TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction.

OBJECTIVE: During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. METHODS: We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. RESULTS: Our results demonstrated that (1) the DA affected the EEG signal in the spatiotemporal domain; (2) ADA was able to completely remove the DA without affecting the TEP waveforms; (3). ICA corrections produced significant changes in peak-to-peak TEP amplitude. CONCLUSIONS: ADA is a reliable solution for the DA correction, especially considering that (1) it does not affect physiological responses; (2) it is completely data-driven and (3) its effectiveness does not depend on the characteristics of the artefact and on the number of recording electrodes. SIGNIFICANCE: We proposed a new reliable algorithm of correction for long-lasting TMS-EEG artifacts.

A four-compartment compartmental model to assess net whole body protein breakdown using a pulse of phenylalanine and tyrosine stable isotopes in humans.

The stable isotopes of phenylalanine (Phe) and tyrosine (Tyr) are often used to study whole body protein metabolism in humans. Noncompartmental approaches give limited physiological insight in the compartmental characteristics. We therefore developed a compartmental mathematical model of Phe/Tyr metabolism to describe protein fluxes by using stable tracer dynamic data in plasma following intravenous bolus of l-[ring-13C6]Phe and l-[ring-2H4]Tyr in healthy subjects. The model consists of four compartments describing Phe/Tyr kinetics. Because the model is a priori nonidentifiable, it is quantified in terms of two uniquely identifiable submodels representing two limit case scenarios, based on known physiology. The two submodels, identified by using the software SAAM II, fit well the experimental data of all individuals and provide an unbiased overview of the metabolic pathway in terms of intervals of validity of the non-uniquely identifiable variables. The model provides estimates of the flux from Phe to Tyr [4.1 ± 1.0 µmol·kg fat-free mass (FFM)-1·h-1 (mean ± SE)] and intervals of validity of the flux and pool estimates. Our preferred submodel yielded protein breakdown flux (50.5 ± 5.2 µmol·kg FFM-1·h-1), net protein breakdown (4.1 ± 1.0 µmol·kg FFM-1·h-1), Tyr from Phe hydroxylation (~12%), hydroxylated Phe (~8%), and flux ratio of Tyr to Phe arising from protein catabolism (0.68), consistent with available literature. The other submodel suggest that the assumptions made by noncompartmental analysis are consistently underestimated. Our accurate and detailed model for estimating Phe/Tyr metabolic pathways in humans might be essential to applications in a variety of scenarios describing whole body protein synthesis and breakdown in health and disease.

Decreased VLDL-Apo B 100 Fractional Synthesis Rate Despite Hypertriglyceridemia in Subjects With Type 2 Diabetes and Nephropathy.

CONTEXT: Subjects with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN) often exhibit hypertriglyceridemia. The mechanism(s) of such an increase are poorly known. OBJECTIVE: We investigated very low-density lipoprotein (VLDL)-Apo B 100 kinetics in T2DM subjects with and without DN, and in healthy controls. DESIGN: Stable isotope (13)C-leucine infusion and modeling analysis of tracer-to-tracee ratio dynamics in the protein product pool in the 6-8-h period following tracer infusion were employed. SETTING: Male subjects affected by T2DM, either with (n = 9) or without (n = 5) DN, and healthy male controls (n = 6), were studied under spontaneous glycemic levels in the post-absorptive state. RESULTS: In the T2DM patients with DN, plasma triglyceride (TG) (mean ± SD; 2.2 ± 0.8 mmol/L) and VLDL-Apo B 100 (17.4 ± 10.4 mg/dL) concentrations, and VLDL-Apo B 100 pool (0.56 ± 0.29 g), were ∼60-80% greater (P < .05 or less) than those of the T2DM subjects without DN (TG, 1.4 ± 0.5 mmol/L; VLDL-Apo B 100, 9.9 ± 2.5 mg/dL; VLDL-Apo B 100 pool, 0.36 ± 0.09 g), and ∼80-110% greater (P < .04 or less) than those of nondiabetic controls (TG, 1.2 ± 0.4 mmol/L; VLDL-Apo B 100, 8.2 ± 1.7 mg/dL; VLDL-Apo B 100, 0.32 ± 0.09 g). In sharp contrast however, in the subjects with T2DM and DN, VLDL-Apo B 100 fractional synthesis rate was ≥50% lower (4.8 ± 2.2 pools/d) than that of either the T2DM subjects without DN (9.9 ± 4.3 pools/d; P < .025) or the control subjects (12.5 ± 9.1 pools/d; P < .04). CONCLUSIONS: The hypertriglyceridemia of T2DM patients with DN is not due to hepatic VLDL-Apo B 100 overproduction, which is decreased, but it should be attributed to decreased apolipoprotein removal.

Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification.

AIMS/HYPOTHESIS: Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients. Our aim was to detect novel genetic variants associated with diabetic nephropathy and ESRD. METHODS: We exploited a novel algorithm, 'Bag of Naive Bayes', whose marker selection strategy is complementary to that of conventional genome-wide association models based on univariate association tests. The analysis was performed on a genome-wide association study of 3,464 patients with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study and subsequently replicated with 4,263 type 1 diabetes patients from the Steno Diabetes Centre, the All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK-Republic of Ireland) and the Genetics of Kidneys in Diabetes US Study (GoKinD US). RESULTS: Five genetic loci (WNT4/ZBTB40-rs12137135, RGMA/MCTP2-rs17709344, MAPRE1P2-rs1670754, SEMA6D/SLC24A5-rs12917114 and SIK1-rs2838302) were associated with ESRD in the FinnDiane study. An association between ESRD and rs17709344, tagging the previously identified rs12437854 and located between the RGMA and MCTP2 genes, was replicated in independent case-control cohorts. rs12917114 near SEMA6D was associated with ESRD in the replication cohorts under the genotypic model (p < 0.05), and rs12137135 upstream of WNT4 was associated with ESRD in Steno. CONCLUSIONS/INTERPRETATION: This study supports the previously identified findings on the RGMA/MCTP2 region and suggests novel susceptibility loci for ESRD. This highlights the importance of applying complementary statistical methods to detect novel genetic variants in diabetic nephropathy and, in general, in complex diseases.

Leucine modulates dynamic phosphorylation events in insulin signaling pathway and enhances insulin-dependent glycogen synthesis in human skeletal muscle cells.

BACKGROUND: Branched-chain amino acids, especially leucine, are known to interact with insulin signaling pathway and glucose metabolism. However, the mechanism by which this is exerted, remain to be clearly defined. In order to examine the effect of leucine on muscle insulin signaling, a set of experiments was carried out to quantitate phosphorylation events along the insulin signaling pathway in human skeletal muscle cell cultures. Cells were exposed to insulin, leucine or both, and phosphorylation events of key insulin signaling molecules were tracked over time so as to monitor time-related responses that characterize the signaling events and could be missed by a single sampling strategy limited to pre/post stimulus events. RESULTS: Leucine is shown to increase the magnitude of insulin-dependent phosphorylation of protein kinase B (AKT) at Ser473 and glycogen synthase kinase (GSK3ß) at Ser21-9. Glycogen synthesis follows the same pattern of GSK3ß, with a significant increase at 100 µM leucine plus insulin stimulus. Moreover, data do not show any statistically significant increase of pGSK3ß and glycogen synthesis at higher leucine concentrations. Leucine is also shown to increase the magnitude of insulin-mediated extracellularly regulated kinase (ERK) phosphorylation; however, differently from AKT and GSK3ß, ERK shows a transient behavior, with an early peak response, followed by a return to the baseline condition. CONCLUSIONS: These experiments demonstrate a complementary effect of leucine on insulin signaling in a human skeletal muscle cell culture, promoting insulin-activated GSK3ß phosphorylation and glycogen synthesis.

Time-frequency analysis of short-lasting modulation of EEG induced by TMS during wake, sleep deprivation and sleep.

The occurrence of dynamic changes in spontaneous electroencephalogram (EEG) rhythms in the awake state or sleep is highly variable. These rhythms can be externally modulated during transcranial magnetic stimulation (TMS) with a perturbation method to trigger oscillatory brain activity. EEG-TMS co-registration was performed during standard wake, during wake after sleep deprivation and in sleep in six healthy subjects. Dynamic changes in the regional neural oscillatory activity of the cortical areas were characterized using time-frequency analysis based on the wavelet method, and the modulation of induced oscillations were related to different vigilance states. A reciprocal synchronizing/desynchronizing effect on slow and fast oscillatory activity was observed in response to focal TMS after sleep deprivation and sleep. We observed a sleep-related slight desynchronization of alpha mainly over the frontal areas, and a widespread increase in theta synchronization. These findings could be interpreted as proof of the interference external brain stimulation can exert on the cortex, and how this could be modulated by the vigilance state. Potential clinical applications may include evaluation of hyperexcitable states such as epilepsy or disturbed states of consciousness such as minimal consciousness.

Frequency and time-frequency analysis of intraoperative ECoG during awake brain stimulation.

Electrocortical stimulation remains the standard for functional brain mapping of eloquent areas to prevent postoperative functional deficits. The aim of this study was to investigate whether the short-train technique (monopolar stimulation) and Penfield's technique (bipolar stimulation) would induce different effects on brain oscillatory activity in awake patients, as quantified by electrocorticography (ECoG). The study population was seven patients undergoing brain tumor surgery. Intraoperative bipolar and monopolar electrical stimulation for cortical mapping was performed during awake surgery. ECoG was recorded using 1 × 8 electrode strip. Spectral estimation was calculated using a parametric approach based on an autoregressive model. Wavelet-based time-frequency analysis was then applied to evaluate the temporal evolution of brain oscillatory activity. Both monopolar and bipolar stimulation produced an increment in delta and a decrease in beta powers for the motor and the sensory channels. These phenomena lasted about 4 s. Comparison between monopolar and bipolar stimulation showed no significant difference in brain activity. Given the importance of quantitative signal analysis for evaluating response accuracy, ECoG recording during electrical stimulation is necessary to characterize the dynamic processes underlying changes in cortical responses in vivo. This study is a preliminary approach to the quantitative analysis of post-stimulation ECoG signals.

Bag of Naïve Bayes: biomarker selection and classification from genome-wide SNP data.

BACKGROUND: Multifactorial diseases arise from complex patterns of interaction between a set of genetic traits and the environment. To fully capture the genetic biomarkers that jointly explain the heritability component of a disease, thus, all SNPs from a genome-wide association study should be analyzed simultaneously. RESULTS: In this paper, we present Bag of Naïve Bayes (BoNB), an algorithm for genetic biomarker selection and subjects classification from the simultaneous analysis of genome-wide SNP data. BoNB is based on the Naïve Bayes classification framework, enriched by three main features: bootstrap aggregating of an ensemble of Naïve Bayes classifiers, a novel strategy for ranking and selecting the attributes used by each classifier in the ensemble and a permutation-based procedure for selecting significant biomarkers, based on their marginal utility in the classification process. BoNB is tested on the Wellcome Trust Case-Control study on Type 1 Diabetes and its performance is compared with the ones of both a standard Naïve Bayes algorithm and HyperLASSO, a penalized logistic regression algorithm from the state-of-the-art in simultaneous genome-wide data analysis. CONCLUSIONS: The significantly higher classification accuracy obtained by BoNB, together with the significance of the biomarkers identified from the Type 1 Diabetes dataset, prove the effectiveness of BoNB as an algorithm for both classification and biomarker selection from genome-wide SNP data. AVAILABILITY: Source code of the BoNB algorithm is released under the GNU General Public Licence and is available at http://www.dei.unipd.it/~sambofra/bonb.html.

Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis.

Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions.

Kinetics and intracellular compartmentalization of HTLV-1 gene expression: nuclear retention of HBZ mRNAs.

Human T-cell leukemia virus type 1 (HTLV-1) codes for 9 alternatively spliced transcripts and 2 major regulatory proteins named Tax and Rex that function at the transcriptional and posttranscriptional levels, respectively. We investigated the temporal sequence of HTLV-1 gene expression in primary cells from infected patients using splice site-specific quantitative RT-PCR. The results indicated a two-phase kinetics with the tax/rex mRNA preceding expression of other viral transcripts. Analysis of mRNA compartmentalization in cells transfected with HTLV-1 molecular clones demonstrated the strict Rex-dependency of the two-phase kinetics and revealed strong nuclear retention of HBZ mRNAs, supporting their function as noncoding transcripts. Mathematical modeling underscored the importance of a delay between the functions of Tax and Rex, which was supported by experimental evidence of the longer half-life of Rex. These data provide evidence for a temporal pattern of HTLV-1 expression and reveal major differences in the intracellular compartmentalization of HTLV-1 transcripts.

Shortened {beta}-cell lifespan leads to {beta}-cell deficit in a rodent model of type 2 diabetes.

Since the fundamental defect in both type 1 and type 2 diabetes is ß-cell failure, there is increasing interest in the capacity, if any, for ß-cell regeneration. Insights into typical ß-cell age and lifespan during normal development and how these are influenced in diabetes is desirable to realistically establish the prospects for ß-cell regeneration as means to reverse the deficit in ß-cell mass in diabetes. We assessed the mean ß-cell age and lifespan by the classical McKendrick-von Foester equation that describes the age-based heterogeneity of ß-cells in terms of the time-varying ß-cell formation and loss estimated by a ß-cell turnover model. This modeling approach was applied to evaluate ß-cell lifespan in a rodent model of type 2 diabetes in comparison with nondiabetic controls. When rats were 10 mo old, mean ß-cell lifespan was 1 mo vs. 6 mo in rats with type 2 diabetes vs. controls. A shortened ß-cell lifespan in a rat model of type 2 diabetes results in a decrease in mean ß-cell age and thus contributes to decreased ß-cell mass.

Ongoing beta-cell turnover in adult nonhuman primates is not adaptively increased in streptozotocin-induced diabetes.

OBJECTIVE: ß-Cell turnover and its potential to permit ß-cell regeneration in adult primates are unknown. Our aims were 1) to measure ß-cell turnover in adult nonhuman primates; 2) to establish the relative contribution of ß-cell replication and formation of new ß-cells from other precursors (defined thus as ß-cell neogenesis); and 3) to establish whether there is an adaptive increase in ß-cell formation (attempted regeneration) in streptozotocin (STZ)-induced diabetes in adult nonhuman primates. RESEARCH DESIGN AND METHODS: Adult (aged 7 years) vervet monkeys were administered STZ (45-55 mg/kg, n = 7) or saline (n = 9). Pancreas was obtained from each animal twice, first by open surgical biopsy and then by euthanasia. ß-Cell turnover was evaluated by applying a mathematic model to measured replication and apoptosis rates. RESULTS: ß-Cell turnover is present in adult nonhuman primates (3.3 ± 0.9 mg/month), mostly (~80%) derived from ß-cell neogenesis. ß-Cell formation was minimal in STZ-induced diabetes. Despite marked hyperglycemia, ß-cell apoptosis was not increased in monkeys administered STZ. CONCLUSIONS: There is ongoing ß-cell turnover in adult nonhuman primates that cannot be accounted for by ß-cell replication. There is no evidence of ß-cell regeneration in monkeys administered STZ. Hyperglycemia does not induce ß-cell apoptosis in nonhuman primates in vivo.

Modeling nonsteady-state metabolism from arteriovenous data.

The use of arteriovenous (AV) concentration differences to measure the production of a substance at organ/tissue level by Fick principle is limited to steady state. Out of steady state, there is the need, as originally proposed by Zierler, to account for the nonnegligible transit time of the substance through the system. Based on this theory, we propose a modeling approach that adopts a parametric description for production and transit time. Once the unknown parameters are estimated on AV data, the transition time of the substance can be assessed and production can be reconstructed. As a case study, we discuss the estimation of pancreatic insulin secretion during a meal from C-peptide concentrations measured in femoral artery and hepatic vein in 12 subjects. Results support the importance of accounting for nonnegligible transit times, even if C-peptide mean transit time across the splanchnic bed is rather limited (3.3 ± 1.3 min), it affects the estimation of pancreatic insulin secretion which shows a significantly different profile in the early portion of the postprandial period when estimated either with the novel modeling approach or with the simplified steady state equation.

Cellular modeling: insight into oral minimal models of insulin secretion.

The oral glucose tolerance test and meal tolerance test are common clinical tests of the glucose-insulin system. Several mathematical models have been suggested as means to extract information about beta-cell function from data from oral tolerance tests. Any such model needs to be fairly simple but should at the same time be linked to the underlying biology of the insulin-secreting beta-cells. The scope of the present work is to present a way to make such a connection using a recent model describing intracellular mechanisms. We show how the three main components of oral minimal secretion models, derivative control, proportional control, and delay, are related to subcellular events, thus providing mechanistic underpinning of the assumptions of the minimal models.

Dynamics of beta-cell turnover: evidence for beta-cell turnover and regeneration from sources of beta-cells other than beta-cell replication in the HIP rat.

Type 2 diabetes is characterized by hyperglycemia, a deficit in beta-cells, increased beta-cell apoptosis, and islet amyloid derived from islet amyloid polypeptide (IAPP). These characteristics are recapitulated in the human IAPP transgenic (HIP) rat. We developed a mathematical model to quantify beta-cell turnover and applied it to nondiabetic wild type (WT) vs. HIP rats from age 2 days to 10 mo to establish 1) whether beta-cell formation is derived exclusively from beta-cell replication, or whether other sources of beta-cells (OSB) are present, and 2) to what extent, if any, there is attempted beta-cell regeneration in the HIP rat and if this is through beta-cell replication or OSB. We conclude that formation and maintenance of adult beta-cells depends largely ( approximately 80%) on formation of beta-cells independent from beta-cell duplication. Moreover, this source adaptively increases in the HIP rat, implying attempted beta-cell regeneration that substantially slows loss of beta-cell mass.

Development of factors to convert frequency to rate for beta-cell replication and apoptosis quantified by time-lapse video microscopy and immunohistochemistry.

An obstacle to development of methods to quantify beta-cell turnover from pancreas tissue is the lack of conversion factors for the frequency of beta-cell replication or apoptosis detected by immunohistochemistry to rates of replication or apoptosis. We addressed this obstacle in islets from 1-mo-old rats by quantifying the relationship between the rate of beta-cell replication observed directly by time-lapse video microscopy (TLVM) and the frequency of beta-cell replication in the same islets detected by immunohistochemistry using antibodies against Ki67 and insulin in the same islets fixed immediately after TLVM. Similarly, we quantified the rate of beta-cell apoptosis by TLVM and then the frequency of apoptosis in the same islets using TdT-mediated dUTP nick-end labeling and insulin. Conversion factors were developed by regression analysis. The conversion factor from Ki67 labeling frequency (%) to actual replication rate (%events/h) is 0.025 +/- 0.003 h(-1). The conversion factor from TdT-mediated dUTP nick-end labeling frequency (%) to actual apoptosis rate (%events/h) is 0.41 +/- 0.05 h(-1). These conversion factors will permit development of models to evaluate beta-cell turnover in fixed pancreas tissue.

A subcellular model of glucose-stimulated pancreatic insulin secretion.

When glucose is raised from a basal to stimulating level, the pancreatic islets respond with a typical biphasic insulin secretion pattern. Moreover, the pancreas is able to recognize the rate of change of the glucose concentration. We present a relatively simple model of insulin secretion from pancreatic beta-cells, yet founded on solid physiological grounds and capable of reproducing a series of secretion patterns from perfused pancreases as well as from stimulated islets. The model includes the notion of distinct pools of granules as well as mechanisms such as mobilization, priming, exocytosis and kiss-and-run. Based on experimental data, we suggest that the individual beta-cells activate at different glucose concentrations. The model reproduces most of the data it was tested against very well, and can therefore serve as a general model of glucose-stimulated insulin secretion. Simulations predict that the effect of an increased frequency of kiss-and-run exocytotic events is a reduction in insulin secretion without modification of the qualitative pattern. Our model also appears to be the first physiology-based one to reproduce the staircase experiment, which underlies 'derivative control', i.e. the pancreatic capacity of measuring the rate of change of the glucose concentration.

Artificial neural networks and robust Bayesian classifiers for risk stratification following uncomplicated myocardial infarction.

OBJECTIVE: To compare artificial neural networks (ANN) and robust Bayesian classifiers (RBC) in predicting outcome following acute myocardial infarction (AMI). METHODS: Clinical, exercise ECG and stress echo variables by 496 patients with AMI were used to predict the cumulative end-point of cardiac death, nonfatal reinfarction and unstable angina. Revascularized patients were censored. Short (200 days)-, medium (400 days)- and long (1000 days)-term observation intervals, including 50%, 75% and 90% of the events, respectively, were considered. At each interval, any patient was binary assigned to the "event" or "no event" class. A multilayer feedforward ANN, trained by a back propagation algorithm, was used. RBC, using the leave-one-out technique, were derived. The accuracy of both techniques was compared to the default accuracy (DA) obtained by assigning all subjects to the largest class. RESULTS: 14 death, 27 reinfarction and 29 unstable angina were observed during a mean follow-up of 24 [95% confidence interval (CI) 19 to 22] months. The accuracy of ANN and RBC and DA were 70%, 81% and 74% at short, 67%, 73% and 56% at medium and 64%, 68% and 62% at long-term follow-up. CONCLUSIONS: (1) ANN do not improve the prognostic classification of patients with uncomplicated AMI as compared to RBC. (2) In particular, short-term prognostic accuracy seems insufficient.