Sepsis

Photocatalytic microrobots for treating bacterial infections deep within sinuses.

Microrobotic techniques are promising for treating biofilm infections located deep within the human body. However, the presence of highly viscous pus presents a formidable biological barrier, severely restricting targeted and minimally invasive treatments. In addition, conventional antibacterial agents exhibit limited payload integration with microrobotic systems, further compromising therapeutic ...

Comprehensive analysis and experimental validation of BST1 as a novel diagnostic biomarker for pediatric sepsis using multiple machine learning algorithms.

Bone marrow stromal cell antigen-1 (BST1) expression is elevated in a variety of human diseases, but its relationship with pediatric sepsis is unclear. This study aimed to investigate the expression of BST1 in pediatric sepsis patients and its value for diagnosing pediatric sepsis. Key neutrophil extracellular traps (NETs)-related genes that accurately diagnose pediatric sepsis patients were ident...

Mortality risk associated with clinical signs of possible serious bacterial infection (PSBI) in young infants in Africa and Asia: protocol for a secondary pooled analysis.

INTRODUCTION: The WHO's Integrated Management of Childhood Illness (IMCI) in young infants <2 months of age includes the identification and management...

Physiological Response of Tissue-Engineered Vascular Grafts to Vasoactive Agents in an Ovine Model.

Tissue-engineered vascular grafts (TEVGs) are emerging as promising alternatives to synthetic grafts, particularly in pediatric cardiovascular surgery...

Enhancing early mortality prediction for sepsis-associated acute respiratory distress syndrome patients via optimized machine learning algorithm: development and multiple databases' validation of the SAFE-Mo.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is associated with high mortality, with sepsis accounts for 31-34% of cases. Given the global b...

Validation of an artificial intelligence-based algorithm for predictive performance and risk stratification of sepsis using real-world data from hospitalised patients: a prospective observational study.

OBJECTIVE: The heterogeneous nature of sepsis renders determining its underlying causes difficult, which may delay diagnosis and intervention. VitalCa...

Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.

Sepsis secondary to urinary tract infection after kidney stone surgery is associated with considerable morbidity. Limited research examines the use o...

Machine learning model for daily prediction of pediatric sepsis using Phoenix criteria.

BACKGROUND: Early sepsis diagnosis is essential for initiating prompt treatment, preventing the progression of organ failure, and improving the surviv...

Morphological Profiling of Imaging Flow Cytometry Data Uncovers Heterogeneity in Infected Gephyrocapsa huxleyi Cultures.

Phytoplankton, such as the coccolitophore Gephyrocapsa huxleyi (G. huxleyi), has a major ecological impact through photosynthesis-the production of ox...

Advancing AMR Surveillance: Confluence of One Health and Big Data Integration : Converging One Health and Big Data for AMR.

Antibiotic resistance poses a critical global health threat, demanding robust surveillance systems to monitor its prevalence, patterns, and trends. Th...

Improving Hepatitis B outcome prediction with ensemble machine learning: A study on predictive models and interpretability.

OBJECTIVE: Hepatitis B virus (HBV) is a significant global health threat, responsible for severe liver diseases such as liver failure, cirrhosis, and ...

Psychedelics, entactogens and psychoplastogens for depression and related disorders.

Currently, the most actively investigated rapidly acting antidepressants, anxiolytics and/or anti PTSD agents, include psychedelics e.g. psilocybin, L...

Chemokine-like factor-like MARVEL transmembrane domain-containing 1 identified as a novel target protein for immune dysregulation in sepsis: A machine learning and molecular dynamics framework for diagnostic biomarkers and therapeutic exploration.

BACKGROUND: Sepsis, a systemic inflammatory response syndrome precipitated by infection, involves critical immune dysregulation. Here, we aimed to eva...

Melioidosis molecular diagnostics: An update.

Melioidosis, a fatal tropical disease, presents a wide array of clinical manifestations, including abscesses, pneumonia, septic shock, bacteraemia, os...

Deciphering the transcriptomic characteristic of lactate metabolism and the immune infiltration landscape in abdominal aortic aneurysm.

BACKGROUND: Abdominal aortic aneurysm (AAA) is a common degenerative vascular disease characterized by progressive dilation of the abdominal aorta, wh...

Fixed-bed studies and artificial neural network modeling for removal of fluoroquinolone antibiotics using a green MWCNT@E adsorbent.

Ciprofloxacin (CIP) and ofloxacin (OFL), commonly used fluoroquinolone antibiotics, have been frequently detected in water sources which can cause env...

Prediction of Antibiotic Resistance Phenotypes and Minimum Inhibitory Concentrations in Using Machine Learning Analysis of Its Pan-Genome and Pan-Resistome Features.

Traditional experimental methods for determining antibiotic resistance phenotypes (ARPs) and minimum inhibitory concentrations (MICs) in bacteria are ...

Identification of bacterial infection types in decomposition stages at various temperatures using pathology images and artificial intelligence algorithms.

Bacterial infections present a significant threat to human health, and accurate identification of infection type is crucial for both clinical and fore...

Exploring the molecular mechanisms of lactylation-related biological functions and immune regulation in sepsis-associated acute kidney injury.

Lactylation, a novel post-translational modification, has been implicated in various pathophysiological processes; however, its role in sepsis-associa...

Clinical correlates of data-driven subtypes of deep gray matter atrophy and dopamine availability in early Parkinson's disease.

Recent machine-learning techniques may be useful to identify subtypes with distinct spatial patterns of biomarker abnormality in the various neurodege...