Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attentions recently. So far, multiple biomarkers have been shown sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51 AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI nonconverters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined

The human carboxylesterase 1 (CES1) gene encodes for the enzyme carboxylesterase 1, a serine esterase governing both metabolic deactivation and activation of numerous therapeutic agents. During the course of a study of the pharmacokinetics of the methyl ester racemic psychostimulant methylphenidate, profoundly elevated methylphenidate plasma concentrations, unprecedented distortions in isomer disposition, and increases in hemodynamic measures were observed in a subject of European descent. These observations led to a focused study of the subject's CES1 gene. DNA sequencing detected two coding region single-nucleotide mutations located in exons 4 and 6. The mutation in exon 4 is located in codon 143 and leads to a nonconservative substitution, p.Gly143Glu. A deletion in exon 6 at codon 260 results in a frameshift mutation, p.Asp260fs, altering residues 260-299 before truncating at a premature stop codon. The minor allele frequency of p.Gly143Glu was determined to be 3.7%, 4.3%, 2.0%, and 0% in white, black, Hispanic, and Asian populations, respectively. Of 925 individual DNA samples examined, none carried the p.Asp260fs, indicating it is an extremely rare mutation. In vitro functional studies demonstrated the catalytic functions of both p.Gly143Glu and p.Asp260fs are substantially impaired, resulting in a complete loss of hydrolytic activity toward methylphenidate. When a more sensitive esterase substrate, p-nitrophenyl acetate was utilized, only 21.4% and 0.6% catalytic efficiency (V(max)/K(m)) were determined in p.Gly143Glu and p.Asp260fs, respectively, compared to the wild-type enzyme. These findings indicate that specific CES1 gene variants can lead to clinically significant alterations in pharmacokinetics and drug response of carboxylesterase 1 substrates.

The poor solubility of paclitaxel (PTX), the commercially most successful anticancer drug, has long been hampering the development of suitable formulations. Here, we present translational evaluation of a nanoformulation of PTX, which is characterized by a facile preparation, extraordinary high drug loading of 50 % wt. and PTX solubility of up to 45 g/L, excellent shelf stability and controllable, sub-100 nm size. We observe favorable in vitro and in vivo safety profiles and a higher maximum tolerated dose compared to clinically approved formulations. Pharmacokinetic analysis reveals that the higher dose administered leads to a higher exposure of the tumor to PTX. As a result, we observed improved therapeutic outcome in orthotopic tumor models including particularly faithful and aggressive “T11” mouse claudin-low breast cancer orthotopic, syngeneic transplants. The promising preclinical data on the presented PTX nanoformulation showcase the need to investigate new excipients and is a robust basis to translate into clinical trials.

89Zr–Tetraazamacrocycle complexes display extraordinary stability.