Interventions focusing on the prevention and treatment of chronic non-communicable diseases are on rise. In the current article, we propose that dysfunction of the mesocortico-limbic reward system contributes to the emergence of the WHO-identified risk behaviors (tobacco use, unhealthy diet, physical inactivity and harmful use of alcohol), behaviors that underlie the evolution of major non-communicable diseases (e.g. cardiovascular diseases, cancer, diabetes and chronic respiratory diseases). Given that dopaminergic neurons of the mesocortico-limbic system are tightly associated with reward-related processes and motivation, their dysfunction may fundamentally influence behavior. While nicotine and alcohol alter dopamine neuron function by influencing some receptors, mesocortico-limbic system dysfunction was associated with elevation of metabolic set-point leading to hedonic over-eating. Although there is some empirical evidence, precise molecular mechanism for linking physical inactivity and mesocortico-limbic dysfunction per se seems to be missing; identification of which may contribute to higher success rates for interventions targeting lifestyle changes pertaining to physical activity. In the current article, we compile evidence in support of a link between exercise and the mesocortico-limbic system by elucidating interactions on the axis of muscle - irisin - brain derived neurotrophic factor (BDNF) - and dopaminergic function of the midbrain. Irisin is a contraction-regulated myokine formed primarily in skeletal muscle but also in the brain. Irisin stirred considerable interest, when its ability to induce browning of white adipose tissue parallel to increasing thermogenesis was discovered. Furthermore, it may also play a role in the regulation of behavior given it readily enters the central nervous system, where it induces BDNF expression in several brain areas linked to reward processing, e.g. the ventral tegmental area and the hippocampus. BDNF is a neurotropic factor that increases neuronal dopamine content, modulates dopamine release relevant for neuronal plasticity and increased neuronal survival as well as learning and memory. Further linking BDNF to dopaminergic function is BDNF's ability to activate tropomyosin-related kinase B receptor that shares signalization with presynaptic dopamine-3 receptors in the ventral tegmental area. Summarizing, we propose that the skeletal muscle derived irisin may be the link between physical activity and reward-related processes and motivation. Moreover alteration of this axis may contribute to sedentary lifestyle and subsequent non-communicable diseases. Preclinical and clinical experimental models to test this hypothesis are also proposed.

COPD is accompanied by limited physical activity, worse quality of life, and increased prevalence of depression. A possible link between COPD and depression may be irisin, a myokine, expression of which in the skeletal muscle and brain positively correlates with physical activity. Irisin enhances the synthesis of brain-derived neurotrophic factor (BDNF), a neurotrophin involved in reward-related processes. Thus, we hypothesized that mood disturbances accompanying COPD are reflected by the changes in the irisin–BDNF axis. Case history, routine laboratory parameters, serum irisin and BDNF levels, pulmonary function, and disease-specific quality of life, measured by St George’s Respiratory Questionnaire (SGRQ), were determined in a cohort of COPD patients (n=74). Simple and then multiple linear regression were used to evaluate the data. We found that mood disturbances are associated with lower serum irisin levels (SGRQ’s Impacts score and reciprocal of irisin showed a strong positive association; β: 419.97; 95% confidence interval [CI]: 204.31, 635.63; P<0.001). This association was even stronger among patients in the lower 50% of BDNF levels (β: 434.11; 95% CI: 166.17, 702.05; P=0.002), while it became weaker for patients in the higher 50% of BDNF concentrations (β: 373.49; 95% CI: -74.91, 821.88; P=0.1). These results suggest that irisin exerts beneficial effect on mood in COPD patients, possibly by inducing the expression of BDNF in brain areas associated with reward-related processes involved in by depression. Future interventional studies targeting the irisin–BDNF axis (eg, endurance training) are needed to further support this notion.

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS).

Background: Asymmetric dimethylarginine (ADMA) assumes a significant role in atherosclerosis by inhibiting the endothelial nitric oxide synthase (eNOS). Moreover, ADMA inhibits the inducible NOS (iNOS), the isoform that triggers atherosclerosis via peroxynitrite formation. Therefore, we investigated whether ADMA is a risk or protective factor in the atherosclerotic process. Intima-media thickness (IMT) of the common carotid artery, a surrogate for vascular diseases, was chosen as the outcome variable of interest. Methods: Sixty patients younger than 55 years having at least 30% stenosis of the internal carotid artery and 30 age- and gender-matched controls were recruited at a community-based neurosonological laboratory. We investigated relatively young patients to circumvent the confounding effect age has in the development of atherosclerosis. Results: The IMT showed a negative correlation with ADMA upon analysis of the pooled data (Spearman correlation coefficient –0.300, p = 0.0041) and the atherosclerotic stratum (Spearman correlation coefficient –0.323, p = 0.012). A multiple linear regression model containing all determinant factors of IMT previously identified by simple regression was used to further quantify the relationship between IMT and ADMA. The negative association between IMT and ADMA remained statistically significant (β: –0.510, CI: –0.894, –0.127; p = 0.010), furthermore it was even stronger in the atherosclerotic stratum (β: –0.67, CI: –1.16, –0.18; p = 0.008). Conclusions: A minimal increase in ADMA concentration may be protective by inhibiting iNOS but not eNOS in states where iNOS is induced, e.g. inflammation accompanying atherosclerosis.

The aim of our study was to predict the epidemic curves (daily new cases) of COVID-19 pandemic using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. We used the publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we used RNNs with gated recurring units (Long Short-Term Memory) to create two prediction models. Information collected in the first t time-steps were aggregated with a fully connected (dense) neural network layer and a consequent regression output layer to determine the next predicted value. We also used Root Mean Squared Logarithmic Errors (RMSLE) to compare the predicted models with the observed data. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seems to be low. The influence of this pandemic is significant worldwide and has already impacted our daily life. Decision makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible.

The term receptor reserve, first introduced and used in the traditional receptor theory, is an integrative measure of response-inducing ability of the interaction between an agonist and a receptor system (consisting of a receptor and its downstream signaling). The underlying phenomenon, i.e., stimulation of a submaximal fraction of receptors can apparently elicit the maximal effect (in certain cases), provides an opportunity to assess the receptor reserve. However, determining receptor reserve is challenging for agonists with short half-lives, such as adenosine. Although adenosine metabolism can be inhibited several ways (in order to prevent the rapid elimination of adenosine administered to construct concentration–effect (E/c) curves for the determination), the consequent accumulation of endogenous adenosine biases the results. To address this problem, we previously proposed a method, by means of which this bias can be mathematically corrected (utilizing a traditional receptor theory-independent approach). In the present investigation, we have offered in silico validation of this method by simulating E/c curves with the use of the operational model of agonism and then by evaluating them using our method. We have found that our method is suitable to reliably assess the receptor reserve for adenosine in our recently published experimental setting, suggesting that it may be capable for a qualitative determination of receptor reserve for rapidly eliminating agonists in general. In addition, we have disclosed a possible interference between FSCPX (8-cyclopentyl-N³-[3-(4-(fluorosulfonyl)benzoyloxy)propyl]-N¹-propylxanthine), an irreversible A₁ adenosine receptor antagonist, and NBTI (S-(2-hydroxy-5-nitrobenzyl)-6-thioinosine), a nucleoside transport inhibitor, i.e., FSCPX may blunt the effect of NBTI.