The correlation of sarcopenia and adverse events of imatinib therapy postoperatively in gastrointestinal stromal tumor through computed tomography quantitative body composition.

PURPOSE: This study aimed to investigate the correlation between sarcopenia and adverse events (AEs) of postoperative imatinib therapy through computed tomography (CT) quantitative body composition for intermediate- and high-risk gastrointestinal stromal tumors (GISTs). METHODS: The study retrospectively analyzed the clinical data of 208 patients with intermediate- and high-risk GIST treated surgically and treated with imatinib afterward at the First Affiliated Hospital of Wenzhou Medical University between October 2011 and October 2021. Images of preoperative CT scans within 1 month were used to determine the body composition of the patients. On the basis of the L3 skeletal muscle index, patients were classified into sarcopenia and nonsarcopenia groups. In 2 groups, AEs related to imatinib were analyzed. RESULTS: The proportion of AEs related to imatinib in the sarcopenia group was higher, and this disparity had a significant statistical significance (P = .013). Sarcopenia was significantly associated with hemoglobin reduction compared with nonsarcopenia (P = .015). There was a significant difference between the sarcopenia group and the nonsarcopenia group in the ratio of severe AEs (grades 3-4). Hemoglobin content (odds ratio [OR], 0.981; 95% CI, 0.963-1.000; P = .045), sex (OR, 0.416; 95% CI, 0.192-0.904; P = .027), and sarcopenia (OR, 5.631; 95% CI, 2.262-14.014; P < .001) were the influential factors of imatinib severe AEs in patients with intermediate- and high-risk GIST within 1 year after imatinib treatment. CONCLUSION: Patients with preoperative sarcopenia have a higher incidence and severity of AEs during adjuvant imatinib therapy.

Assessing myocardial infarction severity from the urban environment perspective in Wuhan, China.

Health inequalities are globally widespread due to the regional socioeconomic inequalities. Myocardial infarction (MI) is a leading health problem causing deaths worldwide. Yet medical services for it are often inequitably distributed by region. Moreover, studies concerning MI's potential spatial risk factors generally suffer from difficulties in focusing on too few factors, inappropriate models, and coarse spatial grain of data. To address these issues, this paper integrates registered 1098 MI cases and urban multi-source spatio-temporal big data, and spatially analyses the risk factors for MI severity by applying an advanced interpretable model, the random forest algorithm (RFA)-based SHapley Additive exPlanations (SHAP) model. In addition, a community-scale model between spatio-temporal risk factors and MI cases is constructed to predict the MI severity of all communities in Wuhan, China. The results suggest that those risk factors (i.e., age of patients, medical quality, temperature changes, air pollution and urban habitat) affect the MI severity at the community scale. We found that Wuhan residents in the downtown area are at risk for high MI severity, and the surrounding suburb areas show a donut-shape pattern of risk for medium-to-high MI severity. These patterns draw our attention to the impact of spatial environmental risk factors on MI severity. Thus, this paper provides three recommendations for urban planning to reduce the risk and mortality from severe MI in the aspect of policy implication.

Binding potentials for vapour nanobubbles on surfaces using density functional theory.

We calculate density profiles of a simple model fluid in contact with a planar surface using density functional theory (DFT), in particular for the case where there is a vapour layer intruding between the wall and the bulk liquid. We apply the method of Hughes et al (2015 J. Chem. Phys. 142 074702) to calculate the density profiles for varying (specified) amounts of the vapour adsorbed at the wall. This is equivalent to varying the thickness h of the vapour at the surface. From the resulting sequence of density profiles we calculate the thermodynamic grand potential as h is varied and thereby determine the binding potential as a function of h. The binding potential obtained via this coarse-graining approach allows us to determine the disjoining pressure in the film and also to predict the shape of vapour nano-bubbles on the surface. Our microscopic DFT based approach captures information from length scales much smaller than some commonly used models in continuum mechanics.

Films, layers, and droplets: The effect of near-wall fluid structure on spreading dynamics.

We present a study of the spreading of liquid droplets on a solid substrate at very small scales. We focus on the regime where effective wetting energy (binding potential) and surface tension effects significantly influence steady and spreading droplets. In particular, we focus on strong packing and layering effects in the liquid near the substrate due to underlying density oscillations in the fluid caused by attractive substrate-liquid interactions. We show that such phenomena can be described by a thin-film (or long-wave or lubrication) model including an oscillatory Derjaguin (or disjoining or conjoining) pressure and explore the effects it has on steady droplet shapes and the spreading dynamics of droplets on both an adsorption (or precursor) layer and completely dry substrates. At the molecular scale, commonly used two-term binding potentials with a single preferred minimum controlling the adsorption layer height are inadequate to capture the rich behavior caused by the near-wall layered molecular packing. The adsorption layer is often submonolayer in thickness, i.e., the dynamics along the layer consists of single-particle hopping, leading to a diffusive dynamics, rather than the collective hydrodynamic motion implicit in standard thin-film models. We therefore modify the model in such a way that for thicker films the standard hydrodynamic theory is realized, but for very thin layers a diffusion equation is recovered.