Willadsen Sandberg (rollanger0)

In recent years a variety of metals (cadmium, chromium, copper, iron) have been demonstrated to modulate coagulation in vitro and in vivo. One group of metals, the platinoids, have not been assessed, and such investigation is justified given the thromboembolic phenomena associated with platinum-based chemotherapy. Thus, the goal of the present investigation was to assess the effects of carboplatin, cisplatin (platinum compounds), NAMI-A, and ruthenium chloride (ruthenium compounds) on human plasmatic coagulation. Human plasma was exposed to clinically relevant, equimolar concentrations of the aforementioned platinum and ruthenium compounds, with changes in plasmatic coagulation assessed via thrombelastography. The first series of experiments demonstrated no significant modulation of coagulation by the platinum compounds, while NAMI-A demonstrated mild hypercoagulability and ruthenium chloride exerted marked hypercoagulability. A second series of experiments utilizing a variety of specialized modifications of thrombelastography focused on ruthenium chloride revealed that this compound enhances prothrombin activation. While the hypercoagulability associated with platinum compounds in vivo do not appear to have a basis in plasmatic biochemistry, it appears that ruthenium compounds can exert procoagulant properties by enhancing the common pathway of human plasmatic coagulation. Future investigation of Ru based chemotherapeutic agents in development to assess procoagulant activity as part of evaluating their potential clinical safety is warranted.Brain atrophy quantification plays a fundamental role in neuroinformatics since it permits studying brain development and neurological disorders. However, the lack of a ground truth prevents testing the accuracy of longitudinal atrophy quantification methods. We propose a deep learning framework to generate longitudinal datasets by deforming T1-w brain magnetic resonance imaging scans as requested through segmentation maps. Our proposal incorporates a cascaded multi-path U-Net optimised with a multi-objective loss which allows its paths to generate different brain regions accurately. We provided our model with baseline scans and real follow-up segmentation maps from two longitudinal datasets, ADNI and OASIS, and observed that our framework could produce synthetic follow-up scans that matched the real ones (Total scans= 584; Median absolute error 0.03 ± 0.02; Structural similarity index 0.98 ± 0.02; Dice similarity coefficient 0.95 ± 0.02; Percentage of brain volume change 0.24 ± 0.16; Jacobian integration 1.13 ± 0.05). Compared to two relevant works generating brain lesions using U-Nets and conditional generative adversarial networks (CGAN), our proposal outperformed them significantly in most cases (p 0.86). Our preliminary results on harmonised datasets showed the potential of our framework to be applied to various data collections without further adjustment.Robotic gastrectomy (RG) is increasingly performed based on expected benefits in short-term outcomes. However, it is still unclear if RG has any advantages over laparoscopic gastrectomy (LG). A retrospective cohort study was performed in patients who underwent minimally invasive gastrectomy between January 2012 and January 2020. A total of 366 patients were enrolled and short-term outcomes were compared between RG and LG. Propensity score matching was conducted to reduce selection bias based on age, sex, body mass index, performance status, physical status, clinical T, clinical N, clinical M, tumor location, neoadjuvant chemotherapy, type of gastrectomy, and extent of lymphadenectomy. A propensity score-matching algorithm was used to select 93 patients for each group. Estimated blood loss was smaller (0 vs. 37 mL, P = 0.001), length of hospital stay was shorter (10 vs. 12 days, P = 0.012), and the time until starting a soft diet was shorter (3 vs. 4 days, P = 0.001) in RG compared to LG. The overall complication rate was also lowe