Exhaled Nitric Oxide is Decreased by Exposure to the Hyperbaric Oxygen Therapy Environment

Exhaled nitric oxide (eNO) detects airway inflammation. Hyperbaric oxygen therapy (HBOT) is used for tissue hypoxia, but can cause lung damage. We measured eNO following inhalation of oxygen at different tensions and pressures. Methods. Part 1, eNO was measured before and after HBOT. Part 2, normal subjects breathed 40% oxygen. Results. Baseline eNO levels in patients prior to HBOT exposure were significantly higher than in normal subjects (P < .05). After HBOT, eNO significantly decreased in patients (15.4 ± 2.0 versus 4.4 ± 0.5 ppb, P < .001), but not in normal subjects, after either 100% O(2) at increased pressure or 40% oxygen, 1 ATA. In an in vitro study, nitrate/nitrite release decreased after 90 minutes HBOT in airway epithelial (A549) cells. Conclusion. HBO exposure causes a fall in eNO. Inducible nitric oxide synthase (iNOS) may cause elevated eNO in patients secondary to inflammation, and inhibition of iNOS may be the mechanism of the reduction of eNO seen with HBOT

The Effects of Pictorial Realism, Delay of Visual Feedback, and Observer Interactivity on the Subjective Sense of Presence

Two experiments examined the effects of pictorial realism, observer interactivity, and delay of visual feedback on the sense of presence. Subjects were presented pairs of virtual enviornments (a simulated driving task) that differed in one or more ways from each other. After subjects had completed the second member of each pair they reported which of the two had produced the greater amount of presence and indicated the size of this difference by means of a 1-100 scale. As predicted, realism and interactivity increased presence while delay of visual feedback diminished it. According to subjects\u27 verbal responses to a postexperiment interview, pictorial realism was the least influential of the three variables examined. Further, although some subjects reported an increase in the sense of presence over the course of the experiment, most said it reamined unchanged or became weaker

Regulatory T Cells Control Effector T Cell Inflammation in Human Prediabetes

A disparate array of plasma/serum markers provides evidence for chronic inflammation in human prediabetes, a condition that is most closely replicated by standard mouse models of obesity and metaflammation. These remain largely nonactionable and contrast with our rich understanding of inflammation in human type 2 diabetes. New data show that inflammatory profiles produced by CD4+ T cells define human prediabetes as a unique inflammatory state. Regulatory T cells (Treg) control mitochondrial function and cytokine production by CD4+ effector T cells (Teff) in prediabetes and type 2 diabetes by supporting T helper (Th)17 or Th1 cytokine production, respectively. These data suggest that Treg control of Teff metabolism regulates inflammation differentially in prediabetes compared with type 2 diabetes. Queries of genes that impact mitochondrial function or pathways leading to transcription of lipid metabolism genes identified the fatty acid importer CD36 as highly expressed in Treg but not Teff from subjects with prediabetes. Pharmacological blockade of CD36 in Treg from subjects with prediabetes decreased Teff production of the Th17 cytokines that differentiate overall prediabetes inflammation. We conclude that Treg control CD4+ T cell cytokine profiles through mechanisms determined, at least in part, by host metabolic status. Furthermore, Treg CD36 uniquely promotes Th17 cytokine production by Teff in prediabetes

Performance and durability tests of smart icephobic coatings to reduce ice adhesion

The accretion of ice can damage applications ranging from power lines and shipping decks; to wind turbines and rail infrastructure. In particular on aircraft, it can change aerodynamic characteristics, greatly affecting the flight safety. Commercial aircraft are therefore required to be equipped with de-icing devices, such as heating mats over the wings. The application of icephobic coatings near the leading edge of a wing can in theory reduce the high power requirements of heating mats, which melt ice that forms there. Such coatings are effective in preventing the accretion of runback ice, formed from airborne supercooled droplets, or the water that the heating mats generate as it is sheared back over the wing’s upper surface. However, the durability and the practicality of applying them over a large wing surface have been prohibitive factors in deploying this technology so far

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1