Blood contact with the prosthetic surfaces of artificial lungs causes extensive activation of molecular and cellular mediators of coagulation and inflammation that can lead to patient morbidity and mortality. To determine the effects of artificial lung fiber bundle shear stress and surface area on blood activation, porcine blood was recirculated for 4 hours through circuits containing mock artificial lungs with bundle shear stresses of 11.6, 7.3, and 3.9 dynes/cm2 and surface areas of 5.2, 3.5, and 1.7 cm2/ml of circuit volume. Blood from these circuits was assayed for platelet and leukocyte counts, soluble P-selectin concentrations, and lactoferrin concentrations to determine the level of platelet and leukocyte adherence to the circuit, platelet activation, and leukocyte activation, respectively. Neither platelet nor leukocyte counts were significantly affected by shear stress or surface area. P-selectin and lactoferrin concentrations were significantly greater at a fiber bundle shear stress of 11.6 dynes/ cm2. P-selectin and lactoferrin concentrations were significantly greater at a fiber bundle surface area of 5.2 cm2/ml of circuit volume. Artificial lungs, therefore, should be designed with average bundle shear stresses < 11.6 dynes/cm2 and with surface areas < 5.2 cm2/ml of circuit volume. Current thoracic artificial lungs meet both these requirements.
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http://dx.doi.org/10.1097/00002480-200211000-00008 | DOI Listing |
Nat Commun
January 2025
Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy.
Objectives: This article aims to evaluate the use and effects of an artificial intelligence system supporting a critical diagnostic task during radiology resident training, addressing a research gap in this field.
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Sci Rep
January 2025
Amrita School of Artificial Intelligences, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Lung cancer is the leading cause of cancer-related fatalities globally, accounting for the highest mortality rate among both men and women. Mutations in the epidermal growth factor receptor (EGFR) gene are frequently found in non-small cell lung cancer (NSCLC). Since curcumin and CB[2]UN support various medicinal applications in drug delivery and design, we investigated the effect of curcumin and CB[2]UN-based drugs in controlling EGFR-mutant NSCLC through a dodecagonal computational approach.
View Article and Find Full Text PDFBr J Anaesth
February 2025
CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Intensive Care Unit, Hospital Universitario La Princesa, Madrid, Spain.
Despite the maturity and sophistication of anaesthesia workstations, improvements in our understanding of intraoperative mechanical ventilation, and use of less invasive surgical techniques, postoperative pulmonary complications (PPCs) are still a common problem in surgical patients of all ages. PPCs are associated with a higher incidence of perioperative morbidity and mortality, longer hospital stays, and higher healthcare costs. PPCs are strongly associated with anaesthesia-induced atelectasis, which predisposes to lung damage when partially collapsed lungs are subjected to mechanical ventilation.
View Article and Find Full Text PDFLung Cancer
January 2025
Institute of Pathology Heidelberg, University Hospital Heidelberg, In Neuenheimer Feld 224 69120 Heidelberg, Germany.
The rapid development of artificial intelligence (AI) based tools in pathology laboratories has brought forward unlimited opportunities for pathologists. Promising AI applications used for accomplishing diagnostic, prognostic and predictive tasks are being developed at a high pace. This is notably true in thoracic oncology, given the significant and rapid therapeutic progress made recently for lung cancer patients.
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