Rapid Screening of Alicyclobacillus acidoterrestris Spoilage of Fruit Juices by Electronic Nose: A Confirmation Study

Early screening of Alicyclobacillus spp. in fruit juices is a major applicative goal for the food industry, since juice contamination can lead to considerable loss of quality, and subsequently, to economic damages for juice producers. This paper presents an accurate study to assess and confirm the EOS507 electronic nose’s (EN) ability of diagnosing Alicyclobacillus acidoterrestris spoilage in artificially contaminated fruit juices. The authors experimental results have shown that the EOS507 can early identify, just after 24 hours from inoculation, contaminated orange and pear juices with an excellent classification rate close to 90% and with a detection threshold as low as 103 cfu/ml. In apple juice the detection threshold was about 105 cfu/ml, thus requiring longer incubation times (72 hours). PLS regression of EOS507 data can be also used to predict with fair accuracy the colony-forming units concentration of the bacteria. These results were supported by the GC/MS/MS measurements of specific chemical markers, such as guaiacol

Noncoding RNAs as novel biomarkers in pancreatic cancer: what do we know?

Pancreatic cancer is an aggressive cancer of the digestive system, which is becoming a serious health problem worldwide. Overall survival for patients with pancreatic cancer is poor, mainly due to a lack of biomarkers to enable early diagnosis and a lack of prognostic markers that can inform decision-making, facilitating personalized treatment and an optimal clinical outcome. ncRNAs play an important role in pancreatic carcinogenesis. Here we review the literature on the role of ncRNAs as biomarkers in pancreatic cancer. We focus on the significance of ncRNAs as markers for early diagnosis, as prognostic biomarkers able to inform clinical management and as targets for novel therapeutics for patients with pancreatic cancer

Direct but not indirect co-culture with osteogenically differentiated human bone marrow stromal cells increases RANKL/OPG ratio in human breast cancer cells generating bone metastases

Background: Bone metastases arise in nearly 70% of patients with advanced breast cancer, but the complex metastatic process has not been completely clarified yet. RANKL/RANK/OPG pathway modifications and the crosstalk between metastatic cells and bone have been indicated as potential drivers of the process. Interactions between tumor and bone cells have been studied in vivo and in vitro, but specific effects of the direct contact between human metastatic cells and human bone cells on RANKL/RANK/OPG pathway have not been investigated. Findings: We directly co-cultured bone metastatic human breast cancer cells (BOKL) with osteo-differentiated human mesenchymal cells (BMSCs) from 3 different donors. BMSCs and BOKL were then enzymatically separated and FACS sorted. We found a significant increase in the RANKL/OPG ratio as compared to control, which was not observed in BOKL cultured in medium conditioned by BMSCs, neither in BOKL directly cultured with fibroblasts or medium conditioned by fibroblasts. Direct co-culture with osteo-differentiated BMSCs caused BOKL aggregation while proliferation was not affected by co-culture. To more specifically associate RANKL expression to osteogenic differentiation degree of BMSCs, we determined their osteogenic markers expression and matrix calcification relative to osteoblasts and fibroblasts. Conclusions: In conclusion, our co-culture model allowed to demonstrate for the first time that direct contact but not paracrine interactions between human metastatic breast cancer cells and bone cells has a significant effect on RANKL/OPG expression in bone metastatic cells. Furthermore, only direct contact with the bone microenvironment induced BOKL clustering without however significantly influencing their proliferation and migration

Environment And Genetics in Lung cancer Etiology (EAGLE) study: An integrative population-based case-control study of lung cancer

Background: Lung cancer is the leading cause of cancer mortality worldwide. Tobacco smoking is its primary cause, and yet the precise molecular alterations induced by smoking in lung tissue that lead to lung cancer and impact survival have remained obscure. A new framework of research is needed to address the challenges offered by this complex disease. Methods/Design: We designed a large population-based case-control study that combines a traditional molecular epidemiology design with a more integrative approach to investigate the dynamic process that begins with smoking initiation, proceeds through dependency/smoking persistence, continues with lung cancer development and ends with progression to disseminated disease or response to therapy and survival. The study allows the integration of data from multiple sources in the same subjects (risk factors, germline variation, genomic alterations in tumors, and clinical endpoints) to tackle the disease etiology from different angles. Before beginning the study, we conducted a phone survey and pilot investigations to identify the best approach to ensure an acceptable participation in the study from cases and controls. Between 2002 and 2005, we enrolled 2101 incident primary lung cancer cases and 2120 population controls, with 86.6% and 72.4% participation rate, respectively, from a catchment area including 216 municipalities in the Lombardy region of Italy. Lung cancer cases were enrolled in 13 hospitals and population controls were randomly sampled from the area to match the cases by age, gender and residence. Detailed epidemiological information and biospecimens were collected from each participant, and clinical data and tissue specimens from the cases. Collection of follow-up data on treatment and survival is ongoing. Discussion: EAGLE is a new population-based case-control study that explores the full spectrum of lung cancer etiology, from smoking addiction to lung cancer outcome, through examination of epidemiological, molecular, and clinical data. We have provided a detailed description of the study design, field activities, management, and opportunities for research following this integrative approach, which allows a sharper and more comprehensive vision of the complex nature of this disease. The study is poised to accelerate the emergence of new preventive and therapeutic strategies with potentially enormous impact on public health

Dose-dependent improvement of myoclonic hyperkinesia due to Valproic acid in eight Huntington's Disease patients: a case series

BACKGROUND: Chorea in Huntington's Disease (HD) is usually treated with antidopaminergic neuroleptics like haloperidol, olanzapine and tiaprid or dopamine depleting drugs like tetrabenazine. Some patients with hyperkinesia, however, react to treatment with antidopaminergic drugs by developing extrapyramidal side effects. In earlier studies valproic acid showed no beneficial effect on involuntary choreatic movements. Myoclonus is rare in HD and is often overseen or misdiagnosed as chorea. METHODS: In this report, we present eight patients whose main symptom is myoclonic hyperkinesia. All patients were treated with valproic acid and scored by using the Unified Huntington's Disease Rating Scale (UHDRS) motor score before and after treatment. In addition to this, two patients agreed to be videotaped. RESULTS: In seven patients myoclonus and, therefore the UHDRS motor score improved in a dose dependent manner. In three of these patients antidopaminergic medication could be reduced. CONCLUSION: In the rare subgroup of HD patients suffering from myoclonic hyperkinesia, valproic acid is a possible alternative treatment

PDRMIP: a precipitation driver and response model intercomparison project - protocol and preliminary results

PDRMIP investigates the role of various drivers of climate change for mean and extreme precipitation changes, based on multiple climate model output and energy budget analyses. As the global temperature increases with changing climate, precipitation rates and patterns are affected through a wide range of physical mechanisms. The globally averaged intensity of extreme precipitation also changes more rapidly than the globally averaged precipitation rate. While some aspects of the regional variation in precipitation predicted by climate models appear robust, there is still a large degree of inter-model differences unaccounted for. Individual drivers of climate change initially alter the energy budget of the atmosphere leading to distinct rapid adjustments involving changes in precipitation. Differences in how these rapid adjustment processes manifest themselves within models are likely to explain a large fraction of the present model spread and needs better quantifications to improve precipitation predictions. Here, we introduce the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where a set of idealized experiments designed to understand the role of different climate forcing mechanisms were performed by a large set of climate models. PDRMIP focuses on understanding how precipitation changes relating to rapid adjustments and slower responses to climate forcings are represented across models. Initial results show that rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers. The PDRMIP results are expected to dramatically improve our understanding of the causes of the present diversity in future climate projections