Advanced cancer patients face a high risk of sepsis due to immune suppression and infection susceptibility. To tackle this challenge, we developed an innovative animal model that simulates the clinical scenario of late-stage cancer complicated by sepsis and designed a sialic acid (SA)-modified paclitaxel (PTX) liposome (PTX-SAL). This formulation specifically targets overactivated peripheral blood neutrophils (PBNs) by binding to L-selectin on their surface. It effectively eliminates hyperactive neutrophils and blocks their migration, thus reducing infiltration into tumor and inflammation sites. In sepsis and melanoma mouse models, PTX-SAL demonstrated superior therapeutic efficacy and a favorable safety profile. Notably, in the late-stage tumor model with sepsis, PTX-SAL significantly improved survival rates, with a 72-hour survival rate of 66.7%. In stark contrast, the PTX solution (PTX-S) group exhibited accelerated mortality, with all animals succumbing within 24 h, highlighting the detrimental effects of PTX-S's non-selective cytotoxicity on immune cells. These findings underscore the superior long-term safety and therapeutic advantage of nanomedicines like PTX-SAL over conventional drug formulations. In summary, SA-modified nanomedicines offer a dual benefit by targeting and eliminating inflammatory neutrophils, addressing both tumor progression and sepsis, and significantly reducing mortality in preclinical models. This innovative strategy fills a critical gap in the treatment of advanced cancer complicated by sepsis.
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http://dx.doi.org/10.1016/j.ijpharm.2025.125211 | DOI Listing |
Expert Rev Mol Diagn
January 2025
Hebei Provincial Center for Clinical Laboratories, Shijiazhuang, China, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, People's Republic of China.
Introduction: Rapid and accurate laboratory diagnosis is essential for the effective treatment of bloodstream infection (BSI).
Areas Covered: This review aims to address novel and traditional approaches that exhibit different performance characteristics in the diagnosis of BSI. In particular, the authors will discuss the pros and cons of the blood culture-based phenotypic methods, nucleic acid-targeted molecular methods, and host response-targeted biomarker detection in the diagnosis of BSI.
Rationale: WW domain-containing oxidoreductase ( ) is a gene associated implicated in both neurologic and inflammatory diseases and is susceptible to environmental stressors. We hypothesize partial loss of Wwox function will result in increased sepsis severity and neuroinflammation.
Methods: mice, generated by CRISPR/Cas9, and mice were treated with intraperitoneal PBS vs LPS (10mg/kg) and euthanized 12 hours post-injection.
Public health alarm concerning the emerging fungus is fueled by its antifungal drug resistance and propensity to cause deadly outbreaks. Persistent skin colonization drives transmission and lethal sepsis although its basis remains mysterious. We compared the skin colonization dynamics of with its relative , quantifying skin fungal persistence and distribution and immune composition and positioning.
View Article and Find Full Text PDFRadiol Case Rep
March 2025
Maimonides Medical Center, Brooklyn, NY, USA.
Thoracic aortic pseudoaneurysms are a rare but serious complication of infectious processes, often resulting from mycotic (infectious) aneurysms, occurring when the vessel wall is compromised by an infection, leading to the formation of a pseudoaneurysm [1]. Mycotic aneurysms typically result from bacteremia or fungemia, with common sources being infective endocarditis or other systemic infections. Tuberculosis, though a common infectious disease worldwide, is an unusual cause of aortic pseudoaneurysm formation.
View Article and Find Full Text PDFJ Inflamm Res
January 2025
Department of Hematology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, Henan Province, People's Republic of China.
Background: Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.
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