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Adhesive Hemostatic Flake Particulates Composed of Calcium Alginate-Starch-Polyacrylamide/Poly(Acrylic Acid) Ionic Networks.
Polymers (Basel)
February 2025
Department of Polymer Science and Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea.
Hemostatic particles have specific advantages when applied to narrow and complicated bleeding sites with convenient usage compared to other types of hemostatic agents such as fabrics, foams, and pastes. However, powdery hemostatic agents are easy to desorb from the bleeding surface due to blood flow, which causes a serious decrease in hemostasis function. Here, we introduce bioresorbable flake particulates composed of calcium alginate, starch and polyacrylamide/poly(acrylic acid) ionic networks as a wound adhesive hemostatic agent.
View Article and Find Full Text PDF"Double Capture" - a technique to differentiate narrow complex Supraventricular Tachycardias.
J Interv Card Electrophysiol
March 2025
Fiona Stanley Hospital, Perth, Australia.
Background: There are various diagnostic manoeuvres to distinguish between atrial tachycardia (AT), atrio-ventricular nodal re-entrant tachycardia (AVNRT) and orthodromic re-entrant tachycardia (ORT) when assessing a narrow complex supraventricular tachycardia (SVT) in the electrophysiology (EP) laboratory. These manoeuvres are commonly used in combination to come to a diagnosis due to the inability of a single test to be able to reliably differentiate between the arrhythmias.
Objective: To determine whether a single captured His-synchronous simultaneous extra-stimulus in the atrium and ventricle ("Double Capture") can reliably distinguish the mechanism of a narrow complex SVT.
A 240-target VEP-based BCI system employing narrow-band random sequences.
J Neural Eng
March 2025
Institute of Semiconductors Chinese Academy of Sciences, Beijing, Beijing, 100083, CHINA.
Objective: In the field of brain-computer interface (BCI), achieving high information transfer rates (ITR) with a large number of targets remains a challenge. This study aims to address this issue by developing a novel code-modulated visual evoked potential (c-VEP) BCI system capable of handling an extensive instruction set while maintaining high performance.
Method: We propose a c-VEP BCI system that employs narrow-band random sequences as visual stimuli and utilizes a convolutional neural network (CNN)-based EEG2Code decoding algorithm.
Lactation Newsmakers: Elaine Petitat-Côté: Making Good Use of Two UN Conventions to Advocate for Breastfeeding Rights.
J Hum Lact
March 2025
Beruffsverband vun den Laktatiounsberoderinnen zu Letzebuerg, Luxembourg.
Elaine Petitat-Côté was born in Canada and has lived for the greatest part of her life in Geneva. She has consistently worked with development, health, and women's organizations and non-governmental organizations (NGOs), advocating for a public health approach to medicine that considers the social and economic realities of communities, and emphasizes the creation of healthy living conditions to ensure long-term, sustainable health outcomes This perspective supports a horizontal approach to medicine, in contrast to the narrower, vertical approach typically employed. As a member of the International Baby Food Action Network (IBFAN) she worked for the Geneva Infant Feeding Association (GIFA) on two main issues related to breastfeeding: maternity protection at work, and the rights of children to the highest attainable standard of health and nutrition, in particular by protecting breastfeeding.
View Article and Find Full Text PDFBinary Classification of Laryngeal Images Utilising ResNet-50 CNN Architecture.
Indian J Otolaryngol Head Neck Surg
February 2025
Department of Electrical Engineering, Perception and Intelligence Lab, Indian Institute of Technology Kanpur, Kanpur, India.
In India, laryngeal cancer is a significant health concern, underlining the critical need for early detection methods. This study introduces a novel approach to classify laryngeal lesions into nine morphological categories; due to data scarcity for all the nine classes, the data is divided into cancer and non-cancer classes, including both non-cancerous and Squamous Cell Carcinoma (SCC), by analysing endoscopy images with advanced convolutional neural networks, deep learning, and image processing techniques. A dataset of 1978 endoscopy images from 960 patients at a tertiary care center in Lucknow, between May 2015 and December 2023, was utilised for this purpose.
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