitoring CTS B and L stage 0 detection may be achieved. This initial study, comprised of 46 breast cancer patients and 20 apparently healthy human subjects, demonstrates the feasibility of protease-activity-based liquid biopsies for early cancer diagnosis

Novel Exopolysaccharide from Marine Bacillus subtilis with Broad Potential Biological Activities: Insights into Antioxidant, Anti-Inflammatory, Cytotoxicity, and Anti-Alzheimer Activity

In the presented study, Bacillus subtilis strain AG4 isolated from marine was identified based on morphological, physiological, phylogenetic characteristics and an examination of 16S rRNA sequences. Novel exopolysaccharide (EPSR4) was extracted and isolated from the Bacillus subtilis strain as a major fraction of exopolysaccharide (EPS). The analysis of structural characterization indicated that EPSR4 is a β-glycosidic sulphated heteropolysaccharide (48.2%) with a molecular weight (Mw) of 1.48 × 104 g/mole and has no uronic acid. Analysis of monosaccharide content revealed that EPSR4 consists of glucose, rhamnose and arabinose monosaccharide in a molar ratio of 5:1:3, respectively. Morphological analysis revealed that EPSR4 possess a high crystallinity degree with a significant degree of porosity, and its aggregation and conformation in the lipid phase might have a significant impact on the bioactivity of EPSR4. The biological activity of EPSR4 was screened and evaluated by investigating its antioxidant, cytotoxicity, anti-inflammatory, and anti-Alzheimer activities. The antioxidant activity results showed that EPSR4 has 97.6% scavenging activity toward DPPH free radicals at 1500 µg/mL, with an IC50 value of 300 µg/mL, and 64.8% at 1500 µg/mL toward hydrogen peroxide free radicals (IC50 = 1500 µg/mL, 30 min). Furthermore, EPSR4 exhibited considerable inhibitory activity towards the proliferation of T-24 (bladder carcinoma), A-549 (lung cancer) and HepG-2 (hepatocellular carcinoma) cancer cell lines with IC50 of 244 µg/mL, 148 µg/mL and 123 µg/mL, respectively. An evaluation of anti-inflammatory activity revealed that EPSR4 has potent lipoxygenase (LOX) inhibitory activity (IC50 of 54.3 µg/mL) and a considerable effect on membrane stabilization (IC50 = 112.2 ± 1.2 µg/mL), while it showed cyclooxygenase (COX2) inhibitory activity up to 125 µg/mL. Finally, EPSR4 showed considerable inhibitory activity towards acetylcholine esterase activity. Taken together, this study reveals that Bacillus subtilis strain AG4 could be considered as a potential natural source of novel EPS with potent biological activities that would be useful for the healthcare system.Faculty of Science, Suez Canal UniversityPrincess Nourah bint Abdulrahman UniversityTaif UniversityPeer Reviewe