AI Article Synopsis

  • Cell-free and concentrated ascites reinfusion therapy (CART) involves filtering and concentrating ascitic fluid from patients before reinfusing it intravenously.
  • A new machine called Plasauto μ has been evaluated in a clinical study with 17 patients suffering from malignant ascites, demonstrating effective filtration of ascitic fluid.
  • The study found that recovery rates for key proteins were acceptable, showing positive results in processing malignant ascites without any reported adverse events related to the machine.

Article Abstract

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8359940PMC
http://dx.doi.org/10.1111/1744-9987.13658DOI Listing

Publication Analysis

Top Keywords

type machine
12
concentrated ascites
12
cell-free concentrated
8
ascites
8
ascites reinfusion
8
reinfusion therapy
8
postmarketing clinical
8
clinical study
8
malignant ascites
8
amounts original
8

Similar Publications

Development of the relationship between visual selective attention and auditory change detection.

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 PDF

Integrating genetics, metabolites, and clinical characteristics in predicting cardiometabolic health outcomes using machine learning algorithms - A systematic review.

Comput 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.

View Article and Find Full Text PDF
Article Synopsis
  • Pseudomonas aeruginosa is problematic in healthcare due to its high antibiotic resistance, highlighting the need for new antimicrobial solutions.
  • A study focused on isolating a new bacteriocin from Enterococcus faecium found in stool samples, which showed promise against multidrug-resistant P. aeruginosa.
  • The purified bacteriocin, enterocin GH, demonstrated significant antibacterial and antibiofilm activity against P. aeruginosa, outperforming controls in laboratory tests.
View Article and Find Full Text PDF

AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus.

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 PDF

Artificial Intelligence in Sepsis Management: An Overview for Clinicians.

J 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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!