Folic acid retention evaluation in preparations with wheat flour and corn submitted to different cooking methods by HPLC/DAD.

Folic acid content was evaluated in food preparations containing wheat and corn flour submitted to baking, deep-frying, and steaming. Commercially fortified flours showed the absence of folic acid. Flours with laboratory folic acid fortification showed 487 and 474 µg of folic acid in 100 g of wheat and corn flours, respectively. In the corn flour preparations, the cake had the highest retention (99%) when compared to couscous (97%). Besides, the cake showed higher retention when compared to the wheat flour preparations due to the interactions of the folic acid with the hydrophobic amino acids of the Zein, a protein found in corn. In wheat flour preparations, vitamin retention was 87%, 80% and 57% in bread, cake, and White sauce respectively. These findings relate to the change of the physicochemical properties of food components that occurs during mixing and cooking of the ingredients.

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor.

BACKGROUND: Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods' restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement. RESULTS: The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions. CONCLUSION: Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors' training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.

Impact of local circumstances on outcome of renal casualties in major disasters.

BACKGROUND: In the aftermath of earthquakes, the cumulative incidence of crush-induced acute kidney injury (AKI) is difficult to predict. Insight into factors determining this risk is indispensable to allow adequate logistical planning, which is a prerogative for success in disaster management. METHODS: Data of 88 crush-related AKI patients in the aftermath of the Kashmir earthquake were collected and outcome measures were analysed. Then the findings were compared with the data of 596 crush-related AKI patients of the Marmara earthquake. RESULTS: The earthquake in Kashmir occurred in a rural area with lack of medical facilities and difficult transportation conditions while the earthquake in Marmara occurred in an urban area with more efficient transport possibilities. In Kashmir we reported fewer patients with treated AKI (1.2 AKI per 1000 deaths, 1.3 AKI per 1000 victims) than in Marmara (34.1 AKI per 1000 deaths; P < 0.001, 13.6 AKI per 1000 victims; P < 0.001). Time lag between earthquake and admission to hospitals was longer in Kashmir (5.8 +/- 5.8 days) than in Marmara (3.5 +/- 3.7 days; P < 0.001). The frequencies of fasciotomies (P < 0.001), amputations (P < 0.001) and dialysis (P = 0.005) were lower in Kashmir, than in Marmara AKI patients. CONCLUSIONS: The cumulative incidence of treated AKI related to number of deaths or victims might differ substantially among earthquakes. Many factors may affect the frequency of AKI: hampered rescue and transport possibilities; destroyed medical facilities on the spot; availability or not of sophisticated therapeutic possibilities and structure of the buildings might all have impacted on different cumulative incidence between Kashmir and Marmara.