Walshmcgowan1378

Background To date, the prognostic significance of acellular mucin pools in tumors from patients with locally advanced rectal cancer (LARC) undergoing preoperative chemoradiotherapy (CRT) and subsequently obtaining pathological complete response (pCR) has not been well determined. Our current study aimed to explore the prognostic impact on these patients of acellular mucin pools. Methods We collected clinical data from 117 consecutive LARC patients who achieved pCR after preoperative CRT and then underwent radical resection. Two groups of patients were generated, according to the presence or absence of acellular mucin pools. The 5-year disease-free survival (DFS) and overall survival (OS) rates were compared between the two groups of patients. Results A total of 27 (23.1%) patients presented with acellular mucin pools. At a median follow-up period of 64 months, patients with acellular mucin pool showed a 5-year DFS rate (96.3% versus 83.7%, p = 0.110) and 5-year OS rate (100% versus 87.5%, p = 0.054) statistically similar to those of patients without acellular mucin pools. In univariable and multivariable Cox regression analyses, the presence of acellular mucin pools was not determined as an independent risk factor for DFS [hazard ratio (HR) 0.222; 95% confidence interval (CI) 0.029-1.864; p = 0.145] or OS (HR 0.033; 95% CI 0.000-9.620; p = 0.238). Conclusions Acellular mucin pools had no significant prognostic impact on LARC patients showing pCR after preoperative CRT. © The Author(s), 2020.Background Recently the American Society for Gastrointestinal Endoscopy addressed the 'resect and discard' strategy, determining that accurate in vivo differentiation of colorectal polyps (CP) is necessary. Previous studies have suggested a promising application of artificial intelligence (AI), using deep learning in object recognition. Therefore, we aimed to construct an AI system that can accurately detect and classify CP using stored still images during colonoscopy. Methods We used a deep convolutional neural network (CNN) architecture called Single Shot MultiBox Detector. Zanubrutinib We trained the CNN using 16,418 images from 4752 CPs and 4013 images of normal colorectums, and subsequently validated the performance of the trained CNN in 7077 colonoscopy images, including 1172 CP images from 309 various types of CP. Diagnostic speed and yields for the detection and classification of CP were evaluated as a measure of performance of the trained CNN. Results The processing time of the CNN was 20 ms per frame. The trained CNN detected 1246 CP with a sensitivity of 92% and a positive predictive value (PPV) of 86%. The sensitivity and PPV were 90% and 83%, respectively, for the white light images, and 97% and 98% for the narrow band images. Among the correctly detected polyps, 83% of the CP were accurately classified through images. Furthermore, 97% of adenomas were precisely identified under the white light imaging. Conclusions Our CNN showed promise in being able to detect and classify CP through endoscopic images, highlighting its high potential for future application as an AI-based CP diagnosis support system for colonoscopy. © The Author(s), 2020.Background Ethanol production through fermentation of gas mixtures containing CO, CO2 and H2 has just started operating at commercial scale. However, quantitative schemes for understanding and predicting productivities, yields, mass transfer rates, gas flow profiles and detailed energy requirements have been lacking in literature; such are invaluable tools for process improvements and better systems design. The present study describes the construction of a hybrid model for simulating ethanol production inside a 700 m3 bubble column bioreactor fed with gas of two possible compositions, i.e., pure CO and a 31 mixture of H2 and CO2. Results Estimations made using the thermodynamics-based black-box model of microbial reactions on substrate threshold concentrations, biomass yields, as well as CO and H2 maximum specific uptake rates agreed reasonably well with data and observations reported in literature. According to the bioreactor simulation, there is a strong dependency of process performance on mass transfer raH2 for syngas fermentations by acetogenic bacteria. The maximization of ethanol productivity in the bioreactor may come with a cost low gas utilization. Exploiting the model flexibility, multi-objective optimizations of bioreactor performance might reveal how process conditions and configurations could be adjusted to guide further process development. © The Author(s) 2020.The recent series of gravitational-wave (GW) detections by the Advanced LIGO and Advanced Virgo observatories launched the new field of GW astronomy. As the sensitivity of GW detectors is limited by quantum noise of light, concepts from quantum metrology have been adapted to increase the observational range. Since 2010, squeezed light with reduced quantum noise has been used for improved sensitivity at signal frequencies above 100 Hz. However, 100 m long optical filter resonators would be required to also improve the sensitivity at lower frequencies, adding significant cost and complexity. Here we report on a proof-of-principle setup of an alternative concept that achieves the broadband noise reduction by using Einstein-Podolsky-Rosen (EPR) entangled states instead. We show that the desired sensitivity improvement can then be obtained with the signal-recycling resonator that is already part of current observatories, providing the viable alternative to high-cost filter cavities.Large-scale single-cell analyses have become increasingly important given the role of cellular heterogeneity in complex biological systems. However, no current techniques enable optical imaging of uniquely-tagged individual cells. Fluorescence-based approaches can only distinguish a small number of distinct cells or cell groups at a time because of spectral crosstalk between conventional fluorophores. Here we investigate large-scale cell tracking using intracellular laser particles as imaging probes that emit coherent laser light with a characteristic wavelength. Made of silica-coated semiconductor microcavities, these laser particles have single-mode emission over a broad range from 1170 to 1580 nm with sub-nm linewidths, enabling massive spectral multiplexing. We explore the stability and biocompatibility of these probes in vitro and their utility for wavelength-multiplexed cell tagging and imaging. We demonstrate real-time tracking of thousands of individual cells in a 3D tumour model over several days showing different behavioural phenotypes.