Nanoscale Structure of Lipid-Gemini Surfactant Mixed Monolayers Resolved with AFM and KPFM Microscopy.

Drug delivery vehicles composed of lipids and gemini surfactants (GS) are promising in gene therapy. Tuning the composition and properties of the delivery vehicle is important for the efficient load and delivery of DNA fragments (genes). In this paper, we studied novel gene delivery systems composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1,2-dipalmitoyl-sn-3-phosphocholine (DPPC), and GS of the type N,N-bis(dimethylalkyl)-α,ω-alkanediammonium dibromide at different ratios. The nanoscale properties of the mixed DOPC-DPPC-GS monolayers on the surface of the gene delivery system were studied using atomic force microscopy (AFM) and Kelvin probe force microscopy (KPFM). We demonstrate that lipid-GS mixed monolayers result in the formation of nanoscale domains that vary in size, height, and electrical surface potential. We show that the presence of GS can impart significant changes to the domain topography and electrical surface potential compared to monolayers composed of lipids alone.

Neonatal Catheter and Tube Placement and Radiographic Assessment Statistics in Relation to Important Anatomic Landmarks.

OBJECTIVE: Neonatal catheters and tubes are commonly used for monitoring and support for intensive care and must be correctly positioned to avoid complications. Position assessment is routinely done by radiography. The objective of this study is to characterize neonatal catheter and tube placement in terms of the proportion of those devices that are malpositioned. STUDY DESIGN: Using an institutional dataset of 723 chest/abdominal radiographs of neonatal intensive care unit (ICU) patients (all within 60 days of birth), we assessed the proportion of catheters that are malpositioned. Many radiographs contained multiple catheter types. Umbilical venous catheters (UVCs; 448 radiographs), umbilical arterial catheters (UACs; 259 radiographs), endotracheal tubes (ETTs; 451 radiographs), and nasogastric tubes (NGTs; 603 radiographs) were included in our analysis. RESULTS: UVCs were malpositioned in 90% of radiographs, while UACs were malpositioned in 36%, ETTs in 30%, and NGTs in just 5%. The most common locations in which UVCs were malpositioned were in the right atrium (31%) and umbilical vein (21%), and for UACs the most common malpositioned tip location was the aortic arch (8%). For the remaining tubes, 5% of ETTs were found to be in the right main bronchus and 4% of NGTs were found in the esophagus. CONCLUSION: A substantial proportion of catheters and tubes are malpositioned, suggesting that optimizing methods of catheter placement and assessment ought to be areas of focus for future work. KEY POINTS: · Neonatal catheters are frequently malpositioned.. · Most umbilical venous catheters need readjustment.. · X-ray and ultrasound are important for assessment.. · Catheter tips should be assessed in all X-rays..

A pictorial review of the radiographic skeletal findings in Morquio syndrome (mucopolysaccharidosis type IV).

Morquio syndrome, also known as Morquio-Brailsford syndrome or mucopolysaccharidosis type IV (MPS IV), is a subgroup of mucopolysaccharidosis. It is an autosomal recessive lysosomal storage disorder. Two subtypes of Morquio syndrome have been identified. In MPS IVA, a deficiency in N-acetylgalactosamine-6-sulfate sulfatase interrupts the normal metabolic pathway of degrading glycosaminoglycans. Accumulated undigested glycosaminoglycans in the tissue and bones result in complications leading to severe skeletal deformity. In MPS IVB, a deficiency in beta-galactosidase results in a milder phenotype than in MPS IVA. Morquio syndrome presents a variety of clinical manifestations in a spectrum of mild to severe. It classically has been considered a skeletal dysplasia with significant skeletal involvement. However, the extraskeletal features can also provide valuable information to guide further work-up to assess the possibility of the disorder. Although the disease involves almost all parts of the body, it most commonly affects the axial skeleton, specifically the vertebrae. The characteristic radiologic findings in MPS IV, such as paddle-shaped ribs, odontoid hypoplasia, vertebral deformity, metaphyseal and epiphyseal bone dysplasia, and steep acetabula, are encompassed in the term "dysostosis multiplex," which is a common feature among other types of MPS and storage disorders. Myelopathy due to spinal cord compression and respiratory airway obstruction are the most critical complications related to mortality and morbidity. The variety of clinical features, as well as overlapping of radiological findings with other disorders, make diagnosis challenging, and delays in diagnosis and treatment may lead to critical complications. Timely imaging and radiologic expertise are important components for diagnosis. Gene therapies may provide robust treatment, particularly if genetic variations can be screened in utero.

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.

Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less attention to the unique aspects of pediatric imaging. The objectives of this study were to (1) identify all publicly available pediatric datasets and determine their potential utility and limitations for pediatric AI studies and (2) systematically review the literature to assess the current state of AI in pediatric chest radiograph interpretation. We searched PubMed, Web of Science and Embase to retrieve all studies from 1990 to 2021 that assessed AI for pediatric chest radiograph interpretation and abstracted the datasets used to train and test AI algorithms, approaches and performance metrics. Of 29 publicly available chest radiograph datasets, 2 datasets included solely pediatric chest radiographs, and 7 datasets included pediatric and adult patients. We identified 55 articles that implemented an AI model to interpret pediatric chest radiographs or pediatric and adult chest radiographs. Classification of chest radiographs as pneumonia was the most common application of AI, evaluated in 65% of the studies. Although many studies report high diagnostic accuracy, most algorithms were not validated on external datasets. Most AI studies for pediatric chest radiograph interpretation have focused on a limited number of diseases, and progress is hindered by a lack of large-scale pediatric chest radiograph datasets.

Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.

We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a split of 81%-9%-10% for training-validation-testing, respectively. We employed ResNet-50 (a CNN), pre-trained on ImageNet. Ground truth labelling was limited to tagging each image to indicate the presence or absence of endotracheal tubes (ETTs), nasogastric tubes (NGTs), and umbilical arterial and venous catheters (UACs, UVCs). The dataset included 561 images containing two or more catheters, 167 images with only one, and 49 with none. Performance was measured with average precision (AP), calculated from the area under the precision-recall curve. On our test data, the algorithm achieved an overall AP (95% confidence interval) of 0.977 (0.679-0.999) for NGTs, 0.989 (0.751-1.000) for ETTs, 0.979 (0.873-0.997) for UACs, and 0.937 (0.785-0.984) for UVCs. Performance was similar for the set of 58 test images consisting of two or more catheters, with an AP of 0.975 (0.255-1.000) for NGTs, 0.997 (0.009-1.000) for ETTs, 0.981 (0.797-0.998) for UACs, and 0.937 (0.689-0.990) for UVCs. Our network thus achieves strong performance in the simultaneous detection of these four catheter types. Radiologists may use such an algorithm as a time-saving mechanism to automate reporting of catheters on radiographs.

Artificial Intelligence Solutions for Analysis of X-ray Images.

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.

Kelvin probe force microscopy to study electrostatic interactions of DNA with lipid-gemini surfactant monolayers for gene delivery.

In novel gene therapy mechanisms utilising gemini surfactants, electrostatic interactions of the surfactant molecules with the DNA strands is a primary mechanism by which the two components of the delivery vehicle bind. In this work, we show for the first time direct evidence of electrostatic interactions of these compounds visualised with Kelvin probe force microscopy (KPFM) and correlated to their topography from atomic force microscopy (AFM). We construct monolayers of lipids and gemini surfactant to simulate interactions on a cellular level, using lipids commonly found in cell membranes, and allow DNA to bind to the monolayer as it is formed on a Langmuir-Blodgett trough. The difference in topography and electrical surface potential between monolayers with and without DNA is striking. In fact, KPFM reveals a strongly positive relative electrical surface potential in between where we identify a background lipid and the DNA strands, evidenced by the height profiles of the domains. Such identification is not possible without KPFM. We conclude that it is likely we are seeing cationic surfactant molecules surrounding DNA strands within a sea of background lipid.

Computer-aided Assessment of Catheters and Tubes on Radiographs: How Good Is Artificial Intelligence for Assessment?

Catheters are the second most common abnormal finding on radiographs. The position of catheters must be assessed on all radiographs because serious complications can arise if catheters are malpositioned. However, due to the large number of radiographs obtained each day, there can be substantial delays between the time a radiograph is obtained and when it is interpreted by a radiologist. Computer-aided approaches hold the potential to assist in prioritizing radiographs with potentially malpositioned catheters for interpretation and automatically insert text indicating the placement of catheters in radiology reports, thereby improving radiologists' efficiency. After 50 years of research in computer-aided diagnosis, there is still a paucity of study in this area. With the development of deep learning approaches, the problem of catheter assessment is far more solvable. This review provides an overview of current algorithms and identifies key challenges in building a reliable computer-aided diagnosis system for assessment of catheters on radiographs. This review may serve to further the development of machine learning approaches for this important use case. Supplemental material is available for this article. © RSNA, 2020.

Nanogenerators based on vertically aligned InN nanowires.

Piezoelectric nanogenerators (NGs) based on vertically aligned InN nanowires (NWs) are fabricated, characterized, and evaluated. In these NGs, arrays of p-type and intrinsic InN NWs prepared by plasma-assisted molecular beam epitaxy (MBE) demonstrate similar piezoelectric properties. The p-type NGs show 160% more output current and 70% more output power product than the intrinsic NGs. The features driving performance enhancement are reduced electrostatic losses due to better NW array morphology, improved electromechanical energy conversion efficiency due to smaller NW diameters, and the higher impedance of intrinsic NGs due to elevated NW surface charge levels. These findings highlight the potential of InN based NGs as a power source for self-powered systems and the importance of NW morphology and surface state in overall NG performance.

Accurate analytic potential and Born-Oppenheimer breakdown functions for MgH and MgD from a direct-potential-fit data analysis.

New high-resolution visible Fourier transform emission spectra of the A (2)Π â†’ X (2)Σ(+) and B' (2)Σ(+) → X (2)Σ(+) systems of (24)MgD and of the B' (2)Σ(+) → X (2)Σ(+) systems of (25,26)MgD and (25,26)MgH have been combined with earlier results for (24)MgH in a multi-isotopologue direct-potential-fit analysis to yield improved analytic potential energy and Born-Oppenheimer breakdown functions for the ground X (2)Σ(+) state of MgH. Vibrational levels of the ground state of (24)MgD were observed up to v" = 15, which is bound by only 30.6 ± 0.10 cm(-1). Including deuteride and minor magnesium isotopologue data allowed us also to determine the adiabatic Born-Oppenheimer breakdown effects in this molecule. The fitting procedure used the recently developed Morse/Long-Range (MLR) potential energy function, whose asymptotic behavior incorporates the correct inverse-power form. A spin-splitting radial correction function to take account of the (2)Σ spin-rotation interaction was also determined. Our refined value for the ground-state dissociation energy of the dominant isotopologue ((24)MgH) is D(e) = 11,104.25 ± 0.8 cm (-1), in which the uncertainty also accounts for the model dependence of the fitted D(e) values for a range of physically acceptable fits. We were also able to determine the marked difference in the well depths of (24)MgH and (24)MgD (with the deuteride potential curve being 7.58 ± 0.30 cm(-1) deeper than that of the hydride) as well as smaller well-depth differences for the minor (25,26)Mg isotopologues. This analytic potential function also predicts that the highest bound level of (24)MgD is v" = 16 and that it is bound by only 2.73 ± 0.10 cm(-1).

Ground state potential energy curve and dissociation energy of MgH.

New high-resolution visible emission spectra of the MgH molecule have been recorded with high signal-to-noise ratios using a Fourier transform spectrometer. Many bands of the A 2Pi-->X 2Sigma+ and B' 2Sigma+-->X 2Sigma+ electronic transitions of 24MgH were analyzed; the new data span the v' = 0-3 levels of the A 2Pi and B'2Sigma+ excited states and the v''=0-11 levels of the X 2Sigma+ ground electronic state. The vibration-rotation energy levels of the perturbed A 2Pi and B' 2Sigma+ states were fitted as individual term values, while those of the X 2Sigma+ ground state were fitted using the direct-potential-fit approach. A new analytic potential energy function that imposes the theoretically correct attractive potential at long-range, and a radial Hamiltonian that includes the spin-rotation interaction were employed, and a significantly improved value for the ground state dissociation energy of MgH was obtained. The v''=11 level of the X 2Sigma+ ground electronic state was found to be the highest bound vibrational level of 24MgH, lying only about 13 cm(-1) below the dissociation asymptote. The equilibrium dissociation energy for the X 2Sigma+ ground state of 24MgH has been determined to be De=11104.7+/-0.5 cm(-1) (1.37681+/-0.00006 eV), whereas the zero-point energy (v''=0) is 739.11+/-0.01 cm(-1). The zero-point dissociation energy is therefore D0=10365.6+/-0.5 cm(-1) (1.28517+/-0.00006 eV). The uncertainty in the new experimental dissociation energy of MgH is more than 2 orders of magnitude smaller than that for the best value available in the literature. MgH is now the only hydride molecule other than H2 itself for which all bound vibrational levels of the ground electronic state are observed experimentally and for which the dissociation energy is determined with subwavenumber accuracy.