Early detection of lung cancer - A challenge

Lung cancer or lung carcinoma, is a common and serious type of cancer caused by rapid cell growth in tissues of the lung. Lung cancer detection at its earlier stage is very difficult because of the structure of the cell alignment which makes it very challenging. Computed tomography (CT) scan is used to detect the presence of cancer and its spread. Visual analysis of CT scan can lead to late treatment of cancer; therefore, different steps of image processing can be used to solve this issue. A comprehensive framework is used for the classification of pulmonary nodules by combining appearance and shape feature descriptors, which helps in the early diagnosis of lung cancer. 3D Histogram of Oriented Gradient (HOG), Resolved Ambiguity Local Binary Pattern (RALBP) and Higher Order Markov Gibbs Random Field (MGRF) are the feature descriptors used to explain the nodule’s appearance and compared their performance. Lung cancer screening methods, image processing techniques and nodule classification using radiomic-based framework are discussed in this paper which proves to be very effective in lung cancer prediction. Good performance is shown by using RALBP descriptor

Case-finding of chronic obstructive pulmonary disease with questionnaire, peak flow measurements and spirometry : a cross-sectional study

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Greenhouse Gas Emissions from Respiratory Treatments : Results from the SABA CARBON International Study

Acknowledgements Medical Writing, Editorial, and Other Assistance Medical writing and editorial support were provided by Tejaswini Subbannayya, PhD, of Cactus Life Sciences (part of Cactus Communications, Mumbai, India), in accordance with Good Publication Practice (GPP3) guidelines (http://www.ismpp.org/gpp3). This support was fully funded by AstraZeneca. Funding AstraZeneca funded the study; was involved in the study design, protocol development, study conduct and statistical analysis; and was given the opportunity to review the manuscript before submission. AstraZeneca also funded medical writing support and the development of the graphical abstract. AstraZeneca funded the journal’s Rapid Service and Open Access fees.Peer reviewedPublisher PD

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity

Experimental Frequency Response Methods for the Demonstration of Thermal Hydraulic Similitude Between Molten Fluoride Salt and Surrogate Fluid Scaled Experiments

This dissertation presents the design, development, and experimental results of the Scaled Heat Exchange Frequency Response Analysis (SHEFRA) experiment, which aimed to investigate the use of frequency response methods for measuring quasi-steady Nusselt number values in forced convection of a surrogate fluid for molten fluoride salt in circular ducts. The dynamic response in the flow channel was initiated by a periodically-varying fluid temperature at the inlet. Initial experimental results obtained from the SHEFRA experiment suggest that quasi-steady conditions are achievable for laminar flow conditions, showing good agreement with steady-state analytical predictions. The theoretical modeling and frequency scaling analysis provided insights into the relevant dimensionless parameters and their impact on the system’s behavior, allowing for optimal experimental parameters and conditions to be determined. Quasi-steady state heat transfer conditions are best approximated at high or low values of a dimensionless parameter, b*, defined as the dimensionless frequency multiplied by the ratio of wall thermal capacitance and fluid thermal conductivity. At the limit of high frequency, the quasi-steady regime approximates the analytically-predicted steady state heat transfer with an isothermal wall temperature boundary condition. Meanwhile, at the limit of low frequency, the quasi-steady state conditions approximate steady state pre- dictions for a constant flux boundary condition. In comparison, high frequency tests resulted in a better approximation of quasi-steady state near the inlet of the test section. The most deviation in the instantaneous Nusselt number exists in the middle frequency range. The results suggest that additional experimental data, covering a range of Prandtl and Reynolds numbers of interest for molten fluoride salt reactor operation, can be used to create Nusselt number correlations that can be compared with prototypical molten salt experiment data and hence qualify the use of surrogate fluids in scaled experiments. Additionally, frequency response parameter estimation techniques presented in this dissertation offer promising av- enues for future research in improving the accuracy of Nusselt number measurements with the potential of estimating other system parameters such as thermophysical properties. The SHEFRA experiment demonstrates the potential of using surrogate fluids and frequency response methods to obtain high-fidelity heat transfer data measurements relevant for molten salt reactor development