How Technical Advances Changed the Concept of Antibodies.

Shaped by advances in scientific instrumentation and experimental techniques, the concept of antibody has undergone profound transformations throughout the history of immunology. Serological assays, separation techniques, protein fragmentation techniques, molecular biology techniques, and other methodological innovations did not only serve to produce data on the structure and function of these molecules but, by framing antibodies into a unique facet of experimental investigation, were effectively redefining and reconceptualizing these molecules for the scientific community. The characteristics and properties of antibodies observed in experimental settings were often directly extrapolated to their presumed nature in living organisms, as exemplified by the literal identification of antibodies with a gamma electrophoretic fraction in the 1930s.

Robotic Laser Tissue Soldering for Atraumatic Soft Tissue Fusion Guided by Fluorescent Nanothermometry.

Minimally invasive surgical techniques, including endoscopic and robotic procedures, continue to revolutionize patient care, for their ability to minimize surgical trauma, thus promoting faster recovery and reduced hospital stays. Yet, the suturing of soft tissues ensuring damage-free tissue bonding during these procedures remains challenging due to missing haptics and the fulcrum effect. Laser tissue soldering has potential in overcoming these issues, offering atraumatic seamless tissue fusion.

Comparison of human hair cortisol concentration stability for 1-year and 2-year test-retest intervals.

Human hair cortisol concentration (HCC) has previously been found to be highly stable for a 1-year interval (r = 0.73) in terms of a product-moment correlation. The present study aimed to replicate this finding and compare HCC stability regarding 1-year and 2-year test-retest intervals.

Stable Deep MRI Reconstruction Using Generative Priors.

Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image priors. We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only.