Insufficient renal urate excretion and/or overproduction of uric acid (UA) are the dominant causes of hyperuricemia. Baicalein (BAL) is widely distributed in dietary plants and has extensive biological activities, including antioxidative, anti-inflammatory and antihypertensive activities.

To investigate the anti-hyperuricemic effects of BAL and the underlying mechanisms in vitro and in vivo.

We investigated the inhibitory effects of BAL on GLUT9 and URAT1 in vitro through electrophysiological experiments and

C-urate uptake assays. To evaluate the impact of BAL on serum and urine UA, the expression of GLUT9 and URAT1, and the activity of xanthine oxidase (XOD), we developed a mouse hyperuricemia model by potassium oxonate (PO) injection. Molecular docking analysis based on homology modeling was performed to explain the predominant efficacy of BAL compared with the other test compounds.

BAL dose-dependently inhibited GLUT9 and URAT1 in a noncompetitive manner with IC

values of 30.17± 8.68μM and 31.56±1.37μM, respectively. BAL (200mg/kg) significantly decreased serum UA and enhanced renal urate excretion in PO-induced hyperuricemic mice. Moreover, the expression of GLUT9 and URAT1 in the kidney was downregulated, and XOD activity in the serum and liver was suppressed. The docking analysis revealed that BAL potently interacted with Trp336, Asp462, Tyr71 and Gln328 of GLUT9 and Ser35 and Phe241 of URAT1.

These results indicated that BAL exerts potent antihyperuricemic efects through renal UA excretal promotion and serum UA production. Thus, we propose that BAL may be a promising treatment for the prevention of hyperuricemia owing to its multitargeted inhibitory activity.

These results indicated that BAL exerts potent antihyperuricemic efects through renal UA excretal promotion and serum UA production. Thus, we propose that BAL may be a promising treatment for the prevention of hyperuricemia owing to its multitargeted inhibitory activity.Assessment and personalised feedback are important components of brief interventions (BIs) for cannabis use. A key outcome is to increase motivation to change during this short interaction. The diversity of available assessments and time burden scoring them pose a challenge for routine use in clinical practice. An instant assessment and feedback (iAx) system was developed to administer assessments informed by bioSocial Cognitive Theory, that were instantly scored and benchmarked against clinical norms, to provide patient feedback and guide treatment planning. This study evaluated the feasibility and additive effectiveness of the iAx on motivation to change cannabis use, when compared to treatment as usual (TAU), in a single-session BI. A randomised controlled trial was conducted in a public hospital alcohol and drug outpatient clinic. AZ 3146 cell line Eighty-seven cannabis users (Mage = 26.41; 66% male) were assigned to the BI utilising the iAx (iAx; n = 44) or to the standard BI (TAU; n = 43). Patients completed pre- and post-BI assessments of motivation to change and a post-BI measure of treatment satisfaction. Practitioners completed a feedback survey. Patients receiving iAx reported a significantly greater increase in motivation to change from pre- to post-BI compared to patients receiving TAU (d = 0.49, p = .03). Treatment satisfaction was high across both conditions, with no significant difference between groups (p = .57). Practitioners also reported a high level of satisfaction with the iAx system. In summary, findings support the feasibility and additive effectiveness of the iAx to enhance patient motivation during cannabis BI.The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method’s clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.Surgical planning of percutaneous interventions has a crucial role to guarantee the success of minimally invasive surgeries. In the last decades, many methods have been proposed to reduce clinician work load related to the planning phase and to augment the information used in the definition of the optimal trajectory. In this survey, we include 113 articles related to computer assisted planning (CAP) methods and validations obtained from a systematic search on three databases. First, a general formulation of the problem is presented, independently from the surgical field involved, and the key steps involved in the development of a CAP solution are detailed. Secondly, we categorized the articles based on the main surgical applications, which have been object of study and we categorize them based on the type of assistance provided to the end-user.The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer’s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years.