Sociodemographic characteristics and diabetes predict invalid self-reported non-smoking in a population-based study of U.S. adults

BACKGROUND: Nearly all studies reporting smoking status collect self-reported data. The objective of this study was to assess sociodemographic characteristics and selected, common smoking-related diseases as predictors of invalid reporting of non-smoking. Valid self-reported smoking may be related to the degree to which smoking is a behavior that is not tolerated by the smoker's social group. METHODS: True smoking was defined as having serum cotinine of 15+ng/ml. 1483 "true" smokers 45+ years of age with self-reported smoking and serum cotinine data from the Mobile Examination Center were identified in the third National Health and Nutrition Examination Survey. Invalid non-smoking was defined as "true" smokers self-reporting non-smoking. To assess predictors of invalid self-reported non-smoking, odds ratios (OR) and 95% confidence intervals (CI) were calculated for age, race/ethnicity-gender categories, education, income, diabetes, hypertension, and myocardial infarction. Multiple logistic regression modeling took into account the complex survey design and sample weights. RESULTS: Among smokers with diabetes, invalid non-smoking status was 15%, ranging from 0% for Mexican-American (MA) males to 22%–25% for Non-Hispanic White (NHW) males and Non-Hispanic Black (NHB) females. Among smokers without diabetes, invalid non-smoking status was 5%, ranging from 3% for MA females to 10% for NHB females. After simultaneously taking into account diabetes, education, race/ethnicity and gender, smokers with diabetes (OR(Adj )= 3.15; 95% CI: 1.35–7.34), who did not graduate from high school (OR(Adj )= 2.05; 95% CI: 1.30–3.22) and who were NHB females (OR(Adj )= 5.12; 95% CI: 1.41–18.58) were more likely to self-report as non-smokers than smokers without diabetes, who were high school graduates, and MA females, respectively. Having a history of myocardial infarction or hypertension did not predict invalid reporting of non-smoking. CONCLUSION: Validity of self-reported non-smoking may be related to the relatively slowly progressing chronic nature of diabetes, in contrast with the acute event of myocardial infarction which could be considered a more serious, major life changing event. These data also raise questions regarding the possible role of societal desirability in the validity of self-reported non-smoking, especially among smokers with diabetes, who did not graduate from high school, and who were NHB females

Predictive Values of Self‐Reported Periodontal Need: National Health and Nutrition Examination Survey III

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142204/1/jper1551.pd

Clinical and Serologic Markers of Periodontal Infection and Chronic Kidney Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141492/1/jper1670.pd

Short Communication: Initiation of an Abacavir-Containing Regimen in HIV-Infected Adults Is Associated with a Smaller Decrease in Inflammation and Endothelial Activation Markers Compared to Non-Abacavir-Containing Regimens

Abacavir has been associated with myocardial infarction in several studies. This may be related to inflammation and endothelial cell activation. We compared changes in inflammation and endothelial activation markers between antiretroviral-naive adults initiating zidovudine, lamivudine, abacavir, and nonnucleoside reverse transcriptase inhibitor (NNRTI) or this regimen without abacavir. Changes in soluble tumor necrosis factor receptors-I, -II (sTNFR-I, -II), high sensitivity C-reactive protein, and soluble vascular cell adhesion molecule-1 (sVCAM-1) from baseline (pre-ART) to a second time point about 24 weeks after initiating antiretroviral therapy (ART) were compared between groups using multivariable linear regression. A total of 37 met eligibility criteria; 12 received abacavir. The median (interquartile range) age was 37 years (27–45). Most were men (32/37), African-American (15/37), or white (15/37). The median nadir CD4+ and baseline HIV-1 RNA were 230 cells/mm3 (180–301) and 82,642 copies/ml (34,400–204,703). In all, 15/30 smoked, 7/37 had hypertension, 1/37 had diabetes, and 1/37 had hyperlipidemia. None had coronary or renal disease. Changes in CD4+ and HIV-1 RNA level and timing of stored samples with regard to ART initiation were not different between groups. In univariable analysis, log transformed percent change in sTNFR-I (p=0.05) and -II (p=0.04) showed significant between-group differences and trended toward significance for sVCAM-1 (p=0.08). These markers decreased less in the abacavir group. After adjustment for confounders, significantly less decrease for sTNFR-II and sVCAM-1 was seen for those receiving the abacavir-containing regimen. When taken with an NNRTI, abacavir induced a smaller decrease in inflammation biomarkers in this cohort, suggesting a possible proinflammatory effect of this nucleoside analogue

How spiking neurons give rise to a temporal-feature map

A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cells’ synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit from the nonlinearity

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Quantal Glutamate Release Is Essential for Reliable Neuronal Encodings in Cerebral Networks

Background: The neurons and synapses work coordinately to program the brain codes of controlling cognition and behaviors. Spike patterns at the presynaptic neurons regulate synaptic transmission. The quantitative regulations of synapse dynamics in spike encoding at the postsynaptic neurons remain unclear. Methodology/Principal Findings: With dual whole-cell recordings at synapse-paired cells in mouse cortical slices, we have investigated the regulation of synapse dynamics to neuronal spike encoding at cerebral circuits assembled by pyramidal neurons and GABAergic ones. Our studies at unitary synapses show that postsynaptic responses are constant over time, such as glutamate receptor-channel currents at GABAergic neurons and glutamate transport currents at astrocytes, indicating quantal glutamate release. In terms of its physiological impact, our results demonstrate that the signals integrated from quantal glutamatergic synapses drive spike encoding at GABAergic neurons reliably, which in turn precisely set spike encoding at pyramidal neurons through feedback inhibition. Conclusion/Significance: Our studies provide the evidences for the quantal glutamate release to drive the spike encodings precisely in cortical circuits, which may be essential for programming the reliable codes in the brain to manage wellorganize

Soft-bound synaptic plasticity increases storage capacity

Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses

Kidins220/ARMS Is a Novel Modulator of Short-Term Synaptic Plasticity in Hippocampal GABAergic Neurons

Kidins220 (Kinase D interacting substrate of 220 kDa)/ARMS (Ankyrin Repeat-rich Membrane Spanning) is a scaffold protein highly expressed in the nervous system. Previous work on neurons with altered Kidins220/ARMS expression suggested that this protein plays multiple roles in synaptic function. In this study, we analyzed the effects of Kidins220/ARMS ablation on basal synaptic transmission and on a variety of short-term plasticity paradigms in both excitatory and inhibitory synapses using a recently described Kidins220 full knockout mouse. Hippocampal neuronal cultures prepared from embryonic Kidins220−/− (KO) and wild type (WT) littermates were used for whole-cell patch-clamp recordings of spontaneous and evoked synaptic activity. Whereas glutamatergic AMPA receptor-mediated responses were not significantly affected in KO neurons, specific differences were detected in evoked GABAergic transmission. The recovery from synaptic depression of inhibitory post-synaptic currents in WT cells showed biphasic kinetics, both in response to paired-pulse and long-lasting train stimulation, while in KO cells the respective slow components were strongly reduced. We demonstrate that the slow recovery from synaptic depression in WT cells is caused by a transient reduction of the vesicle release probability, which is absent in KO neurons. These results suggest that Kidins220/ARMS is not essential for basal synaptic transmission and various forms of short-term plasticity, but instead plays a novel role in the mechanisms regulating the recovery of synaptic strength in GABAergic synapses

Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity

It has been suggested that excitatory and inhibitory inputs to cortical cells are balanced, and that this balance is important for the highly irregular firing observed in the cortex. There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates ‘negative feedback’ that balances excitation and inhibition, which contrasts with the ‘positive feedback’ of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs