NO) and cyclic guanosine monophosphate (cGMP) ameliorate the progression of renal fibrosis. However, the molecular mechanism of this process is not fully understood. We hypothesize that the antifibrotic effects of cGMP and cGMP‐dependent kinase I (cGKI) are mediated via regulation of the TGFβ signalling pathway, both via ERK and the Smad‐dependent route. Kidney fibrosis was induced by unilateral ureter obstruction (UUO) in wild‐type and cGKI‐deficient (cGKI‐KO) mice. The cGMP/cGKI signalling pathway was activated by application of the soluble guanylate cyclase (sGC) stimulator BAY 41‐8543 (BAY), beginning 1 day after UUO. After 7 days, the antifibrotic effects of BAY were analysed by measuring mRNA and protein expression of characteristic fibrotic biomarkers. The effects of cGMP/TGFβ on cultured fibroblasts were also analysed in vitro. BAY application influenced the activity of the extracellular matrix (ECM)‐degrading matrix metalloproteases (MMP2 and MMP9) and their inhibitor tissue inhibitors of metalloproteinase‐1, the secretion of cytokines (e.g. IL‐6) and the expression pattern of ECM proteins  and profibrotic mediators (e.g. connective tissue growth factors and plasminogen‐activator inhibitor‐1). Activation of the cGMP/cGKI signalling pathway showed protective effects against fibrosis which were mediated by inhibition of P‐Erk1/2 and translocation of P‐smad3. The elucidation of these signalling mechanisms might support the development of new therapeutic options regarding cGMP/cGKI‐mediated antifibrotic actions.Agents that enhance production of nitric oxide ( Fibrogenic factors promote the fibrotic process such as transforming growth factors (TGFβ), plasminogen‐activator inhibitor‐1 (PAI‐1) or connective tissue growth factors (CTGF) In addition, synthesis and degradation of ECM proteins are determined by metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs). Their expression pattern is regulated by MAPK/Erk kinase, which promotes the progression of fibrosis et al. We evaluated the effects of the soluble guanylate cyclase (sGC) stimulator BAY 41‐8543 (BAY) on the fibrotic kidney. Under physiological conditions, sGC can be activated by nitric oxide (NO). Activated sGC synthesizes the second messenger cyclic guanosine monophosphate (cGMP) which then stimulates cGMP‐dependent protein kinases (cGK) As marker for fibrosis induction, we examined the mRNA levels of αSMA, fibronectin, collagen1a1 (Col1a1), CTGF, TIMP‐1, PAI‐1, MMP2 and MMP9  was assessed. UUO increased the phosphorylation and the protein expression of Erk1 and Erk2 Fig. A. ImmunoIt has been shown that IL‐6 promotes fibrosis The serum level of creatinine increased significantly after 7 days of UUO. Following BAY administration, serum creatinine was decreased, but there was no significant difference between BAY treated and untreated wt‐mice. Conversely, in cGKI‐KO‐mice, BAY influenced in no way the serum creatinine Fig. .In the present study, we have investigated the functional role of sGC stimulation in regulating renal fibrosis. BAY reduced the mRNA‐ and protein expression of different fibrosis marker. The antifibrotic impact of sGC stimulation was not observed in cGKI‐KO‐mice, suggesting that cGKI mediates the repair process of renal fibrosis.et al. Our study confirmed that the serum creatinine, which is a parameter for renal function, is increased after UUO et al. et al. and Beyer et al. showed that cGMP is not able to decrease the CTGF expression in fibroblasts, It is generally accepted that TGFβ acts by stimulation of its downstream mediator smad2 and smad3. Latest studies report that diminished smad2‐ as well as smad3 phosphorylation results in enhanced renal fibrosis Expression of PAI‐1, which acts profibrotic, is slightly attenuated by BAY. TGFβ1 activates PAI‐1 and PAI‐1, in turn, stimulates TGFβ1 After UUO, the MMP2 mRNA is adjusted much higher than MMP9. Therefore, MMP2 appears to be more crucial in the development of renal fibrosis than MMP9. Of importance is the fact that BAY‐induced increase in TIMP‐1 expression was accompanied by diminished MMP2 activity. TIMPs do not reveal a high specificity for any particular MMP BAY application in rats was previously shown to ameliorate renal injury after relief of ureteral obstruction The results of the present study suggest a therapeutic potential of BAY application in renal fibrosis. The antifibrotic effect of BAY is mediated via cGMP/cGKI by inhibition of Erk and smad3 signalling pathways Fig. .129/Sv‐WT and 129/Sv‐cGKI‐KO‐mice The investigation conforms to the guide for the Care and Use of Laboratory Animals published by the US National Institute of Health. The experimental protocols were approved by the local authorities for animal research  and were conducted according to the German law for animal care.−1) started 24 h after surgery using water, glycerol, PEG400 as vehicle. After 7 days, the fibrotic process was analysed.The renal fibrosis was induced by UUO in 6–12‐week‐old mice as described in 2ΔΔCt) in the fibrotic kidney relating to the opposite healthy kidneys whose mRNA expression was set to one.Real‐time PCR of αSMA, fibronectin, Col1a1, CTGF, PAI‐1, TIMP‐1, MMP2, and MMP9 was performed as previously described Collagen levels were determined by Sirius red/fast green method The fixation, staining and quantification of kidney tissues were performed as previously reported The protein expression of CTGF, PAI‐1 , Erk1/2, P‐Erk1/2  and TIMP‐1  was assayed by western blotting The activity of MMP2 and MMP9 was detected using gelatin zymography, which can distinguish between latent and active forms of proteinases.m NaCl and 2.5% Triton X‐100 in 50 mm Tris‐HCl, pH 7.5) to remove SDS and incubated in a reaction buffer  for enzymatic reaction at 37 °C overnight. Finally, the gel was stained with Coomassie blue, and destained in 10% acetic acid/30% methanol and quantified using imagej software (open source). MMPs in fibrotic tissue were expressed as relative values of markers in kidneys from untreated wt‐mice.Briefly, the culture medium was electrophoresed in a SDS/PAGE gel containing 0.1% gelatin. The gel was loaded with 70 μg/35 μg total protein for MMP2/MMP9. The gel was washed  or vehicle followed by exposure to TGFβ  or vehicle. Nuclei were stained with DAPI . P‐smad3 and P‐smad2 , respectively, were detected using an Alexa647‐conjugated anti‐rabbit secondary‐antibody  for 2 h at room temperature. Coverslips were washed, mounted with glycerol and analysed using an Axiovert 200 microscope . To ensure a valid comparison, images were randomly selected from different fields. The intranuclear and extranuclear fluorescence‐intensity of three to six equal areas was measured respectively. Then, the mean value of intranuclear and extranuclear fluorescence intensity of P‐smad3 was determined respectively. For the quantification of the fluorescence intensity all values were related to values of untreated wt‐fibroblasts (control) using the metamorphic offline software.The fibroblasts of wt‐ and cGKI‐KO‐mice were isolated and stained as described previously m Tris; 150 mm NaCl, 2% Lubrol (nonaethylenglycol‐monododecylether)] containing phosphatase inhibitors  and protease inhibitors. After homogenization and centrifugation  the protein concentration of the supernatant was determined by a Lowry‐based method.The stimulated (TGFβ/cGMP+TGFβ) cells were lysed in 2% Lubrol‐PX buffer . Creatinine eluted after 6.3–6.5 min at a flow rate of 1.0 mL·min−1 (column temperature 35 °C).Serum creatinine was determined by HPLC as previously reported with minor modifications t‐test  was used. If the difference between two groups was statistically significant, then it is indicated by asterisks . n indicates the number of animals.All data are expressed as mean ± SEM. For calculation of statistical differences between two means, the unpaired Student's PS and J‐PS are employees at Bayer Pharma AG. VW is an employee at Novartis Pharma GmbH, Nuremberg. The PhD thesis of VW is funded by Novartis Pharma.ES, VW and JS planned experiments, analysed data and wrote the manuscript; ES, VW, AS and FK performed experiments. FH, PS and HPS contributed reagents or other essential material. All authors critically read the manuscript.Fig. S1. Analysis of renal cGMP levels after BAY application.Click here for additional data file.Fig. S2. Analysis of the whole fluorescence intensity of P‐smad3.Click here for additional data file.


We present tournament results and several powerful strategies for the Iterated Prisoner’s Dilemma created using reinforcement learning techniques . These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also. T > R > P > S and 2R > T + S. The PD is a one round game, but is commonly studied in a manner where the prior outcomes matter. This repeated form is called the Iterated Prisoner’s Dilemma (IPD). The IPD is frequently used to understand the evolution of cooperative behaviour from complex dynamics [The Prisoner’s Dilemma (PD) is a two player game used to model a variety of strategic interactions. Each player chooses between cooperation (C) or defection (D). The payoffs of the game are defined by the matrix dynamics .This manuscript uses the Axelrod library , 3, openThe library is continuously developed and as of version 3.0.0, the library contains over 200 strategies, many from the scientific literature, including classic strategies like Win Stay Lose Shift  and prevhttp://axelrod-tournament.readthedocs.io. This work presents a detailed analysis of tournaments with 176 strategies.Since Robert Axelrod’s seminal tournament , a numbeIn this work we describe how collections of strategies in the Axelrod library have been used to train new strategies specifically to win IPD tournaments. These strategies are trained using generic strategy archetypes based on e.g. finite state machines, arriving at particularly effective parameter choices through evolutionary or particle swarm algorithms. There are several previous publications that use evolutionary algorithms to evolve IPD strategies in various circumstances –22. See The Axelrod library now contains many parametrised strategies trained using machine learning methods. Most are deterministic, use many rounds of memory, and perform extremely well in tournaments as will be discussed in the results Section. Training will be discussed in a later section. These strategies can encode a variety of other strategies, including classic strategies like Tit For Tat , handshan1 moves, the opponent’s last m1 moves, and the players last m2 moves. If n1 > 0 then the player has infinite memory depth, otherwise it has depth max. This is illustrated diagrammatically in The LookerUp strategy is based on a lookup table and encodes a set of deterministic responses based on the opponent’s first n1, m1, and m2 have been tried, the best performance at the time of training was obtained for n1 = m1 = m2 = 2 and generally for n1 > 0. A strategy called EvolvedLookerUp2_2_2 is among the top strategies in the library.Training of this strategy corresponds to finding maps from partial histories to actions, either a cooperation or a defection. Although various combinations of n strategies with the parameters n1 = 0 and m1 = m2 = n. For n = 1, the resulting strategy cooperates if the last round was mutual cooperation and defects otherwise, known as Grim or Grudger.This archetype can be used to train deterministic memory-n1 = 0, m1 = 1, and m2 = 2. The strategy Winner12 emerged in less than 10 generations of training in our framework using a score maximizing objective. Strategies nearly identical to Winner21 arise from training with a Moran process objective.Two strategies in the library, Winner12 and Winner21, from , are basGambler is a stochastic variant of LookerUp. Instead of deterministically encoded moves the lookup table emits probabilities which are used to choose cooperation or defection. This is illustrated diagrammatically in n1 = m1 = m2 = 2 that is mostly deterministic, with 52 of the 64 probabilities being 0 or 1.Training of this strategy corresponds to finding maps from histories to a probability of cooperation. The library includes a strategy trained with n strategies. A memory one strategy called PSOGamblerMem1 was trained, with probabilities (Pr(C | CC), Pr(C | CD), Pr(C | DC), Pr(C | DD)) = . Though it performs well in standard tournaments . Particle swarm algorithms have been used to trained IPD strategies previously .Strategies based on artificial neural networks use a variety of features computed from the history of play:Opponent’s first move is COpponent’s first move is DOpponent’s second move is COpponent’s second move is DPlayer’s previous move is CPlayer’s previous move is DPlayer’s second previous move is CPlayer’s second previous move is DOpponent’s previous move is COpponent’s previous move is DOpponent’s second previous move is COpponent’s second previous move is DTotal opponent cooperationsTotal opponent defectionsTotal player cooperationsTotal player defectionsRound numberThese are then input into a feed forward neural network with one layer and user-supplied width. This is illustrated diagrammatically in Training of this strategy corresponds to finding parameters of the neural network. An inner layer with just five nodes performs quite well in both deterministic and noisy tournaments. The output of the ANN used in this work is deterministic; a stochastic variant that outputs probabilities rather than exact moves could be created.Strategies based on finite state machines are deterministic and computationally efficient. In each round of play the strategy selects an action based on the current state and the opponent’s last action, transitioning to a new state for the next round. This is illustrated diagrammatically in Training this strategy corresponds to finding mappings of states and histories to an action and a state. Figs A variant of finite state machine strategies are called hidden Markov models (HMMs). Like the strategies based on finite state machines, these strategies also encode an internal state. However, they use probabilistic transitions based on the prior round of play to other states and cooperate or defect with various probabilities at each state. This is shown diagrammatically in There are several strategies based on ensemble methods that are common in machine learning called Meta strategies. These strategies are composed of a team of other strategies. In each round, each member of the team is polled for its desired next move. The ensemble then selects the next move based on a rule, such as the consensus vote in the case of MetaMajority or the best individual performance in the case of MetaWinner. These strategies were among the highest performing in the library before the inclusion of those trained by reinforcement learning.Because these strategies inherit many of the properties of the strategies on which they are based, including using knowledge of the match length to defect on the last round(s) of play, not all of these strategies were included in results of this paper. These strategies do not typically outperform the trained strategies described above.The trained strategies (denoted by a * in Appendix A) were trained using reinforcement learning algorithms. The ideas of reinforcement learning can be attributed to the original work of  in whichParticle Swarm Algorithm: .Evolutionary algorithm: .https://pypi.python.org/pypi/pyswarm. This algorithm was used only to train the Gambler archetype.The Particle Swarm Algorithm is implemented using the pyswarm library: All other strategies were trained using evolutionary algorithms. The evolutionary algorithms used standard techniques, varying strategies by mutation and crossover, and evaluating the performance against each opponent for many repetitions. The best performing strategies in each generation are persisted, variants created, and objective functions computed again.The default parameters for this procedure:A population size of 40 individuals (kept constant across the generations);A mutation rate of 10%;10 individuals kept from one generation to the next;A total of 500 generations.https://github.com/Axelrod-Python/axelrod-dojo. There are objective functions for:All implementations of these algorithms are archived at . This sototal or mean payoff,total or mean payoff difference (unused in this work),total Moran process wins (fixation probability). This lead to the strategies named TF1, TF2, TF3 listed in Appendix A.These can be used in noisy or standard environments. These objectives can be further modified to suit other purposes. New strategies could be trained with variations including spatial structure and probabilistically ending matches.This section presents the results of a large IPD tournament with strategies from the Axelrod library, including some additional parametrized strategies (e.g. various parameter choices for Generous Tit For Tat ). These All strategies in the tournament follow a simple set of rules in accordance with earlier tournaments:Players are unaware of the number of turns in a match.Players carry no acquired state between matches.Players cannot observe the outcome of other matches.Players cannot identify their opponent by any label or identifier.Players cannot manipulate or inspect their opponents in any way.Any strategy that does not follow these rules, such as a strategy that defects on the last round of play, was omitted from the tournament presented here (but not necessarily from the training pool).A total of 176 are included, of which 53 are stochastic. A standard tournament with 200 turns and a tournament with 5% noise is discussed. Due to the inherent stochasticity of these IPD tournaments, these tournaments were repeated 50000 times. This allows for a detailed and confident analysis of the performance of strategies. To illustrate the results considered, R, P, T, S) =  thus the specific Prisoner’s Dilemma being played is:The utilities used are  are shown in Pairwise payoff results are given as a heatmap  which shThe strategies that win the most matches  are DefeThe number of wins of the top strategies of Finally, Figs Results from noisy tournaments in which there is a 5% chance that an action is flipped are now described. As shown in Recalling As shown in As shown in Finally, Figs ‘The tournament results indicate that pre-trained strategies are generally better than human designed strategies at maximizing payoff against a diverse set of opponents. An evolutionary algorithm produces strategies based on multiple generic archetypes that are able to achieve a higher average score than any other known opponent in a standard tournament. Most of the trained strategies use multiple rounds of the history of play  and outperform memory-one strategies from the literature. Interestingly, a trained memory one strategy produced by a particle swarm algorithm performs well, better than human designed strategies such as Win Stay Lose Shift and zero determinant strategies .In opposition to historical tournament results and community folklore, our results show that complex strategies can be effective for the IPD. Of all the human-designed strategies in the library, only DBS consistently performs well, and it is substantially more complex than traditional tournament winners like TFT, OmegaTFT, and zero determinant strategies.The generic structure of the trained strategies did not appear to be critical for the standard tournament—strategies based on lookup tables, finite state machines, neural networks, and stochastic variants all performed well. Single layer neural networks performed well in both noisy and standard tournaments though these had some aspect of human involvement in the selection of features. This is in line with the other strategies also where some human decisions are made regarding the structure. For the LookerUp and Gambler archetypes a decision has to be made regarding the number of rounds of history and initial play that are to be used. In contrast, the finite state machines and hidden Markov models required only a choice of the number of states, and the training algorithm can eliminate unneeded states in the case of finite state machines (evidenced by the unconnected nodes in the diagrams for the included representations).Many strategies can be represented by multiple archetypes, however some archetypes will be more efficient in encoding the patterns present in the data. The fact that the Lookerup strategy does the best for the standard tournament indicates that it represents an efficient reduction of dimension which in turn makes its training more efficient. In particular the first rounds of play were valuable bits of information. For the noisy tournament however the dimension reduction represented by some archetypes indicates that some features of the data are not captured by the lookup tables while they are by the neural networks and the finite state machines, allowing the latter to adapt better to the noisy environment. Intuitively, a noisy environment can significantly affect a lookup table based on the last two rounds of play since these action pairs compete with probing defections, apologies, and retaliations. Accordingly, it is not surprising that additional parameter space is needed to adapt to a noisy environment.only in noisy contexts. Empirically we find that DBS (with its default parameters) does not win tournaments at 1% noise. However DBS has a parameter that accounts for the expected amount of noise and a followup study with various noise levels could make a more complete study of the performance of DBS and strategies trained at various noise levels.Two strategies designed specifically to account for noise, DBS and OmegaTFT, perform well and only DBS performs better than the trained strategies and The strategies trained to maximize their average score are generally cooperative and do not defect first. Maximizing for individual performance across a collection of opponents leads to mutual cooperation despite the fact that mutual cooperation is an unstable evolutionary equilibrium for the prisoner’s dilemma. Specifically it is noted that the reinforcement learning process for maximizing payoff does not lead to exploitative zero determinant strategies, which may also be a result of the collection of training strategies, of which several retaliate harshly. Training with the objective of maximizing payoff difference may produce strategies more like zero determinant strategies.For the trained strategies utilizing look up tables we generally found those that incorporate one or more of the initial rounds of play outperformed those that did not. The strategies based on neural networks and finite state machines also are able to condition throughout a match on the first rounds of play. Accordingly, we conclude that first impressions matter in the IPD. The best strategies are nice (never defecting first) and the impact of the first rounds of play could be further investigated with the Axelrod library in future work by e.g. forcing all strategies to defect on the first round.We note that as the library grows, the top performing strategies sometimes shuffle, and are not retrained automatically. Most of the strategies were trained on an earlier version of the library (v2.2.0: ) that didilemma strength) [Finally, whilst we have considered the robustness of our claims and results with respect to noise it would also be of interest to train strategies for different versions of the stage game  , 39. OurThe players used for this study are from Axelrod version 2.13.0 [ϕ—Deterministic—Memory depth: ∞. [epth: ∞. π—Deterministic—Memory depth: ∞. [epth: ∞. e—Deterministic—Memory depth: ∞. [epth: ∞. Stochastic—Memory depth: 1. [ALLCorALLD—epth: 1. Deterministic—Memory depth: ∞. [Adaptive—epth: ∞. Deterministic—Memory depth: ∞. [Adaptive Pavlov 2006—epth: ∞. Deterministic—Memory depth: ∞. [Adaptive Pavlov 2011—epth: ∞. Deterministic—Memory depth: ∞. [Adaptive Tit For Tat: 0.5—epth: ∞. Deterministic—Memory depth: ∞. [Aggravater—epth: ∞. Deterministic—Memory depth: 1. [Alternator—epth: 1. Deterministic—Memory depth: ∞. [Alternator Hunter—epth: ∞. Deterministic—Memory depth: 1. [Anti Tit For Tat—epth: 1. Deterministic—Memory depth: ∞. [AntiCycler—epth: ∞. Deterministic—Memory depth: ∞. [Appeaser—epth: ∞. Stochastic—Memory depth: ∞. [Arrogant QLearner—epth: ∞. Stochastic—Memory depth: ∞. [Average Copier—epth: ∞. Stochastic—Memory depth: ∞. [Better and Better—epth: ∞. Deterministic—Memory depth: 1. [Bully—epth: 1. Stochastic—Memory depth: ∞. [Calculator—epth: ∞. Stochastic—Memory depth: ∞. [Cautious QLearner—epth: ∞. CS)—Deterministic—Memory depth: ∞. [CollectiveStrategy (epth: ∞. CTfT)—Deterministic—Memory depth: 3. [Contrite Tit For Tat —Deterministic—Memory depth: ∞. [Forgiving Tit For Tat —epth: ∞. Stochastic—Memory depth: ∞. [Grofman—epth: ∞. Deterministic—Memory depth: 1. [Grudger—epth: 1. , 53, 54Deterministic—Memory depth: ∞. [GrudgerAlternator—epth: ∞. Deterministic—Memory depth: ∞. [Grumpy: Nice, 10, −10—epth: ∞. Deterministic—Memory depth: ∞. [Handshake—epth: ∞. Deterministic—Memory depth: ∞. [Hard Go By Majority—epth: ∞. Deterministic—Memory depth: 10. [Hard Go By Majority: 10—pth: 10. Deterministic—Memory depth: 20. [Hard Go By Majority: 20—pth: 20. Deterministic—Memory depth: 40. [Hard Go By Majority: 40—pth: 40. Deterministic—Memory depth: 5. [Hard Go By Majority: 5—epth: 5. Deterministic—Memory depth: ∞. [Hard Prober—epth: ∞. HTf2T)—Deterministic—Memory depth: 3. [Hard Tit For 2 Tats (epth: 3. HTfT)—Deterministic—Memory depth: 3. [Hard Tit For Tat —Deterministic—Memory depth: 2. [Tit For 2 Tats (epth: 2. TfT)—Deterministic—Memory depth: 1. [Tit For Tat (epth: 1. Deterministic—Memory depth: 10. [Tricky Cooperator—pth: 10. Deterministic—Memory depth: ∞. [Tricky Defector—epth: ∞. Stochastic—Memory depth: 11. [Tullock: 11—pth: 11. 2TfT)—Deterministic—Memory depth: 2. [Two Tits For Tat (epth: 2. Deterministic—Memory depth: ∞. [VeryBad—epth: ∞. Stochastic—Memory depth: 1. [Willing—epth: 1. WShLSt)—Deterministic—Memory depth: 1. [Win-Shift Lose-Stay: D (epth: 1. WSLS)—Deterministic—Memory depth: 1. [Win-Stay Lose-Shift: C (epth: 1. , 52, 61Deterministic—Memory depth: 2. [Winner12—epth: 2. Deterministic—Memory depth: 2. [Winner21—epth: 2. Stochastic—Memory depth: ∞. [Worse and Worse—epth: ∞. Stochastic—Memory depth: ∞. [Worse and Worse 2—epth: ∞. Stochastic—Memory depth: ∞. [Worse and Worse 3—epth: ∞. Stochastic—Memory depth: 1. [ZD-Extort-2 v2: 0.125, 0.5, 1—epth: 1. Stochastic—Memory depth: 1. [ZD-Extort-2: 0.1111111111111111, 0.5—epth: 1. Stochastic—Memory depth: 1. [ZD-Extort-4: 0.23529411764705882, 0.25, 1—Stochastic—Memory depth: 1. [ZD-GEN-2: 0.125, 0.5, 3—epth: 1. Stochastic—Memory depth: 1. [ZD-GTFT-2: 0.25, 0.5—epth: 1. Stochastic—Memory depth: 1. [ZD-SET-2: 0.25, 0.0, 2—epth: 1.


Hum Brain Mapp 37:3882–3896, 2016. © 2016 Wiley Periodicals, Inc.The frequency of visual gamma oscillations is determined by both the neuronal excitation–inhibition balance and the time constants of GABAergic processes. The gamma peak frequency has been linked to sensory processing, cognitive function, cortical structure, and may have a genetic contribution. To disentangle the intricate relationship among these factors, accurate and reliable estimates of peak frequency are required. Here, a bootstrapping approach that provides estimates of peak frequency reliability, thereby increasing the robustness of the inferences made on this parameter was developed. The method using both simulated data and real data from two previous pharmacological MEG studies of visual gamma with alcohol and tiagabine was validated. In particular, the study by Muthukumaraswamy et al.  (Neurops Synchronization of rhythmic neuronal firing in the gamma range (∼30–90 Hz) is a potential mechanism for information coding in the brain [Buzsáki and Wang, The peak frequency of visual gamma responses is modulated by properties of the visual stimulus such as size [Gieselmann and Thiele, In‐vitro and in‐vivo animal studies have demonstrated a dependency of peak gamma frequency on the time constants of GABAergic processes [Bartos et al., A positive allosteric modulator propofol was found to increase gamma amplitude, but left gamma frequency unchanged [Saxena et al., However, not all human studies are entirely consistent with the animal literature. For example, the GABAIn the present work, we build upon previous research in which the robustness of oscillatory measures in the gamma range was studied with respect to systematic variations of the stimulus configuration [Muthukumaraswamy and Singh, f frequency scaling of the power spectrum. To reproduce the sustained component of visual gamma responses, the second half of each trial was embedded with a sinusoidal signal (1 s), with different frequency in each trial. The frequency of the oscillation was normally distributed across trials, with both mean frequency and mode frequency of 60 Hz, and standard deviation (SD) of frequency increasing exponentially from 2.5 to 20 Hz across six different conditions  to simulate electrophysiological data, as they would be recorded in visual gamma paradigms with MEG. The data resembled the time course of source‐reconstructed cortical activity in the occipital lobe [see Muthukumaraswamy et al., l signal  s, with l signal  s, with l signal  s, with An overview of our approach to peak frequency estimation and quality control (QC) is illustrated schematically in Figure hilbert), to yield the amplitude envelope for this frequency range. The envelopes were baselined, in order to express the response as a percentage change from baseline, and then “stacked” to form a time‐frequency spectrogram . The analysis time‐range was −1.4 to −0.1 s for baseline and 0.3–1.5 s for stimulation. The QC criterion was the same as in the tiagabine analysis . In other words, if 50% or more of the bootstrapped peak frequencies in a given dataset fell within ±1.2 Hz of the bootstrap distribution mode, peak frequency in that dataset was considered reliably estimated. Otherwise, the dataset was marked as of poor quality.We re‐analyzed data from a previous pharmacological MEG study of tiagabine [Muthukumaraswamy et al., α = 0.25) was chosen in order to reduce spectral leakage whilst preserving as much of the signal as possible. The QC criterion used to calculate a reliability measure was based on the frequency resolution of the periodogram (∼1.2 Hz). In other words, if 50% or more of the bootstrapped peak frequencies in a given dataset fell within ±1.2 Hz of the bootstrap distribution mode, peak frequency in that dataset was considered reliably estimated. Otherwise, the dataset was marked as of poor quality.Peak gamma frequency reliability estimates and Bootstrap peak frequency measures were obtained using the QC method as described in the validation study . As the baseline (−0.8 to −0.1 s) and stimulus (0.3–1.0 s) analysis time‐range had a relatively short length of 700 ms, a relatively broad tapered Tukey window  showed that, across the six conditions of exponentially decreasing frequency consistency, the width of the Bootstrap peak frequency distribution increased monotonically Fig. A. LikewiNext, we compared the accuracy in peak frequency estimation between the Bootstrap peak frequency and the Envelope peak frequency. As shown in Figure The QC analysis of the alcohol data  revealed that the estimate of peak frequency did not meet our QC reliability criterion in 5 out of 64 datasets  and Time (two levels: pre and post), with the Drug × Time interaction term being of most interest. To compare how the exclusion of participants and the bootstrap approach to frequency estimation affected the results, the analysis was repeated using the Bootstrap peak frequency  = 13.31, P = 0.004.To test the effect of alcohol on the frequency of visual gamma, peak frequency was analyzed using a 2 × 2 repeated measures ANOVA, with factors ncy Fig. A,B, and ncy Fig. C,D, withncy Fig. A,C, and ncy Fig. B,D. For Drug  = 2.44, P = 0.15), a significant effect of Time  = 5.34, P = 0.041), and a significant Drug × Time interaction effect  = 15.58, P = 0.002). Peak frequency was significantly reduced by alcohol in the post‐alcohol session compared with both the pre‐alcohol (t(11) = −4.68, P = 0.001) and the post‐placebo session (t(11) = −3.63, P = 0.004). There were no significant differences in peak frequency between pre‐ and post‐placebo (t(11) = 0.58, P = 0.57) or between pre‐placebo and pre‐alcohol (t(11) = 0.44, P = 0.67). The absence of a significant difference between pre‐ and post‐placebo indicated that the significant main effect of Time was driven by the significant difference between pre‐ and post‐alcohol alone.The Bootstrap method in the 12 accepted participants, illustrated in Figure Drug  = 1.04, P = 0.32) or Time  = 0.31, P = 0.59), and no significant Drug × Time interaction effect  = 0.58, P = 0.46).The results of the Bootstrap method with inclusion of all sixteen participants Fig. B showed Drug  = 2.51, P = 0.14) or Time  = 2.30, P = 0.16), and a significant Drug × Time interaction effect  = 9.16, P = 0.012). Peak frequency was significantly reduced by alcohol in the post‐alcohol session compared with both the pre‐alcohol (t(11) = −2.57, P = 0.026) and the post‐placebo session (t(11) = −2.71, P = 0.020). There were no significant differences in peak frequency between pre‐ and post‐placebo (t(11) = 0.22, P = 0.83) or between pre‐placebo and pre‐alcohol (t(11) = −0.12, P = 0.91).The Envelope method in the twelve accepted participants Fig. C resulteDrug  = 0.40, P = 0.53) or Time  = 1.26, P = 0.30), and no significant Drug × Time interaction effect  = 0.05, P = 0.82).The results of the Envelope method with inclusion of all 16 participants Fig. D showed To summarize, using the Bootstrap method to estimate peak gamma frequency and after exclusion of participants based on the QC approach, the interaction effect was replicated at a higher level of significance compared with the Envelope method and the Gaussian function fits. Furthermore, our QC approach resulted in the exclusion of four participants, as also reported by Campbell et al.  based onThe QC analysis of the tiagabine data  revealed that the estimate of peak frequency did not meet our QC reliability criterion in 22.5% of the datasets  and Time . In this analysis design, a significant effect of tiagabine is demonstrated by a significant Drug × Time interaction. Results, illustrated in Figure Drug  = 18.8, P = 0.003), a marginally non‐significant effect of Time  = 2.9, P = 0.057), and a significant Drug × Time interaction effect  = 3.7, P = 0.028).To test our main hypothesis of a shift in the frequency of visual gamma, the Bootstrap peak frequency was analyzed in the eight accepted participants using a 2 × 4 repeated measures ANOVA, with factors Drug using paired‐sample t tests at each of the four time points. There was no difference in peak gamma frequency between the pre‐ingestion sessions of the tiagabine and placebo treatments (t(7) = −0.2, P = 0.87). In contrast, peak gamma frequency was significantly reduced with tiagabine, compared with the corresponding placebo sessions, at 1 h (t(7) = 2.4, P = 0.048), at 3 h (t(7) = 6.5, P = 0.0003), and at 5 h post‐ingestion (t(7) = 5.0, P = 0.002).To investigate the temporal profile of drug modulation, given the significant interaction, we analyzed the simple effects of Time with two 1 × 4 repeated measures ANOVAs, separately for each of the two treatments. There was no effect of time in the placebo treatment  = 0.1, P = 0.97), suggesting that peak frequency was estimated reliably over repeated sessions under administration of placebo. In contrast, the effect of Time was significant in the tiagabine treatment  = 7.3, P = 0.002). Compared with the pre‐tiagabine session, peak gamma frequency was significantly reduced at 1 h (t(7) = 2.6, P = 0.036), at 3 h (t(7) = 5.2, P = 0.001), and at 5 h post‐tiagabine (t(7) = 5.5, P = 0.001).Next, we analyzed the simple effects of Drug  = 1.7, P = 0.24), or Time  = 1.5, P = 0.24), and no significant Drug × Time interaction  = 2.6, P = 0.08). The amplitude spectra of percentage change from baseline in the gamma range are illustrated in Figure Subsidiary to our main hypothesis, we tested the effect of tiagabine on gamma amplitude with the same statistical analysis used for peak gamma frequency . As suggested by visual inspection of the SAM spatial images , results showed no significant effect of Overall, these results indicated that peak gamma frequency was significantly reduced by tiagabine at each of the three time points measured after drug administration, whereas gamma amplitude was not affected. Furthermore, peak frequency did not differ statistically across the four measurements in the placebo conditions, or between the pre‐tiagabine and the pre‐placebo sessions. Therefore, peak frequency was estimated reliably both at repeated intervals of 2 h, and between sessions as far as 1 week apart. For comparison, results are shown in Figure A receptor density [Kujala et al., r coefficient to correlate peak gamma frequency, in each experimental session, with peak frequency reliability, as measured by the percentage of bootstrap iterations within ±1.2 Hz of the bootstrap distribution mode. As illustrated in Supporting Information Figure S3A, we found no evidence for a consistent relationship between these two measures, suggesting that the estimated reliability of peak gamma frequency does not depend on the frequency at which gamma peaks occur.Additionally, we asked the question of whether differences in gamma quality could be related to differences in the individual peak gamma frequency, across participants. The latter measure has been proposed as an index of local GABA concentration [Muthukumaraswamy et al., r = 0.48, P = 0.067). As illustrated in Supporting Information Figure S3B  participants whose peak gamma frequency decreased the most tended to show the lowest reliability estimates, at 1 h post‐tiagabine. This would explain why, after participant exclusion, the greatest reduction in peak frequency was observed in the session at 3 h, rather than 1 h after tiagabine  peak frequency from each of the post‐placebo (or post‐tiagabine) sessions, and then correlated this measure with peak frequency reliability. For this purpose, the percentage of bootstrap iterations in the “pre” and “post” sessions were averaged, separately for each correlation. Once again, we found no significant correlations between peak frequency reliability and drug‐induced change in peak frequency. The correlation at 1 h post‐tiagabine was the only positive correlation, and its uncorrected p‐value approached statistical significance  and test how each of these changes impacts on the sensitivity and specificity of the QC results. In particular, sensitivity and specificity can be affected by the width criterion, which determines the threshold for data exclusion. If a strict criterion is chosen, good but not perfect frequency estimates could be inappropriately excluded, potentially leading to a loss of statistical power. On the contrary, the choice of a loose criterion could lead to inclusion of poor estimates and, for example, increased likelihood of false negative results. While the criterion was chosen arbitrarily in this work, with its adequacy being established by visual inspection of the bootstrap distributions, the use of empirically determined criteria in the future would be desirable.In the second part of this work, the alcohol data by Campbell et al.  was consIt should be noticed that, by applying a standard analysis pipeline and avoiding participant exclusion, Muthukumaraswamy et al.  adopted Of course, rejecting datasets based on reliability of peak frequency estimation is still dependent on signal‐to‐noise and hence amplitude , in line with the pharmacokinetics of tiagabine showing maximum plasma concentrations occurring between 45 and 150 min after drug ingestion [Leach and Brodie, A receptor‐induced IPSCs [Roepstorff and Lambert, Overall, these novel tiagabine results are strongly supported by animals models, which demonstrate a close dependency of gamma frequency on the time constants of GABAergic inhibition [Faulkner et al., A receptors in early visual areas [Kujala et al., A modulator [Lozano‐Soldevilla et al., A agonist propofol [Saxena et al., In non‐invasive human studies, the use of magnetic resonance spectroscopy (MRS) to measure the relationship between GABA and gamma frequency has produced controversial results [cf. Cousijn et al., In the current study, however, no significant effects were observed when, subsidiary to our main hypothesis, gamma amplitude was tested with the same analysis used for peak frequency. This could suggest that tiagabine has a specific effect on oscillation frequency via modulation of inhibitory time constants, while leaving other network parameters unaltered. In support of this, animal studies have demonstrated that the duration of IPSCs is prolonged by tiagabine, but IPSC amplitude is not increased [Roepstorff and Lambert, A positive allosteric modulator produced an alteration of the beta rhythm consisting of decreased frequency and increased amplitude [Jensen et al., A partially unresolved question is whether the changes in frequency associated with GABAergic neurotransmission are unique to gamma oscillations, or extend to other frequency ranges. Likewise, it is unclear whether the GABAergic influences on oscillatory dynamics are specific to visual areas or extend to other cortices. In sensorimotor regions, administration of a benzodiazepine GABAThe frequency of gamma oscillations has been previously related to differences in behavioral performance [Dickinson et al., The work presented here highlights the potential impact of objective data quality quantification and paves the way for future methodological developments in this direction. Using a novel approach to peak frequency estimation, we demonstrated a reduction in gamma frequency by tiagabine, in those participants with reliable peak frequency estimates. The result is supported by animal models, and provides additional translational evidence of the GABAergic mechanisms generating gamma oscillations in humans.Supporting Information Figures.Click here for additional data file.


Alcohol warning labels have a limited effect on drinking behavior, potentially because people devote minimal attention to them. We report findings from two studies in which we measured the extent to which alcohol consumers attend to warning labels on alcohol packaging, and aimed to identify if increased attention to warning labels is associated with motivation to change drinking behavior.N = 60) was an exploratory cross-sectional study in which we used eye-tracking to measure visual attention to brand and health information on alcohol and soda containers. In study 2 (N = 120) we manipulated motivation to reduce drinking using an alcohol brief intervention (vs control intervention) and measured heavy drinkers’ attention to branding and warning labels with the same eye-tracking paradigm as in study 1. Then, in a separate task we experimentally manipulated attention by drawing a brightly colored border around health (or brand) information before measuring participants’ self-reported drinking intentions for the subsequent week.Study 1 . Participants who were motivated to reduce drinking paid less attention to alcohol branding and alcohol warning labels. Results from study 2 showed that the alcohol brief intervention decreased attention to branding compared to the control condition, but it did not affect attention to warning labels. Furthermore, the experimental manipulation of attention to health or brand information did not influence drinking intentions for the subsequent week.Alcohol consumers allocate minimal attention to warning labels on alcohol packaging and even if their attention is directed to these warning labels, this has no impact on their drinking intentions. The lack of attention to warning labels, even among people who actively want to cut down, suggests that there is room for improvement in the content of health warnings on alcohol packaging.The online version of this article (doi:10.1186/s12889-017-4055-8) contains supplementary material, which is available to authorized users. In March 2011, alcohol beverage companies in the UK pledged to put warning labels on 80% of alcoholic drink containers as part of the public health responsibility deal –5. SimilIt is possible that warning labels have a limited effect on drinking behaviour because people pay little attention to them. Indeed, participants spent on average 7% of total viewing time looking at warning messages in alcohol advertisements . HoweverUnfortunately, nothing is known about the extent to which alcohol consumers attend to warning labels, how much their attention is related to individual differences in drinking behaviour and motivation to change it, and whether beneficial behaviour change is a likely consequence of increasing attention to warning labels on packaging. The purpose of the studies reported here was to investigate how much attention is paid to warning labels and branding on alcohol beverage containers, and how individual differences in this are associated with individual differences in drinking behaviour and motivation to change it. In both studies, we measured participants’ eye movements towards brand information and warning labels whilst they viewed alcohol beverage containers. Study 1 was an exploratory study that gathered descriptive information about how much attention alcohol consumers pay to health information and investigated correlations between attention and drinking habits. We hypothesized that heightened motivation to reduce drinking would be associated with increased attention to health warnings. In study 2, we experimentally manipulated motivation to reduce drinking and attention to health warnings in order to investigate the causal relationships between them.n = 58). Their mean age was 21.27 (SD = 3.61). They had a mean Alcohol Use Disorders Identification Test (AUDIT) score of 10.67 (SD = 6.54) and drank on average 32.12 (SD = 29.15) UK units in the 14 days prior to the experiment . The study received ethical approval from the University of Liverpool Research Ethics Committee.Sixty participants  were recruited via online advertisements circulated among students and staff of the University of Liverpool. The sample size was based on previous research on attention to warning labels in alcohol print advertisements . ParticiWe photographed 50 beverage containers (bottles or cans) of various brands and types of alcoholic and non-alcoholic beverages that included health/warning labels . We photographed 25 alcohol containers  and 25 soda containers (23 bottles/cans of carbonated soft drinks and 2 bottles of fruit juice). We took four photographs of each container, two of the front and two of the back. One front and one back picture depicted the whole bottle or can, whereas a different picture depicted a close-up of the front label and the back label. The location of the alcohol warning labels varied between the containers. All aspects of the warning labels were visible and readable in the close-up during the viewing task. Most warning labels were in compliance with the guidelines specified in the responsibility deal and included the alcohol content, the daily guidelines for alcohol consumption, a pregnancy warning, an optional link to drinkaware.co.uk, and an optional responsibility statement. Two labels also included nutrition information. Three labels did not meet the minimum requirements: they did not include the daily recommended guidelines, and two of these also did not include a pregnancy warning. Nevertheless, we included these labels in our analyses as research has shown that 22.4% of alcohol warning labels did not comply with the responsibility deal guidelines . TherefoParticipants were asked to view images of beverage containers (viewing phase) before their memory for the containers was tested . In the viewing phase participants viewed 40 containers from the stimulus set . They were instructed to use the arrow keys to manipulate the display of the containers. The left and right arrow keys were used to alternate between front and back. The up arrow was used to zoom in on the label and the down arrow was used to zoom out. Each container was presented for 15 s and participants were free to manipulate the presentation of the container in any way they liked. Whether the ‘zoomed out’ front or back of the container was presented first was randomized on a trial-by-trial basis. To ensure that all participants had the same starting position at image onset, participants were instructed to look at a fixation cross that was presented for 1 s before the trial started. Participants’ eye movements were measured using an ASL Eye-Trac D6  at a sampling rate of 120 Hz.M = 95.83% (SD = 5.38) of the recognition trials.In the recognition phase, participants were shown a second set of 20 images (10 new and 10 of the 40 that had been presented during the viewing phase) and were asked to indicate whether or not each image had been present in the previous set by pressing a “yes” or “no” button. Recognition accuracy was defined as the percentage of correct trials. Participants correctly answered The AUDIT is a 10-item screening instrument assessing hazardous patterns of alcohol use and dependence symptoms. An example of an item is “How often do you have six or more drinks on one occasion?”. Each item is answered in a multiple choice format . Scores range between 0 and 40. AUDIT scores of 8 or higher are indicative of hazardous or harmful drinking patterns . The AUDParticipants were required to sum up for every day of the past two weeks, how many alcoholic drinks they had consumed in UK units. The TLFB has high test-retest reliability and good concurrent validity , 18.The TRI restraint subscale is a 3-item scale answered on a 9-point Likert scale with anchors “never” and “always”. An example of an item is “How often do you attempt to cut down the amount you drink?”. Scores on the TRI restrain subscale range between 3 and 21. The TRI has adequate internal reliability and concurrent validity ..The RTCQ is a questionnaire with three subscales . The subscales are 4-item scales answered on a 5-point Likert scale with anchors “strongly disagree” and “strongly agree”. Examples of items are “I don’t think I drink too much” , “I enjoy my drinking, but sometimes I drink too much” , and “I am trying to drink less than I used to” . Scores on each RTCQ subscale range between−8 and 8. The RTCQ has good internal reliability and concurrent validity .The contemplation ladder is an 11-point scale on which participants are required to indicate their readiness to reduce their drinking (ranging from 0 “No thought of reducing how much I drink per occasion” to 10 “Taking action to reduce the number of drinks I have per occasion”). The contemplation ladder has good concurrent validity .Dietary restraint was measured with the DEBQ Restraint subscale. This is a 10-item scale answered in a multiple choice format . An example of an item is “Do you watch exactly what you eat?”. Scores on the DEBQ Restraint subscale range between 10 and 50. The DEBQ Restraint subscale has high internal reliability and test-rest reliability  and goodr = .53–.80, ps < .001). Finally, participants were thanked and debriefed. Participants received study credits or a £5 shopping voucher.After providing informed consent, participants completed the eye-tracker task. Then, they completed the questionnaire battery on a computer. A motivation to reduce drinking score was created by averaging the TRI restraint subscale, the RTCQ contemplation and action subscales and the contemplation ladder as these scales were strongly correlated  were created by assigning the warning label and any calorie information to the category Health; any brand information, such as the logo and any brand messages to the category Brand; and everything else  to the category Rest. The relative size of each AOI was calculated by dividing the number of pixels in the area by the total number of pixels of the container. The complexity of each AOI was calculated by dividing the compressed file size by the uncompressed file size . BrightnThe different containers varied considerably in their visual characteristics ), order of presentation, size, complexity, brightness, and contrast were level 1 predictors; picture type ) was a level 2 predictor; and motivation to reduce drinking, alcohol consumption, AUDIT scores and dietary restraint were level 3 predictors. The models included random intercepts for all three levels.χ2(24) = 1015.93, p < .001), indicating that both participant characteristics and stimulus characteristics predicted fixation time.In Model 1, we included all level 1 and level 2 predictors and their first and second order interactions with AOI and picture type. Model 1 showed that stimulus characteristics significantly influenced attention to the different AOIs on alcohol and soda packaging. In Model 2, we included all level 3 predictors and their first and second order interactions with AOI and picture type. A chi-squared test showed that Model 2 was a significantly better fit than Model 1 . A drink type  × AOI  repeated measures ANOVA revealed significant main effects of drink type  = 63.97, p < .001, η2p = .53) and AOI  = 84.47, p < .001, η2p = .60) that were qualified by a significant interaction  = 71.09, p < .001, η2p = .56). Post-hoc comparisons showed that participants spent less time viewing health information than brand information  = 14.36, p < .001, d = 1.87; soda t(56) = 7.17, p < .001, d = 0.95) and the rest of the packaging  = 10.62, p < .001, d = 1.38; soda t(56) = 12.95, p < .001, d = 1.71). Participants also looked longer at alcohol branding than the rest of the packaging, t(58) = 6.73, p < .001, d = 0.88, but less long at soda branding than the rest of the packaging, t(56) = 2.21, p = .03, d = 0.29. Participants attended more to alcohol than soda branding, t(56) = 11.78, p < .001, d = 1.56, and less to the rest of alcohol than soda packaging, t(56) = 4.44, p < .001, d = 0.59, but spent similar amounts of time viewing health warnings on alcohol and soda products, t(56) = .91, p = .37, d = 0.12.Over the 15 s viewing period, participants looked at alcohol warning labels for 1.03 s  × picture type interaction  after the alcohol intervention than the control intervention. Second, we manipulated attention to alcohol packaging so that participants either had to attend to warning labels or brand information. We used drinking intentions as the outcome measure, because they predict consumption  and are 120 participants  were recruited via online advertisements circulated among students and staff at the University of Liverpool .We used the same stimuli and questionnaires as in study 1. Because the contemplation ladder was administered after the manipulation, baseline motivation to reduce drinking was defined as the average of the TRI restraint scale and RTCQ contemplation and action subscales, which were strongly correlated . The scThe eye-tracker task was the same as in study 1, with the exception that participants only viewed 30 containers during the viewing phase  and 12 containers in the recognition phase.Participants were informed about their AUDIT scores and alcohol consumption, and the associated health risks, before receiving advice about population norms and the benefits of cutting down, followed by individualised tips to reduce their drinking. For the control condition, participants received brief advice on study habits. The advice closely followed the SIPS procedure, providing participants with information about different ways to study and their associated benefits and tailored tips to improve their own study habits , whereas in the brand exposure condition, the border was around the brand information.After giving consent, participants filled out the alcohol diary, AUDIT, TRI, and RTCQ. Then, half of the participants received brief advice on sensible alcohol consumption , whilst the other half received brief advice about study habits (control condition). Then, participants did the viewing task. They were asked to indicate their motivation to reduce drinking on the contemplation ladder before and after the task. After this, participants received the manipulation of attention task. Half of the participants in the alcohol advice and control condition were allocated to the brand exposure condition and the other half were allocated to the health exposure condition. Allocation to the advice conditions and attention conditions was randomized. Then, participants completed the drinking intentions questionnaire and the DEBQ, followed by a bogus memory task to corroborate the stated aim of the manipulation of attention task. Finally, participants were thanked and debriefed. Participants received study credits or a £5 high street voucher.χ2(12) = 31.72, p < .001), which indicates that the level 3 variables predicted fixation time above and beyond stimulus characteristics.We employed the same data preparation and analysis strategy for the viewing task as in study 1. Trials in which participants spent less than 50% of the viewing time looking at the stimuli were removed due to inaccurate tracking (9%). A model with the level 1 and level 2 predictors (Model 1) was compared with Model 2, which also included condition , and baseline motivation to reduce drinking as participant-level predictors (level 3). A chi-squared test showed that Model 2 was a significantly better fit than Model 1  = 4.20, p = .04, η2p = .04), which were qualified by a significant advice × exposure condition interaction on motivation to reduce drinking  = 5.99, p = .02, η2p = .05). Post-hoc t-tests showed that participants in the control condition had a stronger baseline motivation to reduce drinking than participants in the alcohol advice condition, t(118) = 2.02, p = .046, d = .37. This difference between advice conditions was only significant among participants in the brand exposure condition, t(58) = 3.39, p = .001, d = .88, and not among those in the health exposure condition, t(58) = .27, p = .79, d = .07, see Table ps > .23).A 2  × 2  MANOVA with age, recent alcohol consumption, AUDIT scores and the baseline measure of motivation to reduce drinking as dependent variables revealed significant baseline differences between conditions. There were significant group differences in motivation to reduce drinking for the advice conditions  and the control condition , t(118) = 1.46, p = .15, d = .27. Therefore, the SIPS manipulation did not increase motivation to reduce drinking.An independent samples SD = 0.81, 8%). There was a significant AOI brand × condition interaction, indicating that participants who received alcohol advice spent less time viewing brand information than those in the control condition  than health information  and participants in the health attention condition fixated longer on health  than brand information  = 133.24, p < .001, η2p = .58). Therefore, the manipulation of attention was successful.Participants in the brand attention condition fixated longer on brand  = .47, p = .62, η2p = .01). Neither did condition  = 1.94, p = .15, η2p = .03), or the interaction between exposure and condition  = .64, p = .53, η2p = .01).A 2  by 2  MANOVA with binge drinking intentions and intended consumption as the DVs showed that exposure did not significantly affect drinking intentions . In study 1, we demonstrated that self-reported motivation to reduce drinking reduced attention to both branding and warning labels on alcohol packaging. Although we did not replicate this association in study 2, we did demonstrate that a brief alcohol intervention reduced attention to branding, although this effect was not specific for alcohol packaging and the brief alcohol intervention did not influence participants’ motivation to reduce drinking. Contrary to hypotheses, our experimental manipulation that encouraged participants to focus their attention on warning labels did not affect their drinking intentions for the subsequent week.A possible explanation is that participants do not particularly notice warning labels, due to their current design , 32. OurIndeed, Al-hamdani and Smith  demonstrThese studies have some limitations. The viewing task in both studies comprised a 15 s viewing period for each beverage container and it was framed as a memory task to ensure that participants would attend to the packaging. The length of exposure and the instructions might have increased attention to areas that participants would normally ignore. Additionally, the alcohol advice manipulation did not increase motivation to reduce drinking, which means that the significant effect of advice condition cannot be interpreted as an effect of motivation to reduce drinking. Finally, when viewing multiple products at the same time, people pay more attention to the product they prefer . We did To conclude, our studies show that people pay minimal attention to current UK warning labels on alcohol packaging. Motivation to reduce drinking decreases attention to branding, but does not increase attention to warning labels. Drinking intentions were not affected by attention to warning labels, even when participants had to attend to them. Changes in warning label design that make the label more visually salient and content are advised.


HCC) and might play a tumor‐suppressive role. In this study, we sought to validate the decreased expression with a larger cohort and to explore potential molecular mechanisms. GEO and TCGA databases were used to gather miR‐146a‐5p expression data in HCC, which included 762 HCC and 454 noncancerous liver tissues. A meta‐analysis of the GEO‐based microarrays, TCGA‐based RNA‐seq data, and additional qRT‐PCR data validated the down‐regulation of miR‐146a‐5p in HCC and no publication bias was observed. Integrated genes were generated by overlapping miR‐146a‐5p‐related genes from predicted and formerly reported HCC‐related genes using natural language processing. The overlaps were comprehensively analyzed to discover the potential gene signatures, regulatory pathways, and networks of miR‐146a‐5p in HCC. A total of 251 miR‐146a‐5p potential target genes were predicted by bioinformatics platforms and 104 genes were considered as both HCC‐ and miR‐146a‐5p‐related overlaps. RAC1 was the most connected hub gene for miR‐146a‐5p and four pathways with high enrichment  were denoted for the overlapped genes. The down‐regulation of miR‐146a‐5p in HCC has been validated with the most complete data possible. The potential gene signatures, regulatory pathways, and networks identified for miR‐146a‐5p in HCC could prove useful for molecular‐targeted diagnostics and therapeutics.Our previous research has demonstrated that miR‐146a‐5p is down‐regulated in hepatocellular carcinoma ( AUCarea under the curveEMTepithelial mesenchymal transitionFCsfold changesGEOgene expression omnibusHCChepatocellular carcinomaKEGGKyoto Encyclopedia of Genes and GenomesmiRsmicroRNAsNLPnatural language processingROCreceiver operator characteristicTCGAThe Cancer Genome AtlasHepatocellular carcinoma (HCC) is considered to be the fifth most frequent cancer globally and takes the third place for cancer‐related mortality MicroRNAs (miRs) are an ample variety of short, noncoding RNA molecules of 18–25 nucleotides, which mediate numerous cellular processes, such as cell proliferation, migration, and apoptosis Believed to be promising in cancer diagnostics and prognosis predicting, gene signatures help to provide the molecular backgrounds, regulatory pathways, and networks of cellular activities in HCC A succession of resources and techniques in bioinformatics and computational biology were applied in the study, which includes GEO and TCGA data aggregation, comprehensive meta‐analyses, NLP analysis, target genes prediction, analytic integration, and bioinformatics analyses. We aimed to validate the down‐regulation of miR‐146a‐5p in HCC with the most complete data currently available and to present the audience with the intriguing gene signatures, regulatory pathways, and networks of miR‐146a‐5p in the carcinogenesis, metastasis, prognosis, recurrence, survival, and drug‐resistance (sorafenib and bevacizumab) of HCC.The present study consists of several processes sequentially Fig. , that ishttp://www.ncbi.nlm.nih.gov/geo/) with the closing date of 10 September 2016. The search strategy formulated in the GEO datasets (http://www.ncbi.nlm.nih.gov/gds/) was as follows:  AND (hepatocellular OR liver OR hepatic OR HCC). Inclusion criteria were listed below: (a) HCC tissues were included in each dataset with each group containing more than two samples, regardless of the inclusion of adjacent noncancerous tissues ; (b) the dataset sample organism was Homo sapiens; (c) the expression data of miR‐146a (hsa‐miR‐146a or hsa‐miR‐146a‐5p) from the experimental and control groups could be provided or calculated. Meanwhile, the following conditions might cause the exclusion of related datasets: (a) datasets without information on miR‐146a‐5p; (b) datasets without complete data for analysis; (c) samples based on cell lines; (d) not all the subjects of the included studies were human; or (e) miR‐146a‐5p was determined in the HCC patients without a comparison. Expression values of miR‐146a‐5p and sample size in both test and control groups were calculated. Moreover, means and standard deviations of these values were extracted to estimate the different levels of miR‐146a‐5p in case and control groups by using Review Manager 5.3 with random‐effects model. The chi‐square test and the I2 statistics were applied to evaluate the heterogeneity across studies. It was considered to be heterogeneous when the P value <0.05 or I2 > 50%. Furthermore, SMD and its 95% CI were pooled to assess the stability of the analysis. It was considered to be statistically significant if the corresponding 95% CI for the pooled SMD did not overlap 1 or ‐1. Additionally, sensitivity analysis was conducted by eliminating each study to evaluate the source of heterogeneity.All the functional genomics data of miR‐146a‐5p were requested and assembled from the GEO Database (http://cancergenome.nih.gov/), we downloaded and extracted the data of miR‐146a‐5p expression from miRNASeqV2 (level 3), on 15 July 2016, through bulk download mode. MiR‐146a‐5p expression data were presented as upper quartile normalized Expectation‐Maximization (RSEM) count estimates P value <0.05 in Student's t‐test.From the TCGA (in vitro or in vivo studies were excluded. (c) We also excluded the studies with no information on the difference of miR‐146a‐5p between HCC and controls. (d) Since we had downloaded and evaluated TCGA data by ourselves, those studies based on TCGA data were excluded for the meta‐analysis based on literature.Comprehensive meta‐analyses were performed based on the data gathered from GEO, TCGA, and relevant literature. Related studies were selected by comprehensively searching through the online databases PubMed, Embase, Web of Science, Wiley Online Library, Cochrane Library, Science Direct, Chinese WanFang Database, Chinese VIP Database, Chinese Biomedical Literature Database, and Chinese CNKI Database up to 15 July 2016, independently. The following combination of keywords and entry words was employed: (a) (miR‐146a OR miRNA‐146a OR microRNA‐146a OR miR146a OR miRNA146a OR microRNA146a OR ‘miR 146a’ OR ‘miRNA 146a’ OR ‘microRNA 146a'OR miR‐146a‐5p OR miRNA‐146a‐5p OR microRNA‐146a‐5p); (b) (hepatocellular OR liver OR hepatic OR HCC); (c) . In addition, some references of relevant articles were manually searched for further studies. Whichever articles fulfilled all the following criteria were considered to be included: (a) There was no language restriction of the publications. (b) Patients with HCC were included. (c) The difference of miR‐146a‐5p expression between HCC and noncancerous controls was estimated. (d) If the study of the same patient cohort was published twice or more, only the most complete and recently published one would be included. Listed below were situations which caused the exclusion of related articles: (a) Reviews, letters, comments, case reports, editorials, expert opinions, and conference abstracts without original data were excluded. (b) Articles of experimental spss 22.0 software . The final data after calculation were presented as the means ± SD. Student's t‐test was used for a comparative analysis of two independent groups. To differentiate the expression data between controls and HCC tissues, the diagnostic value was identified using a receiver operator characteristic (ROC) curve. Any P value <0.05 denoted statistical significance. The meta‐analysis was performed using revman 5.3  5.3. A standard mean difference (SMD) and a 95% confidence interval (CI) were utilized to measure continuous outcomes. Fixed or random‐effects models were applied to pool the effect sizes. Cochrane's Q test  and inconsistency (I²) test were conducted to assess heterogeneity. A P < 0.05 or I² > 50% indicated significant heterogeneity, and a random‐effects model was applied. Otherwise, the fixed effects model would be selected. A funnel plot was generated to evaluate publication bias. A P value <0.05 was considered to indicate statistical significance.The statistics were analyzed using Generally, the bioinformatics analyses were conducted as formerly described http://pages.cs.wisc.edu/~bsettles/abner/) as well as gene name normalization according to Entrez Database developed by NCBI p=1−∑i=0k−1p and p=(n!(N−n)!m!(N−m)!)/((n−i)!i!(n−m)!(N−n−m+i)!N!). N was defined as the total number of articles in PubMed. The letters m and n stood for the occurrence frequencies of relevant genes and HCC in PubMed, respectively. K was denoted as the co‐occurrence frequency of a certain gene and HCC at the same time in actual cases. Thus, we could calculate the probability of cocitation occurrence frequency greater than k under completely randomized conditions. The frequency of occurrence was output for each gene respectively: the higher frequency a certain gene demonstrated, the greater opportunity the gene harbored to be HCC‐related. The above methods for NLP procedure and corresponding results for HCC have been reported in our previous research article First of all, we conducted the document mining in PubMed, which included all related articles published between 1 January 1980 and 25 May 2015. The combination of keywords used was as listed: (hepatocellular carcinoma) AND  and (‘1980/01/01’ [PDAT]: ‘2015/05/25’ [PDAT]). A detailed list of relevant proteins and genes was created afterwards. Later on, obtained data went through multiple processes; that is, gene mention tagging and conjunction resolution by ABNER  Pathway Database and calculated the enrichment The gene connectivity was calculated as a quantitative index to demonstrate the degree of interactions among genes and proteins.https://www.r-project.org/) with the keggsoap package (http://www.bioconductor.org/packages/2.4/bioc/html/KEGGSOAP.html) undergoing a genome‐wide interaction analysis . (b) Data from high‐throughput experiments: the MIPS Mammalian Protein–Protein Interaction Database (http://mips.helmholtz-muenchen.de/proj/ppi/) were employed for the protein–protein interactions data. (c) Existing data regarding gene interactions: data were processed with the hypergeometric distribution algorithm.There were three different types of interaction relationships to construct the regulatory networks: (a) Available data from KEGG Database: we achieved the relevant data from KEGG Pathway Database and ported them into R  in form of networks.All the above factors and data were analyzed comprehensively and visualized by the http://www.ncbi.nlm.nih.gov/geo/) after relevant assessments and evaluation. Eventually, nine microarrays were included in this part  demonstrated that the miR‐146a‐5p expression was significantly lower in HCC tissues than that in noncancerous tissues. Two microarrays (GSE21362 and GSE22058) . The detailed information of the included studies for the meta‐analysis was summarized in Table P = 0.033, Fig. P value of the heterogeneity test was <0.001 (I2 = 79%). The funnel plot shown in Fig. P = 0.917; Egger's test: P = 0.760).A total of 2705 microarrays were identified during the primary searching, among which 22 were later downloaded from the GEO database (http://cancergenome.nih.gov/publications/publicationguidelines). The non‐HCC samples or samples with data deficiency were excluded; and 354 HCC patients were finally included in this study. Besides, the data of 50 normal liver tissues were retrieved for comparison. The expression of miR‐146a‐5p in HCC was down‐regulated (8.0304 ± 1.6810), as compared to its expression in normal liver tissues . The funnel plot shown in Fig. P = 0.876; Egger's test: P = 0.460). In summary, the current meta‐analysis further confirmed the down‐regulation of miR‐146a‐5p in HCC.The combination of 762 HCC and 454 noncancerous liver tissues was included for the meta‐analysis, which was from the various recourses such as GEO database, TCGA dataset, the previous article As formerly reported The prediction of miR‐146a‐5p target genes was performed with a union of 11 bioinformatics platforms as described. A certain gene would only be included if nominated by at least four prediction solutions. Accordingly, 251 genes were deemed eligible as potential miR‐146a‐5p target genes for the succeeding analyses.The comprehensive integration yielded a total of 104 genes Table  by overlP ≤ 0.05) and scientifically valuable, namely neurotrophin signaling pathway , adherens junction , VEGF signaling pathway , and toll‐like receptor (TLR) signaling pathway . As to HCC‐related genes obtained from NLP procedure, 24 statistically significant pathways (P ≤ 0.05) were reported previously Pathway enrichment analyses were performed for both miR‐146a‐5p predicted target genes and HCC‐related genes. For miR‐146a‐5p predicted target genes, a panel of 59 pathways Table  was idenz‐test, P = 0.007305, Fig. The gene connectivity analysis provided us with a quantitative interface to understand the interacting degree of related genes and proteins. As to miR‐146a‐5p predicted target genes, the gene connectivity of RAC1 ranked top  after rigorous screening procedures. Three microarrays  presented statistically significant results that the miR‐146a‐5p expression was decreased in HCC tissues as compared to the noncancerous counterparts. Two microarrays (GSE21362 and GSE22058) demonstrated the significant diagnostic value of miR‐146a‐5p in HCC . Meanwhile, in TCGA Database, the RNA‐seq data of 354 randomized HCC and 50 normal tissues were finally included. The miR‐146a‐5p expression in HCC was significantly down‐regulated as compared to its expression in normal liver tissues . Furthermore, a moderate diagnostic value of miR‐146a‐5p in HCC was perceived via ROC . Besides, a new combined cohort using qRT‐PCR was established by pooling data from the previous research t = −7.911, P < 0.001) and the remarkable diagnostic value of miR‐146a‐5p in HCC should be denoted .In relation to the data acquisition, we employed the collaboration of GEO, TCGA, and reported qRT‐PCR as well as newly included pairs. In GEO Database, we included nine microarrays with 319 HCC and 315 noncancerous liver tissues (P = 0.001) was presented by the random‐effects model. The P value of the heterogeneity test was <0.001 (I2 = 76%) and no publication bias was observed . Based on the above in the meta‐analysis, we can safely reach the conclusion that miR‐146a‐5p is down‐regulated in HCC, which is consistent with our previous findings For the comprehensive meta‐analysis, a large cohort of 762 HCC and 454 noncancerous liver tissues from GEO database, TCGA dataset, the previous article, The integrated computation consists of three parts; the natural language processing, the miR‐146a‐5p target genes’ prediction, and the comprehensive integration of overlapped genes. The NLP P = 0.000627; IRAK1, NRAS, RAC1, SORT1, TRAF6), adherens junction , VEGF signaling pathway , and TLR signaling pathway . In the connectivity test for miR‐146a‐5p, RAC1 ranked top as the most interacted gene among all the 20 hub genes of miR‐146a‐5p , interplaying with 10 other genes altogether . Regulatory networks constructed for the overlapping genes demonstrated that miR‐132 in HCC might interact with RAC1, PTGS2, and NRAS via VEGF signaling pathway, mediate biological processes with SMAD4, YES1, and IQGAP1 via adherens junction, associate with TRAF6 and IRAK1 via TLR signaling pathway, and interplay with SORT1 via neurotrophin signaling pathway.Three comprehensive bioinformatics analyses were applied in the study; pathway enrichment test, gene connectivity test, and regulatory network construction. First of all, four pathways were highlighted among all the 59 pathways identified in the pathway enrichment analysis for miR‐146a‐5p predicted target genes; that is, neurotrophin signaling pathway  and validated the down‐regulation of miR‐146a‐5p in HCC, which is the first of its kind. Secondly, the study provided us with the gene signatures for HCC‐ and miR‐146a‐5p‐related overlapped genes, mapping out the potentially primary picture for the further exploration into the mechanisms. Thirdly, the powerful combination of 11 bioinformatics tools for prediction maximized the reliability of miR‐146a‐5p target gene prediction results, since other previous articles only used the utmost of three prediction platforms. Last but not least, the predicted target genes of miR‐146a‐5p and the related informatics analyses have all been made available in the current article along with the supplementary files, which are easily accessible and reusable for further study purposes. Still, The study gathered the most complete data currently available from multiple resources , validated the down‐regulation of miR‐146a‐5p and denoted its diagnostic values in HCC. An outstanding union of 11 bioinformatics platforms predicted a total of 251 potential target genes of miR‐146a‐5p. With the HCC‐related genes from NLP, the overlaps of 104 genes were generated, which are considered both HCC‐ and miR‐146a‐5p related. Last but not least, the bioinformatics analyses highlighted RAC1 as the most connected hub gene for miR‐146a‐5p and four pathways with high enrichment were featured for the overlapped genes, both of which could prove useful for future molecule‐based diagnostics and therapeutics of HCC.XZ, YD, and GC conceived and designed the experiments. XZ, ZY, HL, FR, PL, YD, and GC performed the experiments. XZ, ZY, HL, FR, PL, YD, and GC analyzed the data. XZ, ZY, HL, FR, PL, YD, and GC contributed reagents/materials/analysis tools. XZ, YD, and GC wrote the manuscript. All authors read and approved the final manuscript.Table S1. List of all the 251 potential miR‐146a‐5p target genes. A total of 251 genes were predicted by at least four bioinformatics platforms, making them eligible potential miR‐146a‐5p target genes.Click here for additional data file.


Little is known about the clinical value of late gadolinium enhancement (LGE), in children affected by dilated cardiomyopathy (DCM).U test .We retrospectively evaluated 15 patients  with diagnosis of DCM who underwent cardiac magnetic resonance since 2014. All scans were performed with a 1.5 T system . Study protocol included cine steady-state free precession sequences, followed by administration of 0.2 mmol/kg of gadolinium-based contrast agent. Inversion recovery Turbo Flash sequences, in the same position of cine images, were acquired 10–15 min after the injection of contrast agent, in order to assess the presence of LGE. The latter was considered positive with a signal intensity >6 SD from normal myocardial tissue. Indexed end-diastolic volume (EDVi) and end-systolic volume (ESVi), and left ventricle (LV) ejection fraction (EF) were calculated by using dedicated software on off-line workstation. Global longitudinal strain and diastolic function were evaluated by echocardiography. Clinical follow-up, including death, transplant, and listing for heart transplant [major adverse cardiac events (MACE)], were evaluated. Patients were divided into two different subgroups: negative (Group A) and positive (Group B) for presence of LGE. Statistical analysis was performed by using Mann–Whitney E/e′ ratio = 10 ± 3, MACE = 1. Group B: EDVi = 130 ± 60 ml, ESVi = 89 ± 43 ml, LV EF = 31 ± 6%, global longitudinal strain = −13 ± 4%, E/e′ ratio = 9 ± 3, MACE = 3. There was no statistically significant difference between the two groups, in terms of EDVi (p: 0.2), ESVi (p: 0.2), and E/e′ ratio (0.9), whereas a significant difference of LV EF, presence of significative mitral regurgitation, and global longitudinal strain were observed .Seven patients (47%) showed LGE. A global diffuse subendocardial pattern was evident in all patients presenting LGE . The following main LV indexes were observed in the two subgroups. Group A: EDVi = 96 ± 33 ml, ESVi = 56 ± 29 ml, LV EF = 45 ± 10%, global longitudinal strain = −16 ± 5%, In our population of children with DCM, LGE shows a global diffuse subendocardial pattern. Presence of LGE seems to play a role in these patients determining a worst global systolic function. Dilated cardiomyopathy (DCM) is defined as a ventricular dilatation associated with systolic dysfunction not secondary to other cardiac abnormalities such as coronary artery disease, valvular, or congenital heart disease. Incidence of DCM in children is extremely rare   has gained a crucial role in adults with DCM, since CMR provides reliable information about cardiac function and muscle tissue characterization . In factIn adult patients, various studies demonstrated not only a diagnostic but also a prognostic role of LGE especially in non-ischemic disease , 8–11.Cardiac magnetic resonance assessment of myocardial fibrosis has been reported to be useful in patients with congenital heart disease –15 and cHence, our aim was to investigate the potential role of CMR and LGE in children affected by DCM.We retrospectively evaluated all patients affected by DCM who underwent cardiac MRI, between December 2014 and March 2016, at our institution.Z score >2 and LV EF <50%.Diagnosis of DCM was based on echocardiographic findings with LV end-diastolic dimension Patients younger than 8 years old underwent cardiac mitral regurgitation (MR) under general anesthesia.This study was carried out in accordance with the recommendations of the “Ethical Committee of the Ospedale Pediatrico Bambino Gesù” with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki.2, or frequent ventricular arrhythmias, were excluded from the study.Cardiac magnetic resonance was performed with 1.5 T magnet . Enrolled patients met the eligibility requirements, according to the guidelines of the American College of Radiology of 2013 . Patient2, slice thickness 6–8 mm, acquisition matrix 256 × 173, voxel size 1.5 mm × 1.5 mm × 7 mm, echo/repetition time (TE/TR) 1.1/40 ms, readout bandwidth 930 Hz/pixel, and flip angle 69°), followed by a “stack” of contiguous SSFP cine images, with the same technical parameters, acquired along cardiac short axis, to cover the whole ventricle—from base to apex.Our protocol included cine steady-state free precession (SSFP) sequences acquired on the long-axis cardiac planes: four chambers, two chambers, and three chambers  250–425 ms], were acquired along the same planes of the cine SSFP images, in order to evaluate the presence of LGE.After 10–15 min of intravenous administration of contrast agent , at 0.2 mmol/kg, T1-weighted inversion recovery sequences , but the small sample size did not allow any survival analysis.This study demonstrates that in children affected by DCM, despite comparable LV volumes, the presence of diffuse subendocardial LGE is associated with a decreased LV global systolic function and worst global longitudinal strain. Interestingly, a global diffuse subendocardial pattern, involving majority of LV segments, papillary muscles, and trabeculae, was evident in all patients presenting LGE.A typical pediatric cardiomyopathy, quite similar to our finding, had been already described in autopsy since the 1950s, and it was called primary endocardial fibroelastosis (EFE) . Such enOur findings seem to confirm this report and corroborate the hypothesis that in the presence of EFE the ventricular mechanic is compromised, leading to impaired contraction and expansibility of the cardiac cavity.Moreover, development of this peculiar LGE pattern does not seem to be related to disease duration, since age of the two subgroups is not significantly different.Our study also shows that significant MR is more frequent in patients with significant LGE. This finding should not be linked to a functional mechanism, since ventricular dilation is comparable in the two subgroups. Therefore, the increasing leakage seems to be related to a fibrotic involvement of the valve and subvalvar apparatus. This is in keeping with the original description of primary EFE where papillary muscles involvement and valve leaflets thickening were described. Our data hence confirm that the presence of global diffuse EFE in the setting of children affected by DCM identifies a subgroup of patients with a worse disease clinical expression.The etiology of EFE is still debated in literature. Some articles attribute the development of this condition to an increased response of fetal myocardium in case of viral illnesses including mumps, during pregnancy . The othE/e′ ratio. EFE is commonly believed to be a cause of diastolic dysfunction , and it shows a characteristic global diffuse subendocardial pattern. Despite comparable LV volumes, the presence of fibrosis seems to play a key role in these patients, determining a worst clinical expression of disease. If LGE is present, patients have a significant lower global systolic function and more frequently show a significant MR. More data confirming these findings on a larger population and investigating the prognostic role of LGE in this setting are needed.The authors declare that this study was performed in accordance with the research ethical guidelines. The study was conducted in retrospective analysis.GM, PC, and AS wrote the manuscript. DM, CN, and TS collected data and analyzed the images. GR, MC, and BL analyzed data and were involved in manuscript editing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.