Left Ventricular Mechanics Differ in Subtypes of Aortic Stenosis Following Transcatheter Aortic Valve Replacement

BackgroundLeft ventricular (LV) mechanics are impaired in patients with severe aortic stenosis (AS). We hypothesized that there would be differences in myocardial mechanics, measured by global longitudinal strain (GLS) recovery in patients with four subtypes of severe AS after transcatheter aortic valve replacement (TAVR), stratified based upon flow and gradient.MethodsWe retrospectively evaluated 204 patients with severe AS who underwent TAVR and were followed post-TAVR at our institution for clinical outcomes. Speckle-tracking transthoracic echocardiography was performed pre- and post-TAVR. Patients were classified as: (1) normal-flow and high-gradient, (2) normal-flow and high-gradient with reduced LV ejection fraction (LVEF), (3) classical low-flow and low-gradient, or (4) paradoxical low-flow and low-gradient.ResultsBoth GLS (−13.9 ± 4.3 to −14.8 ± 4.3, P < 0.0001) and LVEF (55 ± 15 to 57 ± 14%, P = 0.0001) improved immediately post-TAVR. Patients with low-flow AS had similar improvements in LVEF (+2.6 ± 9%) and aortic valve mean gradient (−23.95 ± 8.34 mmHg) as patients with normal-flow AS. GLS was significantly improved in patients with normal-flow (−0.93 ± 3.10, P = 0.0004) compared to low-flow AS. Across all types of AS, improvement in GLS was associated with a survival benefit, with GLS recovery in alive patients (mean GLS improvement of −1.07 ± 3.10, P < 0.0001).ConclusionsLV mechanics are abnormal in all patients with subtypes of severe AS and improve immediately post-TAVR. Recovery of GLS was associated with a survival benefit. Patients with both types of low-flow AS showed significantly improved, but still impaired, GLS post-TAVR, suggesting underlying myopathy that does not correct post-TAVR.

A biogeographic 16S rRNA survey of bacterial communities of ureolytic biomineralization from California public restrooms

In this study, we examined the total bacterial community associated with ureolytic biomineralization from urine drainage systems. Biomineral samples were obtained from 11 California Department of Transportation public restrooms fitted with waterless, low-flow, or conventional urinals in 2019. Following high throughput 16S rRNA Illumina sequences processed using the DADA2 pipeline, the microbial diversity assessment of 169 biomineral and urine samples resulted in 3,869 reference sequences aggregated as 598 operational taxonomic units (OTUs). Using PERMANOVA testing, we found strong, significant differences between biomineral samples grouped by intrasystem sampling location and urinal type. Biomineral microbial community profiles and alpha diversities differed significantly when controlling for sampling season. Observational statistics revealed that biomineral samples obtained from waterless urinals contained the largest ureC/16S gene copy ratios and were the least diverse urinal type in terms of Shannon indices. Waterless urinal biomineral samples were largely dominated by the Bacilli class (86.1%) compared to low-flow (41.3%) and conventional samples (20.5%), and had the fewest genera that account for less than 2.5% relative abundance per OTU. Our findings are useful for future microbial ecology studies of urine source-separation technologies, as we have established a comparative basis using a large sample size and study area.

Implications of variations in stream specific conductivity for estimating baseflow using chemical mass balance and calibrated hydrograph techniques

Baseflow to rivers comprises regional groundwater and lower-salinity intermediate water stores such as interflow, soil water, and bank return flows. Chemical mass balance (CMB) calculations based on the specific conductivity (SC) of rivers potentially estimate the groundwater contribution to baseflow. This study discusses the application of the CMB approach in rivers from south-eastern Australia and assesses the feasibility of calibrating recursive digital filters (RDFs) and sliding minima (SM) techniques based on streamflow data to estimate groundwater inflows. The common strategy of assigning the SC of groundwater inflows based on the highest annual river SC may not always be valid due to the persistent presence of lower-salinity intermediate waters. Rather, using the river SC from low-flow periods during drought years may be more realistic. If that is the case, the estimated groundwater inflows may be lower than expected, which has implications for assessing contaminant transport and the impacts of near-river groundwater extraction. Probably due to long-term variations in the proportion of groundwater in baseflow, the RDF and SM techniques cannot generally be calibrated using the CMB results to estimate annual baseflow proportions. Thus, it is not possible to extend the estimates of groundwater inflows using those methods, although in some catchments reasonable estimates of groundwater inflows can be made from annual streamflows. Short-term variations in the composition of baseflow also lead to baseflow estimates made using the CMB method being far more irregular than expected. This study illustrates that estimating baseflow, especially groundwater inflows, is not straightforward.

Beef tallow injection matrix for serial crystallography

Serial crystallography (SX) enables the visualization of the time-resolved molecular dynamics of macromolecular structures at room temperature while minimizing radiation damage. In SX experiments, the delivery of a large number of crystals into an X-ray interaction point in a serial and stable manner is key. Sample delivery using viscous medium maintains the stable injection stream at low flow rates, markedly reducing sample consumption compared with that of a liquid jet injector and is widely applied in SX experiments with low repetition rates. As the sample properties and experimental environment can affect the stability of the injection stream of a viscous medium, it is important to develop sample delivery media with various characteristics to optimize the experimental environment. In this study, a beef tallow injection matrix possessing a higher melting temperature than previously reported fat-based shortening and lard media was introduced as a sample delivery medium and applied to SX. Beef tallow was prepared by heat treating fats from cattle, followed by the removal of soluble impurities from the extract by phase separation. Beef tallow exhibited a very stable injection stream at room temperature and a flow rate of < 10 nL/min. The room-temperature structures of lysozyme and glucose isomerase embedded in beef tallow were successfully determined at 1.55 and 1.60 Å, respectively. The background scattering of beef tallow was higher than that of previously reported fat-based shortening and lard media but negligible for data processing. In conclusion, the beef tallow matrix can be employed for sample delivery in SX experiments conducted at temperatures exceeding room temperature.

Thermal Suitability of the Los Angeles River for Cold Water Resident and Migrating Fish Under Physical Restoration Alternatives

Anthropogenic development has adversely affected river habitat and species diversity in urban rivers, and existing habitats are jeopardized by future uncertainties in water resources management and climate. The Los Angeles River (LAR), for example, is a highly modified system that has been mostly channelized for flood control purposes, has altered hydrologic and hydraulic conditions, and is thermally altered (warmed), which severely limits the habitat suitability for cold water fish species. Efforts are currently underway to provide suitable environmental flows and improve channel hydraulic conditions, such as depth and velocity, for adult fish migration from the Pacific Ocean to upstream spawning areas. However, the thermal responses of restoration alternatives for resident and migrating cold water fish have not been fully investigated. Using a mechanistic model, we simulated the LAR’s water temperature under baseline conditions and future alternative restoration scenarios for migration of the native, anadromous steelhead trout in Southern California and the historically resident Santa Ana sucker. We considered three scenarios: 1) increasing roughness of the low-flow channel, 2) increasing the depth and width of the low-flow channel, and 3) allowing subsurface inflow to the river at a soft bottom reach in the LA downtown area. Our analysis indicates that the maximum weekly average temperature (MaxWAT) in the baseline condition was 28.9°C, suggesting that the current river temperatures would act as a limiting factor during the steelhead migration season and habitat for Santa Ana sucker. The MaxWAT dropped about 3%–28°C after applying all the considered scenarios at the study site, which is 3°C higher than the determined steelhead survival threshold. Our simulations suggest that without consideration of thermal restoration, restoring hydraulic conditions may be insufficient to support cold water fish migration or year-round resident native fish populations, particularly with potential river temperature increases due to climate change.

Parsimonious statistical learning models for low-flow estimation

Statistical learning methods offer a promising approach for low-flow regionalization. We examine seven statistical learning models (Lasso, linear, and nonlinear-model-based boosting, sparse partial least squares, principal component regression, random forest, and support vector regression) for the prediction of winter and summer low flow based on a hydrologically diverse dataset of 260 catchments in Austria. In order to produce sparse models, we adapt the recursive feature elimination for variable preselection and propose using three different variable ranking methods (conditional forest, Lasso, and linear model-based boosting) for each of the prediction models. Results are evaluated for the low-flow characteristic Q95 (Pr(Q>Q95)=0.95) standardized by catchment area using a repeated nested cross-validation scheme. We found a generally high prediction accuracy for winter (RCV2 of 0.66 to 0.7) and summer (RCV2 of 0.83 to 0.86). The models perform similarly to or slightly better than a top-kriging model that constitutes the current benchmark for the study area. The best-performing models are support vector regression (winter) and nonlinear model-based boosting (summer), but linear models exhibit similar prediction accuracy. The use of variable preselection can significantly reduce the complexity of all the models with only a small loss of performance. The so-obtained learning models are more parsimonious and thus easier to interpret and more robust when predicting at ungauged sites. A direct comparison of linear and nonlinear models reveals that nonlinear processes can be sufficiently captured by linear learning models, so there is no need to use more complex models or to add nonlinear effects. When performing low-flow regionalization in a seasonal climate, the temporal stratification into summer and winter low flows was shown to increase the predictive performance of all learning models, offering an alternative to catchment grouping that is recommended otherwise.

Remdesivir and dexamethasone as tools to relieve hospital care systems stressed by COVID-19: A modelling study on bed resources and budget impact

Remdesivir and dexamethasone are the only drugs providing reductions in the lengths of hospital stays for COVID-19 patients. We assessed the impacts of remdesivir on hospital-bed resources and budgets affected by the COVID-19 outbreak. A stochastic agent-based model was combined with epidemiological data available on the COVID-19 outbreak in France and data from two randomized control trials. Strategies involving treating with remdesivir only patients with low-flow oxygen and patients with low-flow and high-flow oxygen were examined. Treating all eligible low-flow oxygen patients during the entirety of the second wave would have decreased hospital-bed occupancy in conventional wards by 4% [2%; 7%] and intensive care unit (ICU)-bed occupancy by 9% [6%; 13%]. Extending remdesivir use to high-flow-oxygen patients would have amplified reductions in ICU-bed occupancy by up to 14% [18%; 11%]. A minimum remdesivir uptake of 20% was required to observe decreases in bed occupancy. Dexamethasone had effects of similar amplitude. Depending on the treatment strategy, using remdesivir would, in most cases, generate savings (up to 722€) or at least be cost neutral (an extra cost of 34€). Treating eligible patients could significantly limit the saturation of hospital capacities, particularly in ICUs. The generated savings would exceed the costs of medications.

Longitudinal River Monitoring and Modelling Substantiate the Impact of Weirs on Nitrogen Dynamics

The fluvial nitrogen dynamics at locations around weirs are still rarely studied in detail. Eulerian data, often used by conventional river monitoring and modelling approaches, lags the spatial resolution for an unambiguous representation. With the aim to address this knowledge gap, the present study applies a coupled 1D hydrodynamic–water quality model to a 26.9 km stretch of an upland river. Tailored simulations were performed for river sections with water retention and free-flow conditions to quantify the weirs’ influences on nitrogen dynamics. The water quality data were sampled with Eulerian and Lagrangian strategies. Despite the limitations in terms of required spatial discretization and simulation time, refined model calibrations with high spatiotemporal resolution corroborated the high ammonification rates (0.015 d−1) on river sections without weirs and high nitrification rates (0.17 d−1 ammonium to nitrate, 0.78 d−1 nitrate to nitrite) on river sections with weirs. Additionally, using estimations of denitrification based on typical values for riverbed sediment as a reference, we could demonstrate that in our case study, weirs can improve denitrification substantially. The produced backwater lengths can induce a means of additional nitrogen removal of 0.2-ton d−1 (10.9%) during warm and low-flow periods.

Imaging Challenges in Patients with Severe Aortic Stenosis and Heart Failure: Did We Find a Way Out of the Labyrinth?

Aortic stenosis (AS) is the most frequent degenerative valvular disease in developed countries. Its incidence has been constantly rising due to population aging. The diagnosis of AS was considered straightforward for a very long time. High gradients and reduced aortic valve area were considered as “sine qua non” in diagnosis of AS until a growing body of evidence showed that patients with low gradients could also have severe AS with the same or even worse outcome. This completely changed the paradigm of AS diagnosis and involved large numbers of parameters that had never been used in the evaluation of AS severity. Low gradient AS patients may present with heart failure (HF) with preserved or reduced left ventricular ejection fraction (LVEF), associated with changes in cardiac output and flow across the aortic valve. These patients with low-flow low-gradient or paradoxical low-flow low-gradient AS are particularly challenging to diagnose, and cardiac output and flow across the aortic valve have become the most relevant parameters in evaluation of AS, besides gradients and aortic valve area. The introduction of other imaging modalities in the diagnosis of AS significantly improved our knowledge about cardiac mechanics, tissue characterization of myocardium, calcium and inflammation burden of the aortic valve, and their impact on severity, progression and prognosis of AS, not only in symptomatic but also in asymptomatic patients. However, a variety of novel parameters also brought uncertainty regarding the clinical relevance of these indices, as well as the necessity for their validation in everyday practice. The aim of this review is to summarize the prevalence of HF in patients with severe AS and elaborate on the diagnostic challenges and advantages of comprehensive multimodality cardiac imaging to identify the patients that may benefit from surgical or transcatheter aortic valve replacement, as well as parameters that may help during follow-up.

Analytical Solving a Nonlinear Model of the Glow Discharge

A nonlinear model has been introduced for the positive column of DC glow discharge in apure sealed, or low flow, gas media by including the diffusion, recombination, attachment, detachment,process and having the two-step ionization process of the metastable excited states, too. By thecombination of the system of the nonlinear continuity equations of the system, using some physicalestimations, and degrading the resulted nonlinear PDE in polar and rectangular systems of coordinatethe steady-state nonlinear ODE have been derived. Using a series-based solution, an innovativenonlinear recursion relation has been proposed for calculating the sentence of series. Using the stateof elimination of free charge on the outer boundary of the discharge vessel, the universal equation ofthe characteristic energy of the electrons versus the similarity variable, using the maximum degree ofionization as the parameter, has been derived.