Background. Colorectal carcinoma is the third cause of cancer deaths in the world. For diagnosis, invasive methods like colonoscopy and sigmoidoscopy are used, and noninvasive screening tests are not very accurate. We decided to study the potential of 1HNMR spectroscopy with metabolomics and chemometrics as a preliminary noninvasive test. We obtained a distinguishing pattern of metabolites and metabolic pathways between colon cancer patient and normal. Methods. Sera were obtained from confirmed colon cancer patients and the same number of healthy controls. Samples were sent for 1HNMR spectroscopy and analysis was carried out Chenomex and MATLAB software. Metabolites were identified using Human Metabolic Data Base (HDMB) and the main metabolic cycles were identified using Metaboanalyst software. Results. 15 metabolites were identified such as pyridoxine, orotidine, and taurocholic acid. Main metabolic cycles involved were the bile acid biosynthesis, vitamin B6 metabolism, methane metabolism, and glutathione metabolism. Discussion. The main detected metabolic cycles were also reported earlier in different cancers. Our observations corroborated earlier studies that suggest the importance of lowering serum LCA/DCA and increasing vitamin B6 intake to help prevent colon cancer. This work can be looked upon as a preliminary step in using 1HNMR analysis as a screening test before invasive procedures.

Malaria is responsible for estimated 584,000 deaths in 2013. Researchers are working on new drugs and medicinal herbs due to drug resistance that is a major problem facing them; the search is on for new medicinal herbs. Cinnamon is the bark of a tree with reported antiparasitic effects. Metabonomics is the simultaneous study of all the metabolites in biological fluids, cells, and tissues detected by high throughput technology. It was decided to determine the mechanism of the effect of aqueous extract of cinnamon on the metabolome of Plasmodium falciparum in vitro using 1HNMR spectroscopy. Prepared aqueous extract of cinnamon was added to a culture of Plasmodium falciparum 3D7 and its 50% inhibitory concentration determined, and, after collection, their metabolites were extracted and 1HNMR spectroscopy by NOESY method was done. The spectra were analyzed by chemometric methods. The differentiating metabolites were identified using Human Metabolome Database and the metabolic cycles identified by Metaboanalyst. 50% inhibitory concentration of cinnamon on Plasmodium falciparum was 1.25 mg/mL with p < 0.001. The metabolites were identified as succinic acid, glutathione, L-aspartic acid, beta-alanine, and 2-methylbutyryl glycine. The main metabolic cycles detected were alanine and aspartame and glutamate pathway and pantothenate and coenzyme A biosynthesis and lysine biosynthesis and glutathione metabolism, which are all important as drug targets.

Intravenous laser blood irradiation (ILBI) is widely applied in the treatment of different pathologies including diabetes mellitus. The aim of this study is to evaluate the effects of ILBI on the metabolites of blood in diabetic type 2 patients using metabolomics. We compared blood samples of nine diabetic type 2 patients, using metabolomics, before and after ILBI with blue light laser. The results showed significant decrease in glucose, glucose 6 phosphate, dehydroascorbic acid, R-3-hydroxybutyric acid, L-histidine, and L-alanine and significant increase in L-arginine level in blood and blood sugar in the patients have reduced significantly (p < 0.05). This study clearly demonstrated a significant positive effect of ILBI on metabolites of blood in diabetic type 2 patients. These findings support the therapeutic potential of ILBI in diabetic patients.

Background: The modern life style despite its comforts has made cancer a leading public health problem. As chemotherapeutics has many side effects including resistance, recent attention has focused on plants which may provide a good opportunity for complementary cancer treatment. Aloe vera has been described as a wonder plant due to its many advantages. It has been reported to have anticancer properties. Metabolomics is the measurement and analysis of metabolites in a biological sample by the use of throughput technology like

Vaccines require a period of at least three months for clinical trials, hence a method that can identify elicitation of immune response a few days after the first dose is a necessity. Evolutionary variable selections are modeling approaches for proper manipulation of available data which were used to set up an animal model for classification of time dependent 1HNMR metabolomic profiles and pattern recognition of fluctuations of metabolites in two groups of male rabbits. One group of rabbits was immunized with human red blood cells and the other used as control. Blood was obtained every 48 h from each rabbit for a period of six weeks and the serum monitored for antibodies and metabolites by 1HNMR spectra. Evaluation of data was carried out using orthogonal signal correction followed by principal component analysis and partial least square. A neural network was also set up to predict immunization profiles. A distinct separation in patterns of significant metabolites was obtained between the two groups, just a few days after the first and the second dose. These metabolites were used as targets of neural networks where each sample was used as test, validation and training and their quantitative influence predicted by regression. This model could be used for prediction of immunization in rabbits a few days after the first dose with 96% accuracy. Similar animals and human vaccine trials would assist greatly in reaching early conclusions in advance of the usual two month immunization schedule; resulting in an appreciable saving of cost and time.