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Antimicrobial resistance is one of the major human health threats, with significant impacts on the global economy. Antibiotics are becoming increasingly ineffective as drug-resistance spreads, imposing an urgent need for new and innovative antimicrobial agents. Metal complexes are an untapped source of antimicrobial potential. Rhenium complexes, amongst others, are particularly attractive due to their low in vivo toxicity and high antimicrobial activity, but little is known about their targets and mechanism of action. In this study, a series of rhenium di- and tricarbonyl diimine complexes were prepared and evaluated for their antimicrobial potential against eight different microorganisms comprising Gram-negative and -positive bacteria. Our data showed that none of the Re dicarbonyl or neutral tricarbonyl species have either bactericidal or bacteriostatic potential. In order to identify possible targets of the molecules, and thus possibly understand the observed differences in the antimicrobial efficacy of the molecules, we computationally evaluated the binding affinity of active and inactive complexes against structurally characterized membrane-bound S. aureus proteins. The computational analysis indicates two possible major targets for this class of compounds, namely lipoteichoic acids flippase (LtaA) and lipoprotein signal peptidase II (LspA). Our results, consistent with the published in vitro studies, will be useful for the future design of rhenium tricarbonyl diimine-based antibiotics.

Background Computed tomography (CT) enables quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, helping in outcome prediction. Methods From 1 to 22 March 2020, patients with pneumonia symptoms, positive lung CT scan, and confirmed SARS-CoV-2 on reverse transcription-polymerase chain reaction (RT-PCR) were consecutively enrolled. Clinical data was collected. Outcome was defined as favourable or adverse (i.e., need for mechanical ventilation or death) and registered over a period of 10 days following CT. Volume of disease (VoD) on CT was calculated semi-automatically. Multiple linear regression was used to predict VoD by clinical/laboratory data. To predict outcome, important features were selected using a priori analysis and subsequently used to train 4 different models. Results A total of 106 consecutive patients were enrolled (median age 63.5 years, range 26–95 years; 41/106 women, 38.7%). Median duration of symptoms and C-reactive protein (CRP) was 5 days (range 1–30) and 4.94 mg/L (range 0.1–28.3), respectively. Median VoD was 249.5 cm3 (range 9.9–1505) and was predicted by lymphocyte percentage (p = 0.008) and CRP (p < 0.001). Important variables for outcome prediction included CRP (area under the curve [AUC] 0.77), VoD (AUC 0.75), age (AUC 0.72), lymphocyte percentage (AUC 0.70), coronary calcification (AUC 0.68), and presence of comorbidities (AUC 0.66). Support vector machine had the best performance in outcome prediction, yielding an AUC of 0.92. Conclusions Measuring the VoD using a simple CT post-processing tool estimates SARS-CoV-2 burden. CT and clinical data together enable accurate prediction of short-term clinical outcome.

Cloud Computing has assumed a relevant role in the ICT, profoundly influencing the life-cycle of modern applications in the manner they are designed, developed, and deployed and operated. In this article, we tackle the problem of supporting the design-time analysis of Cloud applications to identify a cost-optimized strategy for allocating components onto Cloud Virtual Machine infrastructural services, taking performance requirements into account. We present an approach and a tool, SPACE4Cloud, that supports users in modeling the architecture of an application, in defining performance requirements as well as deployment constraints, and then in mapping each architecture component into a corresponding VM service, minimizing total costs. An optimization algorithm supports the mapping and determines the Cloud configuration that minimizes the execution costs of the application over a daily time horizon. The benefits of this approach are demonstrated in the context of an industrial case study. Furthermore, we show that SPACE4Cloud leads to a cost reduction up to 60 percent, when compared to a first-principle technique based on utilization thresholds, like the ones typically used in practice, and that our solution is able to solve large problem instances within a time frame compatible with a fast-paced design process (less than half an hour in the worst case). Finally, we show that SPACE4Cloud is suitable to model even microservice-based applications and to compute the corresponding optimized deployment configuration which is compared with a state-of-the art meta-heuristic alternative method, achieving savings between 21 and 85 percent.

The focus of this work is to design a deeply quantized anomaly detector of oil leaks that may happen at the junction between the wind turbine high-speed shaft and the external bracket of the power generator. We propose a block-based binary shallow echo state network (BBS-ESN) architecture belonging to the reservoir computing (RC) category and, as we believe, it also extends the extreme learning machines (ELM) domain. Furthermore, BBS-ESN performs binary block-based online training using fixed and minimal computational complexity to achieve low power consumption and deployability on an off-the-shelf micro-controller (MCU). This has been achieved through binarization of the images and 1-bit quantization of the network weights and activations. 3D rendering has been used to generate a novel publicly available dataset of photo-realistic images similar to those potentially acquired by image sensors on the field while monitoring the junction, without and with oil leaks. Extensive experimentation has been conducted using a STM32H743ZI2 MCU running at 480 MHz and the results achieved show an accurate identification of anomalies, with a reduced computational cost per image and memory occupancy. Based on the obtained results, we conclude that BBS-ESN is feasible on off-the-shelf 32 bit MCUs. Moreover, the solution is also scalable in the number of image cameras to be deployed and to achieve accurate and fast oil leak detections from different viewpoints.

This repositoryincludes the the code and the datasets we used as reported in the experimental results of our paper

Silica aerogel is the ultimate thermal insulator thanks to its record-breaking low thermal conductivity (λ), open porosity and hydrophobicity. Silica aerogel's thermal conductivity is lowest at intermediate densities (ρ ≈ 0.11 g/cm ) and, because of the strong, power-law dependence of the E modulus on density, this rather low density so far led to low E moduli. Even with polymer reinforcement, increasing stiffness is only possible at higher density, thus higher conductivity. This paper explores the synthesis of silica aerogel granules using ambient pressure drying to provide enhanced mechanical stiffness whilst maintaining thermal conductivities well below 20 mW m K . The aging and drying conditions affect the interplay between mechanical and thermal properties, and are varied to optimize the physical properties. The dense (ρ≤0.29 g/cm ), but superinsulating (λ≈15 mW m K ) silica aerogels presented in this paper challenge the community's understanding of heat transport in aerogels, and do not rely on polymer reinforcement. The underlying microscopic structural parameters affecting the mechanical and thermal transport properties are investigated by modelling and simulation of the aerogel back-bone. Short aging times reduce the cross-section of, and heat transport through, inter-particle necks, leading to an overall decrease in thermal conductivity through the solid skeleton (λ ). In addition, short-aged gels undergo a partial pore collapse during ambient pressure drying of the pore fluid due to less aged, hence weaker network structures. The resulting denser structure contains additional point contacts that increase stiffness, by up to an order of magnitude. However, heat transport through these newly formed point-contacts is limited and the gas phase conduction (λ ) is further suppressed due to the even smaller pore sizes. Strong and superinsulating particles are ideal fillers for aerogel composites, concrete and renders. The optimized APD aerogels, available as granules, are finally compiled in a composite thermal insulation board with an effective thermal conductivity down to 20 mW m K with improved strength: a 2-fold increase for E, compared to a board produced from classical silica aerogel granulate. The possibility to improve mechanical properties of pure silica aerogels can help aerogels to break into new high-strength, superinsulating structural applications needed to reduce carbon emissions of the built environment., Acta Materialia, 213, ISSN:1359-6454

Ureido-functionalized compounds play an indispensable role in important biochemical processes, as well as chemical synthesis and production. Isocyanates, and KOCN in particular, are the preferred reagents for the ureido functionalization of amine-bearing compounds. In this study, we evaluate the potential of urea as a reagent to graft ureido groups onto amines at relatively low temperatures (

Graph analytics are an emerging class of irregular applications. Operating on very large datasets, they present unique behaviors, such as fine-grained, unpredictable memory accesses, and highly unbalanced task-level parallelism, that make existing general-purpose processors or accelerators (e.g., GPUs) suboptimal or difficult to program. To address these issues, research and industry are more and more relying on designs based on reconfigurable devices (Field Programmable Gate Arrays), sometimes even partially employing High-Level Synthesis (HLS) methods to accelerate the development of the accelerators. In this paper, we propose a novel architecture template for the automatic generation of accelerators for graph analytics and irregular applications. The architecture template includes a dynamic task scheduler, a parallel array of accelerators that enables supporting task-level parallelism with context switching, and a related multi-channel memory interface that decouples communication from computation and provides support for fine-grained atomic memory operations. We discuss the integration of the architectural template in an HLS flow, presenting the necessary modifications to enable automatic generation of the accelerators starting from OpenMP annotated code. We evaluate our approach by synthesizing custom designs for a set of graph database benchmark queries. We compare the synthesized accelerators with previous state-of-the-art methodologies for the synthesis of parallel architectures.

The use of plasmonic nanoparticles for biological applications has been gaining momentum in the last two decades. The ability of these particles to generate heat when exposed to light with a given ...

The azo moiety is receiving increasing attention as a stimuli-responsive trigger. Herein, we present an investigation of the mechanoresponsive behavior of a series of polyurethanes containing multiple randomly distributed azo motifs as scissile mechanophores, i.e., an entity that is preferentially cleaved upon application of a mechanical force. We made a systematic comparison of the ultrasound-induced cleavage of azo-containing polymers of different molecular weights and with varying azo content. We developed a mathematical model to describe the scission kinetics and the analysis of the rate constants showed that site-specific cleavage at the azo position was favored over random bond scission events. The proposed mathematical model appears to be a broadly useful method to characterize the ultrasound-induced molecular weight decrease of polymers containing multiple scissile mechanophores.

Some marine plankton called dinoflagellates emit light in response to the movement of surrounding water, resulting in a phenomenon called milky seas or sea sparkle. The underlying concept, a shear‐stress induced permeabilisation of biocatalytic reaction compartments, is transferred to polymer‐based nanoreactors. Amphiphilic block copolymers that carry nucleobases in their hydrophobic block are self‐assembled into polymersomes. The membrane of the vesicles can be transiently switched between an impermeable and a semipermeable state by shear forces occurring in flow or during turbulent mixing of polymersome dispersions. Nucleobase pairs in the hydrophobic leaflet separate when mechanical force is applied, exposing their hydrogen bonding motifs and therefore making the membrane less hydrophobic and more permeable for water soluble compounds. This polarity switch is used to release payload of the polymersomes on demand, and to activate biocatalytic reactions in the interior of the polymersomes., Polymersomes with nucleobase pairs in their membrane become transiently permeable for substrates when shear forces are applied. These force‐responsive polymersomes allow to switch on enzymatic reactions by turbulent mixing of vesicle dispersions, which is demonstrated with colorimetric and chemoluminescent reactions, as well as the curing of hydrogels.

Differently to the common belief, the industry quest for ultra-low-power neural networks is just at the beginning. Examples are the efforts carried by open communities such as TinyML and TinyMLPerf currently focusing on deep learning frameworks for Machine Learning (ML) and associated applications targeting micro-controllers (MCUs). However little attention has been put on deep learning frameworks and applications to enable ultra-low precision ML. These are enabling technologies to target uW hardware implementations. This work aims to compare two Deep Learning frameworks with support to deep quantization, QKeras and Larq, that abstract Tensorflow and Keras frameworks. Currently, Tensorflow is one of the most used deep learning tools by the research and industry communities aimed at deploying ML on the field. Two applications are presented with associated deeply quantized neural networks: Human Activity Recognition (HAR) exploiting a Hybrid Binary Neural Network (HBN) and Anomaly Detection for Industry 4.0 based on a Hybrid Binary AutoEncoder (HBAE). The pros and cons of the frameworks will be discussed during their usage on those applications. Results show an accuracy of up to 98.6% for the HBN and a PSNR up to 111.2 for the HBAE. The inference time has also been measured on different ARM-based architecture.

Magnetosomes are near-perfect intracellular magnetite nanocrystals found in magnetotactic bacteria. Their synthetic imitation, known as superparamagnetic iron oxide nanoparticles (SPIONs), have found applications in a variety of (nano)medicinal fields such as magnetic resonance imaging contrast agents, multimodal imaging and drug carriers. In order to perform these functions in medicine, shape and size control of the SPIONs is vital. We sampled SPIONs at ten-minutes intervals during the high-temperature thermal decomposition reaction. Their shape (sphericity and anisotropy) and geometric description (volume and surface area) were retrieved using three-dimensional imaging techniques, which allowed to reconstruct each particle in three dimensions, followed by stereological quantification methods. The results, supported by small angle X-ray scattering characterization, reveal that SPIONs initially have a spherical shape, then grow increasingly asymmetric and irregular. A high heterogeneity in volume at the initial stages makes place for lower particle volume dispersity at later stages. The SPIONs settled into a preferred orientation on the support used for transmission electron microscopy imaging, which hides the extent of their anisotropic nature in the axial dimension, there by biasing the interpretation of standard 2D micrographs. This information could be feedback into the design of the chemical processes and the characterization strategies to improve the current applications of SPIONs in nanomedicine.

The efficacy of magnetic hyperthermia treatment depends on the optimal available magnetic nanoparticles (MNPs) that are excited in a given alternating magnetic field and viscosity of the region of interest. In this regard, assessing the relevant relaxation parameters is of upmost importance and could improve the speed of development of efficient applications. Here, we demonstrate how to deduce all relevant magnetic parameters from fast, independent, and simple experimental measurements such as dynamic light scattering, vibrating sample magnetometer, and lock-in thermography. We study the thermal behaviour of two MNPs with different forms, i.e. spherical and cubical, synthesized in-house by thermal decomposition and coated with 4 different surface agents. By determination of specific absorption rate (SAR) values, hydrodynamic diameters and M−H curves it is possible to compute the magnetic particle volume, dominant relaxation time and magnetocrystalline anisotropy constant. The calculated SAR values derived from these parameters, show good agreement with the experimental determined SAR data, demonstrating the applicability of the reported procedure. Additionally, our results indicate that surface coatings can have minor impacts on the thermal dissipation of Néel relaxation dominated MNPs.

Nowadays, the big data paradigm is consolidating its central position in the industry, as well as in society at large. As big data applications gain more and more importance over time and given the dynamic nature of cloud resources, it is fundamental to develop an intelligent resource management system to provide Quality of Service guarantees to end-users. This paper presents a set of run-time optimization-based resource management policies for advanced big data analytics. Users submit Spark applications characterized by a priority and by a hard or soft deadline. Optimization policies address two scenarios: i) identification of the minimum capacity to run a Spark application within the deadline; ii) re-balance of the cloud resources in case of heavy load, minimising the weighted soft deadline application tardiness. The results obtained in the first scenario demonstrate that the percentage error of the prediction of the optimal resource usage with respect to system measurement and exhaustive search is the range 4%-29% while literature-based techniques present an average error in the range 6%-63%. Moreover, in the second scenario, the proposed algorithms can address complex scenarios with an error of 8% on average while literature-based approaches obtain an average error of about 57%.

In this work, a method is established to calibrate a model that describes the basic dynamics of DNA damage and repair. The model can be used to extend planning for radiotherapy and hyperthermia in order to include the biological effects. In contrast to “syntactic” models (e.g., describing molecular kinetics), the model used here describes radiobiological semantics, resulting in a more powerful model but also in a far more challenging calibration. Model calibration is attempted from clonogenic assay data (doses of 0–6 Gy) and from time-resolved comet assay data obtained within 6 h after irradiation with 6 Gy. It is demonstrated that either of those two sources of information alone is insufficient for successful model calibration, and that both sources of information combined in a holistic approach are necessary to find viable model parameters. Approximate Bayesian computation (ABC) with simulated annealing is used for parameter search, revealing two aspects that are beneficial to resolving the calibration problem: (1) assessing posterior parameter distributions instead of point-estimates and (2) combining calibration runs from different assays by joining posterior distributions instead of running a single calibration run with a combined, computationally very expensive objective function.

As a highly interdisciplinary field, working with nanoparticles in a biomedical context requires a robust understanding of soft matter physics, colloidal behaviors, nano-characterization methods, biology, and bio-nano interactions. When reporting results, it can be easy to overlook simple, seemingly trivial experimental details. In this context, we set out to understand how in vitro technique, specifically the way we administer particles in 2D culture, can influence experimental outcomes. Gold nanoparticles coated with poly(vinylpyrrolidone) were added to J774A.1 mouse monocyte/macrophage cultures as either a concentrated bolus, a bolus then mixed via aspiration, or pre-mixed in cell culture media. Particle-cell interaction was monitored via inductively coupled plasma-optical emission spectroscopy and we found that particles administered in a concentrated dose interacted more with cells compared to the pre-mixed administration method. Spectroscopy studies reveal that the initial formation of the protein corona upon introduction to cell culture media may be responsible for the differences in particle-cell interaction. Modeling of particle deposition using the in vitro sedimentation, diffusion and dosimetry model helped to clarify what particle phenomena may be occurring at the cellular interface. We found that particle administration method in vitro has an effect on particle-cell interactions (i.e. cellular adsorption and uptake). Initial introduction of particles in to complex biological media has a lasting effect on the formation of the protein corona, which in turn mediates particle-cell interaction. It is of note that a minor detail, the way in which we administer particles in cell culture, can have a significant effect on what we observe regarding particle interactions in vitro.

ObjectiveFew randomized trials have evaluated the use of non-invasive ventilation (NIV) for early acute respiratory failure (ARF) in non-intensive care unit (ICU) wards. The aim of this study is to test the hypothesis that early NIV for mild-moderate ARF in non-ICU wards can prevent development of severe ARF.DesignPragmatic, parallel group, randomized, controlled, multicenter trial.SettingNon-intensive care wards of tertiary centers.PatientsNon-ICU ward patients with mild to moderate ARF without an established indication for NIV.InterventionsPatients will be randomized to receive or not receive NIV in addition to best available care.Measurements and main resultsWe will enroll 520 patients, 260 in each group. The primary endpoint of the study will be the development of severe ARF. Secondary endpoints will be 28-day mortality, length of hospital stay, safety of NIV in non-ICU environments, and a composite endpoint of all in-hospital respiratory complications.ConclusionsThis trial will help determine whether the early use of NIV in non-ICU wards can prevent progression from mild-moderate ARF to severe ARF.

Oral presentation In this work, a method is presented to map a set of experimentally obtained, time-resolved distributions to a dynamic model. Specifically, time-resolved comet assay readouts of cancer cells after application of ionizing radiation are mapped to the Multi-Hit-Repair model, a radiobiologically motivated dynamic model used to predict DNA damage and repair. Differential evolution is used for parameter-search to showcase the potential of this method, producing a prediction close to the experimental measurement. The results obtained from the parameter search are used to characterize aspects of the repair process. The method is compared to prior attempts of finding model parameters from dose-response curves, revealing that calibration is required to render the two comparable.

High Level Synthesis is a set of methodologies aimed at generating hardware descriptions starting from specifications written in high-level languages. While these methodologies share different elements with traditional compilation flows, there are characteristics of the addressed problem which require ad hoc management. In particular, differently from most of the traditional compilation flows, the complexity and the execution time of the High Level Synthesis techniques are much less relevant than the quality of the produced results. For this reason, fixed-point analyses, as well as successive refinement optimizations, can be accepted, provided that they can improve the quality of the generated designs. This article presents a design flow engine for the description and the execution of complex and customized synthesis flows. It supports dynamic addition of passes and dependencies, cyclic dependencies, and selective pass invalidation. Experimental results show the benefits of such type of design flows with respect to static linear design flows when applied to High Level Synthesis.

Deep learning (DL) methods have recently gained popularity and been used in commonplace applications; voice and face recognition, among the others. Despite the growing popularity of DL and the associated hardware acceleration techniques, GPU-based systems still have very high costs. Moreover, while the cloud represents a cost-effective and flexible solution, in large settings operations costs can be further optimized by carefully managing and fostering resource sharing. This work addresses the online joint problem of capacity planning of virtual machines (VMs) and DL training jobs scheduling, and proposes a Mixed Integer Linear Programming (MILP) formulation. In particular, DL jobs are assumed to feature a deadline, while multiple VM types are available from a cloud provider catalog, and each VM has, possibly, multiple GPUs. Our solutions optimize the operations costs by (i) right-sizing the VM capacities; (ii) partitioning the set of GPUs among multiple concurrent jobs running on the same VM, and (iii) determining a deadline-aware job schedule. Our approach is evaluated using an ad-hoc simulator and a prototype environment, and compared against first-principle approaches, resulting in a cost reduction of 45-80%.

Data analytics applications increasingly are complex workflows composed of phases with very different program behaviors (e.g., graph algorithms and machine learning, algorithms operating on sparse and dense data structures, etc). To reach the levels of efficiency required to process these workflows in real time, upcoming architectures will need to leverage even more workload specialization. If, at one end, we may find even more heterogenous processors composed by a myriad of specialized processing elements, at the other end we may see novel reconfigurable architectures, composed of sets of functional units and memories interconnected with (re)configurable on-chip networks, able to adapt dynamically to adapt the workload characteristics. Field Programmable Gate Arrays are more and more used for accelerating various workloads and, in particular, inferencing in machine learning, providing higher efficiency than other solutions. However, their fine-grained nature still leads to issues for the design software and still makes dynamic reconfiguration impractical. Future, more coarse-grained architectures could offer the features to execute diverse workloads at high efficiency while providing better reconfiguration mechanisms for dynamic adaptability. Nevertheless, we argue that the challenges for reconfigurable computing remain in the software. In this position paper, we describe a possible toolchain for reconfigurable architectures targeted at data analytics.

The integration of Field Programmable Gate Arrays (FPGAs) in an aerospace system improves its efficiency and its flexibility thanks to their programmability, but increases the design complexity. The design flows indeed have to be composed of several steps to fill the gap between the starting solution, which is usually a reference sequential implementation, and the final heterogeneous solution which includes custom hardware accelerators. Among these steps, there are the analysis of the application to identify the functionalities that gain advantages in execution on hardware and the generation of their implementations by means of Hardware Description Languages. Generating these descriptions for a software developer can be a very difficult task because of the different programming paradigms of software programs and hardware descriptions. To facilitate the developer in this activity, High Level Synthesis techniques have been developed aiming at (semi-)automatically generating hardware implementations of specifications written in high level languages (e.g., C). With respect to other embedded systems scenarios, the aerospace systems introduce further constraints that have to be taken into account during the design of these heterogeneous systems. In this type of systems explicit data transfers to and from FPGAs are preferred to the adoption of a shared memory architecture. The first approach indeed potentially improves the predictability of the produced solutions, but the sizes of all the data transferred to and from any devices must be known at design time. Identifying the sizes in presence of complex C applications which use pointers can be a not so easy task. In this paper, a semi-automatic design flow based on the integration of an aerospace design flow, an application analysis technique, and High Level Synthesis methodologies is presented. The initial reference application is analyzed to identify which are the sizes of the data exchanged among the different components of the application. Next, starting from the high level specification and from the results of this analysis, High Level Synthesis techniques are applied to automatically produce the hardware accelerators.

Surface chemistry is believed to be the key parameter affecting the aggregation and breakage of colloidal suspensions when subjected to shear. To date, only a few works dealt with the understanding of the role of the physical and chemical properties of the particles’ surface upon aggregation under shear. Previous studies suggested that surface modifications strongly affect polymer particles’ adhesion, but it was very challenging to demonstrate this effect and monitor these alterations upon prolonged exposure to shear forces. More importantly, the mechanisms leading to these changes remain elusive. In this work, shear-induced aggregation experiments of polymer colloidal particles have been devised with the specific objective of highlighting material transfer and clarifying the role of the softness of the particle’s surface. To achieve this goal, polymer particles with a core–shell structure comprising fluorescent groups have been prepared so that the surface’s softness could be tuned by the addition of monomer acting as a plasticizer and the percentage of fluorescent particles could be recorded over time via confocal microscopy to detect eventual material transfer among different particles. For the first time, material exchange occurring on the soft surface of core–shell polymer microparticles upon aggregation under shear was observed and proved. More aptly, starting from a 50% labeled/nonlabeled mixture, an increase in the percentage of particles showing a fluorescent signature was recorded over time, reaching a fraction of 70% after 5 h.

The mechanical reinforcement of nanocomposites containing nanorods-like fillers such as cellulose nanocrystals (CNCs) is often interpreted by adapting the classical parallel–series model, assuming a simple hyperbolic dependence between the percolation threshold and aspect ratio. However, such assumptions are valid only for nanorods with high aspect ratio and often are misinterpreting the reinforcement obtained at low volume fraction of filler loading. To elucidate this intriguing scenario, we proposed a new approach and validated it by compiling and reinterpreting some of available literature that represent the experimental reinforcement with CNCs. Our approach showed better accuracy, specifically for the cases of CNC nanorods with lower aspect ratio. We conclude that this route permits a more realistic evaluation of the mechanical reinforcement, where a physical parameter accounting the polymer filler association is introduced. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45254.

BACKGROUND: Prone positioning and positive end-expiratory pressure can improve pulmonary gas exchange and respiratory mechanics. However, they may be associated with the development of intracranial hypertension. Intracranial pressure (ICP) can be noninvasively estimated from the sonographic measurement of the optic nerve sheath diameter (ONSD) and from the transcranial Doppler analysis of the pulsatility (ICPPI) and the diastolic component (ICPFVd) of the velocity waveform. METHODS: The effect of the prone positioning and positive end-expiratory pressure on ONSD, ICPFVd, and ICPPI was assessed in a prospective study of 30 patients undergoing spine surgery. One-way repeated measures analysis of variance, fixed-effect multivariate regression models, and receiver operating characteristic analyses were used to analyze numerical data. RESULTS: The mean values of ONSD, ICPFVd, and ICPPI significantly increased after change from supine to prone position. Receiver operating characteristic analyses demonstrated that, among the noninvasive methods, the mean ONSD measure had the greatest area under the curve signifying it is the most effective in distinguishing a hypothetical change in ICP between supine and prone positioning (0.86±0.034 [0.79 to 0.92]). A cutoff of 0.43 cm was found to be a best separator of ONSD value between supine and prone with a specificity of 75.0 and a sensitivity of 86.7. CONCLUSIONS: Noninvasive ICP estimation may be useful in patients at risk of developing intracranial hypertension who require prone positioning.

We report on theoretical and experimental considerations on bacteria capturing and enrichment via magnetic separation enabling integrated diagnosis and treatment of blood stream infections. We show optimization of carrier-pathogen interactions based on a mathematical model followed by an experimental proof-of-concept study along with investigations on the process safety.