The success of extracorporeal membrane oxygenation (ECMO) for the treatment of acute respiratory failure has led to consideration of the development of a more portable, and perhaps even implantable, artificial lung. The authors suggest a bioregenerative life support system that includes a photo-synthetic organism that can remove CO2 and produce O2 in the presence of an energy source. To build a model of such a photosynthetic artificial lung, the photosynthetic capability of a high temperature strain of the algae Chlorella pyrenoidosa was maximized at a cell density of 25 million cells/ml to serve as the O2 producer and CO2 remover. The "patient" in this model was comprised of 1 L of medium or 350 ml of blood, interfaced with the photosynthetic system across a gas transfer membrane. The experiments demonstrated the ability of the plant cells to supply O2 and remove CO2 from the "patient" with a maximum rate of 0.55 mmoles/L/hr under the most favorable measured operating conditions. The projected rate of 1.0 mmoles/L/hr required for physiologic applications is not totally ab absurd idea, with a slightly modified set-up. Modifications may be in the form of regulating the photosynthetic pathway or genetically engineering a hybrid strain with enhanced O2 producing and suppressed photoinhibition capacity.
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Antiviral Res
January 2025
CIRI, Centre International de Recherche en Infectiologie, INSERM U1111, CNRS, UMR5308, Univ Lyon, Université Claude Bernard Lyon 1, École Normale Supérieure de Lyon, 21 Avenue Tony Garnier, 69007 Lyon, France.
Nipah virus (NiV) is a lethal zoonotic paramyxovirus that can be transmitted from person to person through the respiratory route. There are currently no licensed vaccines or therapeutics. A lipopeptide-based fusion inhibitor was developed and previously evaluated for efficacy against the NiV-Malaysia strain.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
View Article and Find Full Text PDFInt J Surg
January 2025
Carcinoma Department of Traditional Chinese Medicine, Dianjiang People's Hospital of Chongqing, Chongqing, PR China.
The widespread adoption of high-resolution computed tomography (CT) screening has led to increased detection of small pulmonary nodules, necessitating accurate localization techniques for surgical resection. This review examines the evolution, efficacy, and safety of various localization methods for small pulmonary nodules. Studies focusing on localization techniques for pulmonary nodules ≤30 mm in diameter were included, with emphasis on technical success rates and complication profiles.
View Article and Find Full Text PDFJ Intern Med
January 2025
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Background: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.
Purpose: To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).
Material And Methods: We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.
J Magn Reson Imaging
January 2025
Department of Radiology, Peking University Third Hospital, Beijing, China.
Background: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.
Purpose: To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.
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