A prospective study of stomach cancer death in relation to green tea consumption in Japan

To evaluate whether green tea consumption provides protection against stomach cancer death, relative risks were calculated using Cox proportional hazards regression analysis in the Japan Collaborative Study for Evaluation of Cancer Risk, sponsored by the Ministry of Health and Welfare (JACC Study). The study was based on 30 370 men and 42 481 women aged 40–79. After adjustment for age, smoking status, history of peptic ulcer, family history of stomach cancer along with certain dietary items, the risks associated with drinking one or two, three or four, five to nine, and 10 or more cups of green tea per day, relative to those of drinking less than one cup per day, were 1.6 (95% CI: 0.9–2.9), 1.1 (95% CI: 0.6–1.9), 1.0 (95% CI: 0.5–2.0), and 1.0 (95% CI: 0.5–2.0), respectively, in men (P for trend=0.669), and 1.1 (95% CI: 0.5–2.5), 1.0 (95% CI: 0.5–2.5), 0.8 (95% CI: 0.4–1.6), and 0.8 (95% CI: 0.3–2.1), respectively, in women (P for trend=0.488). We found no inverse association between green tea consumption and the risk of stomach cancer death

Usefulness of the Palliative Prognostic Index in patients with lung cancer.

The usefulness of the Palliative Prognostic Index (PPI) has been successfully validated in a variety of clinical settings. However, while lung cancer is the leading cause of death worldwide, patients with lung cancer accounted for only 6.9-25.8 % of the study populations in these previous studies. We conducted a retrospective study to evaluate the usefulness of the PPI for survival prediction in patients with lung cancer. Patients with lung cancer who were admitted to our hospital between 2009 and 2013 to receive palliative care were enrolled. The association between the Palliative Prognostic Index, determined based on the data recorded in the clinical charts at the last admission to our hospital, and survival was evaluated. The patient group with a PPI of >6 showed a significantly shorter survival time than the patient group with a PPI of ≤ 6 (P < 0.0001, log-rank test). The sensitivity and specificity of the PPI determined using the cutoff value of 6 for predicting less than 3 weeks of survival were 61.3 and 86.8 %, respectively. However, the sensitivity decreased to 50.0 % when the assessment was carried out in only patients with small cell lung carcinoma. Our findings suggest the existence of a close association between the PPI and survival in patients with lung cancer receiving palliative care. However, the sensitivity of the index for predicting less than 3 weeks of survival was relatively low in patients with small cell lung carcinoma

Geldanamycin Derivative Ameliorates High Fat Diet-Induced Renal Failure in Diabetes

Diabetic nephropathy is a serious complication of longstanding diabetes and its pathogenesis remains unclear. Oxidative stress may play a critical role in the pathogenesis and progression of diabetic nephropathy. Our previous studies have demonstrated that polyunsaturated fatty acids (PUFA) induce peroxynitrite generation in primary human kidney mesangial cells and heat shock protein 90β1 (hsp90β1) is indispensable for the PUFA action. Here we investigated the effects of high fat diet (HFD) on kidney function and structure of db/db mice, a widely used rodent model of type 2 diabetes. Our results indicated that HFD dramatically increased the 24 h-urine output and worsened albuminuria in db/db mice. Discontinuation of HFD reversed the exacerbated albuminuria but not the increased urine output. Prolonged HFD feeding resulted in early death of db/db mice, which was associated with oliguria and anuria. Treatment with the geldanamycin derivative, 17-(dimethylaminoehtylamino)-17-demethoxygeldanamycin (17-DMAG), an hsp90 inhibitor, preserved kidney function, and ameliorated glomerular and tubular damage by HFD. 17-DMAG also significantly extended survival of the animals and protected them from the high mortality associated with renal failure. The benefit effect of 17-DMAG on renal function and structure was associated with a decreased level of kidney nitrotyrosine and a diminished kidney mitochondrial Ca2+ efflux in HFD-fed db/db mice. These results suggest that hsp90β1 is a potential target for the treatment of nephropathy and renal failure in diabetes

Chronic treatment with 17-DMAG improves balance and coordination in a new mouse model of Machado-Joseph disease

Machado-Joseph disease (MJD) or spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disease currently with no treatment. We describe a novel mouse model of MJD which expresses mutant human ataxin-3 at near endogenous levels and manifests MJD-like motor symptoms that appear gradually and progress over time. CMVMJD135 mice show ataxin-3 intranuclear inclusions in the CNS and neurodegenerative changes in key disease regions, such as the pontine and dentate nuclei. Hsp90 inhibition has shown promising outcomes in some neurodegenerative diseases, but nothing is known about its effects in MJD. Chronic treatment of CMVMJD mice with Hsp90 inhibitor 17-DMAG resulted in a delay in the progression of their motor coordination deficits and, at 22 and 24 weeks of age, was able to rescue the uncoordination phenotype to wild-type levels; in parallel, a reduction in neuropathology was observed in treated animals. We observed limited induction of heat-shock proteins with treatment, but found evidence that 17-DMAG may be acting through autophagy, as LC3-II (both at mRNA and protein levels) and beclin-1 were induced in the brain of treated animals. This resulted in decreased levels of the mutant ataxin-3 and reduced intranuclear aggregation of this protein. Our data validate this novel mouse model as a relevant tool for the study of MJD pathogenesis and for pre-clinical studies, and show that Hsp90 inhibition is a promising therapeutic strategy for MJD.We would like to thank to Dr. Henry Paulson for providing the anti-ataxin-3 serum, Dr. Monica Sousa for the pCMV vector and to Eng. Lucilia Goreti Pinto, Lu s Martins, Miguel Carneiro and Celina Barros for technical assistance. This work was supported by Fundacao para a Ciencia e Tecnologia through the projects FEDER/FCT, POCI/SAU-MMO/60412/2004 and PTDC/SAU-GMG/64076/2006. This work was supported by Fundacao para a Ciencia e Tecnologia through fellowships SFRH/BPD/91562/2012 to A.S-F., SFRH/BD/78388/2011 to S. D-S., SFRH/BD/51059/2010 to A.N-C., and SFRH/BPD/79469/2011 to A.T-C.

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments