Privacy preservation for federated learning in health care.

Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns.

Platelet factor 4 improves survival in a murine model of antibiotic-susceptible and methicillin-resistant peritonitis.

The complement receptor CR3, also known as integrin Mac-1 (CD11b/CD18), is one of the major phagocytic receptors on the surface of neutrophils and macrophages. We previously demonstrated that in its protein ligands, Mac-1 binds sequences enriched in basic and hydrophobic residues and strongly disfavors negatively charged sequences. The avoidance by Mac-1 of negatively charged surfaces suggests that the bacterial wall and bacterial capsule possessing net negative electrostatic charge may repel Mac-1 and that the cationic Mac-1 ligands can overcome this evasion by acting as opsonins.

PLATELET FACTOR 4 (PF4) IMPROVES SURVIVAL IN A MURINE MODEL OF ANTIBIOTIC-SUSCEPTIBLE AND METHICILLIN-RESISTANT PERITONITIS.

The complement receptor CR3, also known as integrin Mac-1 (CD11b/CD18), is one of the major phagocytic receptors on the surface of neutrophils and macrophages. We previously demonstrated that in its protein ligands, Mac-1 binds sequences enriched in basic and hydrophobic residues and strongly disfavors negatively charged sequences. The avoidance by Mac-1 of negatively charged surfaces suggests that the bacterial wall and bacterial capsule possessing net negative electrostatic charge may repel Mac-1 and that the cationic Mac-1 ligands can overcome this evasion by acting as opsonins.

The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms.