Explore the vast range of titles available.

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95–1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0–15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38–0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

The contrast in the electrical properties of healthy and malignant lung tissue could provide a methodology for intraoperative surgical margin assessment to reduce positive margins and improve clinical outcomes. However, the tissue is often compressed and deformed during intraoperative procedures. This study explores the impact applied pressure has on the electrical properties of ex vivo human lung tissue. Impedance spectra spanning 100 Hz to 10 MHz were recorded from freshly resected lung tissue specimens including lesion (n=3), perilesion (n=3), and healthy (n=4) samples, under an applied load and converted into resistivity. A linear regression was performed for each resistivity-pressure curve for all tissue types. A significant difference (p<0.05) was found between regression coefficients of lesion and perilesion tissues and both regression coefficient and constant for lesion and healthy tissue. No significant differences were found between the perilesion and healthy tissue coefficient or constant. These finding suggests that resistivity increases with pressure at different rates for malignant and healthy tissue that may be sufficient for surgical margin assessment.

An original modeling-based microwave imaging technique for determining the volume fraction of solid material in dielectric powders is described. The desired characteristic is determined by analyzing S-parameter measurements in a waveguide containing the sample with the help of an artificial neural network trained by data from 3D FDTD simulation. The powder sample is represented by a mixture of air and millimeter-scale particles reproduced in the FDTD model. Computational tests with 20 to 40 mm cubic samples of SiC and ZrO2 powders in WR340 show that the solids volume fraction is determined with less than 5% error.

We explain how the problem of improving image reconstruction in electrical impedance mammography can be studied by comparing measured data with predictions from the continuum model, ave-gap model, and complete electrode model. The data is measured by the RPI ACT4 system using 60 electrodes arranged in a mammography geometry. Each model's accuracy can be determined by comparing the computed eigenvalues of its Neumann-to-Dirichlet map with those found from experimentally measured N-to-D maps. It will be explained how the different models can be used to reconstruct images of the electrical conductivity and permittivity inside a region bounded by a pair of electrode arrays in a mammography geometry. Reconstructions from experimental data are presented.

Upgrading successful processes of microwave-assisted organic synthesis to the level of industrial technology is currently slowed by difficulties in experimental development of largescale and highly-productive reactors. This paper proposes to address this issue by developing microwave chemistry reactors as microwave systems, rather than as black-box-type units for chemical reactions. We suggest an approach based on the application of a neural network optimization technique to a microwave system in order to improve its coupling (and thus energy efficiency). The RBF network optimization with CORS sampling introduced in our earlier work and capable of exceptionally quick convergence to the optima due to a dramatically reduced number of underlying 3D FDTD analyses, is upgraded here to account for an additional practically important condition requiring optimal design of the reactor for different reactants. Viability of the approach is illustrated by three examples of finding the geometry of a conventional 99% energy efficient microwave reactor for 3/3/6 different materials; with 1/5/1 liter reactants, seven-parameter optimization yields the best configurations taking only 16/38/115 hours of CPU time of a regular PC.

Purpose - To outline different versions of a novel method for accurate and efficient determining the dielectric properties of arbitrarily shaped materials.Design methodology approach - Complex permittivity is found using an artificial neural network procedure designed to control a 3D FDTD computation of S-parameters and to process their measurements. Network architectures are based on multilayer perceptron and radial basis function nets. The one-port solution deals with the simulated and measured frequency responses of the reflection coefficient while the two-port approach exploits the real and imaginary parts of the reflection and transmission coefficients at the frequency of interest.Findings - High accuracy of permittivity reconstruction is demonstrated by numerical and experimental testing for dielectric samples of different configuration.Research limitations implications - Dielectric constant and the loss factor of the studied material should be within the ranges of corresponding parameters associated with the database used for the network training. The computer model must be highly adequate to the employed experimental fixture.Practical implications - The method is cavity-independent and applicable to the sample fixture of arbitrary configuration provided that the geometry is adequately represented in the model. The two-port version is capable of handling frequency-dependent media parameters. For materials which can take some predefined form computational cost of the method is very insignificant.Originality value - A full-wave 3D FDTD modeling tool and the controlling neural network procedure involved in the proposed approach allow for much flexibility in practical implementation of the method.

Purpose To outline different versions of a novel method for accurate and efficient determining the dielectric properties of arbitrarily shaped materials. Designmethodologyapproach Complex permittivity is found using an artificial neural network procedure designed to control a 3D FDTD computation of Sparameters and to process their measurements. Network architectures are based on multilayer perceptron and radial basis function nets. The oneport solution deals with the simulated and measured frequency responses of the reflection coefficient while the twoport approach exploits the real and imaginary parts of the reflection and transmission coefficients at the frequency of interest. Findings High accuracy of permittivity reconstruction is demonstrated by numerical and experimental testing for dielectric samples of different configuration. Research limitationsimplications Dielectric constant and the loss factor of the studied material should be within the ranges of corresponding parameters associated with the database used for the network training. The computer model must be highly adequate to the employed experimental fixture. Practical implications The method is cavityindependent and applicable to the samplefixture of arbitrary configuration provided that the geometry is adequately represented in the model. The twoport version is capable of handling frequencydependent media parameters. For materials which can take some predefined form computational cost of the method is very insignificant. Originalityvalue A fullwave 3D FDTD modeling tool and the controlling neural network procedure involved in the proposed approach allow for much flexibility in practical implementation of the method.

The recent successes of immunotherapy have shifted the paradigm in cancer treatment, but because only a percentage of patients are responsive to immunotherapy, it is imperative to identify factors impacting outcome. Obesity is reaching pandemic proportions and is a major risk factor for certain malignancies, but the impact of obesity on immune responses, in general and in cancer immunotherapy, is poorly understood. Here, we demonstrate, across multiple species and tumor models, that obesity results in increased immune aging, tumor progression and PD-1-mediated T cell dysfunction which is driven, at least in part, by leptin. However, obesity is also associated with increased efficacy of PD-1/PD-L1 blockade in both tumor-bearing mice and clinical cancer patients. These findings advance our understanding of obesity-induced immune dysfunction and its consequences in cancer and highlight obesity as a biomarker for some cancer immunotherapies. These data indicate a paradoxical impact of obesity on cancer. There is heightened immune dysfunction and tumor progression but also greater anti-tumor efficacy and survival after checkpoint blockade which directly targets some of the pathways activated in obesity. Obesity promotes tumor growth yet simultaneously increases susceptibility to PD-1-targeted cancer immunotherapy.

Glucocorticoid (GC) induced osteoporosis (GIOP) and osteonecrosis remain a significant health issue with few approved therapies. Here, we investigate the cellular and molecular processes by which GCs affect osteogenesis and angiogenesis. We find that GC treatment reduces bone mass through decreased bone formation by skeletal stem cells (SSCs). Concomitantly, endothelial cells increase in number but display distorted phenotypical features. Transplantation studies of SSCs combined with molecular analysis by single cell RNA-sequencing and functional testing of primary human cells tie GC-induced skeletal changes to altered stem cell differentiation dynamics. This in turn perpetuates reduced osteogenesis and vascular malformation through direct SSC-endothelial crosstalk mediated at least in part by Basigin. The genetic deletion of Basigin in the skeletal lineage as well as antibody-mediated blockade of Basigin during GC treatment prevents bone loss. Intriguingly, when administered to 2-year-old mice, anti-Basigin therapy reinstates bone remodeling to significantly improve bone mass. These findings provide therapeutic vantage points for GIOP and potentially other conditions associated with bone loss. Glucocorticoid (GC) induced osteoporosis (GIOP) remains a significant clinical problem. Here, the authors find that GCs disrupt skeletal stem cell–endothelial crosstalk via Basigin that can be targeted as a potential therapy for GIOP.

PD-1 expression is a hallmark of both early antigen-specific T cell activation and later chronic stimulation, suggesting key roles in both naive T cell priming and memory T cell responses. Although significant similarities exist between T cells and NK cells, there are critical differences in their biology and functions reflecting their respective adaptive and innate immune effector functions. Expression of PD-1 on NK cells is controversial despite rapid incorporation into clinical cancer trials. Our objective was to stringently and comprehensively assess expression of PD-1 on both mouse and human NK cells under multiple conditions and using a variety of readouts. We evaluated NK cells from primary human tumor samples, after ex vivo culturing, and from multiple mouse tumor and viral models using flow cytometry, quantitative reverse-transcriptase PCR (qRT-PCR), and RNA-Seq for PD-1 expression. We demonstrate that, under multiple conditions, human and mouse NK cells consistently lack PD-1 expression despite the marked upregulation of other activation/regulatory markers, such as TIGIT. This was in marked contrast to T cells, which were far more prominent within all tumors and expressed PD-1. These data have important implications when attempting to discern NK from T cell effects and to determine whether PD-1 targeting can be expected to have direct effects on NK cell functions.