This work studied the performance of the artificial digestion method in terms of recovery and viability of third-stage larvae (L3) when previous treatments given to the infected fish muscle may accidentally render viable larvae. For that: a) hake mince was spiked with 10 L3/75g mince, frozen at -10, -15, -20, and -30 °C and immediately thawed, or stored for 12 or 24 h, and subjected to pepsin digestion; b) the mince was spiked under the same conditions, frozen at the above temperatures and thawed immediately. After manual recovery, L3 were assessed for viability, used to spike again the minced fish and subjected to pepsin digestion; c) the mince was spiked with 10 L3 which were: i) living (i.e. chilled), ii) freeze-surviving (live L3 had been previously recovered after freezing at -10 °C), or iii) dead (frozen at -30 °C or - 80 °C), and then subjected to pepsin digestion. Results showed that the artificial digestion method kills a significant number of larvae that may have survived freezing and thus may underestimate the number of viable larvae in a given batch. The method may also underestimate the infection level of fish batches containing dead larvae. It is suggested to take these limitations into account when designing digestion protocols for specific applications, especially when there is a risk of insufficiently treated or cooked fish batches or ready-to-eat foods.
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http://dx.doi.org/10.1016/j.fawpar.2021.e00121 | DOI Listing |
Nat Commun
January 2025
Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
Infertility affects one-in-six couples, often necessitating in vitro fertilization treatment (IVF). IVF generates complex data, which can challenge the utilization of the full richness of data during decision-making, leading to reliance on simple 'rules-of-thumb'. Machine learning techniques are well-suited to analyzing complex data to provide data-driven recommendations to improve decision-making.
View Article and Find Full Text PDFFront Artif Intell
December 2024
Department of Medical Education and Clinical Skills, California University of Science and Medicine, Colton, CA, United States.
Introduction: As artificial intelligence systems like large language models (LLM) and natural language processing advance, the need to evaluate their utility within medicine and medical education grows. As medical research publications continue to grow exponentially, AI systems offer valuable opportunities to condense and synthesize information, especially in underrepresented areas such as Sleep Medicine. The present study aims to compare summarization capacity between LLM generated summaries of sleep medicine research article abstracts, to summaries generated by Medical Student (humans) and to evaluate if the research content, and literary readability summarized is retained comparably.
View Article and Find Full Text PDFNat Commun
January 2025
Center for Applied Medical Research, University of Navarra, PIO XII 55 Ave, Pamplona, Spain.
Diabetologia
January 2025
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Aims/hypothesis: A positive association between sugar-sweetened beverages (SSBs) and diabetes risk has been shown, with inconsistent evidence between artificially sweetened beverages (ASBs) and diabetes. Moreover, it is uncertain if physical activity can mitigate the negative effects of these beverages on diabetes development. Therefore, we aimed to evaluate the independent and joint associations between SSB or ASB consumption and physical activity on the risk of type 2 diabetes.
View Article and Find Full Text PDFJ Clin Gastroenterol
February 2025
Swedish Medical Center, Seattle, WA.
Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for detecting and managing gastrointestinal conditions. Recent advancements involve using Artificial Neural Network models to enhance predictive accuracy for severe lower gastrointestinal (LGI) bleeding outcomes, including the need for surgery. To this end, artificial intelligence (AI)-guided predictive models have shown promise in improving management outcomes.
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