Diabetic nephropathy: early markers for monitoring and prevention

Background: Type 2 diabetes, with its complications is perpetually on the rise more so in India .Diabetic Nephropathy progresses silently, and manifests at a stage where, patient can be offered only renal replacement. This study was undertaken to detect early markers of Diabetic Nephropathy. Aims and objective of the study was to study early nephropathy by UACR (urinary albumin/creatinine ratio), RFT (renal function test) and e-GFR in Type 2 diabetic patients of more than 2 years duration, with and without hypertension.Methods: A hospital based cross-sectional observational study, of 100 patients, 18-60 years of age, of type 2 Diabetes of 2 year duration and above, of which 50 were only diabetic and 50 had diabetes and hypertension. Patients who had an established renal disease were excluded from study.Results: Our study of 100 patients, 18-60 years of age, had 23 male and 77 female patients. Maximum patients were in age group 41-50 years, and 52% had diabetes of 2-4 years duration. Of the renal parameters studied, BUN was normal in 72% and S. Creatinine normal in 67%. UACR was normal in only 38%, and e-GFR was normal in 49%.Conclusions: In our study age and sex, duration of Diabetes and HbA1c did not have any bearing on renal parameters. UACR followed by e-GFR, were deranged early. UACR was more deranged in diabetics with hypertension.

Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy.

In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

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Not AvailableThis research work was carried out to compare the various physicochemical parameters of two species, camel and buffalo. Camel milk samples were collected at National Research Centre on Camel, Bikaner and buffaloes milk samples were collected from the surroundings villages of Bikaner. After collection milk samples were brought to the laboratory of NRCC Bikaner and they were analyzed for fat, SNF (Solid Not Fat), protein, lactose, total ash and pH using milk analyzer (Lactoscan). Camel milk had 2.71±0.11 fat, 6.91±0.03 SNF, 2.23±0.02 protein, 3.86±0.02 lactose, 0.79±0.004 total ash and 6.95±0.01 pH while buffalo milk had 8.71±0.82 fat, 8.44±0.19 SNF, 4.11±0.02 protein, 4.46±0.15 lactose, 0.98±0.05 total ash and 7.59±0.02 pH. Fat, SNF, protein and pH of buffalo milk was significantly (P<0.001) higher than camel milk. Lactose and total ash in buffalo milk was also higher than camel milk but at P<0.05 level. So it can be concluded that all the studied parameters were high in buffalo milk than camel milkNot Availabl

Identification of small molecules using accurate mass MS/MS search

Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed

Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy

Abstract In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important