Hur man skiljer på typ 1 och typ 2 diabetes vid diagnos hos patienter över 16 år

Mål: Diabetes är en sjukdom som uppstår då bukspottkörteln inte kan producera tillräckliga mängder av insulin för att upprätthålla en fysiologisk nivå på blodsockret. Olika patofyisologiska mekanismer ligger bakom detta tillstånd och är beroende på typen av diabetes. För optimal vård av diabetes, är det viktigt att vid ett tidigt skede kunna utgöra vilken typ av diabetes en patient har insjuknat i. Metod: I denna studie undersökte vi vilka kliniska variabler är mest betydande för att skilja mellan typ 1 och typ 2 diabetes, vid tidpunkten av diagnos. Vi använder dessa variabler för att träna och validerar en CART maskininlärnigsmodell för att kunna skilja på typ 1 och typ 2 diabetes, speciellt i fall där det är oklart vilken subtyp patenten hör till. Resultat: Blodsockernivå, C-peptidnivå samt deras förhållande och BMI samt ålder vid insjunkning i diabetes visade sig vara de mest signifikanta kliniska variablerna. Vår modell klarade av att skilja på typ 1 och typ 2 diabetes med 91,8 % noggrannhet av testdatat som bestod av 1175 patienter. CART modellen är således en användbar modell för att differentiera diabetes typer vid tidpunkten av diagnos hos patienter över 16 år. (190 ord

Expedited evolution of soil bacteria exposed to organic contaminants

We tested two hypotheses for which support can be found in the literature. (1) the degradation of organic contaminants is faster in humus soil than in mineral soil and (2) the degradation of organic contaminants is faster in previously contaminated soil than in similar but previously non-contaminated soil

Fenton's reaction-based chemical oxidation in suboptimal conditions can lead to mobilization of oil hydrocarbons but also contribute to the total removal of volatile compounds

Fenton's reaction-based chemical oxidation is in principle a method that can be utilized for all organic fuel residues thus making it a potential all-purpose, multi-contaminant, in situ application for cases in which storage and distribution of different types of fuels have resulted in contamination of soil or groundwater. Since peroxide breakdown reactions are also expected to lead to a physical transport of the target compound, this secondary physical removal, or rebound concentrations related to it, is prone to be affected by the chemical properties of the target compound. Also, since soil conditions are seldom optimal for Fenton's reaction, the balance between chemical oxidation and transport may vary. In this study, it was found that, with a high enough hydrogen peroxide concentration (5 M), methyl tert-butyl ether-spiked groundwater could be treated even under suboptimal conditions for chemical mineralization. In these cases, volatilization was not only contributing to the total removal but also leading to rebound effects similar to those associated with air sparging techniques. Likewise for diesel, temporal transport from soil to the aqueous phase was found to lead to false positives that outweighed the actual remediation effect through chemical mineralization.Peer reviewe

Universal ligation-detection-reaction microarray applied for compost microbes

<p>Abstract</p> <p>Background</p> <p>Composting is one of the methods utilised in recycling organic communal waste. The composting process is dependent on aerobic microbial activity and proceeds through a succession of different phases each dominated by certain microorganisms. In this study, a ligation-detection-reaction (LDR) based microarray method was adapted for species-level detection of compost microbes characteristic of each stage of the composting process. LDR utilises the specificity of the ligase enzyme to covalently join two adjacently hybridised probes. A zip-oligo is attached to the 3'-end of one probe and fluorescent label to the 5'-end of the other probe. Upon ligation, the probes are combined in the same molecule and can be detected in a specific location on a universal microarray with complementary zip-oligos enabling equivalent hybridisation conditions for all probes. The method was applied to samples from Nordic composting facilities after testing and optimisation with fungal pure cultures and environmental clones.</p> <p>Results</p> <p>Probes targeted for fungi were able to detect 0.1 fmol of target ribosomal PCR product in an artificial reaction mixture containing 100 ng competing fungal ribosomal internal transcribed spacer (ITS) area or herring sperm DNA. The detection level was therefore approximately 0.04% of total DNA. Clone libraries were constructed from eight compost samples. The LDR microarray results were in concordance with the clone library sequencing results. In addition a control probe was used to monitor the per-spot hybridisation efficiency on the array.</p> <p>Conclusion</p> <p>This study demonstrates that the LDR microarray method is capable of sensitive and accurate species-level detection from a complex microbial community. The method can detect key species from compost samples, making it a basis for a tool for compost process monitoring in industrial facilities.</p

Lead (Pb) contamination alters richness and diversity of the fungal, but not the bacterial community in pine forest soil

Peer reviewe

Simulation of Microbial Response to Accidental Diesel Spills in Basins Containing Brackish Sea Water and Sediment

The brackish Baltic Sea is under diesel oil pollution risk due to heavy ship traffic. The situation is exasperated by densely distributed marinas and a vigorous although seasonal recreational boating. The seasonality and physical environmental variations hamper the monitoring of microbial communities in response to diesel oil spills. Hence, an 8-week simulation experiment was established in metal basins (containing 265 L sea water and 18 kg quartz sand or natural shore sand as the littoral sediment) to study the effect of accidental diesel oil spills on microbial communities. Our results demonstrated that microbial communities in the surface water responded to diesel oil contamination, whereas those in the littoral sediment did not, indicating that diesel oil degradation mainly happened in the water. Diesel oil decreased the abundance of bacteria and fungi, but increased bacterial diversity in the water. Time was the predominant driver of microbial succession, attributable to the adaption strategies of microbes. Bacteria were more sensitive to diesel oil contamination than fungi and archaea. Diesel oil increased relative abundances of bacterial phyla, Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Flavobacteriia and Cytophagia, and fungal phylum Ascomycota in the surface water. Overall, this study improves the understanding of the immediate ecological impact of accidental diesel oil contamination, providing insights into risk management at the coastal area.Peer reviewe