Adriamycin-loaded albumin-heparin conjugate microspheres for intraperitoneal chemotherapy

Adriamycin-loaded albumin-heparin conjugate microspheres (ADR-AHCMS) were evaluated as possible intraperitoneal (i.p.) delivery systems for site-specific cytotoxic action. The biocompatibility of the microspheres after intraperitoneal injection was tested first. 1 day after i.p. administration of empty as well as drug-loaded AHCMS to male Balb/c mice, only a moderate increase in i.p. neutrophils was measured. 3 days after injection neutrophil levels were comparable with the controls. No significant increases in the numbers of other cell types were observed, indicating an acute inflammatory response which can be considered to be mild. Antitumour efficacy was tested in an L1210 tumour-bearing mouse model and in a CC531 tumour-bearing rat model. The use of ADR-AHCMS leads to longer survival times of mice and improved tumour growth delay in rats, as compared with untreated controls and free drug treated animals. In both animal models higher adriamycin doses were initially tolerated if the drug was formulated in microspheres, although long-term adriamycin toxicity effects were evident in all treated groups. Doses and dosage schedules may be optimized to further reduce the toxic effects of the drug

Mass measurements of neutron-deficient Y, Zr, and Nb isotopes and their impact on rp and νp nucleosynthesis processes

© 2018 The Authors. Published by Elsevier B.V. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Using isochronous mass spectrometry at the experimental storage ring CSRe in Lanzhou, the masses of 82Zr and 84Nb were measured for the first time with an uncertainty of ∼10 keV, and the masses of 79Y, 81Zr, and 83Nb were re-determined with a higher precision. The latter are significantly less bound than their literature values. Our new and accurate masses remove the irregularities of the mass surface in this region of the nuclear chart. Our results do not support the predicted island of pronounced low α separation energies for neutron-deficient Mo and Tc isotopes, making the formation of Zr–Nb cycle in the rp-process unlikely. The new proton separation energy of 83Nb was determined to be 490(400) keV smaller than that in the Atomic Mass Evaluation 2012. This partly removes the overproduction of the p-nucleus 84Sr relative to the neutron-deficient molybdenum isotopes in the previous νp-process simulations.Peer reviewe

Hypothalamic loss of Snord116 recapitulates the hyperphagia of Prader-Willi syndrome.

Profound hyperphagia is a major disabling feature of Prader-Willi syndrome (PWS). Characterization of the mechanisms that underlie PWS-associated hyperphagia has been slowed by the paucity of animal models with increased food intake or obesity. Mice with a microdeletion encompassing the Snord116 cluster of noncoding RNAs encoded within the Prader-Willi minimal deletion critical region have previously been reported to show growth retardation and hyperphagia. Here, consistent with previous reports, we observed growth retardation in Snord116+/-P mice with a congenital paternal Snord116 deletion. However, these mice neither displayed increased food intake nor had reduced hypothalamic expression of the proprotein convertase 1 gene PCSK1 or its upstream regulator NHLH2, which have recently been suggested to be key mediators of PWS pathogenesis. Specifically, we disrupted Snord116 expression in the mediobasal hypothalamus in Snord116fl mice via bilateral stereotaxic injections of a Cre-expressing adeno-associated virus (AAV). While the Cre-injected mice had no change in measured energy expenditure, they became hyperphagic between 9 and 10 weeks after injection, with a subset of animals developing marked obesity. In conclusion, we show that selective disruption of Snord116 expression in the mediobasal hypothalamus models the hyperphagia of PWS

Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts

BACKGROUND AND PURPOSE: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. METHODS: Data were harmonized from 4 stroke studies (follow-up range, ≈12–18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. RESULTS: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). CONCLUSIONS: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution)

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Continental-scale geographic change across zealandia during paleogene subduction initiation

Data from International Ocean Discovery Program (IODP) Expedition 371 reveal vertical movements of 1-3 km in northern Zealandia during early Cenozoic subduction initiation in the western Pacific Ocean. Lord Howe Rise rose from deep (~1 km) water to sea level and subsided back, with peak uplift at 50 Ma in the north and between 41 and 32 Ma in the south. The New Caledonia Trough subsided 2-3 km between 55 and 45 Ma. We suggest these elevation changes resulted from crust delamination and mantle flow that led to slab formation. We propose a "subduction resurrection" model in which (1) a subduction rupture event activated lithospheric-scale faults across a broad region during less than ~5 m.y., and (2) tectonic forces evolved over a further 4-8 m.y. as subducted slabs grew in size and drove plate-motion change. Such a subduction rupture event may have involved nucleation and lateral propagation of slip-weakening rupture along an interconnected set of preexisting weaknesses adjacent to density anomalies

HDAC6 modulates myofibril stiffness and diastolic function of the heart

Passive stiffness of the heart is determined largely by extracellular matrix and titin, which functions as a molecular spring within sarcomeres. Titin stiffening is associated with the development of diastolic dysfunction (DD), while augmented titin compliance appears to impair systolic performance in dilated cardiomyopathy. We found that myofibril stiffness was elevated in mice lacking histone deacetylase 6 (HDAC6). Cultured adult murine ventricular myocytes treated with a selective HDAC6 inhibitor also exhibited increased myofibril stiffness. Conversely, HDAC6 overexpression in cardiomyocytes led to decreased myofibril stiffness, as did ex vivo treatment of mouse, rat, and human myofibrils with recombinant HDAC6. Modulation of myofibril stiffness by HDAC6 was dependent on 282 amino acids encompassing a portion of the PEVK element of titin. HDAC6 colocalized with Z-disks, and proteomics analysis suggested that HDAC6 functions as a sarcomeric protein deacetylase. Finally, increased myofibril stiffness in HDAC6-deficient mice was associated with exacerbated DD in response to hypertension or aging. These findings define a role for a deacetylase in the control of myofibril function and myocardial passive stiffness, suggest that reversible acetylation alters titin compliance, and reveal the potential of targeting HDAC6 to manipulate the elastic properties of the heart to treat cardiac diseases

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications