Antihypertensive drugs in blood donations in Serbia - Observational study.

INTRODUCTION: Most of the blood banks worldwide do not defer donors for their medication, with the exception of the teratogenic and platelet aggregation-inhibiting drugs use. In Serbia, where around 245.000 units of blood are collected each year, it is not common practice to consider the medication of potential blood donors. Therefore, the aim of this study was to quantify the presence of antihypertensive drugs in blood samples collected from blood donors treated for hypertension and to raise the issue of the recipient risks posed by drug residues in blood products. METHODS: Serum samples were obtained from 450 volunteer blood donors collected during the year 2017 who reported the use of antihypertensive drugs. All blood donors were required to interrupt regular antihypertensive therapy for 24 h before blood donation and LCMS determination of antihypertensive drugs was performed. RESULTS: Beta blockers were detected in 81 out of 203 samples which tested positive for the presence of antihypertensive drugs. Concentrations above the limit of quantification were determined in 58% of samples positive on beta blockers, containing metoprolol and bisoprolol in amounts sufficient to produce a therapeutic effect in the recipient. CONCLUSION: Therefore, the obtained results suggested that the safety of blood donation from individuals with treated hypertension should not be neglected. A solution for this problem might be the establishment of a standard LCMS screening procedure as a tool for testing the blood of donors taking drugs.

Hemodialysis Clinic Social Networks, Sex Differences, and Renal Transplantation.

This study describes patient social networks within a new hemodialysis clinic and models the association between social network participation and kidney transplantation. Survey and observational data collected between August 2012 and February 2015 were used to observe the formation of a social network of 46 hemodialysis patients in a newly opened clinic. Thirty-two (70%) patients formed a social network, discussing health (59%) and transplantation (44%) with other patients. While transplant-eligible women participated in the network less often than men (56% vs. 90%, p = 0.02), women who participated discussed their health more often than men (90% vs. 45.5%, p = 0.02). Patients in the social network completed a median of two steps toward transplantation compared with a median of 0 for socially isolated patients (p = 0.003). Patients also completed more steps if network members were closely connected (ß = 2.23, 95% confidence interval [CI] 0.16-4.29, p = 0.03) and if network members themselves completed more steps (ß = 2.84, 95% CI 0.11-5.57, p = 0.04). The hemodialysis clinic patient social network had a net positive effect on completion of transplant steps, and patients who interacted with each other completed a similar number of steps.

A search for interaction among combinations of drugs of abuse and the use of isobolographic analysis.

WHAT IS KNOWN AND OBJECTIVE: Individuals who abuse drugs usually use more than one substance. Toxic consequences of single and multi-drug use are well documented in the Treatment Episodes Data Set that lists drug combinations that result in hospital admissions. Using this list as a guide, we focused our attention on combinations that result in the most hospital admissions and searched the PubMed database with the objective of determining the number of such publications and, in particular, those that used the term synergism in their titles or abstracts. COMMENT: Using the search criteria produced an extensive list of published articles. However, a further intersection of the search terms with the term isobole revealed a surprisingly small number of literature reports. WHAT IS NEW AND CONCLUSION: Because the method of isoboles is the most common quantitative method for distinguishing between drug synergism and simple additivity, the small number of investigations that actually employed this quantification suggests that the term synergism is not properly documented in describing the toxicity among abused substances. The possible reasons for this lack of quantification may be related to a misunderstanding of the modelling equations. To help rectify this possible hurdle to understanding and clinical utility, the theory and modelling are discussed here.

The mathematical analysis of the heart rate and blood lactate curves during incremental exercise testing.

This paper describes a new mathematical approach for the analysis of HR (heart rate) and BL (blood lactate) curves during incremental exercise testing using a HR/BL curve and its derivatives, taking into account the native shape of all curves, without any linear approximation. Using this approach the results indicate the appearance of three characteristic points (A, B and C) on the HR/BL curve. The point A on the HR/BL curve which is the value that corresponds to the load (12.73 ± 0.46 km h-1) at which BL starts to increase above the resting levels (0.9 ± 0.06 mM), and is analogous to Lactate Turn Point 1 (LTP1). The point C on the HR/BL curve which corresponds to a BL of approximately 4mM, and is analogous to LTP2. The point B on the HR/BL curve, which corresponds to the load (16.32 ± 0.49 km h-1) at which the moderate increase turns into a more pronounced increase in BL. This point has not been previously recognized in literature. We speculate this point represents attenuation of left ventricular ejection fraction (LVEF) increase, accompanied by the decrease in diastolic time duration during incremental exercise testing. Proposed mathematical approach allows precise determination of lactate turnpoints during incremental exercise testing.

Mathematical analysis of the heart rate performance curve during incremental exercise testing.

In this study we performed laboratory treadmill protocols of increasing load. Heart rate was continuously recorded and blood lactate concentration was measured for determination of lactate threshold by means of LTD-max and LT4.0 methods.Our results indicate that the shape of heart rate performance curve (HRPC) during incremental testing depends on the applied exercise protocol (change of initial speed and the step of running speed increase, with the constant stage duration). Depending on the applied protocol, the HRPC can be described by linear, polynomial (S-shaped), and exponential mathematical expression.We presented mathematical procedure for estimation of heart rate threshold points at the level of LTD-max and LT4.0, by means of exponential curve and its relative deflection from the initial trend line (tangent line to exponential curve at the point of starting heart rate). The relative deflection of exponential curve from the initial trend line at the level of LTD-max and/or LT4.0 can be defined, based on the slope of the initial trend line. Using originally developed software that allows mathematical analysis of heart rate-load relation, LTD-max and/or LT4.0 can be estimated without direct measurement of blood lactate concentration.

Retrospective estimation of the concentration of 241Pu in air sampled at a Belgrade site following the Chernobyl accident.

The surface air activity concentration of 241Pu for the period May 01-15, 1986, for a monitoring site in Vinca, Belgrade was retrospectively estimated. The results were obtained by re-measurement of plutonium fraction alpha-spectrometric sources, with 236Pu as a tracer, used almost 13 years earlier for the determination of (239,240)Pu concentration in surface air immediately after the Chernobyl accident. The estimated 241Pu concentration, based on the 241Am in-growth method for air samples, ranged from 240 to 7800 microBq/m3. The average activity ratio 241Pu/(239,240)Pu originating from the Chernobyl accident at Belgrade site was approximately 100 at the collection time.

Exploring bias in the Protein Data Bank using contrast classifiers.

In this study we analyzed the bias existing in the Protein Data Bank (PDB) using the novel contrast classifier approach. We trained an ensemble of neural network classifiers, called a contrast classifier, to learn the distributional differences between non-redundant sequence subsets of PDB and SWISS-PROT. Assuming that SWISS-PROT is a representative of the sequence diversity in nature while the PDB is a biased sample, output of the contrast classifier can be used to measure whether the properties of a given sequence or its region are underrepresented in PDB. We applied the contrast classifier to SWISS-PROT sequences to analyze the bias in PDB towards different functional protein properties. The results showed that transmembrane, signal, disordered, and low complexity regions are significantly underrepresented in PDB, while disulfide bonds, metal binding sites, and sites involved in enzyme activity are overrepresented. Additionally, hydroxylation and phosphorylation posttranslational modification sites were found to be underrepresented while acetylation sites were significantly overrepresented. These results suggest the potential usefulness of contrast classifiers in the selection of target proteins for structural characterization experiments.

Intrinsically disordered protein.

Proteins can exist in a trinity of structures: the ordered state, the molten globule, and the random coil. The five following examples suggest that native protein structure can correspond to any of the three states (not just the ordered state) and that protein function can arise from any of the three states and their transitions. (1) In a process that likely mimics infection, fd phage converts from the ordered into the disordered molten globular state. (2) Nucleosome hyperacetylation is crucial to DNA replication and transcription; this chemical modification greatly increases the net negative charge of the nucleosome core particle. We propose that the increased charge imbalance promotes its conversion to a much less rigid form. (3) Clusterin contains an ordered domain and also a native molten globular region. The molten globular domain likely functions as a proteinaceous detergent for cell remodeling and removal of apoptotic debris. (4) In a critical signaling event, a helix in calcineurin becomes bound and surrounded by calmodulin, thereby turning on calcineurin's serine/threonine phosphatase activity. Locating the calcineurin helix within a region of disorder is essential for enabling calmodulin to surround its target upon binding. (5) Calsequestrin regulates calcium levels in the sarcoplasmic reticulum by binding approximately 50 ions/molecule. Disordered polyanion tails at the carboxy terminus bind many of these calcium ions, perhaps without adopting a unique structure. In addition to these examples, we will discuss 16 more proteins with native disorder. These disordered regions include molecular recognition domains, protein folding inhibitors, flexible linkers, entropic springs, entropic clocks, and entropic bristles. Motivated by such examples of intrinsic disorder, we are studying the relationships between amino acid sequence and order/disorder, and from this information we are predicting intrinsic order/disorder from amino acid sequence. The sequence-structure relationships indicate that disorder is an encoded property, and the predictions strongly suggest that proteins in nature are much richer in intrinsic disorder than are those in the Protein Data Bank. Recent predictions on 29 genomes indicate that proteins from eucaryotes apparently have more intrinsic disorder than those from either bacteria or archaea, with typically > 30% of eucaryotic proteins having disordered regions of length > or = 50 consecutive residues.

Sequence complexity of disordered protein.

Intrinsic disorder refers to segments or to whole proteins that fail to self-fold into fixed 3D structure, with such disorder sometimes existing in the native state. Here we report data on the relationships among intrinsic disorder, sequence complexity as measured by Shannon's entropy, and amino acid composition. Intrinsic disorder identified in protein crystal structures, and by nuclear magnetic resonance, circular dichroism, and prediction from amino acid sequence, all exhibit similar complexity distributions that are shifted to lower values compared to, but significantly overlapping with, the distribution for ordered proteins. Compared to sequences from ordered proteins, these variously characterized intrinsically disordered segments and proteins, and also a collection of low-complexity sequences, typically have obviously higher levels of protein-specific subsets of the following amino acids: R, K, E, P, and S, and lower levels of subsets of the following: C, W, Y, I, and V. The Swiss Protein database of sequences exhibits significantly higher amounts of both low-complexity and predicted-to-be-disordered segments as compared to a non-redundant set of sequences from the Protein Data Bank, providing additional data that nature is richer in disordered and low-complexity segments compared to the commonness of these features in the set of structurally characterized proteins.

Intrinsic protein disorder in complete genomes.

Intrinsic protein disorder refers to segments or to whole proteins that fail to fold completely on their own. Here we predicted disorder on protein sequences from 34 genomes, including 22 bacteria, 7 archaea, and 5 eucaryotes. Predicted disordered segments > or = 50, > or = 40, and > or = 30 in length were determined as well as proteins estimated to be wholly disordered. The five eucaryotes were separated from bacteria and archaea by having the highest percentages of sequences predicted to have disordered segments > or = 50 in length: from 25% for Plasmodium to 41% for Drosophila. Estimates of wholly disordered proteins in the bacteria ranged from 1% to 8%, averaging to 3 +/- 2%, estimates in various archaea ranged from 2 to 11%, plus an apparently anomalous 18%, averaging to 7 +/- 5% that drops to 5 +/- 3% if the high value is discarded. Estimates in the 5 eucarya ranged from 3 to 17%. The putative wholly disordered proteins were often ribosomal proteins, but in addition about equal numbers were of known and unknown function. Overall, intrinsic disorder appears to be a common, with eucaryotes perhaps having a higher percentage of native disorder than archaea or bacteria.

Comparing predictors of disordered protein.

More than 6,000 amino acid sequence attributes were ranked by their conditional probabilities for indicating ordered or disordered protein structure. The top 10 each from several different groups of attributes were merged with still other attributes and then subjected to selection by logistic regression. Evidently, the determination of order or disorder results from the interplay among several attributes, such as average Coordination Number, aromatic content and the numbers of non-polar amino acids, all of which favor the ordered state, and others like Net Charge, Flexibility Index, and the presence of certain polar amino acids, all of which favor disorder. The top 12 selected attributes were used as inputs for artificial neural network (ANN) predictors. Five predictors were developed, compared with each other, and with previous work. The best of these shows substantially improved generalization compared to our previously published predictor.

Predicting Protein Disorder for N-, C-, and Internal Regions.

Logistic regression (LR), discriminant analysis (DA), and neural networks (NN) were used to predict ordered and disordered regions in proteins. Training data were from a set of non-redundant X-ray crystal structures, with the data being partitioned into N-terminal, C-terminal and internal (I) regions. The DA and LR methods gave almost identical 5-cross validation accuracies that averaged to the following values: 75.9 +/- 3.1% (N-regions), 70.7 +/- 1.5% (I-regions), and 74.6 +/- 4.4% (C-regions). NN predictions gave slightly higher scores: 78.8 +/- 1.2% (N-regions), 72.5 +/- 1.2% (I-regions), and 75.3 +/- 3.3% (C-regions). Predictions improved with length of the disordered regions. Averaged over the three methods, values ranged from 52% to 78% for length = 9-14 to >/= 21, respectively, for I-regions, from 72% to 81% for length = 5 to 12-15, respectively, for N-regions, and from 70% to 80% for length = 5 to 12-15, respectively, for C-regions. These data support the hypothesis that disorder is encoded by the amino acid sequence.

Predicting Binding Regions within Disordered Proteins.

Disordered regions are sequences within proteins that fail to fold into a fixed tertiary structure and have been shown to be involved in a variety of biological functions. We recently applied neural network predictors of disorder developed from X-ray data to several protein sequences characterized as disordered by NMR (Garner, Cannon, Romero, Obradovic and Dunker, Genome Informatics, 9:201-213, 1998). A few predictions on the NMR-characterized disordered regions were noted to contain false negative indications of order that correlated with regions of function. These and additional examples are examined in more detail here. Overall, 8 of 9 functional segments in 5 disordered proteins were identified or partially identified by this approach. The functions of these regions appear to involve binding to DNA, RNA, and proteins. These regions are known to undergo disorder-to-order transitions upon binding. This apparent ability of the predictors to identify functional regions in disordered proteins could be due to the existence of different flavors, or sub-classes of disorder, originating from the sequence of the disordered regions and perhaps owing to local inclinations toward order. These different flavors may be a characteristic that could be used to identify binding regions within proteins that are difficult to characterize structurally.

Folding minimal sequences: the lower bound for sequence complexity of globular proteins.

Alphabet size and informational entropy, two formal measures of sequence complexity, are herein applied to two prior studies on the folding of minimal proteins. These measures show a designed four-helix bundle to be unlike its natural counterparts but rather more like a coiled-coil dimer. Segments from a simplified sarc homology 3 domain and more than 2000000 segments from globular proteins both have lower bounds for alphabet size of 10 and for entropy near 2.9. These values are therefore suggested to be necessary and sufficient for folding into globular proteins having both rigid side chain packing and biological function.

Thousands of proteins likely to have long disordered regions.

Neural network predictors of protein disorder using primary sequence information were developed and applied to the Swiss Protein Database. More than 15,000 proteins were predicted to contain disordered regions of at least 40 consecutive amino acids, with more than 1,000 having especially high scores indicating disorder. These results support proposals that consideration of structure-activity relationships in proteins need to be broadened to include unfolded or disordered protein.

Protein disorder and the evolution of molecular recognition: theory, predictions and observations.

Observations going back more than 20 years show that regions in proteins with disordered backbones can play roles in their binding to other molecules; typically, the disordered regions become ordered upon complex formation. Thought-experiments with Schulz Diagrams, which are defined herein, suggest that disorder-to-order transitions are required for natural selection to operate separately on affinity and specificity. Separation of affinity and specificity may be essential for fine-tuning the molecular interaction networks that comprise the living state. For low affinity, high specificity interactions, our analysis suggests that natural selection would parse the amino acids conferring flexibility in the unbound state from those conferring specificity in the bound state. For high affinity, low specificity or for high affinity, multiple specificity interactions, our analysis suggests that the disorder-to-order transitions enable alternative packing interactions between side chains to accommodate the different binding targets. Disorder-to-order transitions upon binding also have significant kinetic implications as well, by having complex effects on both on- and off-rates. Current data are insufficient to decide on these proposals, but sequence and structure analysis on two examples support further investigations of the role of disorder-to-order transitions upon binding.

The Sequence Attribute Method for Determining Relationships Between Sequence and Protein Disorder.

The conditional probability, P(s|x), is a statement of the probability that the event, s, will occur given prior knowledge for the value of x. If x is given and if s is randomly distributed, then an empirical approximation of the true conditional probability can be computed by the application of Bayes' Theorem. Here s represents one of two structural classes, either ordered, s (o), or disordered, s (d), and x represents an attribute value calculated over a window of 21 amino acids. Plots of P(s|x) versus x provide information about the correlation between the given sequence attribute and disorder or order. These conditional probability plots allow quantitative comparisons between individual attributes for their ability to discriminate between order and disorder states. Using such quantitative comparisons, 38 different sequence attributes have been rank-ordered. Attributes based on cysteine, the aromatics, flexible tendencies, and charge were found to be the best attributes for distinguishing order and disorder among those tested so far.

Predicting Disordered Regions from Amino Acid Sequence: Common Themes Despite Differing Structural Characterization.

Using ordered and disordered regions identified either by X-ray crystallography or by NMR spectroscopy, we trained neural networks to predict order and disorder from amino acid sequence. Although the NMR-based predictor initially appeared to be much better than the one based on the X-ray data, both predictors yielded similar overall accuracies when tested on each other's training sets, and indicated similar regions of disorder upon each sequence. The predictors trained with X-ray data showed similar results for a 5-cross validation experiment and for the out-of-sample predictions on the NMR characterized data. In contrast, the predictor trained with NMR data gave substantially worse accuracies on the out-of-sample X-ray data as compared to the accuracies displayed by the 5-cross validation during the network training. Overall, the results from the two predictors suggest that disordered regions comprise a sequence-dependant category distinct from that of ordered protein structure.