Find odd man out citric acid formic acid lactic acid nitric acid
Jaypeedigital site offers to our students a huge number of medical publications such as textbooks With user-friendly interface, Jaypeedigital provides extensive coverage in medicine, dentistry and Jaypeedigital platform presents an interesting and valuable collection of medical publications that All Rights Reserved.SEE VIDEO BY TOPIC: Что такое синтол? Синтольные качки мутанты. Перекачанные бодибилдеры, стероидные монстры
- US9790167B2 - Vinyl substituted fatty acids - Google Patents
- Frankland and Kolbe
- EP1072608A1 - Glutathione derivatives and dosage forms thereof - Google Patents
- Carboxylic acid
- Metabolic modulation predicts heart failure tests performance.
- Metabolic modulation predicts heart failure tests performance
US9790167B2 - Vinyl substituted fatty acids - Google Patents
Plos one , 20 Jun , 14 6 : e DOI: Metabolic Pathway Analysis MetPA used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway PPP.
Metabolite Set Enrichment Analysis MSEA found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance.
The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio. The prevalence of heart failure HF has increased over time in the aging population. In people older than 20, the incidence of HF has increased from 5.
Investigations into diagnosis of HF has revealed promising cardiopulmonary tests and biomarkers that allow better disease management following diagnosis[ 3 , 4 ]. However, the complex association of these HF tests with changes in the peripheral metabolism of compromised individuals is still under investigation and has failed to reveal the value of circulating metabolites as HF biomarkers[ 6 ].
Impaired cardiorespiratory fitness measured during cardiopulmonary exercise testing CPET is a hallmark manifestation of heart failure[ 7 ] and exercise training reduces all-cause mortality in patients with heart failure and reduced left ventricular ejection fraction HFrEF [ 8 ]. However, a potential disadvantage of the CPET and HFBio use is that the evaluation of their response alone is insufficient to indicate risk of heart disease nor is it enough to diagnose a heart problem[ 12 , 13 ].
It is now known that cardiac and peripheral metabolic abnormalities may contribute to the pathogenesis of heart failure[ 14 ]. Studies of the metabolic profile of HF patients have indicated a rich metabolic modulation that can be identified and used as putative biomarkers[ 15 — 17 ]. However, little is known about the relationship of cardiorespiratory fitness and global metabolic profile in HFrEF. Efforts to correlate clinical and metabolic data are still necessary to fully integrate metabolomics as a translational medicine apparatus[ 18 ].
We hypothesized that deep metabolomic and lipidomic phenotyping would reveal novel metabolic and lipid mediators of cardiorespiratory fitness in patients with HFrEF.
Although MRA does not imply causality for the HF performance, this analysis intends to reveal the metabolic changes underlying the complex HF pathology to maximize the relevance of the HF tests and its potential for HF outcome prognosis.
Written informed consent was obtained from all participants. The present analysis includes plasma samples collected from 49 patients at baseline prior to randomization, since we were interested in the baseline analysis only without the cofounding randomization for drugs and place groups of the original study.
Also, there were not health matched control group in the REDHART study, the reason for the absence of comparison with health subject in our analysis.
All patients underwent maximal aerobic exercise testing using a metabolic cart and a treadmill according to a conservative ramping treadmill protocol. Blood plasma lipids extraction was carried out using a biphasic solvent system of cold methanol, methyl tert -butyl ether MTBE , and water with some modifications[ 20 ].
Upon vortexing 20s the sample was centrifuged at 12, rpm for 2 min. Next, the mixture was centrifuged for 2min at 14, rcf. The supernatant was transferred to a polypropylene tube and subjected to drying in a cold trap. Negative and positive electrospray ionization ESI modes were applied with nitrogen serving as the desolvation gas and the collision gas. The mobile phases consisted of A water with 10 mM ammonium formate, 0.
Chromatography separation was achieved on a 30 m long, 0. Rtx-5Sil MS column 0. Lipidomic and metabolomic data were presented as peak heights normalized by mTIC, a form of sample normalization[ 21 ]. Using mTIC allows for the merger of the two databases.
Metabolic data were filtered to exclude detected prescribed drugs, and from the lipidomic data only phospholipids, with detected fatty acids composition, were used in the analysis. The final lipids and metabolites were used in the analysis. Search of published threshold references confirmed the clinical importance of the models.
Pathway enrichment analysis revealed the main metabolic pathways and associated diseases enriched using the set of metabolites predicting HF performance. A metabolic network was derived from the analysis confirming several metabolic dysfunctions related to HF described in the literature.
Covariate and cofounders bias was not taken in account in the MRA because the aim of the study was not focused in causal analysis, but rather in the lipid and metabolite associations with HF tests to reveal the underlying metabolic modulation. Therefore, unadjusted regression models were utilized in this study, and no control group was included. To avoid bias due to presence of outliers, subjects presenting extreme values in the HF tests were excluded for the specific model.
The number of observations and predictors in each model is depicted in Table 1. To enable comparison, the different models were standardized by mean centering of their coefficients, and the mean response of their dependent variables was estimated. Coefficient of Variation CV was calculated as the ratio of RMSE to mean response of the dependent variable with its result suggesting good model fit and allowing for intermodal comparison.
We also performed a statistical cross-validation to determine the predicted R 2. This procedure is executed by removing a data point from the dataset, calculating the regression equation, and then evaluating how well the model predicts the missing observation.
This is repeated for all data points in the dataset and a predicted R 2 is generated. The mean response are values of the CPET and HFBio calculated from the regression parameters and a given value of the predictors that best fit the model. RMSE is presented as an absolute measure of fit in the same unit as the mean response. Threshold references for test prediction of CHF outcome is presented as a measure of comparison of the mean response with peer reviewed publications.
To discover the metabolic pathways associated with HF, two methods of pathway enrichment analysis were performed. Coupled with this method, a pathway topological analysis with an out-degree centrality algorithm was used to measure the centrality of a metabolite in a metabolic pathway, estimating the mean importance of each matched metabolite impacting the pathway. The second method was a Metabolite Set Enrichment Analysis[ 23 ] that allows the incorporation into the analysis of pre-existing biological knowledge contained in metabolite-set libraries from studies of human metabolism.
The analysis facilitated hypothesis generation and aided in interpretation of the metabolic models. Metabolite Set Enrichment Analysis used a reference metabolome from metabolite-set libraries to calculate a background distribution, and determine if the matched metabolite set in the model is more enriched for certain metabolites compared to random chance. The study cohort included mostly African Americans with diabetes, hyperlipidemia, and hypertension Table 2 , characterizing this particular HF population for this single center study.
The metabolic modulation underlying the HF test performance of this particular cohort is the main finding in our study. The higher the value of the coefficient, the higher the slope of the plot line, indicating the sensitivity of changes of the predictor value to estimate the test performance.
The predictors of all HF tests performance are listed in Table 3 by order of the highest to the lowest coefficient absolute values. All of our models rendered a predicted R 2 higher than 0. The 73 predictors taken together give an overall view of the metabolic state of HF patients, since the tests encompass cardiovascular and respiratory physiology.
Standardized regression coefficient indicates the impact of one individual predictor over the specific test if all other predictors remain constant. Negative coefficient values are indicative of inverse correlation. When there are higher values of CE , CE Acylcarnitine C, hydroxyproline dipeptide, oxoproline,transhydroxyproline, and indoleacetate, as well as lower values of CE , LPC , 1-monoolein, propionic acid, xanthine, and phenylethylamine, the CPET test predicts poor performance.
Therefore, the metabolic modulation can be used to estimate the HF tests mean response as a threshold cut-off. To be able to compare models we used the coefficient of variance, where the model with the smaller CV has predicted values that are closer to the actual values. This results indicates that CPET can be reasonably explained by their metabolites predictors, while HFbio are weekly explained. Fig 3 shows that 13 pathways are significantly involved in prediction of HF test performance.
Aminoacyl-tRNA, amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, along with sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, as well as glutathione metabolism and pentose phosphate pathway were revealed as the more relevant pathways. The plot shows matched pathways according to the p-values from the pathway enrichment analysis and pathway impact values from pathway topology analysis.
The pathways with the lowest p-values and highest match status predictors present in the pathways are listed in the table along with their FDR correction and impact score.
To explore the similarity of other diseases metabolic dysfunction to HF metabolic modulation, a pathway enrichment analysis was utilized Fig 4. This analysis revealed 14 disorders statistically significant and with high impact that demonstrated similar metabolic perturbation to that observed by us for HF.
Most of these identified diseases were related to brain dysfunction, such as acute seizure disorders and epilepsy-like metabolic profiles indicating the compromise of brain function. Enzymes deficiencies were also detected associated to lactic acidosis. Peritoneal dialysis and early markers of myocardial injury were present with lower impact. These results indicate that the metabolic profile of HF patients is mostly similar to brain- like dysfunction, and renal and cardiac abnormalities.
The majority of the statistically significant enriched diseases are related to brain dysfunction. Lactic acidosis-related diseases were also found with high impact in the analysis. Predictors of HF tests performance, supported by pathway enrichment analysis, were also used to propose a metabolic network suggestive of the metabolic modulations associated with HF test performance Fig 5. The metabolic network was built linking relevant pathways revealed in the study, and the direction of pathways were suggested by metabolites elevation or decrease.
The metabolic changes affecting heart failure patients based in heart failure test performance includes glutathione anti-oxidative pathway, branched-chain amino acid BCAA biosynthesis, pentose cycle, tricarboxylic acid cycle TCA , fatty acid FA metabolism, sphingolipids and glycerophospholipids metabolism, and tryptophan metabolism.
Arrow represents predicted elevation or decrease variables in poor test performance. Only predictors with coefficients higher than 0. Some metabolites not detected in the analysis were included in the figure to complement the metabolic pathways. Direction of pathways are proposed based in the metabolic modulation found in the study. We chose to base our analyses focused in the metabolic modulations in poor test performance as an indication of HF poor prognosis.
Our results indicates an overall profile of oxidative stress, lactic acidosis, and metabolic syndrome, coupled with mitochondria dysfunction. There are signs of glutathione depletion, represented by decreased cysteine-glycine dipeptide and glutamate, and elevation of methionine, cysteine, glycine, and 5-oxoproline. The proposed oxidative stress could induce ribose elevation from the pentose phosphate pathway PPP and decreased xanthine and consequent elevation of uric acid in poor performance.
Glutamate catabolism is indicated by elevation of downstream products such as pyrrolecarboxilic acid and hydroxyproline dipeptide.
In our study, poor performance is associated with low levels of the branched-chain amino acids BCAAs leucine and valine, except elevation of isoleucine. Other amino acids modulations are elevated threonine and serine and decreased asparagine. Elevation of a compound similar to metabolites of BCAA catabolism, 2-hydroxyvaleric acid, was also detected.
We also detected consumption of 3-hydroxybutyric acid, a ketone body linked to BCAAs metabolism, and decreased tryptophan levels. It appears that tryptophan catabolism is leading to elevation of indoleacetate and indolelactate in an oxidative stressed environment. Pyruvate was also elevated in poor performance. The decreased citrate in TCA cycle suggest that anaplerotic reactions are present.
Therefore, the destiny of accumulated pyruvate could be responsible for malic acid elevation, but also responsible for elevation of lactate indicating lactic acidosis as a metabolic profile of poor HF test performance.
Frankland and Kolbe
A fourth bond links the carbon atom to a hydrogen H atom or to some other univalent combining group. The chief chemical characteristic of the carboxylic acids is their acidity. They are generally more acidic than other organic compounds containing hydroxyl groups but are generally weaker than the familiar mineral acids e.
No eBook available Pustak Mahal Amazon. Account Options Sign in. My library Help Advanced Book Search. Get print book.
EP1072608A1 - Glutathione derivatives and dosage forms thereof - Google Patents
Translate texts with the world's best machine translation technology, developed by the creators of Linguee. Look up words and phrases in comprehensive, reliable bilingual dictionaries and search through billions of online translations. Look up in Linguee Suggest as a translation of "acid oxidation" Copy. DeepL Translator Linguee. Open menu. Translator Translate texts with the world's best machine translation technology, developed by the creators of Linguee. Linguee Look up words and phrases in comprehensive, reliable bilingual dictionaries and search through billions of online translations. Blog Press Information Linguee Apps.
Metabolic Pathway Analysis MetPA used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway PPP. Metabolite Set Enrichment Analysis MSEA found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio.
Nice post! This is a very nice that I will definitively come back to more times this year! Thanks for informative post. Science Curriculum.
Metabolic modulation predicts heart failure tests performance.
The data comes from registration dossiers submitted to ECHA by the date indicated as last update. The Total Tonnage Band is compiled from all the dossiers with two exceptions; any tonnages claimed confidential and any quantity used as an intermediate to produce a different chemical. The Total Tonnage band published does not necessarily reflect the registered tonnage band s.
A Lactic acid done clear. B Acetic acid done clear. C Tartaric acid done clear. D Oxalic add done clear. A Nitric acid done clear. B Sulphuric acid done clear.
Metabolic modulation predicts heart failure tests performance