In the early 1800s, an awareness of potential ventricular failure stimulated interest in artificial heart replacement. In 1937 the first total artificial heart (TAH) was implanted into the chest of a dog by Russian physicians. The primary driving force for mechanical cardiac assistance developed from the necessity for circulatory assistance in order to perform corrective cardiac surgery. In 1953 the first successful closure of an atrial septal defect using extracorporeal circulation was performed. During the following decade the concept of using mechanical devices to assist the failing heart was aggressively pursued. This culminated in the first implant of a TAH in a human in 1969 as a bridge to transplant. Clinical implant of the TAH as a permanent device was performed in 1982 by researchers at the University of Utah. This patient lived for 112 days. Three successive permanent implants were performed in Louisville, Kentucky, with one patient surviving for 620 days. All of these permanent TAH patients suffered from device-related complications including bleeding, infection, and thromboembolic events. It became apparent that the present configuration of the TAH with its external drive lines and large air console was not ideal for long-term support. In 1985 the first implant of the Symbion J-7-100 TAH (Jarvik-7) as a bridge to transplant was performed. This patient was supported by the device for 9 days and was successfully transplanted and discharged home. Since 1985 more than 170 patients have been bridged using the Symbion J-7 TAH with more than 70% of these patients being successfully transplanted. The incidence of thromboembolic events has dramatically reduced with better understanding of anticoagulation requirements. Infection continues to be the greatest potential complication with these patients. In spite of this, the pneumatic TAH has proved to be an adequate bridge to transplant device.
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http://dx.doi.org/10.4037/15597768-1991-3022 | DOI Listing |
EClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
View Article and Find Full Text PDFRSC Adv
January 2025
Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
Surface-enhanced Raman spectroscopy (SERS) is widely recognized as a powerful analytical technique, offering molecular identification by amplifying characteristic vibrational signals, even at the single-molecule level. While SERS has been successfully applied for a wide range of targets including pesticides, dyes, bacteria, and pharmaceuticals, it has struggled with the detection of molecules with inherently low Raman scattering cross-sections. Urea, a key nitrogen-containing biomolecule and the diamide of carbonic acid, is a prime example of such a challenging target.
View Article and Find Full Text PDFCommun Med (Lond)
January 2025
Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
Background: The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.
Methods: We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated.
Sci Rep
January 2025
Department of Computer Science, Faculty of Computers and Information, Suez University, P. O. Box 43221, Suez, Egypt.
Diabetes is a long-term condition characterized by elevated blood sugar levels. It can lead to a variety of complex disorders such as stroke, renal failure, and heart attack. Diabetes requires the most machine learning help to diagnose diabetes illness at an early stage, as it cannot be treated and adds significant complications to our health-care system.
View Article and Find Full Text PDFJACC Adv
January 2025
Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, California, United States.
Background: Observational data have suggested that patients with moderate to severe ischemia benefit from revascularization. However, this was not confirmed in a large, randomized trial.
Objectives: Using a contemporary, multicenter registry, the authors evaluated differences in the association between quantitative ischemia, revascularization, and outcomes across important subgroups.
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