Cell-free and concentrated ascites reinfusion therapy (CART) is performed by collecting the ascites from the patient, followed by filtration and concentration. Thereafter, concentrated cell-free ascites is reinfused into the patient intravenously. The new type of machine, Plasauto μ, for managing the process of CART was launched onto the market. We have evaluated the machine through postmarketing clinical study in 17 patients with malignant ascites. The amounts of original and concentrated ascites were 3673 ± 1920 g and 439 ± 228 g, respectively. Recovery rates were acceptable regarding values of total protein, albumin, and IgG that were 55.6% ± 17.3%, 60.2% ± 20.8%, and 58.2% ± 20.5%, respectively. Recovery rates were positively associated with amounts of original ascites and negatively associated with total protein concentration. No adverse events related to the machine were observed. The new type of machine showed preferable performance in processing malignant ascites.
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http://dx.doi.org/10.1111/1744-9987.13658 | DOI Listing |
Neuroimage
January 2025
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. Electronic address:
Understanding the developmental trajectories of the auditory and visual systems is crucial to elucidate cognitive maturation and its associated relationships, which are essential for effectively navigating dynamic environments. Our one recent study has shown a positive correlation between the event-related potential (ERP) amplitudes associated with visual selective attention (posterior contralateral N2) and auditory change detection (mismatch negativity) in adults, suggesting an intimate relationship and potential shared mechanism between visual selective attention and auditory change detection. However, the evolution of these processes and their relationship over time remains unclear.
View Article and Find Full Text PDFComput Biol Med
January 2025
Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6DZ, UK; Institute for Food, Nutrition and Health (IFNH), University of Reading, Reading, RG6 6AH, UK. Electronic address:
Background: Machine learning (ML) integration of clinical, metabolite, and genetic data reveals variable results in predicting cardiometabolic health (CMH) outcomes. Therefore, we aim to (1) evaluate whether a multi-modal approach incorporating all three data types using ML algorithms can improve CMH outcome prediction compared to single-modal or paired-modal models, and (2) compare the methodologies used in existing prediction models.
Methods: We systematically searched five databases from 1998 to 2024 for ML predictive modelling studies using the multi-modal approach for CMH outcomes.
Cell Mol Biol (Noisy-le-grand)
January 2025
Department Medical Laboratory Technology, College of Medical Technology, University of Al-Farahidi, Baghdad, Iraq.
Methods
January 2025
Department of Physiology, Ajou University School of Medicine, Suwon 16499 Republic of Korea; Department of Molecular Science and Technology, Ajou University, Suwon 16499 Republic of Korea. Electronic address:
Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharides, rapidly raising blood sugar levels and contributing to type 2 diabetes mellitus (T2DM). Synthetic inhibitors of carbohydrate-digesting enzymes are used to manage T2DM but may harm organ function over time. Bioactive peptides offer a safer alternative, avoiding such adverse effects.
View Article and Find Full Text PDFJ Clin Med
January 2025
Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.
Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms.
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