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Nyholm Egeberg posted an update 6 months ago
Coronavirus (COVID-19) has spread throughout the world, causing mayhem from January 2020 to this day. Owing to its rapidly spreading existence and high death count, the WHO has classified it as a pandemic. Biomedical engineers, virologists, epidemiologists, and people from other medical fields are working to help contain this epidemic as soon as possible. The virus incubates for five days in the human body and then begins displaying symptoms, in some cases, as late as 27 days. In some instances, CT scan based diagnosis has been found to have better sensitivity than RT-PCR, which is currently the gold standard for COVID-19 diagnosis. Lung conditions relevant to COVID-19 in CT scans are ground-glass opacity (GGO), consolidation, and pleural effusion. In this paper, two segmentation tasks are performed to predict lung spaces (segregated from ribcage and flesh in Chest CT) and COVID-19 anomalies from chest CT scans. A 2D deep learning architecture with U-Net as its backbone is proposed to solve both the segmentation tasks. It is observed that change in hyperparameters such as number of filters in down and up sampling layers, addition of attention gates, addition of spatial pyramid pooling as basic block and maintaining the homogeneity of 32 filters after each down-sampling block resulted in a good performance. The proposed approach is assessed using publically available datasets from GitHub and Kaggle. Model performance is evaluated in terms of F1-Score, Mean intersection over union (Mean IoU). It is noted that the proposed approach results in 97.31% of F1-Score and 84.6% of Mean IoU. The experimental results illustrate that the proposed approach using U-Net architecture as backbone with the changes in hyperparameters shows better results in comparison to existing U-Net architecture and attention U-net architecture. The study also recommends how this methodology can be integrated into the workflow of healthcare systems to help control the spread of COVID-19.Desmoplastic small round cell tumor (DSRCT) is a rare and aggressive mesenchymal malignancy, usually affecting young males. There is no consensus on the best therapeutic approach. We seek to characterize a cohort of nonpediatric patients with DSRCT treated at a large Brazilian cancer center. We performed a retrospective analysis of patients with histologically confirmed DSRCT referred to our institution (2007-2020). Clinical and imaging data were extracted and summarized with descriptive statistics. Survival analyses were conducted by the Kaplan-Meier method and compared with the log-rank test. We included 19 patients with DSRCT, the median age at diagnosis was 26 years (range 15-41 years), and 68% were male. Ninety percent presented with abdominopelvic masses, and 32% had extra-abdominal metastasis at diagnosis. Eleven patients (58%) underwent surgery, four patients (21%) received whole abdominal adjuvant radiotherapy, and five patients (26%) had hyperthermic intraperitoneal chemotherapy. Median OS was 27 months (interquartile range 18-51 m). The five-year OS rate was 12%. Our data confirm the aggressiveness of DSRCT despite intense multimodality treatment. Outcomes of patients treated in a reference cancer center in a developing country are similar to cancer centers in developed nations. Multicenter cooperation is urgent to the development of clinical trials and to improve diagnosis and treatment efficacy.This study examined spatial variations of precipitation accumulation and chemistry for six sites located on the West and East Coasts of the U.S., and one site each on the islands of Hawaii, Bermuda, and Luzon of the Philippines (specifically Manila). The nine coastal sites ranged widely in both mean annual precipitation accumulation, ranging from 40 cm (Mauna Loa, Hawaii) to 275 cm (Washington), and in terms of monthly profiles. The three island sites represented the extremes of differences in terms of chemical profiles, with Bermuda having the highest overall ion concentrations driven mainly by sea salt, Hawaii having the highest SO 4 2 – mass fractions due to the nearby influence of volcanic SO2 emissions and mid-tropospheric transport of anthropogenic pollution, and Manila exhibiting the highest concentration of non-marine ions ( NH 4 + non-sea salt SO 4 2 – , nss Ca2+, NO 3 – , nss K+, nss Na+, nss Mg2+) linked to anthropogenic, biomass burning, and crustal emissions. GLXC-25878 nmr The Manila site exhibited the most variability in composition throughout the year due to shifting wind directions and having diverse regional and local pollutant sources. In contrast to the three island sites, the North American continental sites exhibited less variability in precipitation composition with sea salt being the most abundant constituent followed by some combination of SO 4 2 – , NO 3 – , and NH 4 + . The mean-annual pH values ranged from 4.88 (South Carolina) to 5.40 (central California) with NH 4 + exhibiting the highest neutralization factors for all sites except Bermuda where dust tracer species (nss Ca2+) exhibited enhanced values. The results of this study highlight the sensitivity of wet deposition chemistry to regional considerations, elevation, time of year, and atmospheric circulations.This study examines 14 years (2004-2017) of surface aerosol composition data from the EPA IMPROVE network with a focus on the monthly profile, sources, and chemical nature of extreme dust events (>92nd percentile of fine soil concentration each month) impacting ten sites along the United States East Coast ranging in latitude from Florida to Maine. Based on trajectory, remote sensing, and reanalysis data, dust events were categorized into four source categories African, Asian, Mix (African + Asian), and Other (anything other than African and Asian). The results reveal that extreme dust events account for between 3.3% and 4.6% of total available days depending on the site. March-April-May (MAM) had the most (174) dust events, followed by June-July-August (JJA) with 172, and then by September-October-November (SON) with 160 and December-January-February (DFJ) with 150. There is a variability in the predominant dust sources based on latitude, with African and Other sources more influential from North Carolina to the south, while Asian and Other were most important from New Jersey to the north.
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