Search

To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer. This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists. The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model: 65.5 ± 19.3 cm3 and STAPLE: 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88. The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer. [ABSTRACT FROM AUTHOR]

Industrial activities often disrupt the natural equilibrium of water bodies, with discharged wastewater introducing dissolved organic matter (DOM) and pollutants. This study investigated the water quality along a riverine system subjected to heavy industrial influence by collecting samples from distinct points at before, during, and after industrial discharge across four seasons. By employing EEM-PARAFAC, four fluorescent components were identified at the discharge point including C1 (microbial/ terrestrial humic-like), C2 (anthropogenic humic-like materials), C3 (microbial humic-like), and C4 (wastewater/nutrient enrichment tracer). Specifically, C2 and C4 which contained wastewater and anthropogenic-like material are not found at the pre-pollutant point. Additionally, fluorescence characteristics parameters such as FI, HIX, and BIX are analyzed. Kernel Principal Component Analysis was employed to elucidate the non-linear relationships inherent in the data that able to covered up to 97 % of the data variance, offering nuanced insights into complex behavior of water characteristics across seasonal variations. Two-dimensional correlation spectroscopy (2D-COS) was further employed to evaluate the sequence variation of DOM components and further identify the temporal effect across season with moving window 2D-COS. The sequence order of the DOM fractions was identified as C2→C4→C1→C3 by following the Noda's rule. In order to understand the interactions between the water characteristics and the DOM components, an empirical polynomial regression model is developed with an average MAPE of 2.2 %. The interaction between DOM-related parameters was identified using structural equation modeling. These findings underscore the dynamic nature of DOM composition under industrial influences considering the spatial-temporal impact. [Display omitted] • DOM identified by EEM-PARAFAC targeting pollutants and anthropogenic materials. • Kernel PCA offers insights into nonlinear spatial-temporal water dynamics. • Hetero 2D-COS revealed the sequence variation of PARAFAC components. • Seasonal behavior of PARAFAC components is identified with MW 2D-COS. • SEM provides causal relationship between DOM impacted by industrial pollution. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an optimum design model for the green construction of reinforced concrete two-way slabs in buildings. The model was applied to various design cases of two-way slabs in residential, office, and commercial buildings to derive designs that minimize environmental impact. The sustainability and economic feasibility of the derived designs were extensively compared with those of the conventional design method in relation to the material composition of concrete and steel in slabs, design parameters, and dominant design factors. As compared to the conventional design method, the optimum sustainable design method achieved average CO 2 emission reductions of 4.94%, 11.40%, and 19.96% for residential, office, and commercial buildings, respectively, for all design cases considered in this study. Moreover, a multi-objective optimum design method that could simultaneously minimize embodied carbon emissions, cost, and vibration responses was created to consider the serviceability for vibrations induced by human activity in buildings. The design method was applied for the design cases of slabs in residential and office buildings, which are sensitive to human-induced vibrations. A design with increased concrete strength was derived to satisfy vibration criteria in the case of a residential building. This resulted in an increase of 8.89% in embodied carbon emissions compared with the conventional design method. On the contrary, in the case of an office building, the derived design showed a reduction of 15.79% in embodied carbon emissions while satisfying vibration criteria. [ABSTRACT FROM AUTHOR]

We introduce a novel approach for the high-value production of nano/micro hierarchical structured Sn anodes for lithium-ion batteries (LIBs) by utilizing microalgal biomass residues that collaterally form during oil extraction for biofuel production. The Sn/C composites made from the oil-extracted microalgal biomass residues (the extracted Sn/C) exhibit the following advantages as high-energy-density anodes: 1) a homogeneous distribution of Sn nanoparticles in the carbon matrix (Sn/C), which efficiently relieves the strain caused by volume changes of the active materials; 2) a high porosity of Sn/C composites; and 3) a homogeneous distribution of the hetero elements N and P in the carbon matrix. Overall, the extracted Sn/C exhibit improved electrochemical performance in LIBs compared with the Sn/C composites made from the microalgal biomass residues without oil extraction (non-extracted Sn/C). The extracted Sn/C have improved rate capabilities (160.0 and 72.9 mAh g −1 for the extracted Sn/C and the non-extracted Sn/C, respectively, at the 80th cycle, 3.5 A g −1 ) and improved cycle performances (511.7 and 493.2 mAh g −1 for the extracted Sn/C and the non-extracted Sn/C, respectively, at the 300th cycle, 200 mA g −1 ). [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the effect of graphitic character of ordered mesoporous carbons (OMCs) on the performances of OMC supported catalysts for direct methanol fuel cells (DMFC). Two OMC samples with hexagonal mesostructure were prepared from phenanthrene and sucrose by nano-replication method using mesoporous silica as a template. Structural characterizations revealed that both OMCs exhibited large BET surface area and uniform mesopores, while the OMC synthesized from phenanthrene exhibited lower sheet resistance than the OMC derived from sucrose. The Pt nanoparticles were supported on both OMCs with very high dispersion, as the particle size was estimated under 3nm despite high metal loading of 60wt.%. In DMFC single cell test, the OMC supported Pt catalysts exhibited much higher performance than the commercial catalyst, which may be attributed to the high surface area and uniform mesopore networks of OMC. In particular, it was found that the performance of OMC supported catalysts can be significantly enhanced by lowering the resistance of OMC. [Copyright &y& Elsevier]

Massive vaccination against COVID-19 has become a global priority. Simultaneously, concerns regarding the safety of vaccines are growing. We describe two patients who developed sensory Guillain-Barre syndrome (GBS) shortly after the first dose of the ChAdOx1 vaccine. We also summarize 12 published cases of GBS after ChAdOx1 vaccination, highlighting their unique clinical and paraclinical features. We propose a possible association between the risk of GBS and the ChAdOx1 vaccine and recommend surveillance for GBS following vaccination. Population-based studies are needed to determine causality and whether specific subpopulations are susceptible. [Display omitted] • We describe two sensory Guillain-Barre syndromes (GBS) after the ChAdOx1 vaccine. • We highlight the unique features of published GBS cases after the ChAdOx1 vaccine. • We propose a possible association between the vaccine and GBS. • To determine causality, data accumulation and epidemiologic analyses are mandatory. [ABSTRACT FROM AUTHOR]

Korean comprehensive nursing-caregiving service, which is covered by national insurance, has been officially initiated in 2016. Following interim analysis presented at JSMO 2018, we report our 1-year experience of cancer-oriented comprehensive nursing-caregiving unit´s outcomes and issues. and Results Our institution is a general hospital (827 beds) with 246 doctors and 703 RNs. We are running cancer-oriented comprehensive nursing-caregiving service (46 beds with 25 RNs and 12 nursés aides) since December 2017. This special ward is caring only medical oncology patients. For 12 months, 11,946 inpatients (average 32.4 patients/day, bed turnover rate 85.7%) and 5,682 patients received chemotherapy in our unit. We evaluated our Results in three categories: structure, process, and outcome. First, structural (environmental) improvements were found in more interval between beds (1.5m), fully automatic beds with fall prevention sensors, comfortable patient lounge. Second, process improvements such as nursing time, quality of oncologic nursing is improved. For example, missed nursing care has been improved (nursing caregiving ward vs. general ward: 0.1 vs. 1.3 events/month). Finally, patient and nurse outcome are much improved when compared with general wards. Patient satisfaction (will to revisit: 91.7% vs. 78.4%, will to recommend to others: 95.0% vs. 76.8%) have been increased. Safety indicators such as pressure ulcer (0.3 vs. 0.8 events/month), falls (1.6 vs. 1.5 events/month) have dropped. Nurse outcomes such as resignation rate have been stabilized (resignation rate: 0 vs. 14.9%). Issues during unit operation were found in still heavy workloads for nursing staff and low compensation by the government compared with other countries. Comprehensive nursing-caregiving service for medical oncology patients is a key to success in both improved quality of cancer care and nursing staff´s job satisfaction. [ABSTRACT FROM AUTHOR]