Validation of a novel extraction method for studying hexahydro-1,3,5-trinitro-1,3,5 triazine (RDX) biodegradation by ruminal microbiota

A simple, fast liquid-liquid extraction method was developed for studying hexahydro-1,3,5-trinitro-1,3,5 triazine (RDX) biodegradation using small sample volumes. The method was tested in vitro with anaerobic incubations of RDX with whole rumen fluid (WRF) and a commercial S. acetigenes strain in methanogenic media for RDX. Additionally, validation experiments were conducted in deionized water in order to show applicability towards various aqueous matrices. Conditions for extraction were as follows: 300 μL of sample were mixed with an equal volume of a 0.34 M ammonium hydroxide solution to reach a basic pH, extracted with a hexane/ethyl acetate 1:1 (v/v) solution (1 mL) and shaken vigorously for 10 secs. The resulting organic phase was transferred, then dried under a constant flow of N₂ and reconstituted with acetonitrile (300μL) for HPLC- UV and LC-MS/MS analysis. Percent recovery values were obtained (83-101%) in all matrices for RDX. In WRF (n =3 animals), RDX degradation was observed with almost 100% elimination of RDX after 4 h. The five nitroso and ring cleavage metabolites (were observed by mass spectrometry. Liquid cultures of S. acetigenes did not show significant RDX biodegradation activity. Deionized water extractions indicated acceptable recoveries with low variability, suggesting suitability of the method for aqueous matrices. Overall, the new method demonstrated acceptable efficiency and reproducibility across three matrices, providing an advantageous alternative for studies where complex matrices and small volume samples are in use.Keywords: HPLC-UV, Biodegradation, RDX, Organic extraction, Ovine ruminal microbiot

Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and national survey

BACKGROUND: Administrative databases are widely available and have been extensively used to provide estimates of chronic disease prevalence for the purpose of surveillance of both geographical and temporal trends. There are, however, other sources of data available, such as medical records from primary care and national surveys. In this paper we compare disease prevalence estimates obtained from these three different data sources. METHODS: Data from general practitioners (GP) and administrative transactions for health services were collected from five Italian regions (Veneto, Emilia Romagna, Tuscany, Marche and Sicily) belonging to all the three macroareas of the country (North, Center, South). Crude prevalence estimates were calculated by data source and region for diabetes, ischaemic heart disease, heart failure and chronic obstructive pulmonary disease (COPD). For diabetes and COPD, prevalence estimates were also obtained from a national health survey. When necessary, estimates were adjusted for completeness of data ascertainment. RESULTS: Crude prevalence estimates of diabetes in administrative databases (range: from 4.8% to 7.1%) were lower than corresponding GP (6.2%-8.5%) and survey-based estimates (5.1%-7.5%). Geographical trends were similar in the three sources and estimates based on treatment were the same, while estimates adjusted for completeness of ascertainment (6.1%-8.8%) were slightly higher. For ischaemic heart disease administrative and GP data sources were fairly consistent, with prevalence ranging from 3.7% to 4.7% and from 3.3% to 4.9%, respectively. In the case of heart failure administrative estimates were consistently higher than GPs' estimates in all five regions, the highest difference being 1.4% vs 1.1%. For COPD the estimates from administrative data, ranging from 3.1% to 5.2%, fell into the confidence interval of the Survey estimates in four regions, but failed to detect the higher prevalence in the most Southern region (4.0% in administrative data vs 6.8% in survey data). The prevalence estimates for COPD from GP data were consistently higher than the corresponding estimates from the other two sources. CONCLUSION: This study supports the use of data from Italian administrative databases to estimate geographic differences in population prevalence of ischaemic heart disease, treated diabetes, diabetes mellitus and heart failure. The algorithm for COPD used in this study requires further refinement

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio