Martinsenogden9062

Elastic properties are of particular interest during the development of tablets especially for the definition of the formulation and of the process parameters. Impulse excitation, which is used in several industrial fields to determine elastic properties of materials, is presented in this article as a new fast and relatively cheap technology for the determination of elastic constants of pharmaceutical tablets. This technique is based on the detection of the natural resonance frequencies of solids. read more It was found in the present work that, for tablets obtained using different products under different compaction pressures, it was possible to detect clearly at least 3 resonance frequencies. Moreover, the shape of the resonance peaks obtained in the spectrum could be a sign of the viscoelastic nature of the tablet. With the two first resonance frequencies, it was possible, under the assumption of isotropy, to calculate Young's modulus and Poisson's ratio for each tablet using the methodology presented in the norm ASTM E1876-01. The values obtained were found independent of the tablet size as expected, and were consistent with those presented in the literature using other methodologies. Moreover, using FEM simulation, it was found that the difference between the experimental value of the third resonance frequency and the value obtained numerically was well correlated with the expected anisotropy of the tablet. Impulse excitation could thus be an interesting methodology to study tablet anisotropy.Granule structure has a key influence on tablet critical quality attributes. The ability to control this structure through excipient choice is an important part of formulation development. Mannitol is a popular diluent and the choice of input grade has been shown to impact granule properties. Allopurinol formulations containing two grades of mannitol (Pearlitol 160C and 200SD) were prepared by wet-granulation (twin-screw and high-shear) at different liquid/solid ratios (0.3 and 0.6 g/g). The particle and bulk properties were characterised by a range of techniques and linked to flow performance and tablet tensile strength during compression on a rotary tablet press. During granulation, 200SD underwent a polymorphic transition from a mixture of α and β to predominantly β. This transition was accompanied by a morphology change. Mannitol needles were formed, giving more porous granules with a higher specific surface area, which led to poorer flow properties but higher tablet tensile strength. This study concludes that understanding the effect of mannitol grade is a crucial part of formulation selection.Aminopeptidase N (APN/CD13) plays an important role in neoangiogenic process in malignancies. Our previous studies have already shown that 68Ga-labelled NOTA conjugated asparagine-glycine-arginine peptide (c[KNGRE]-NH2) specifically bind to APN/CD13 expressing tumors. The aim of this study was to evaluate and compare the APN/CD13 specificity of newly synthesized 68Ga-labelled NGR derivatives in vivo by PET/MRI imaging using hepatocellular carcinoma (He/De) and mesoblastic nephroma (Ne/De) tumor models. PET/MRI and ex vivo biodistribution studies were performed 11 ± 1 days after subcutaneous injection of tumor cells and 90 min after intravenous injection of 68Ga-NOTA-c(NGR), 68Ga-NODAGA-c(NGR), 68Ga-NODAGA-c(NGR) (MG1) or 68Ga-NODAGA-c(NGR) (MG2). The APN/CD13 selectivity was confirmed by blocking experiments and the APN/CD13 expression was verified by immunohistochemistry. 68Ga-labelled c(NGR) derivatives were produced with high specific activity and radiochemical purity. In control animals, low radiotracer accumulation was found in abdominal and thoracic organs. Using tumor-bearing animals we found that the 68Ga-NOTA-c(NGR), 68Ga-NODAGA-c(NGR), and 68Ga-NODAGA-c(NGR) (MG1) derivatives showed higher uptake in He/De and Ne/De tumors, than that of the accumulation of 68Ga-NODAGA-c(NGR) (MG2). APN/CD13 is a very promising target in PET imaging, however, the selection of the appropriate 68Ga-labelled NGR-based radiopharmaceutical is critical for the precise detection of tumor neo-angiogenesis and for monitoring the efficacy of anticancer therapy.Analyzing disease-disease relationships plays an important role for understanding disease mechanisms and finding alternative uses for a drug. A disease is usually the result of abnormal state of multiple molecular process. Since biological networks can model the interplay of multiple molecular processes, network-based methods have been proposed to uncover the disease-disease relationships recently. Given a disease and a network, the disease could be represented as a subnetwork constructed by the disease genes involved in the given network, named disease subnetwork. Because it is difficult to learn the feature representation of disease subnetworks, most existing methods are unsupervised ones without using labeled information. To fill this gap, we propose a novel method named SubNet2vec to learn the feature vectors of diseases from their corresponding subnetwork in the biological network. By utilizing the feature representation of disease subnetwork, we can analyze disease-disease relationships in a supervised fashion. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on disease-disease/disease-drug association prediction. The source code and data are available athttps//github.com/MedicineBiology-AI/SubNet2vec.git.

To conduct a meta-analysis of resting-state functional magnetic resonance imaging (R-fMRI) studies in children and adolescents with attention-deficit/hyperactivity disorder (ADHD) and in adults with ADHD to assess spatial convergence of findings from available studies.

Based on a preregistered protocol in PROSPERO (CRD42019119553), a large set of databases were searched up to April 9, 2019, with no language or article type restrictions. Study authors were systematically contacted for additional unpublished information/data. Resting-state functional magnetic resonance imaging studies using seed-based connectivity (SBC) or any other method (non-SBC) reporting whole-brain results of group comparisons between participants with ADHD and typically developing controls were eligible. Voxelwise meta-analysis via activation likelihood estimation with cluster-level familywise error (voxel-level p< .001; cluster-level p< .05) was used.

Thirty studies (18 SBC and 12 non-SBC), comprising 1,978 participants (1,094 with ADHD; 884 controls) were retained.