THE FABRICATION AND CHARACTERIZATION OF METAL OXIDE NANOPARTICLES EMPLOYED IN ENVIRONMENTAL TOXICITY AND POLYMERIC NANOCOMPOSITE APPLICATIONS

Ceria (cerium oxide) nanomaterials, or nanoceria, have commercial catalysis and energy storage applications. The cerium atoms on the surface of nanoceria can store or release oxygen, cycling between Ce3+ and Ce4+, and can therefore act as a therapeutic to relieve oxidative stress within living systems. Nanoceria dissolution is present in acidic environments in vivo. In order to accurately define the fate of nanoceria in vivo, nanoceria dissolution or stabilization is observed in vitro using acidic aqueous environments. Nanoceria stabilization is a known problem even during its synthesis; in fact, a carboxylic acid, citric acid, is used in many synthesis protocols. Citric acid adsorbs onto nanoceria surfaces, capping particle formation and creating stable dispersions with extended shelf lives. Nanoceria was shown to agglomerate in the presence of some carboxylic acids over a time scale of up to 30 weeks, and degraded in others, at pH 4.5 (representing that of phagolysosomes). Sixteen carboxylic acids were tested: citric, glutaric, tricarballylic, α-hydroxybutyric, β-hydroxybutyric, adipic, malic, acetic, pimelic, succinic, lactic, tartronic, isocitric, tartaric, dihydroxymalonic, and glyceric acid. Each acid was introduced as 0.11 M, into pH 4.5 iso-osmotic solutions. Controls such as ammonium nitrate, sodium nitrate, and water were also tested to assess their effects on nanoceria dissolution and stabilization. To further test stability, nanoceria suspensions were subject to light and dark milieu, simulating plant environments and biological systems, respectively. Light induced nanoceria agglomeration in some, but not all ligands, and is likely to be a result of UV irradiation. Light initiates free radicals generated from the ceria nanoparticles. Some of the ligands completely dissolved the nanoceria when exposed to light. Citric and malic acids form coordination complexes with cerium on the surface of the ceria nanoparticle that can inhibit agglomeration. This approach identifies key functional groups required to prevent nanoceria agglomeration. The impact of each ligand on nanoceria was analyzed and will ultimately describe the fate of nanoceria in vivo. In addition, simulated biological fluid (SBF) exposure can change nanoceria’s surface properties and biological activity. The citrate-coated nanoceria physicochemical properties such as size, morphology, crystallinity, surface elemental composition, and charge were determined before and after exposure to simulated lung, gastric, and intestinal fluids. SBF exposure resulted in either loss or overcoating of nanoceria’s surface citrate by some of the SBF components, greater nanoceria agglomeration, and small changes in the zeta potential. Nanocomposites are comprised of a polymer matrix embedded with nanoparticles. These nanoparticles can alter material and optical properties of the polymer. SR-399 (dipentaerythritol pentaacrylate) is a fast cure, low skin irritant monomer that contains five carbon-carbon double bonds (C=C). It is a hard, flexible polymer, and also resistant to abrasion. It can be used as a sealant, binder, coating, and as a paint additive. In this case, metal oxide nanoparticles were added to the monomer prior to polymerization. Titania nanoparticles are known to absorb UV light due to their photocatalytic nature. Titania nanoparticles were chosen due to their high stability, non-toxicity, and are relatively quick, easy, and inexpensive to manufacture. Channels in thin monomer films were created using a ferrofluid manipulated by magnetic fields. The mechanical properties of a microfluidic device by rapid photopolymerization is dependent on the crosslinking gradient observed throughout the depth of the film. Quantitative information regarding the degree of polymerization of thin film polymers polymerized by free radical polymerization through the application of UV light is crucial to estimate material properties. In general, less cure leads to more flexibility, and more cure leads to brittleness. The objective was to quantify the degree of polymerization to approximate the C=C concentration and directly relate it to the mechanical properties of the polymer. Polymerization of C=C groups was conducted using a photoinitiator and an UV light source from one surface of a thin film of a multifunctional monomer. The C=C fraction in the film was found to vary with film depth and UV light intensity. The extents of conversion and crosslinking estimates were compared to local mechanical moduli and optical properties. A mathematical model linking the mechanical properties to the degree of polymerization, C=C composition, as a function of film depth and light intensity was then developed. For a given amount of light energy, one can predict the hardness and modulus of elasticity. The correlation between the photopolymerization and the mechanical properties can be used to optimize the mechanical properties of thin films within the manufacturing and energy constraints, and should be scalable to other multifunctional monomer systems

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015 : a systematic analysis for the Global Burden of Disease Study 2015

Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography-year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61.7 years (95% uncertainty interval 61.4-61.9) in 1980 to 71.8 years (71.5-72.2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11.3 years (3.7-17.4), to 62.6 years (56.5-70.2). Total deaths increased by 4.1% (2.6-5.6) from 2005 to 2015, rising to 55.8 million (54.9 million to 56.6 million) in 2015, but age-standardised death rates fell by 17.0% (15.8-18.1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14.1% (12.6-16.0) to 39.8 million (39.2 million to 40.5 million) in 2015, whereas age-standardised rates decreased by 13.1% (11.9-14.3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42.1%, 39.1-44.6), malaria (43.1%, 34.7-51.8), neonatal preterm birth complications (29.8%, 24.8-34.9), and maternal disorders (29.1%, 19.3-37.1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000-183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000-532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Copyright (C) The Author(s). Published by Elsevier Ltd.Peer reviewe

Extracellular gluco-oligosaccharide degradation by Caulobacter crescentus

The oligotrophic bacterium Caulobacter crescentus has the ability to metabolize various organic molecules, including plant structural carbohydrates, as a carbon source. The nature of β-glucosidase (BGL)-mediated gluco-oligosaccharide degradation and nutrient transport across the outer membrane in C. crescentus was investigated. All gluco-oligosaccharides tested (up to celloheptose) supported growth in M2 minimal media but not cellulose or CM-cellulose. The periplasmic and outer membrane fractions showed highest BGL activity, but no significant BGL activity was observed in the cytosol or extracellular medium. Cells grown in cellobiose showed expression of specific BGLs and TonB-dependent receptors (TBDRs). Carbonyl cyanide 3-chlorophenylhydrazone lowered the rate of cell growth in cellobiose but not in glucose, indicating potential cellobiose transport into the cell by a proton motive force-dependent process, such as TBDR-dependent transport, and facilitated diffusion of glucose across the outer membrane via specific porins. These results suggest that C. crescentus acquires carbon from cellulose-derived gluco-oligosaccharides found in the environment by extracellular and periplasmic BGL activity and TBDR-mediated transport. This report on extracellular degradation of gluco-oligosaccharides and methods of nutrient acquisition by C. crescentus supports a broader suite of carbohydrate metabolic capabilities suggested by the C. crescentus genome sequence that until now have not been reported

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype