Despite numerous advances in medical treatment, sepsis remains one of the leading causes of death worldwide. Sepsis is characterized by the involvement of all organs and tissues as a consequence of blood poisoning, resulting in organ failure and eventually death. Effective treatment remains an unmet need and novel approaches are urgently needed. The growing evidence of clinical and biological heterogeneity of sepsis suggests precision medicine as a possible key for achieving therapeutic breakthroughs. In this scenario, biomimetic nanomedicine represents a promising avenue for the treatment of inflammatory diseases, including sepsis. We investigated the role of macrophage-derived biomimetic nanoparticles, namely leukosomes, in a lipopolysaccharide-induced murine model of sepsis. We observed that treatment with leukosomes was associated with significantly prolonged survival. In vitro studies elucidated the potential mechanism of action of these biomimetic vesicles. The direct treatment of endothelial cells (ECs) with leukosomes did not alter the gene expression profile of EC-associated cell adhesion molecules. In contrast, the interaction of leukosomes with macrophages induced a decrease of pro-inflammatory genes (IL-6, IL-1b, and TNF-α), an increase of anti-inflammatory ones (IL-10 and TGF-β), and indirectly an anti-inflammatory response on ECs. Taken together, these results showed the ability of leukosomes to regulate the inflammatory response in target cells, acting as a bioactive nanotherapeutic.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1039/c9nr04253a | DOI Listing |
Tissue Barriers
January 2025
Sepsis Translational Medicine Key Laboratory of Hunan Province, Department of Pathophysiology, School of Basic Medicine Science, Central South University, Changsha, Hunan, PR China.
Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are the result of an exaggerated inflammatory response triggered by a variety of pulmonary and systemic insults. The lung tissues are comprised of a variety of cell types, including alveolar epithelial cells, pulmonary vascular endothelial cells, macrophages, neutrophils, and others. There is mounting evidence that these diverse cell populations within the lung interact to regulate lung inflammation in response to both direct and indirect stimuli.
View Article and Find Full Text PDFAnn Intensive Care
January 2025
Department of Intensive Care Medicine, Universitaire Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium.
Background: Continuous veno-venous hemodiafiltration (CVVHDF) is used in critically ill patients, but its impact on O₂ and CO₂ removal, as well as the accuracy of resting energy expenditure (REE) measurement using indirect calorimetry (IC) remains unclear. This study aims to evaluate the effects of CVVHDF on O₂ and CO₂ removal and the accuracy of REE measurement using IC in patients undergoing continuous renal replacement therapy.
Design: Prospective, observational, single-center study.
Forensic Sci Med Pathol
January 2025
Department of Medical and Surgical Sciences, Unit of Legal Medicine, University of Bologna, Via Irnerio 49, 40126, Bologna, Italy.
The diagnosis of septic arthritis remains challenging in the clinical setting, often leading to a suspicion for medical liability. Our purpose is to describe an unusual case of a post-mortem diagnosis of P. multocida fatal septic arthritis, in a healthy 67-year-old woman presenting with pain in the right shoulder.
View Article and Find Full Text PDFAnn Emerg Med
January 2025
Department of Emergency Medicine, Kaiser Permanente San Diego Medical Center, San Diego, CA.
Study Objective: This study analyzes emergency medicine airway management trends and outcomes among community emergency departments.
Methods: A multicenter, retrospective chart review was conducted on 11,475 intubations from 15 different community emergency departments between January 1, 2015, and December 31, 2022. Data collected included patient's age, sex, rapid sequence intubation medications, use of cricoid pressure, method of intubation, number of attempts, admission diagnosis, and all-cause mortality rates.
J Clin Med
January 2025
Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.
Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!