The UniScept API system was evaluated for agreement of visual versus automated readings of both its identification panels and its antimicrobial susceptibility panels. The biochemical responses of 340 oxidase-negative and oxidase-positive fermentative bacterial cultures were read both visually and automatically in the UniScept API 20E system. Automated and visual readings agreed with 99.3% of the biochemicals. Of the 45 tests that disagreed, the tests for indole and citrate were most often in disagreement. A total of 470 fermentative and nonfermentative cultures were used in the UniScept MIC system to compare visual and automated readings of susceptibility results with 17 antimicrobial agents. Agreement within +/- 1 dilution occurred with 94.1% of the enteric fermenters and with 91.7% of the other cultures. Comparison of visual and automated readings resulted in very major discrepancies in 0.95% of the readings, with the largest percentage of discrepancies associated with glucose nonfermenters (1.8%). It was felt that an automated reading is an acceptable alternative to a visual reading of the biochemicals but that 0.95% was just within the acceptable range of the 1% allowable very major discrepancies in the automated reading of susceptibilities.
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http://dx.doi.org/10.1128/jcm.28.3.452-454.1990 | DOI Listing |
Anal Chem
January 2025
Department of Chemistry, Faculty of Science, Masaryk University, Brno 625 00, Czech Republic.
Obtaining high-quality matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) images and the reproducibility of the technique depend strongly on the sample preparation protocol. The most crucial part is the application of the MALDI matrix, which often relies on expensive spraying or sublimation coaters. In this work, we present a new dual-polarity matrix for MALDI mass spectrometry imaging (MSI): Basic Blue 7 (BB7), which belongs to the group of triarylmethane dyes.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Imaging Laboratory (iLab), Varian Medical Systems, Siemens Healthcare, Baden, Switzerland.
. To develop an augmentation method that simulates cone-beam computed tomography (CBCT) related motion artifacts, which can be used to generate training-data to increase the performance of artificial intelligence models dedicated to auto-contouring tasks.The augmentation technique generates data that simulates artifacts typically present in CBCT imaging.
View Article and Find Full Text PDFBMC Emerg Med
January 2025
Department of Emergency Medicine, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, No.138, Sheng Li Road, Tainan city, 704, Taiwan.
Background: Out-of-hospital cardiac arrest (OHCA) presents significant challenges with low survival rates, emphasizing the need for effective bystander CPR training. In Basic Life Support (BLS) training, the role of instructors is pivotal as they assess and correct learners' cardiopulmonary resuscitation (CPR) techniques to ensure proficiency in life-saving skills. This study evaluates the concordance between CPR quality assessments by Basic Life Support (BLS) instructors and those determined through Quantitative CPR (QCPR) devices, utilizing data from BLS courses conducted at National Cheng Kung University Hospital from October 2017 to April 2018.
View Article and Find Full Text PDFSci Data
January 2025
Shanghai Artificial Intelligence Research Institute Co., Ltd., Shanghai, 200240, China.
Academic data processing is crucial in scientometrics and bibliometrics, such as research trending analysis and citation recommendation. Existing datasets in this domain have predominantly concentrated on textual data, overlooking the importance of visual elements. To bridge this gap, we introduce a multidisciplinary multimodal aligned dataset (MMAD) specifically designed for academic data processing.
View Article and Find Full Text PDFDatabase (Oxford)
January 2025
Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.
The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data.
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