Stalk formation as a function of lipid composition studied by X-ray reflectivity

AbstractWe have investigated the structure and interaction of solid-supported multilamellar phospholipid bilayers in view of stalk formation as model systems for membrane fusion. The multi-component bilayers were composed of ternary and quaternary mixtures, containing phosphatidylcholines, phosphatidylethanolamines, sphingomyelin, cholesterol, diacylglycerol, and phosphatidylinositol. Analysis of the obtained electron density profiles and the pressure–distance curves reveals systematic changes in structure and hydration repulsion. The osmotic pressure needed to induce stalk formation at the transition from the fluid lamellar to the rhombohedral phase indicates how membrane fusion properties are modified by bilayer composition

Stalk structures in lipid bilayer fusion studied by x-ray diffraction

Softcover, 182 S.: 44,00 €Softcover, 17x24The fusion of two biological membranes is an important step in many processes on the cellular and sub-cellular level. Understanding the involved interplay of different lipid species, a specialized protein machinery and water on length scales of few nanometers poses a significant challenge to current structural biology. Among several complementary approaches, one strategy is to study the structural rearrangements of the lipid matrix. As the initial step, lipid bilayers must be forced into close contact to form a non-bilayer intermediate termed a stalk. This has been the subject of numerous theoretical studies and simulations, but experimental data on stalks are largely lacking. Currently, the only way to obtain structural information at the required sub-nanometer resolution is x-ray diffraction on the recently discovered “stalk phase” formed by certain lipids. We apply this method to elucidate the effect of lipid composition on stalk geometry and the repulsive forces between lipid bilayers prior to stalk formation. An approach based on differential geometry of electron density isosurfaces is introduced to analyze the curvatures and bending energies of the lipid monolayers. For the first time, this connects experiment-based structures of stalks and the associated bending and hydration energies. In addition, this thesis aims to provide a self-contained introduction to the required background in x-ray diffraction on lipid mesophases and electron density reconstruction

Radiation damage studies in cardiac muscle cells and tissue using microfocused X-ray beams: experiment and simulation

Soft materials are easily affected by radiation damage from intense, focused synchrotron beams, often limiting the use of scanning diffraction experiments to radiation-resistant samples. To minimize radiation damage in experiments on soft tissue and thus to improve data quality, radiation damage needs to be studied as a function of the experimental parameters. Here, the impact of radiation damage in scanning X-ray diffraction experiments on hydrated cardiac muscle cells and tissue is investigated. It is shown how the small-angle diffraction signal is affected by radiation damage upon variation of scan parameters and dose. The experimental study was complemented by simulations of dose distributions for microfocused X-ray beams in soft muscle tissue. As a simulation tool, the Monte Carlo software package EGSnrc was used that is widely used in radiation dosimetry research. Simulations also give additional guidance for a more careful planning of dose distribution in tissue

Measurement of Scapholunate Joint Space Width on Real-Time MRI—A Feasibility Study

Introduction: The scapholunate interosseous ligament is pivotal for wrist stability, and its impairment can result in instability and joint degeneration. This study explores the application of real-time MRI for dynamic assessment of the scapholunate joint during wrist motion with the objective of determining its diagnostic value in efficacy in contrast to static imaging modalities. Materials and Methods: Ten healthy participants underwent real-time MRI scans during wrist ab/adduction and fist-clenching maneuvers. Measurements were obtained at proximal, medial, and distal landmarks on both dynamic and static images with statistical analyses conducted to evaluate the reliability of measurements at each landmark and the concordance between dynamic measurements and established static images. Additionally, inter- and intraobserver variabilities were evaluated. Results: Measurements of the medial landmarks demonstrated the closest agreement with static images and exhibited the least scatter. Distal landmark measurements showed a similar level of agreement but with increased scatter. Proximal landmark measurements displayed substantial deviation, which was accompanied by an even greater degree of scatter. Although no significant differences were observed between the ab/adduction and fist-clenching maneuvers, both inter- and intraobserver variabilities were significant across all measurements. Conclusions: This study highlights the potential of real-time MRI in the dynamic assessment of the scapholunate joint particularly at the medial landmark. Despite promising results, challenges such as measurement variability need to be addressed. Standardization and integration with advanced image processing methods could significantly enhance the accuracy and reliability of real-time MRI, paving the way for its clinical implementation in dynamic wrist imaging studies

Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology

Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues.*This article is a product of a Special Interest Group of the Society of Toxicologic Pathology (STP). The views expressed in this article are those of the authors and do not necessarily represent the policies, positions, or opinions of the STP