Moringa oleifera has been used in traditional medicine to treat diabetes. However, few studies have been conducted to relate its antidiabetic properties to proteins. In this study, a leaf protein isolate was obtained from M. oleifera leaves, named Mo-LPI, and the hypoglycemic and antioxidant effects on alloxan-induced diabetic mice were assessed. Mo-LPI was obtained by aqueous extraction, ammonium sulphate precipitation and dialysis. The electrophoresis profile and proteolytic hydrolysis confirmed its protein nature. Mo-LPI showed hemagglutinating activity, cross-reaction with anti-insulin antibodies and precipitation after zinc addition. Single-dose intraperitoneal (i.p.) administration of Mo-LPI (500 mg/kg·bw) reduced the blood glucose level (reductions of 34.3%, 60.9% and 66.4% after 1, 3 and 5 h, respectively). The effect of Mo-LPI was also evidenced in the repeated dose test with a 56.2% reduction in the blood glucose level on the 7th day after i.p. administration. Mo-LPI did not stimulate insulin secretion in diabetic mice. Mo-LPI was also effective in reducing the oxidative stress in diabetic mice by a decrease in malondialdehyde level and increase in catalase activity. Mo-LPI (2500 mg/kg·bw) did not cause acute toxicity to mice. Mo-LPI is a promising alternative or complementary agent to treat diabetes.
1H-MRSI enables a simultaneous acquisition of MR-spectra from multiple spatial locations inside the brain. While 1H-MRSI is increasingly used in the human brain, its implementation in preclinical setting is limited because of the smaller size of rodent brain. At UHF for humans, 1H-FID-MRSI acquisitions are increasingly used (T2 and J-evolution minimization, increased SNR). We present the first implementation of fast 1H-FID-MRSI in the rat brain at 14.1T and exploit its potential for an increased brain coverage, reliable and accurate quantification results and metabolic maps. Our results set the grounds for a wider application of 1H-FID-MRSI in the preclinical setting.
1H-MRSI is highly challenging and the constant appetite for higher spatial resolution leads to increased search for post-processing methods aiming to reduce the noise variance in 1H-MRSI. The aim of the present study was to implement and test the feasibility of two noise-reduction techniques on preclinical 14.1T fast 1H-FID-MRSI datasets: the MP-PCA based denoising and the low-rank TGV reconstruction. Our results are promising showing an enormous potential of the two noise-reduction techniques towards novel and fast MRSI developments. Further studies will be performed to evaluate if the “apparent” increase in spectral SNR translates in true lower uncertainty in metabolite concentrations.
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