The present review of the foreign and domestic literature is concerned with the application of the method of enhanced external counterpulsation (EECP) therapy for the treatment of the patients presenting with various diseases. It is shown that many recent publications report extensive investigations of the clinical and neurophysiological aspects of the application of this method for the combined regenerative treatment of the patients surviving after ischemic stroke (IS). The possibility of the influence of EECP therapy on the system of regulation of the cerebral blood flow, the formation of collateral circulation in the ischemic tissue, and the cellular-humoral mechanisms are considered. It is concluded that the introduction of enhanced external counterpulsation therapy into the program of the combined rehabilitative treatment on an individual basis for the patients surviving after ischemic stroke is pathogenetically substantiated as promoting regression of clinical, neurological, and neuropsychological disorders.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.17116/kurort2015345-52 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease.
Gastro Hep Adv
October 2024
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California.
Background And Aims: Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement.
View Article and Find Full Text PDFA Deep Learning Framework for Automated Classification and Archiving of Orthodontic Diagnostic Documents.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFA protein corona modulates the function of mineralization-competent matrix vesicles.
JBMR Plus
February 2025
Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto 14040-901, Brazil.
Mineralizing cells release a special class of extracellular vesicles known as matrix vesicles (MV), crucial for bone mineralization. Following their release, MV anchor to the extracellular matrix (ECM), where their highly specialized enzymatic machinery facilitates the formation of seed mineral within the MV's lumen, subsequently releasing it onto the ECM. However, how MV propagate mineral onto the collagenous ECM remains unclear.
View Article and Find Full Text PDFUnlabelled: Transparent and accurate reporting in early phase dose-finding (EPDF) clinical trials is crucial for informing subsequent larger trials. The SPIRIT statement, designed for trial protocol content, does not adequately cover the distinctive features of EPDF trials. Recent findings indicate that the protocol contents in past EPDF trials frequently lacked completeness and clarity.
View Article and Find Full Text PDFRisk prediction models for feeding intolerance in patients with enteral nutrition: a systematic review and meta-analysis.
Front Nutr
January 2025
Department of Epidemiology and Health Statistics, Tianjin Medical University, Tianjin, China.
Background: Although more risk prediction models are available for feeding intolerance in enteral-nourishment patients, it is still unclear how well these models will work in clinical settings. Future research faces challenges in validating model accuracy across populations, enhancing interpretability for clinical use, and overcoming dataset limitations.
Objective: To thoroughly examine studies that have been published on feeding intolerance risk prediction models for enteral nutrition patients.
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!