Alternative Treatment with Red Yeast Rice to Reduce Hyperlipidemia

• First line treatment of cardiovascular disease is lifestyle modification followed by the pharmacologic intervention of HMG-CoA reductase inhibitors or statins • Statins are commonly associated with intolerable side effects such as myalgia leading to medication non-compliance • RYR preparations claim to inhibit cholesterol synthesis without causing myalgia • RYR preparations have naturally occurring monacolins such as monacolin K which is chemically identical to lovastatin • RYR is not regulated by the FDA leading to questionable manufacturing practices producing varying ingredient composition • The purpose of this study is to investigate the role RYR in hyperlipidemia treatment compared to statins by evaluating efficacy, side effects, and the potential to reduce medication non-compliance in adults • Clinicians potentially could recommend RYR as an alternative treatment to hyperlipidemia in patients unable to comply with statin treatment to decrease cholesterol levels and reduce the progression of atherosclerosishttps://commons.und.edu/pas-grad-posters/1132/thumbnail.jp

Synaptic plasticity and cognitive function are disrupted in the absence of Lrp4.

Lrp4, the muscle receptor for neuronal Agrin, is expressed in the hippocampus and areas involved in cognition. The function of Lrp4 in the brain, however, is unknown, as Lrp4-/- mice fail to form neuromuscular synapses and die at birth. Lrp4-/- mice, rescued for Lrp4 expression selectively in muscle, survive into adulthood and showed profound deficits in cognitive tasks that assess learning and memory. To learn whether synapses form and function aberrantly, we used electrophysiological and anatomical methods to study hippocampal CA3-CA1 synapses. In the absence of Lrp4, the organization of the hippocampus appeared normal, but the frequency of spontaneous release events and spine density on primary apical dendrites were reduced. CA3 input was unable to adequately depolarize CA1 neurons to induce long-term potentiation. Our studies demonstrate a role for Lrp4 in hippocampal function and suggest that patients with mutations in Lrp4 or auto-antibodies to Lrp4 should be evaluated for neurological deficits

Horseweed (Erigeron canadensis) Control in No-Till Soybean Systems on a Coarse Textured Soil

Horseweed (Erigeron canadensis) is a competitive winter or summer annual broadleaf weed. When uncontrolled, horseweed can reduce soybean (Glycine max) yields by 93%. Research was conducted to advance our knowledge on horseweed growth stage response to foliar-active and residual herbicides, fall applications, and the utility of differing herbicide technologies. Greenhouse results determined that herbicide efficacy was greatest when applied to early rosette horseweed providing an average control of 70% across herbicide treatments. Field trials determined that preventing new emergence with flumioxazin, added with dicamba or paraquat to kill existing plants in the fall, increased control to 99% the following spring. Field trials also determined that dicamba, applied PRE or POST, provided excellent horseweed control and was an effective soybean technology system for horseweed-infested fields. Saflufenacil controlled existing plants, but residual benefits were unclear. Further research must be done to investigate residual activity of PRE herbicides applied before horseweed emergence

Enhancing Value-Based Healthcare with Reconstructability Analysis: Predicting Cost of Care in Total Hip Replacement

Legislative reforms aimed at slowing growth of US healthcare costs are focused on achieving greater value per dollar. To increase value healthcare providers must not only provide high quality care, but deliver this care at a sustainable cost. Predicting risks that may lead to poor outcomes and higher costs enable providers to augment decision making for optimizing patient care and inform the risk stratification necessary in emerging reimbursement models. Healthcare delivery systems are looking at their high volume service lines and identifying variation in cost and outcomes in order to determine the patient factors that are driving this variation and generating increased cost. One way to improve predictions is through enhanced modeling methods. Current modeling is predominantly done with logistic regression (LR). This project applied Reconstructability Analysis (RA) to data from hospital based hip and knee replacement surgery. RA is partially similar to LR, but has some unique features. RA is a data mining method that searches for relations in data, especially non-linear and higher ordinality relations, by decomposing the frequency distribution of the data into projections, several of which taken together define a model, which is then assessed for statistical significance. The predictive power of the model is expressed as the percent reduction of uncertainty (Shannon entropy) of the dependent variable (the DV) gained by knowing the values of the predictive independent variables (the IVs). RA predictive models were then generated for the total cost of the hospital episode. RA generated continuous predictions for cost by calculating expected values. Models included novel comorbidity variables, non-hypothesized interaction terms, and often resulted in substantial reductions in uncertainty. Predictive variables consisted of both delivery system variables and binary patient comorbidity variables. Delivery system variables (surgeon, location, and surgeon volume) were found to be the predominant predictors of total cost rather than individual patient risk factors. Results suggest that provider practice patterns have a larger effect than previously considered. Improving hospital and provider efficiency may be more strategic than cherry picking low risk patients. Risk ratios were generated as an additional measure of effect size. These risk ratios were used to classify the IV states of the models as indicating higher or lower risk of adverse outcomes. Some IV states showed nearly 25% of patients at increased risk, while other IV states showed over 75% of patients at decreased risk. In real time, such risk predictions could support clinical decision making and custom tailored utilization of services. Future research might address the limitations of this project’s data and employ additional RA techniques and training-test splits. Implementation of predictive models is also discussed, with considerations for data supply lines, maintenance of models, organizational buy-in, and the acceptance of model output by clinical teams for use in real time clinical practice. If outcomes and risk are adequately predicted, areas for potential improvement become clearer, and focused changes can be made to drive improvements in patient care. Better predictions, such as those resulting from the RA methodology, can thus support improvement in value – better outcomes at a lower cost. As reimbursement increasingly evolves into value-based programs, understanding the outcomes achieved, and customizing patient care to reduce unnecessary costs while improving outcomes, will be an active area for clinicians, healthcare administrators, researchers, and data scientists for many years to come

Developmental sensory experience balances cortical excitation and inhibition.

Early in life, neural circuits are highly susceptible to outside influences. The organization of the primary auditory cortex (A1) in particular is governed by acoustic experience during the critical period, an epoch near the beginning of postnatal development throughout which cortical synapses and networks are especially plastic. This neonatal sensitivity to the pattern of sensory inputs is believed to be essential for constructing stable and adequately adapted representations of the auditory world and for the acquisition of language skills by children. One important principle of synaptic organization in mature brains is the balance between excitation and inhibition, which controls receptive field structure and spatiotemporal flow of neural activity, but it is unknown how and when this excitatory-inhibitory balance is initially established and calibrated. Here we use whole-cell recording to determine the processes underlying the development of synaptic receptive fields in rat A1. We find that, immediately after the onset of hearing, sensory-evoked excitatory and inhibitory responses are equally strong, although inhibition is less stimulus-selective and mismatched with excitation. However, during the third week of postnatal development, excitation and inhibition become highly correlated. Patterned sensory stimulation drives coordinated synaptic changes across receptive fields, rapidly improves excitatory-inhibitory coupling and prevents further exposure-induced modifications. Thus, the pace of cortical synaptic receptive field development is set by progressive, experience-dependent refinement of intracortical inhibition

Temporal Modulation of Spike-Timing-Dependent Plasticity

Spike-timing-dependent plasticity (STDP) has attracted considerable experimental and theoretical attention over the last decade. In the most basic formulation, STDP provides a fundamental unit – a spike pair – for quantifying the induction of long-term changes in synaptic strength. However, many factors, both pre- and postsynaptic, can affect synaptic transmission and integration, especially when multiple spikes are considered. Here we review the experimental evidence for multiple types of nonlinear temporal interactions in STDP, focusing on the contributions of individual spike pairs, overall spike rate, and precise spike timing for modification of cortical and hippocampal excitatory synapses. We discuss the underlying processes that determine the specific learning rules at different synapses, such as postsynaptic excitability and short-term depression. Finally, we describe the success of efforts toward building predictive, quantitative models of how complex and natural spike trains induce long-term synaptic modifications

Dendritic Synapse Location and Neocortical Spike-Timing-Dependent Plasticity

While it has been appreciated for decades that synapse location in the dendritic tree has a powerful influence on signal processing in neurons, the role of dendritic synapse location on the induction of long-term synaptic plasticity has only recently been explored. Here, we review recent work revealing how learning rules for spike-timing-dependent plasticity (STDP) in cortical neurons vary with the spatial location of synaptic input. A common principle appears to be that proximal synapses show conventional STDP, whereas distal inputs undergo plasticity according to novel learning rules. One crucial factor determining location-dependent STDP is the backpropagating action potential, which tends to decrease in amplitude and increase in width as it propagates into the dendritic tree of cortical neurons. We discuss additional location-dependent mechanisms as well as the functional implications of heterogeneous learning rules at different dendritic locations for the organization of synaptic inputs

Intrinsic Stability of Temporally Shifted Spike-Timing Dependent Plasticity

Spike-timing dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains and it generates competition among synapses. However, STDP has an inherent instability because strong synapses are more likely to be strengthened than weak ones, causing them to grow in strength until some biophysical limit is reached. Through simulations and analytic calculations, we show that a small temporal shift in the STDP window that causes synchronous, or nearly synchronous, pre- and postsynaptic action potentials to induce long-term depression can stabilize synaptic strengths. Shifted STDP also stabilizes the postsynaptic firing rate and can implement both Hebbian and anti-Hebbian forms of competitive synaptic plasticity. Interestingly, the overall level of inhibition determines whether plasticity is Hebbian or anti-Hebbian. Even a random symmetric jitter of a few milliseconds in the STDP window can stabilize synaptic strengths while retaining these features. The same results hold for a shifted version of the more recent “triplet” model of STDP. Our results indicate that the detailed shape of the STDP window function near the transition from depression to potentiation is of the utmost importance in determining the consequences of STDP, suggesting that this region warrants further experimental study

Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity

Long-term modifications of neuronal connections are critical for reliable memory storage in the brain. However, their locus of expression—pre- or postsynaptic—is highly variable. Here we introduce a theoretical framework in which long-term plasticity performs an optimization of the postsynaptic response statistics toward a given mean with minimal variance. Consequently, the state of the synapse at the time of plasticity induction determines the ratio of pre- and postsynaptic modifications. Our theory explains the experimentally observed expression loci of the hippocampal and neocortical synaptic potentiation studies we examined. Moreover, the theory predicts presynaptic expression of long-term depression, consistent with experimental observations. At inhibitory synapses, the theory suggests a statistically efficient excitatory-inhibitory balance in which changes in inhibitory postsynaptic response statistics specifically target the mean excitation. Our results provide a unifying theory for understanding the expression mechanisms and functions of long-term synaptic transmission plasticity