A multi-proxy approach for revealing recent climatic changes in the Russian Altai

For the first time we present a multi-proxy data set for the Russian Altai, consisting of Siberian larch tree-ring width (TRW), latewood density (MXD), δ13C and δ18O in cellulose chronologies obtained for the period 1779–2007 and cell wall thickness (CWT) for 1900–2008. All of these parameters agree well between each other in the high-frequency variability, while the low-frequency climate information shows systematic differences. The correlation analysis with temperature and precipitation data from the closest weather station and gridded data revealed that annual TRW, MXD, CWT, and δ13C data contain a strong summer temperature signal, while δ18O in cellulose represents a mixed summer and winter temperature and precipitation signal. The temperature and precipitation reconstructions from the Belukha ice core and Teletskoe lake sediments were used to investigate the correspondence of different independent proxies. Low frequency patterns in TRW and δ13C chronologies are consistent with temperature reconstructions from nearby Belukha ice core and Teletskoe lake sediments showing a pronounced warming trend in the last century. Their combination could be used for the regional temperature reconstruction. The long-term δ18O trend agrees with the precipitation reconstruction from the Teletskoe lake sediment indicating more humid conditions during the twentieth century. Therefore, these two proxies could be combined for the precipitation reconstruction.

A metabolomic approach to the metabolism of the areca nut alkaloids arecoline and arecaidine in the mouse

The areca alkaloids comprise arecoline, arecaidine, guvacoline, and guvacine. Approximately 600 million users of areca nut products, for example, betel quid chewers, are exposed to these alkaloids, principally arecoline and arecaidine. Metabolism of arecoline (20 mg/kg p.o. and i.p.) and arecaidine (20 mg/kg p.o. and i.p.) was investigated in the mouse using a metabolomic approach employing ultra-performance liquid chromatography-time-of-flight mass spectrometric analysis of urines. Eleven metabolites of arecoline were identified, including arecaidine, arecoline N-oxide, arecaidine N-oxide, N-methylnipecotic acid, N-methylnipecotylglycine, arecaidinylglycine, arecaidinylglycerol, arecaidine mercapturic acid, arecoline mercapturic acid, and arecoline N-oxide mercapturic acid, together with nine unidentified metabolites. Arecaidine shared six of these metabolites with arecoline. Unchanged arecoline comprised 0.3-0.4%, arecaidine 7.1-13.1%, arecoline N-oxide 7.4-19.0%, and N-methylnipecotic acid 13.5-30.3% of the dose excreted in 0-12 h urine after arecoline administration. Unchanged arecaidine comprised 15.1-23.0%, and N-methylnipecotic acid 14.8%-37.7% of the dose excreted in 0-12 h urine after arecaidine administration. The major metabolite of both arecoline and arecaidine, N-methylnipecotic acid, is a novel metabolite arising from carbon-carbon double-bond reduction. Another unusual metabolite found was the monoacylglyceride of arecaidine. What role, if any, that is played by these uncommon metabolites in the toxicology of arecoline and arecaidine is not known. However, the enhanced understanding of the metabolic transformation of arecoline and arecaidine should contribute to further research into the clinical toxicology of the areca alkaloids.

Multimodal approach in the use of clinical scoring, morphological MRI and biochemical T2-mapping and diffusion-weighted imaging in their ability to assess differences between cartilage repair tissue after microfracture therapy and matrix-associated autologous chondrocyte transplantation: a pilot study

OBJECTIVE: The aim of the present pilot study is to show initial results of a multimodal approach using clinical scoring, morphological magnetic resonance imaging (MRI) and biochemical T2-relaxation and diffusion-weighted imaging (DWI) in their ability to assess differences between cartilage repair tissue after microfracture therapy (MFX) and matrix-associated autologous chondrocyte transplantation (MACT). METHOD: Twenty patients were cross-sectionally evaluated at different post-operative intervals from 12 to 63 months after MFX and 12-59 months after MACT. The two groups were matched by age (MFX: 36.0+/-10.4 years; MACT: 35.1+/-7.7 years) and post-operative interval (MFX: 32.6+/-16.7 months; MACT: 31.7+/-18.3 months). After clinical evaluation using the Lysholm score, 3T-MRI was performed obtaining the MR observation of cartilage repair tissue (MOCART) score as well as T2-mapping and DWI for multi-parametric MRI. Quantitative T2-relaxation was achieved using a multi-echo spin-echo sequence; semi-quantitative diffusion-quotient (signal intensity without diffusion-weighting divided by signal intensity with diffusion weighting) was prepared by a partially balanced, steady-state gradient-echo pulse sequence. RESULTS: No differences in Lysholm (P=0.420) or MOCART (P=0.209) score were observed between MFX and MACT. T2-mapping showed lower T2 values after MFX compared to MACT (P=0.039). DWI distinguished between healthy cartilage and cartilage repair tissue in both procedures (MFX: P=0.001; MACT: P=0.007). Correlations were found between the Lysholm and the MOCART score (Pearson: 0.484; P=0.031), between the Lysholm score and DWI (Pearson:-0.557; P=0.011) and a trend between the Lysholm score and T2 (Person: 0.304; P=0.193). CONCLUSION: Using T2-mapping and DWI, additional information could be gained compared to clinical scoring or morphological MRI. In combination clinical, MR-morphological and MR-biochemical parameters can be seen as a promising multimodal tool in the follow-up of cartilage repair.