Using United States Pharmacopeia-National Formulary (USP-NF) general method <1223> guidance, the Soleris(®) automated system and reagents (Nonfermenting Total Viable Count for bacteria and Direct Yeast and Mold for yeast and mold) were validated, using a performance equivalence approach, as an alternative to plate counting for total microbial content analysis using five representative microbes: Staphylococcus aureus, Bacillus subtilis, Pseudomonas aeruginosa, Candida albicans, and Aspergillus brasiliensis. Detection times (DTs) in the alternative automated system were linearly correlated to CFU/sample (R(2) = 0.94-0.97) with ≥70% accuracy per USP General Chapter <1223> guidance. The LOD and LOQ of the automated system were statistically similar to the traditional plate count method. This system was significantly more precise than plate counting (RSD 1.2-2.9% for DT, 7.8-40.6% for plate counts), was statistically comparable to plate counting with respect to variations in analyst, vial lots, and instruments, and was robust when variations in the operating detection thresholds (dTs; ±2 units) were used. The automated system produced accurate results, was more precise and less labor-intensive, and met or exceeded criteria for a valid alternative quantitative method, consistent with USP-NF general method <1223> guidance.
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http://dx.doi.org/10.5740/jaoacint.16-0142 | DOI Listing |
Front Robot AI
December 2024
School of Food Science and Environmental Health, Technological University Dublin, Dublin, Ireland.
Collaborative intelligence (CI) involves human-machine interactions and is deemed safety-critical because their reliable interactions are crucial in preventing severe injuries and environmental damage. As these applications become increasingly data-driven, the reliability of CI applications depends on the quality of data, shaping the system's ability to interpret and respond in diverse and often unpredictable environments. In this regard, it is important to adhere to data quality standards and guidelines, thus facilitating the advancement of these collaborative systems in industry.
View Article and Find Full Text PDFComput Graph
December 2024
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, Aurora, CO 80045, USA.
3D photogrammetry is a cost-effective, non-invasive imaging modality that does not require the use of ionizing radiation or sedation. Therefore, it is specifically valuable in pediatrics and is used to support the diagnosis and longitudinal study of craniofacial developmental pathologies such as craniosynostosis - the premature fusion of one or more cranial sutures resulting in local cranial growth restrictions and cranial malformations. Analysis of 3D photogrammetry requires the identification of craniofacial landmarks to segment the head surface and compute metrics to quantify anomalies.
View Article and Find Full Text PDFDiabet Med
December 2024
Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Aims: Diabetes distress (DD) is prevalent among people with diabetes. While automated insulin delivery systems (AIDs) improve glycaemic control, their impact on DD is unclear. We aimed to investigate the effect of AIDs on DD in people with diabetes and their caregivers.
View Article and Find Full Text PDFBiomed Eng Online
December 2024
The Laboratory for Rehabilitation Engineering, Institute for Human Centred Engineering, Bern University of Applied Sciences, Biel, Switzerland.
The aim of this study was to evaluate the feasibility of using a biofeedback-enhanced robotics-assisted tilt table (RATT) to investigate time- and intensity-dependent changes in heart rate variability (HRV) at rest and during heart rate-controlled exercise in patients recovering from a stroke. Twelve patients (age 55.3 years ± 15.
View Article and Find Full Text PDFBiotechnol Lett
December 2024
Jiangsu Key Laboratory for Pathogens and Ecosystems, College of Life Sciences, Nanjing Normal University, No.1 Wenyuan Rd., Xixia District, Nanjing, 210023, Jiangsu, People's Republic of China.
Recombineering (recombination-mediated genetic engineering) is a powerful strategy for bacterial genomic DNA and plasmid DNA modifications. CoS-MAGE improved over MAGE (multiplex automated genome engineering) by co-electroporation of an antibiotic resistance repair oligo along with the oligos for modification of the Escherichia coli chromosome. After several cycles of recombineering, the sub-population of mutants were selected among the antibiotic resistant colonies.
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