Federal University of São Carlos

About: Federal University of São Carlos is a education organization based out in São Carlos, Brazil. It is known for research contribution in the topics: Population & Microstructure. The organization has 16471 authors who have published 34057 publications receiving 456654 citations. The organization is also known as: UFSCar & Federal University of São Carlos.

Short term culture from solid tissues of fishes

Abstract: SUMMARYIn fish cytogenetics the methods of chromosome preparations still need to be improved in order to obtain good metaphase figures for banding technics and more accurate analysis. In this paper we describe a very fast and simples culture method from solid fish tissues. Results of its application to different species as well as about 5-BrdU incorporation, both for chromosome banding and sister chromatid differentiation are reported. The data emphasized the advantage of this short term culture method wich provide excellent chromosome preparations suitables for basic and applied cytogenetic studies.

Lean healthcare in developing countries: evidence from Brazilian hospitals.

Abstract: The present study evaluates how five sectors of two Brazilian hospitals have implemented lean healthcare concepts in their operations. The main characteristics of the implementation process are analyzed in the present study: the motivational factor for implementation, implementation time, form (consultancy or internal), team (hospital and consultants), lean implementation continuity/sustainability, lean healthcare tools and methods implemented, problems/improvement opportunities, lean healthcare barriers faced during the implementation process, and critical factors that affected the implementation and the results obtained in each case. The case studies indicate that reducing patient lead times and costs and making financial improvements were the primary factors that motivated lean healthcare implementation in the hospitals studied. Several tools and methods were used in the cases studied, especially value stream mapping and DMAIC. The barriers found in both hospitals are primarily associated with the human factor. Additionally, the results obtained after implementation were analyzed and improvements in financial aspects, productivity and capacity, and lead time reduction of the analyzed sectors were observed. Further, this study also exhibited four propositions elaborated from the results obtained from the cases that highlighted barriers and challenges to lean healthcare implementation in developing countries. Two of these barriers are hospital organizational structure (and, consequently, how the senior management works with medical staff), and outsourcing hospital activities. This study also concluded that the initialization and maintenance of lean healthcare implementation rely heavily on external support because lean healthcare subject knowledge is not yet available in the healthcare organization, which represents a challenge. Copyright © 2015 John Wiley & Sons, Ltd.

Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus

Abstract: Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9% on 14 cases with neoplastic BE. This paper follows up on our prior publication on the application of AI and deep learning in the evaluation of BE.1 2 In our initial publications, we developed a computer-aided diagnosis (CAD) model and demonstrated promising performance scores in the classification and segmentation domains during BE assessment.1 2 However, these results were achieved on optimal endoscopic images, which may not mirror the real-life situation sufficiently. To enable the seamless integration of AI-based image classification into the clinical workflow, our previous system was developed further to increase the speed of image analysis for classification and the resolution of the dense prediction, which shows the color-coded spatial distribution of cancer probabilities.1 2 Still based on deep convolutional neural nets (CNNs) and a residual net (ResNet) architecture with DeepLab V.3+, a state-of-the-art encoder–decoder network was adapted.3 To transfer the endoscopic livestream to our AI system, a capture card (Avermedia, Taiwan) was plugged to the endoscopic monitor. Online supplementary video 1 shows the setting of AI-based BE evaluation in the endoscopy room of the University Hospital Augsburg (figure 1). The AI prediction can be started at any time using either a button on the keyboard or a foot switch. The video clip shows examples of …