FGF23-klotho axis as predictive factors of fractures in type 2 diabetics with early chronic kidney disease

Background: The aim of our study was to evaluate the relevance of FGF23-klotho axis in the predisposition for bone fractures in type 2 diabetic patients with early chronic kidney disease. Methods: In a prospective study we included 126 type 2 diabetic patients with CKD stages 2-3 (from 2010 to 2017). We used descriptive statistics, ANOVA and chi-square test. Our population was divided into two groups according to the occurrence of a bone fracture event or not, and the groups were compared considering several biological and laboratorial parameters. We employed a multiple regression model to identify risk factors for bone fracture events and hazard ratios (HR) were calculated using a backward stepwise likelihood ratio (LR) Cox regression. Results: Patients with a fracture event displayed higher levels of FGF-23, Phosphorus, PTH, TNF-alpha, OxLDL, HOMA-IR, calcium x phosphorus product and ACR and lower levels of Osteocalcin, alpha-Klotho, 25(OH)D3 and eGFR compared with patients without a fracture event (p < 0.001). The number of patients with a fracture event was higher than expected within inclining CKD stages (chi 2, p = 0.06). The occurrence of fracture and the levels of TNF-alpha, klotho, 25(OH)D3 and OxLDL were found to predict patient entry into RRT (p < 0.05). Age, osteocalcin, alpha-Klotho and FGF-23 independently influenced the occurrence of bone fracture (p < 0.05). Conclusions: alpha-Klotho and FGF-23 levels may have a good clinical use as biomarkers to predict the occurrence of fracture events. (C) 2019 Elsevier Inc. All rights reserved.info:eu-repo/semantics/publishedVersio

Asymptotology of Chemical Reaction Networks

The concept of the limiting step is extended to the asymptotology of multiscale reaction networks. Complete theory for linear networks with well separated reaction rate constants is developed. We present algorithms for explicit approximations of eigenvalues and eigenvectors of kinetic matrix. Accuracy of estimates is proven. Performance of the algorithms is demonstrated on simple examples. Application of algorithms to nonlinear systems is discussed.Comment: 23 pages, 8 figures, 84 refs, Corrected Journal Versio

Le FORUM, Vol. 39 No. 4

https://digitalcommons.library.umaine.edu/francoamericain_forum/1047/thumbnail.jp

Stereoselective ketone rearrangements with hypervalent iodine reagents

The first stereoselective version of an iodine(III)-mediated rearrangement of arylketones in the presence of orthoesters is described. The reaction products, α-arylated esters, are very useful intermediates in the synthesis of bioactive compounds such as ibuprofen. With chiral lactic acid-based iodine(III) reagents product selectivities of up to 73 % ee have been achieved

Le FORUM, Vol. 39 No. 2

https://digitalcommons.library.umaine.edu/francoamericain_forum/1045/thumbnail.jp

Comparing hydrological frameworks for simulating crop biomass, water and nitrogen dynamics in a tile drained soybean-corn system: Cascade vs computational approach

Biophysical agricultural models are needed for assessing science-based mitigation options to improve the efficiency and sustainability of agricultural cropping systems. It is crucial that they can accurately simulate soil hydrology and nutrient flows which strongly influence crop growth, biogeochemical processes and water quality. The purpose of this study was to compare the performance of the DeNitrification DeComposition model (DNDC), which utilizes simplified hydrologic processes, to a more comprehensive water flow model, the Root Zone Water Quality Model (RZWQM2), to determine which processes are sufficient for simulating water and nitrogen dynamics and recommend improvements. Both models were calibrated and validated for simulating soil hydrology, nitrogen loss to tile drains and crop biomass using detailed observations from a corn (Zea mays L.) -soybean (Glycine max (L.) Merr.) rotation in Iowa, with and without cover crops. DNDC performed adequately across a wide range of metrics in comparison to a more hydrologically complex model. Soybean and corn yield, and corn biomass over the growing season were well simulated by both models (NRMSE < 25%). Soybean yields were also very well simulated by both models (NRMSE < 20%); however, soybean biomass was over-predicted by RZWQM2 in the validation treatments. The magnitude of winter rye biomass and N uptake was well simulated but the timing of growth initiation in the spring was inaccurate at times. The annual and monthly estimation of tile flow and nitrogen loss to tiles drains were well simulated by both models; however, RZWQM2 performed better for simulating soil water content, and the dynamics of daily water flow to tile drains (DNDC: NSE −0.32 to 0.24; RZWQM2: NSE 0.35–0.69). DNDC overestimated soil water content near the soil surface and underestimated it in the deeper profile. We recommend that developments be carried out for DNDC to include improved root density and penetration functions, a heterogeneous and deeper soil profile, a fluctuating water table and mechanistic tile drainage. However, the inclusion of computationally intensive processes needs to be assessed in context to improved accuracy weighed against the model’s broad applicability. Keywords: DNDC, RZWQM2, Tile drain, Soil hydrology, Water quality, Crop biomas