Real-time functional magnetic resonance imaging (rtfMRI) with neurofeedback allows investigation of human brain neuroplastic changes that arise as subjects learn to modulate neurophysiological function using real-time feedback regarding their own hemodynamic responses to stimuli. We investigated the feasibility of training healthy humans to self-regulate the hemodynamic activity of the amygdala, which plays major roles in emotional processing. Participants in the experimental group were provided with ongoing information about the blood oxygen level dependent (BOLD) activity in the left amygdala (LA) and were instructed to raise the BOLD rtfMRI signal by contemplating positive autobiographical memories. A control group was assigned the same task but was instead provided with sham feedback from the left horizontal segment of the intraparietal sulcus (HIPS) region. In the LA, we found a significant BOLD signal increase due to rtfMRI neurofeedback training in the experimental group versus the control group. This effect persisted during the Transfer run without neurofeedback. For the individual subjects in the experimental group the training effect on the LA BOLD activity correlated inversely with scores on the Difficulty Identifying Feelings subscale of the Toronto Alexithymia Scale. The whole brain data analysis revealed significant differences for Happy Memories versus Rest condition between the experimental and control groups. Functional connectivity analysis of the amygdala network revealed significant widespread correlations in a fronto-temporo-limbic network. Additionally, we identified six regions — right medial frontal polar cortex, bilateral dorsomedial prefrontal cortex, left anterior cingulate cortex, and bilateral superior frontal gyrus — where the functional connectivity with the LA increased significantly across the rtfMRI neurofeedback runs and the Transfer run. The findings demonstrate that healthy subjects can learn to regulate their amygdala activation using rtfMRI neurofeedback, suggesting possible applications of rtfMRI neurofeedback training in the treatment of patients with neuropsychiatric disorders. Real-time functional magnetic resonance imaging (rtfMRI), in which fMRI data processing and display are performed at a speed that makes them concomitant with image acquisition Few studies, however, have explored the feasibility of rtfMRI for training individuals to self-regulate activity in brain structures relevant to emotional processing. In one recent study, the researchers identified emotion-related networks using a functional localizer run and then asked participants (n = 13) to upregulate blood oxygen level dependent (BOLD) activity in individually (for each subject) selected region such as insula, amygdala, and ventrolateral prefrontal cortex (PFC) In the present study, we investigate whether healthy volunteers could be trained to control the BOLD activity level in the amygdala by receiving rtfMRI neurofeedback while contemplating positive autobiographical memories. An extensive body of research in both humans and experimental animals has established that the amygdala plays a central role in several aspects of emotion processing, such as recognition of both positively- and negatively-valenced emotional stimuli, reward learning, and appetitive or aversive conditioning The involvement of the amygdala during mood self-induction has been reported in several studies Twenty-eight right-handed, medically- and psychiatrically-healthy male volunteers (age 28.0±9.0 years) participated in the study. Subjects were excluded from the study if they had: 1) current or past history of any major psychiatric disorder, 2) major medical or neurological disorders, 3) exposure to drugs likely to influence cerebral blood flow or neurological function within 3 weeks, 4) a history of drug or alcohol abuse within 1 year or a lifetime history of drug or alcohol dependence, 5) general MRI exclusion criteria. The participants' mean educational level attained was 5.3±0.9, based on the following scale: 0: no school; 1: less than 7 years of school; 2: junior high school ; 3: some high school ; 4: high school graduate ; 5: some college or technical school; 6: college graduate; 7: graduate professional training (master's degree or higher). All the volunteers were naïve to fMRI neurofeedback.t(26) = −0.27, P<0.790) and education (t(26) = −0.60, P<0.551). Although the control group underwent the same rtfMRI neurofeedback training as the experimental group, this group received sham rtfMRI neurofeedback information presented with rtfMRI data acquired from a different region that putatively was not involved in emotion regulation (see Regions of Interest Placement below).The participants were randomly assigned to either an experimental group  or a control (sham) group , that were matched on age (The study was conducted at the Laureate Institute for Brain Research. The research protocol (protocol #: 14845) was approved by the University of Oklahoma Institutional Review Board (IRB). All the participants provided written informed consent as approved by the University of Oklahoma IRB. The subjects received financial compensation for their participation.The participants were given detailed instructions about the goal of the study and the experimental paradigm. They were instructed to retrieve positive autobiographical memories that potentially would help them learn to control the level of activity in the target brain region. Prior to scanning, each subject was asked to write down three happy autobiographical memories that could be evoked during the rtfMRI neurofeedback runs (the subjects were asked to keep details of their memories private). The participants were instructed that they would be asked to use those happy memories during scanning while attempting to increase the hemodynamic activity in the target brain region. Moreover, they would receive ongoing information about the level of neurophysiological activity in this brain area. The subjects were further instructed not to move, but instead to relax to minimize potential motion-related artifacts in the image data. Finally, it was explained to the participants that the rtfMRI neurofeedback signal is inherently delayed with respect to their mental activity by a few seconds due to the intrinsically slow hemodynamics governing the BOLD fMRI signal.Happy Memories condition involving neurofeedback, the cue “Happy” and two color bars  were displayed on the screen. The red bar represented the actual neurofeedback signal, which was updated continuously by changing the height of the bar either upward or downward based on the corresponding level of BOLD activity. This neurofeedback signal was also indicated by a number shown above the red bar. The participants were instructed to retrieve and contemplate the positive autobiographical memories while also attempting to increase the level of the red bar to that of the fixed target level displayed by the blue bar. Because the Happy Memories condition required memory recall and rumination on those memories could potentially not be stopped quickly Count condition, the subjects were shown the cue with a specific instruction to count backwards from 100 by a subtracting a specified integer. This number was 1, 2, 3, and 4 for Run 1, Run 2, Run 3, and the Transfer run, respectively . During the subsequent Rest condition, the participants were presented with the cue “Rest” and were asked to relax and breathe regularly while looking at the display screen  agreed after the session to provide a general account  of the memories that were most effective at helping them to feel happy and raise the neurofeedback bar level. Based on their reports, two categories of positive autobiographical memories appeared to work best for the purpose of the present study: memories involving close family members and memories of specific joyful events .Rest run (RE), a resting state paradigm was employed, and the participants were instructed to let their minds wander while fixating at the display screen. During the second Practice run (PR), the subjects were given an opportunity to become comfortable with the rtfMRI neurofeedback procedure. The Practice run consisted of alternating blocks of Rest (5 blocks lasting 40 seconds each) and Happy Memories (4 blocks lasting 80 seconds each) conditions (Run 1 (R1), Run 2 (R2), and Run 3 (R3) — the participants underwent the rtfMRI neurofeedback training as they were instructed during the pre-training session. Those three runs consisted of alternating blocks of Rest (5 blocks), Happy Memories (4 blocks), and Count (4 blocks) conditions, each lasting 40 seconds (Transfer run (TR), the participants were instructed to perform the same task as during the neurofeedback training, but rtfMRI neurofeedback information was not provided for the blocks of the Happy Memories condition and the bars were not shown. The Transfer run was performed to assess the transfer of the learned control and to check whether the training effect generalized to situations where no feedback was available.The rtfMRI neurofeedback experiment consisted of six fMRI runs each lasting 8 minutes 40 seconds . During nditions . For the seconds . The subBefore the rtfMRI neurofeedback training, the subjects were asked to complete the 20-item Toronto Alexithymia Scale (TAS-20), as well as the 15-item Emotional Contagion (EC) Scale. TAS-20 assesses an individual's difficulty in understanding, processing, or describing emotions left amygdala , right amygdala , and left horizontal segment of the intraparietal sulcus , as illustrated in The rtfMRI neurofeedback procedure was based on an MRI-based region-of-interest (ROI) approach. Three ROIs were defined as spheres of 7 mm radius in the stereotaxic array of Talairach and Tournoux All functional and structural MR images were collected at the Laureate Institute for Brain Research using a General Electric Discovery MR750 whole-body 3 Tesla MRI scanner. The scanner is equipped with a scalable 32-channel digital MRI receiver capable of performing massively-parallel fMRI in real time TR/TE = 2000/30 ms, SENSE acceleration factor R = 2 in the phase encoding (anterior-posterior) direction, flip angle = 90°, sampling bandwidth = 250 kHz, number of volumes = 263. Each functional scan time lasted 8 min 40 sec. Three EPI volumes (6 sec) were added at the beginning of each fMRI run to allow the fMRI signal to reach steady state, and were excluded from data analysis. The EPI images were reconstructed into a 128×128 matrix, in which the resulting fMRI voxel volume was 1.875×1.875×2.9 mm3. Additionally, simultaneous physiological pulse oximetry TR/TE = 5/1.9 ms, acceleration factor R = 2, flip angle = 10°, delay time TD = 1400 ms, inversion time TI = 725 ms, sampling bandwidth = 31.2 kHz, scan time = 4 min 58 sec.A single-shot gradient-recalled EPI sequence with Sensitivity Encoding (SENSE) http://afni.nimh.nih.gov/) http://www-01.ibm.com/software/analytics/spss) and MATLAB Statistics Toolbox .The image data analyses were performed using Analysis of Functional NeuroImages  group, unaware that they were presented with the sham neurofeedback, performed an identical training sequence. The sham information consisted of fMRI data derived from the left HIPS ROI instead of the left amygdala ROI and, therefore, was not expected to correlate with the performance in the mood self-induction task.ß coefficients were computed for each voxel using the 3dDeconvolve AFNI program and then converted to percent signal changes for Happy versus Rest, Count versus Rest, and Happy versus Count contrasts. The resulting fMRI percent signal change maps for each run were spatially transformed to the stereotaxic array of Talairach and Tournoux 3 isotropic voxel size. They were subsequently used for whole-brain statistical group analyses. The voxel-wise percent signal change data were also averaged within the three ROIs  and used as a performance measure.Pre-processing of single-subject fMRI data included correction of cardiorespiratory artifacts using AFNI implementation of the RETROICOR method t-tests comparing the percent signal change data to zero activation level were employed to generate statistical activation maps for the Happy versus Rest, Count versus Rest, and Happy versus Count contrasts. The statistical activation maps were corrected for multiple comparisons using the false discovery rate (FDR) approach t-test was also applied to Happy versus Rest activation data for the experimental and control (sham) groups to examine statistical differences between the groups.In preparation for the whole-brain statistical group analysis, the spatially-normalized fMRI percent signal change maps were spatially smoothed using a Gaussian kernel with full width at half maximum (FWHM) of 5 mm. Group t-test for percent signal changes between the Transfer run (TR) and the last training run (R3). Associations between average percent signal changes for LA ROI and sub-scores of the TAS-20 and EC psychological scales were determined using Pearson bivariate correlations.Inferential statistical analyses were applied to the average activation results for the three ROIs. First, the training effect was evaluated by applying a three-way 4 (Training)×2 (ROI)×2 (Group) ANOVA for repeated measures on percent signal changes with Training  and ROI  as within-subjects factors and Group  as a between-subjects factor. Second, specificity of the training effect to the LA ROI was evaluated (within each group) by applying a two-way 4 (Training)×2 (ROI) ANOVA for repeated measures on percent signal changes with Training  and ROI  as within-subjects factors. Third, specificity of the training effect to the experimental group was evaluated (for each ROI) by using a two-way 4 (Training)×2 (Group) ANOVA for repeated measures with Training  as a within-subject factor and Group  as a between-subjects factor. Fourth, monotonic properties of the participants' control over brain activation across all runs were evaluated (for each ROI and Group) by using a one-way ANOVA trend analysis for repeated measures on percent signal changes with Time  as a within-subjects factor. Finally, generalization of the training effect beyond the actual training was evaluated (for the LA and RA ROIs) by applying a paired R-squared statistics were converted to correlation coefficient values r and the resulting correlation maps for each run were transformed to the Talairach space, re-sampled, and spatially smoothed (5 mm FWHM). For statistical analyses, the correlation coefficient values were converted to z scores using the Fisher r-to-z transformation. Group t-test with respect to zero level was employed to determine the functional connectivity pattern for each run. Correction for multiple comparisons was based on FDR.To determine functional connectivity of the amygdala network, a GLM-based functional connectivity analysis was applied using a seed ROI in the left amygdala region. The seed ROI was defined as a sphere of 5 mm radius in the Talairach space. After transformation to an individual subject's EPI image space, this ROI contained approximately 50 voxels. The volume-registered and slice-timing-corrected single-subject fMRI data from each run were low-pass filtered at 0.08 Hz. The time course of the mean fMRI signal from the seed ROI was used as a stimulus regressor. The GLM model for each run also included six motion parameters, five polynomial terms for modeling the baseline, and time courses from two additional ROIs defined, respectively, within the deep white matter and the CSF of the lateral ventricles. The GLM-based t values in the statistical group connectivity analysis of the experimental group for the Transfer run. For each of these ROIs, a one-way ANOVA trend analysis for repeated measures on mean correlation coefficients was applied (for each group) with Training  as a within-subjects factor.To identify regions within the network, for which the functional connectivity with the left amygdala increased during the experiment, mean values of the correlation coefficients for several spherical ROIs (of 5 mm radius) were determined for each run. The ROIs were centered at locations that were characterized by peak Results of the neurofeedback experiment based on the ROI analysis are exhibited in F = 0.52, P<0.669), ROI  = 0.16, P<0.689), and Group  = 3.38, P<0.078), and a non-significant interaction effects for Training×Group  = 0.73, P<0.540), Training×ROI  = 0.51, P<0.679), and ROI×Group  = 2.33, P<0.139). However, a significant three-way interaction effect was evident for Training×ROI×Group  = 3.73, P<0.015). This result suggests that the experimental and control (sham) groups differed in their neurofeedback training effects based on the specific target brain region .Several statistical tests were performed to evaluate the data in F = 0.38, P<0.766), but a significant effect for ROI  = 10.28, P<0.007), and a significant interaction effect for Training×ROI  = 3.05, P<0.040). For the control group (CG), in contrast, non-significant main and interaction effects  = 0.95, P<0.426; ROI: F = 0.35, P<0.567; Training×ROI: F = 1.31, P<0.287) were found. These results indicate a significant training effect in the target region (LA) for the experimental group.Second, the two-way 4 ×2  ANOVA for the experimental group (EG) showed a non-significant main effect for Training  = 0.55, P<0.652), but a significant main effect for Group  = 4.71, P<0.039) as well as a significant interaction effect for Training×Group  = 3.05, P<0.033). Independent t-tests of the LA ROI activations for EG and CG for each of the six runs (RE: t(26) = 1.16, P<0.257; PR: t(26) = 0.60, P<0.557; R1: t(26) = 1.17, P<0.254; R2: t(26) = 2.16, P<0.040; R3: t(26) = 2.70, P<0.012; TR: t(26) = 2.47, P<0.020) showed significant differences in mean LA ROI activation levels between the two groups for Run 2, Run 3, and the Transfer run. A similar two-way 4×2 ANOVA analysis for the RA ROI revealed a non-significant main effect for Training  = 2.11, P<0.105), a marginally significant main effect for Group  = 3.81, P<0.062), and a non-significant interaction effect for Training×Group  = 0.44, P<0.722). A marginally significant difference in mean RA ROI activations between EG and CG was found for Run 3 (RE: t(26) = 0.24, P<0.814; PR: t(26) = 0.88, P<0.388; R1: t(26) = 1.41, P<0.171; R2: t(26) = 1.06, P<0.300; R3: t(26) = 1.83, P<0.079; TR: t(26) = 1.63, P<0.115). These results demonstrate that the experimental and control groups differed significantly in the neurofeedback training effects on LA activation, and less significantly – in the effects on RA activation.Third, the two-way 4 ×2  ANOVA for the LA ROI revealed a non-significant main effect for Training  = 2.23, P<0.062; Linear: F = 11.00, P<0.006; Quadratic: F = 0.09, P<0.767; Cubic: F = 0.12, P<0.734) for the experimental group, but not for the control group  = 1.51, P<0.198; Linear: F = 0.30, P<0.595; Quadratic: F = 1.26, P<0.281; Cubic: F = 5.48, P<0.036). A marginally significant linear trend across all runs was found for the RA ROI activation in the experimental group  = 1.78, P<0.130; Linear: F = 5.00, P<0.043; Quadratic: F = 0.85, P<0.374; Cubic: F = 0.63, P<0.442). In contrast, no significant trend was found for the HIPS ROI in the experimental group  = 0.34, P<0.886; Linear: F = 0.54, P<0.474; Quadratic: F = 0.001, P<0.980; Cubic: F = 0.50, P<0.494). These results indicate a monotonic increase in activations of both LA and RA across all runs for the experimental group.Fourth, one-way ANOVA trend analysis across all runs  for the LA ROI activation showed a t-test of the LA ROI activation levels for the the Transfer run vs. the last neurofeedback training run (TR vs. R3) within the experimental group showed no difference in activation means (t(13) = 0.01, P<0.992). A similar effect was found for the RA ROI activation (t(13) = 0.31, P<0.765). These results suggest that the neurofeedback training effects in both LA and RA persisted during the Transfer run (without neurofeedback).Finally, the paired significant negative correlation with the Difficulty Identifying Feelings score  of TAS-20 was found  or the Externally Oriented Thinking score . Note that the experimental and control groups did not differ in any of these psychological measures (DIF: t(26) = 0.71, P<0.482; DDF: t(26) = 1.34, P<0.193; EOT: t(26) = 0.12, P<0.908). Negative correlation was also found between the left amygdala activation and the Susceptibility to Anger score  for the experimental group .as found , indicatal group , though t = 0.84, P<0.409; PR: t = 0.63, P<0.538; R1: t = 0.82, P<0.418; R2: t = 0.87, P<0.396; R3: t = 0.72, P<0.476; t = 0.52, P<0.607).t = −0.81, P<0.428; PR: t = −0.74, P<0.468; R1: t = −1.17, P<0.252; R2: t = −0.63, P<0.534; R3: t = −0.95, P<0.351; t = −1.01, P<0.323). Note that both cardiac and respiratory rates for the Happy Memories condition exhibit the largest relative (%) increase over the corresponding rates for the Rest condition during Run 1 in both groups , bilateral ventrolateral prefrontal cortex (VLPFC), right medial frontal polar cortex (MFPC), right dorsomedial prefrontal cortex (DMPFC), and right ventromedial prefrontal cortex (VMPFC) in the frontal lobe; bilateral superior temporal gyrus (STG) and bilateral middle temporal gyrus (MTG) in the temporal lobe; and left amygdala (encompassing the LA ROI), bilateral hippocampus (HC), left parahippocampal gyrus (PHG), left pregenual anterior cingulate cortex (ACC), left posterior cingulate cortex (PCC), and right subgenual anterior cingulate cortex (ACC) in the limbic lobe. The Count>Happy contrast revealed activations in bilateral inferior parietal lobule  and left parieto-occipital transition cortex in the parietal lobe.Results of group activation analysis of the fMRI data for the experimental group are shown in t-test was applied to the Happy versus Rest percent signal change data from the two groups. The test results for the Transfer run are summarized in p<0.05 (t = ±2.057) are reported, with the minimum cluster size set to 20 voxels. According to To compare activation results between the experimental and control groups, a group q<0.001 and the minimum cluster size of 20 voxels. The functional connectivity pattern for the left amygdala reveals a fronto-temporo-limbic network, including bilateral SFG, bilateral VLPFC, bilateral MFPC, bilateral DMPFC, bilateral lateral orbitofrontal cortex (LOFC), left middle frontal gyrus (MidFG), and right VMPFC in the frontal lobe; bilateral MTG in the temporal lobe; and bilateral amygdala, bilateral PHG, bilateral PCC, left pregenual ACC, right subgenual ACC, and right HC in the limbic lobe. The connectivity pattern also includes thalamus and bilateral insula. For several regions in Results of group functional connectivity analysis for the Transfer run within the experimental group are shown in significant increase in functional connectivity over the course of the neurofeedback training. The average correlation coefficient values (±s.e.m.) for the corresponding ROIs are exhibited in F = 3.95, P<0.007; Linear: F = 22.0, P<0.0004; Quadratic: F = 0.26, P<0.622; Cubic: F = 0.33, P<0.576); (2) right dorsomedial prefrontal cortex  = 5.10, P<0.002; Linear: F = 22.0, P<0.0004; Quadratic: F = 0.02, P<0.883; Cubic: F = 2.41, P<0.145); (3) left dorsomedial prefrontal cortex  = 5.46, P<0.001; Linear: F = 28.0, P<0.0001; Quadratic: F = 0.007, P<0.934; Cubic: F = 9.03, P<0.010); (4) left pregenual anterior cingulate cortex  = 5.44, P<0.001; Linear: F = 14.0, P<0.002; Quadratic: F = 2.85, P<0.115; Cubic: F = 3.03, P<0.105); (5) right superior frontal gyrus  = 4.58, P<0.003; Linear: F = 14.5, P<0.002; Quadratic: F = 0.56, P<0.467; Cubic: F = 6.07, P<0.028); (6) left superior frontal gyrus  = 3.06, P<0.025; Linear: F = 7.63, P<0.016; Quadratic: F = 0.09, P<0.767; Cubic: F = 1.30, P<0.275). Note that the overall within-subject connectivity enhancement effect and the linear trend effect for these regions are significant across all experimental runs , as demonstrated by the following statistics for the same six regions, respectively: (1) F = 3.34, P<0.01  = 10.48, P<0.006); (2) F = 3.53, P<0.007  = 11.12, P<0.005); (3) F = 4.15, P<0.002  = 13.06, P<0.003); (4) F = 5.20, P<0.0004  = 21.73, P<0.0004); (5) F = 3.69, P<0.005  = 4.68, P<0.050); (6) F = 2.64, P<0.031  = 6.37, P<0.025). It should be noted also that all six regions, demonstrating significant connectivity enhancement with the left amygdala are located near the brain's medial plane  were performed . For the experimental group, six regions within the amygdala network demonstrated a al plane .F = 0.33, P<0.855  = 0.19, P<0.671); (2) right dorsomedial prefrontal cortex: F = 0.38, P<0.854  = 0.28, P<0.607); (3) left dorsomedial prefrontal cortex: F = 0.23, P<0.921  = 0.67, P<0.428); (4) left pregenual anterior cingulate cortex: F = 0.85, P<0.502  = 0.29, P<0.598); (5) right superior frontal gyrus: F = 0.42, P<0.792  = 0.006, P<0.937); (6) left superior frontal gyrus: F = 0.09, P<0.984  = 0.13, P<0.725). Therefore, no significant changes in functional connectivity with the left amygdala were observed for the same six regions for the control (sham) group.To examine whether functional connectivity with the left amygdala region was affected by sham neurofeedback, the same statistical analyses were performed for the control group using the same ROIs to determine average correlation coefficient values. The results are exhibited in In this study, we investigated the feasibility of training healthy volunteers to regulate the level of activation in their left amygdala using rtfMRI neurofeedback. The results of this study demonstrated that, given appropriate direction, practice, and rtfMRI neurofeedback information, individuals learned to significantly enhance the regional BOLD activity in the amygdala by contemplating positive autobiographical memories within a short training session. The BOLD fMRI signal in the left amygdala increased with the number of neurofeedback runs, resembling a progressive learning effect reported by previous studies for other brain regions: the right anterior insula The amygdala plays major roles in the neural processing underlying emotional behavior, and plays crucial roles in evaluating the salience of experiential stimuli, aversive and appetitive conditioning Although previous rtfMRI neurofeedback training studies investigated the possibility of training healthy humans to control amygdala activity during self-induced sadness left side in response to positively valenced stimuli, such as to happy face stimuli presented below the level of conscious awareness Recent evidence from quantitative meta-analyses of functional neuroimaging studies has suggested a functional dissociation between left and right amygdala in terms of temporal dynamics The enhanced control over left amygdala BOLD activity appeared to specifically result from rtfMRI-induced learning. The control group underwent the same rtfMRI neurofeedback procedure as the experimental group, but received sham neurofeedback information corresponding to BOLD activity in the left HIPS, a region that has been consistently implicated in numeric processing The observed training effect generalized to the Transfer run, in which neurofeedback was no longer provided. During this run, the participants were instructed to contemplate positive autobiographical memories in the absence of rtfMRI neurofeedback. The results suggested that the subjects continued to use the learning acquired during the preceding neurofeedback trials. A similar transfer effect was recently reported for the anterior insula during rtfMRI neurofeedback training Although the right amygdala showed a nominal increase in BOLD activity across the neurofeedback trials, this effect was only marginally significant. The statistically significant self-regulation was only specific to the target ROI—the left amygdala. These data are compatible with those from studies in psychiatric disorders which showed a lack of correlation between the left and right amygdala metabolism Analysis of the participants' cardiac and respiratory waveforms, recorded simultaneously with fMRI for each run, revealed no statistically significant differences between the experimental and control groups in terms of their cardiac and respiration rates for any of the runs. Additionally, no correlations were found between the left amygdala BOLD activations and the subjects' cardiac and respiratory rates. These results suggest that the pronounced differences in the left amygdala activation levels across Runs 1–3 between the two groups cannot be attributed to differences in cardiorespiratory effects observed using the standard physiological recordings and simple physiological rate analyses. It should be noted also that the largest relative variations in both cardiac and respiratory rates between the Happy Memories and Rest conditions occurred during Run 1, i.e. close to the middle of the neurofeedback experiment.The whole-brain voxel-wise analyses showed that the training to modulate left amygdala activity while contemplating positive autobiographical memories engaged a fronto-temporo-limbic network that is implicated in emotion processing and autobiographical memory retrieval Analysis of functional connectivity of the amygdala network revealed connectivity with fronto-temporo-limbic network , as well as sub-lobar structures such as the thalamus and insula. These findings are consistent with a previous meta-analytic connectivity modeling analysis that investigated the functional connectivity of the amygdala based on human neuroimaging studies and anatomical studies in nonhuman primates Some limitations of our study design merit comment. First, the participants' actual emotional experience was not independently assessed in the present study. Thus we cannot comment on whether their mood state became more positive while contemplating positive autobiographical memories. Second, individual performance during neurofeedback training depended on the subjects' ability to alter left amygdala activity by contemplating positive autobiographical memories while receiving neurofeedback information. This led to a relatively large inter-subject variability of the results. For the participants in the experimental group, the average left amygdala activation for all Happy Memories conditions across Runs 1–3 varied from +0.60% (the best performance) to −0.13% (the worst performance). While the differences in the subjects' ability to identify feelings partly accounted for this variability , other iIn summary, our findings demonstrate that healthy, neurofeedback-naive subjects can learn to regulate their amygdala activation using positive autobiographical memory retrieval while receiving rtfMRI neurofeedback. In contrast to the sham feedback from the HIPS region, the feedback provided from the left amygdala resulted in a significant monotonic BOLD signal increase during rtfMRI neurofeedback training, and this effect persisted during the Transfer run, in which no feedback was provided. Across the individual subjects from the experimental group, the training effect in the LA BOLD activity correlated inversely with scores on the Difficulty Identifying Feelings subscale of the Toronto Alexithymia Scale, suggesting that the better subjects rated their ability to identify their emotions, the more effectively they learned to regulate LA activity via training. Furthermore, the whole brain data analysis revealed significant group differences  for the Happy Memories versus Rest condition. The comparison of the Happy Memories and Count conditions revealed significant Happy Memories>Count contrast in a fronto-temporo-limbic network, and significant Count>Happy contrast in a parietal network. Functional connectivity analysis of the amygdala network demonstrated significant widespread correlations among regional BOLD signal changes in a fronto-temporo-limbic network. Additionally, we identified six regions – right MFPC, bilateral DMPFC, left ACC, and bilateral SFG – where the functional connectivity with the left amygdala increased across the rtfMRI neurofeedback runs and the Transfer run.Further studies are needed to determine whether the findings provided in this proof-of-concept study have the potential to significantly advance our understanding of the pathophysiology of neuropsychiatric disorders. For example, the modulation of amygdala activity using rtfMRI neurofeedback training may be particularly relevant for the development of novel approaches for optimizing cognitive-behavioral therapeutic interventions in post-traumatic stress disorder (PTSD) or major depressive disorders. Because exaggerated hemodynamic responses of the amygdala to fearful faces or traumatic reminders are consistent pathological constructs in PTSD


Recently, our group demonstrated that naringenin inhibits hepatitis C virus (HCV) production, while others demonstrated its potential in the treatment of hyperlipidemia and diabetes. However, naringenin suffers from low oral bioavailability critically limiting its clinical potential. In this study, we demonstrate that the solubility of naringenin is enhanced by complexation with β-cyclodextrin, an FDA approved excipient. Hydroxypropoyl-β-cyclodextrin (HPβCD), specifically, increased the solubility of naringenin by over 400-fold, and its transport across a Caco-2 model of the gut epithelium by 11-fold. Complexation of naringenin with HPβCD increased its plasma concentrations when fed to rats, with AUC values increasing by 7.4-fold and Cmax increasing 14.6-fold. Moreover, when the complex was administered just prior to a meal it decreased VLDL levels by 42% and increased the rate of glucose clearance by 64% compared to naringenin alone. These effects correlated with increased expression of the PPAR co-activator, PGC1α in both liver and skeletal muscle. Histology and blood chemistry analysis indicated this route of administration was not associated with damage to the intestine, kidney, or liver. These results suggest that the complexation of naringenin with HPβCD is a viable option for the oral delivery of naringenin as a therapeutic entity with applications in the treatment of dyslipidemia, diabetes, and HCV infection.The abundant flavonoid aglycone, naringenin, which is responsible for the bitter taste in grapefruits, has been shown to possess hypolipidemic and anti-inflammatory effects both  In this study, β-cyclodextrins were examined as potential excipients to enhance the solubility and enteral uptake of the flavonoid. Cyclodextrins are a family of cyclic oligosaccharides that create a 3-dimensional toroid structure, providing a cavity that can accommodate small hydrophobic molecules, such as cholesterol or steroids. Cyclodextrins can therefore be used as excipients to improve the solubility of hydrophobic drugs with similar structure Molecules similar to naringenin in structure and size were previously shown to be solubilized by complexation with β-cyclodextrin. To explore if naringenin is similarly solublized we generated complexes with β-cyclodextrin (βCD), methyl β-cyclodextrin (mβCD), and 2-hydroxypropyl-β-cyclodextrin (HPβCD). UV analysis indicated that complexation with cyclodextrins resulted in a very small shift in naringenin's absorption spectrum  on which cells formed differentiated monolayers, expressing major tight junction proteins, microvilli, and drug transporters  HPβCD-naringenin complex, using an oral gavage. Blood samples were collected sequentially for 10 hrs from the carotid artery using the previously placed catheter into tubes containing heparin. Immediately after collection, plasma was separated and stored at −80°C for further analysis. At the conclusion of the experiment, all animals were sacrificed, and liver, kidney, and bowel specimens were collected for histology. In an additional experiment, animals were placed in metabolic cages and urine was collected and pooled. Total naringenin (flavonoid and glucuronide) was determined by LC-MS as described above.0-10 of naringenin from 2.0±0.5 hr×µg/ml to 15.0±4.9 hr×µg/ml representing a 7.4-fold increase in bioavailability . Naringenin's maximal concentration, Cmax, increased from 0.3±0.1 µg/ml to 4.3±1.2 µg/ml representing a 14.6-fold increase . The calculated half-life for naringenin in plasma remained unchanged in both conditions at 2.3 hours, consistent with values previously reported in humans The complexation of HPβCD with naringenin significantly affected the plasma concentration versus time profile of the flavonoid . Interestingly, rats that were administered the HPβCD–naringenin complex showed a significantly 64% higher  rates of glucose clearance, compared to rats given naringenin alone  and glucose (1 g/kg). Glucose levels were measured sequentially for 2 hrs after the meal  and 118±60% in skeletal muscle and liver, respectively . Histological characterization by a blind observer demonstrated that the small intestine, kidney, and liver sections showed no evidence of tissue injury or inflammation in both groups. Liver sections showed no evidence of hepatocyte damage or neutrophil infiltration to the portal area, while kidney and intestine sections show no tubular/glomerular damage, edema or epithelial damage, respectively Figure 4Comprehensive metabolic analysis was carried out on serum samples taken from rats, 10 hours after the treatment with HPβCD-naringenin, naringenin alone, as well as rats treated with saline as a control. The biochemical examination revealed no major changes Table 2.citrus paradisi). The compound is highly soluble and can be found in grapefruit juice at concentrations of up to 0.5 g/L Naringin is an abundant flavonone-glycoside known to cause the bitter taste in grapefruit , methyl β-cyclodextrin (mβCD), and 2-hydroxypropyl-β-cyclodextrin (HPβCD) were purchased from Sigma-Aldrich Chemicals . Caco-2 human epithelial colorectal adenocarcinoma cells were purchased from the American Type Culture Collection . Unless otherwise noted, all other chemicals were purchased from Invitrogen Life Technologies .Stock solutions of naringenin were prepared in ethanol. A calibration curve was prepared by measuring the UV absorbance of the naringenin stock solutions (0.1 to 0.6 mM) at 290 nm using a ND-1000 spectrophotometer . Standard deviations between triplicate measurements were less than 5%.Improvements in naringenin solubility when complexed with cyclodextrin were determined and evaluated as follows; stock solutions of βCD, mβCD, and HPβCD were prepared in distilled water. None of the cyclodextrins absorbed at 290 nm for concentrations from 0 to 50 mM (data not shown). Next, excess amounts of naringenin powder were added to solutions containing variable amounts of each cyclodextrin, vortexed, and incubated with shaking at 37°C for 3-5 hrs. Naringenin-cyclodextrin solutions were filtered through a 0.45 µm filter to remove the undissolved naringenin, diluted by 20 or 50-fold, and absorbance was measured at 290 nm. The complex stability constant K was calculated from the linear portion of the solubility diagram assuming a 1:1 complex.2. Cells between 30 and 53 passages were used.Caco-2 cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum, 1% nonessential amino acids, and 4 mM glutamine without antibiotics. The cultures were grown in a humidified incubator at 37°C and 5% CO2 growth area; Corning Costar Co.) at a cell density of 1x105 cells/filter. Cell growth and maintenance were performed as previously described For the transport studies, Caco-2 cells were seeded on Transwell . Upon arrival, each rat was isolated for 3–5 days towards adaptation to the new environment. Animals were housed under 12h cycle of day/night with free access to drinking water and fed To measure the pharmacokinetic profile of naringenin, rats weighing between 280 and 300 g were anaesthetized using intraperitoneal injections of ketamine and xylazine at 110 and 0.4 mg/kg, respectively. The left carotid artery was cannulated using a 0.76-mm diameter ×60-cm length heparanized catherter. The catheter was tunneled subcutaneously from the opening made in the anterior face of the neck to the dorsal site of the neck and permanently anchored in the skin. The catheter was secured by the use of a rat jacket. Animals were placed in their cages during the term of the study. Animals were orally administered with 20 mg/kg body weight of naringenin in either water or complexed with 320 mg/kg body weight HPbCD using a rat oral gavage . Blood samples (0.5 ml) were collected at 0, 15, 30, 60, 120, 240, 360, 510, and 600 min from the carotid artery using the previously placed catheter. In two additional experiments, animals were placed in metabolic cages and urine was collected and pooled for the duration of the experiment.To measure metabolic changes, rats weighing between 270 to 290 g were fasted for 16 hrs, but allowed free access to water. Animals were orally administered with 20 mg/kg body weight of naringenin in either water or complexed with 320 mg/kg body weight HPbCD using a rat oral gavage. Precisely 30 min after the oral administration of naringenin, the rats were administered 1 ml/kg of olive oil suspended in PBS with 1 g/kg of glucose using rat oral gavage. Glucose was measured using a single tail snip and repeated scratching on an Accu-Chek Sensor  prior to the experiment and at 0, 15, 30, 60, 90, and 120 min from the meal. Rats were anaesthetized 200 min following the meal using intraperitoneal injection of ketamine and xylazine followed by terminal blood draw and tissue collection.LC-MS analysis was performed on an Agilent Technologies series 1100 LC-MSD system , which included an Agilent 1100 quaternary pump, autosampler, column oven, on-line vacuum degassor, and single quadrupole mass spectrometer equipped with electrospray ion source (ESI).Mass spectrometry conditions: Electrospray ionization (ESI), positive, selected ion monitoring scan (SIM); SIM: naringenin m/z 273.1. LC conditions: Eclipse XDB-C18 column . The mobile phase was composed of methanol-water with 0.1% formic acid . The isocratic flow rate was set at 0.8 ml/min and injection volume was only 10 µl.To each 100 µl of rat serum sample, 100 µl of 0.1N sodium acetate (pH = 5.0) and 100 µl of β-glucuronidase enzyme  were added and vortexed for 5 seconds. This process hydrolyzes the conjugated form of naringenin to determine total naringenin in plasma. After addition of 20 µl IS buffer solution (5 µg/ml), the sample was then incubated at 37°C water bath for 18 h.The sample was extracted with 0.8 ml of ethyl acetate after 18 h incubation, and centrifuged at 13000 rpm for 10 min. The supernatant was collected and evaporated to dryness under nitrogen at room temperature. The residue was reconstituted with 100 µl of mobile phase and filtered through a micro nylon n filter (0.45 µm). 10 µl of the filtrate was forwarded to LC-MS analysis. A calibration curve was established and QC samples conducted (data not shown). Data acquisition was performed using ChemStation software (Agilent). Linear regression (weighted by 1/x) between serum concentration and peak area ratio of naringenin to IS was constructed using SPSS11.0 statistical software. The concentrations of naringenin in samples were calculated by interpolation of the linear equation.GACCCCAGAGTCACCAAATGA and reverse GGCCTGCAGTTCCAGAGAGT, while β-Actin (ACTB) used were, forward AGCCATGTACGTAGCCATCCA and reverse TCTCCGGAGTCCATCACAATG . Expression was normalized to β-Actin using ΔΔCt.Liver and skeletal muscle tissues were ground in liquid nitrogen, homogenized and RNA was purified using BioRad Aurum Total RNA Fatty and Fibrous Tissue Kit . Total RNA was quantified on NanoDrop Technologies, ND-1000 spectrophotometer  and mRNA transcript abundance was measured on a BioRad CFX96 real-time PCR Detection System using Bio-Rad SsoFast™ EvaGreen® supermix , according to the manufacturers' instructions. PGC1α (PPARGC1a) primers used were, forward Liver metabolic and lipid plasma levels were analyzed using Piccolo Blood Analyzer  and confirmed by A.M.L veterinary department . Levels of rat apolipoprotein B100 (APOB100) were measured using ELISA .Histological sections of each organ were taken 10 hours after treatment. Formalin-fixed, paraffin-embedded liver, intestine, and kidney samples were sectioned at 4 µm and stained with hematoxylin & eosin (H&E). Histological characterization was performed by a blinded observer using standard assessment of damage.P-value of 0.05 was used for statistical significance.Data are expressed as the mean ± standard deviation. Statistical significance was determined by a one-tailed Student's t-test. A Figure S1Glucose plasma concentrations over time in rats administered a high fat (1 ml/kg) high glucose (1 g/kg) meal at time zero. 30 minutes prior to the meal, the rats were administered either naringenin alone or HPβCD-naringenin complex. Curves represent average ± standard deviation of 3 rats in each group.(PDF)Click here for additional data file.


Bdellovibrio bacteriovorus is a gram-negative bacterium that preys other gram-negative species for survival, acting as a population-balancer. It was found in terrestrial/aquatic ecosystems, and in animal intestines, postulating its presence also in the human gut.Members of the human intestinal microbiota are key players in maintaining human health. Alterations in the composition of gut microbial community (dysbiosis) have been linked with important human diseases. Understanding the underlying processes that control community structure, including the bacterial interactions within the microbiota itself, is essential. B. bacteriovorus in intestinal and faecal biopsy specimens from 92 paediatric healthy subjects and patients, suffering from Inflammatory Bowel Diseases (IBD), Celiac disease and Cystic fibrosis (CF).The present study was aimed to evaluate, by end-point PCR and qPCR, the presence of B. bacteriovorus was present and abundant only in healthy individuals, while it was heavily reduced in patients, as in the case of IBD and Celiac, while in CF patients and relative controls we observed comparable results; ii) B. bacteriovorus seemed to be mucosa-associated, because all IBD and Celiac biopsies (and related controls) were treated with mucus-removing agents, leaving only the mucosa-attached microflora; iii) B. bacteriovorus abundance was district-dependent, with a major preponderance in duodenum, and gradually decreasing up to rectum; iv) B. bacteriovorus levels significantly dropped in disease status, in duodenum and ileum.i) Bdellovibrio as a probiotic.Results obtained in this study could represent the first step for new therapeutic strategies aimed to restore a balance in the intestinal ecosystem, utilizing  Alterations in the development or composition of the microbiota could affect the cross-talk between microbiota and host compromising human health. Microbiota ecosystem unbalance, named dysbiosis, has been found in different pathologies such as Inflammatory Bowel Diseases (IBD) Clostridium IXa and IV groups, Bacteroides, Bifidobacteria) and a concomitant increase in detrimental bacteria  Bdellovibrio bacteriovorus is a predatory gram-negative bacterium belonging to the δ-proteobacteriaceae sub-family, which attacks other gram-negative bacteria and invade their periplasm, using them as substrates for growth and reproduction B. bacteriovorus to feed without competition and, through its high lytic capability, can rapidly reduce gram-negative populations. From its discovery in 1963 B. bacteriovorus was under investigation for decades, but only in recent years it took the stage B. bacteriovorus divides into progeny which then lyses the residue cells and swim away to chase new hosts B. bacteriovorus is probably involved in keeping the balance between the different bacterial species living together in a community, acting as an ecological balancer Bdellovibrio delivered orally to live birds caused an increase in some gram-positive bacteria, a reduction in gram-negative bacteria  and a parallel reduction in mucosal inflammation, without affecting the bird health B. bacteriovorus population levels respond to the Lotka-Volterra prey-predator oscillation B. bacteriovorus expansions B. bacteriovorus is ubiquitous in terrestrial and aquatic environments, even if at low counts B. bacteriovorus. To this purpose, we evaluated by PCR the presence of the bacterial predator B. bacteriovorus in bioptic samples collected from IBD and Celiac patients, as well as in faecal samples taken from Cystic fibrosis patients. Sex- and age- matched controls were also enrolled to compare the prevalence and relative abundance of B. bacteriovorus in the healthy status.The largest collection of microbes resides in our gut, which harbours trillions of bacteria Intestinal biopsies were obtained from gastro-duodenoscopy (for ‘Celiac group’) and colonoscopy (for ‘IBD group’) procedures carried out at the ‘Policlinico Umberto I’ Hospital at Rome, Italy. Ethics approval for this study was granted by the Ethics Committees of the ‘Sapienza’ University and ‘Policlinico Umberto I’ Hospital, Italy. Written informed consent was obtained from parents of all subjects enrolled in this study who were under 18 years of age, while the written informed consent was given autonomously by subjects over  18 years. In any case, written informed consent was obtained upon instructions on ethics, aims, and methodologies employed in the study.Twenty-three paediatric patients referred to the Paediatric Gastroenterology and Liver Unit of the Sapienza University of Rome, Italy, for suspected IBD were studied: active CD was diagnosed in 9, and active UC was diagnosed in 6. The remaining 8 subjects with functional gastrointestinal disorders (lymphonodular hyperplasia), and normal colonoscopy and histology findings, served as controls. In Two groups of children referred to the Paediatric Gastroenterology and Liver Unit of the “Sapienza” University of Rome were included in this study: 10 Celiac patients in active state and 8 controls undergoing upper gastrointestinal endoscopy for functional dyspepsia. Controls tested negative for antitransglutaminase and antiendomysial antibodies with normal IgA levels, while histology of duodenum did not reveal features of Celiac disease. Diagnosis of Celiac disease had been performed according to ESPGHAN criteria Thirty-five CF patients, referred to the Cystic Fibrosis Centre of the Department of Paediatrics at Hospital ‘Policlinico Umberto I’ of Rome, were enrolled after written informed consent. Sixteen age- and sex-matched controls were also enrolled. The diagnostic work up of CF was according to international protocols. The study protocol was approved by the Committee on Ethical Practice of the ‘Sapienza’ University of Rome and ‘Policlinico Umberto I’ hospital, as reported in ‘Ethics statement’. All patients did not received antibiotics during 2 months prior to the beginning of the study, and were fasting prior faecal sampling. Faecal samples were collected in a 50 mL tube, and immediately frozen at −80°C.P = 0.117), indicating a similar amount of extracted DNA in all the cohorts (‘IBD group’ and ‘Celiac group’).Biopsy specimens were taken from fasting patients  and proper age−/sex-matched controls by the same gastro-endoscopist, and immediately stored in 2 mL tubes at −80°C, as described above. Within 2 hours from sampling, biopsies were first quickly washed three times in 500 µL of physiologic saline with 0.016% dithiothreitol (DTT) to remove luminal bacteria and the mucus layer, and then utilized for DNA extraction procedure by DNeasy tissue kit  according to the manufacturer’s instructions. In order to obtain maximum yield of Gram-negative bacteria, a special step in DNA purification protocol was added, following DNeasy tissue kit manual. Briefly, 180 µL of ATL buffer were added to sample followed by 180 µL volume of enzymatic lysis buffer , and incubated for 30 minutes at 37°C. Next, 25 µL of proteinase K solution and 200 µL of buffer AL were added, followed by an incubation step at 56°C for 30 minutes. DNA concentration was determined using a NanoDrop spectrophotometer  at 260 nm. The 260/280 ratio was also measured to ensure a proper DNA purity. We obtained similar DNA concentrations after kit extraction both from patients and controls biopsies: a Mann-Whitney U test was performed on total DNA concentration (P = 0.348), indicating a similar amount of extracted DNA in both cohorts within the ‘CF group’.Faecal samples were taken from fasting CF patients and age- and sex-matched controls in a sterile environment placed into the ‘Policlinico Umberto I’ hospital, collected in a 50 mL tube, and immediately frozen at −80°C. Total DNA was extracted within 1 hour from sampling by QIAmp Stool Mini Kit  following manufacturer’s instructions. Starting faecal amount was set at 500 mg, picked up from different chunks within the sample itself, in order to minimize the sampling error. Upon extraction, DNA concentration was quantified by a NanoDrop spectrophotometer  at 260 nm, along with the 260/280 ratio, and integrity checked through 1% agarose gel electrophoresis containing EtBr 0.5 µg/ml. We obtained similar DNA concentrations after kit extraction both from CF patients and controls biopsies. A Mann-Whitney U test was performed on total DNA concentration (B. bacteriovorus strain HD 100 (DSM No.: 12732) taken from Deutsche Sammlung von Mikroorganismen und Zellkulturen , maintained as plaques in double-layered diluted nutrient broth (DNB) agar, a 1∶10 dilution of nutrient broth amended with 3 mM of MgCl2·6H2O and 2 mM of CaCl2·2H2O [pH 7.2] and agar (0.6% agar in the top layer) B. bacteriovorus plaque to washed prey cells in DNB and incubated at 30°C on a rotary shaker set at 200 rpm until the co-culture became clear (stock lysate). To harvest the predators, co-cultures were prepared in which 2 ml of washed host cells (109 CFU/ml) was incubated with 2 ml of stock lysate in 20 ml of DNB. The co-cultures were incubated for 18 h to reach a final concentration of 108 plaque-forming units (PFU)/ml of predator. At this point, the lysate was passed three times through a 0.45-µm Millex pore-size filter  to remove residual prey and cell debris (filtered lysate). As a control, filtered sterilized lysate was prepared by sequentially passing the lysates through three 0.22-µm pore-size filters. After filtration, no predator, as judged by PFU, could be detected B. bacteriovorus through thermal lysis, and used in subsequent PCR as positive control.The predatory bacteria used was B. bacteriovorus in intestinal and faecal biopsy specimens from paediatric healthy subjects and patients suffering from IBD, Celiac disease and CF. To this purpose, we carried out the following PCR reactions: i) ‘β-globin gene amplification’ on the total DNA extracted from all samples collected, in order to verify DNA suitability for amplification; ii) Bdellovibrio species-specific end-point PCR and qPCR on the total DNA extracted from all samples collected, in order to verify the Bdellovibrio presence; iii) 16S rDNA amplification on negative Bdellovibrio species-specific PCR samples, in order to asses if overall bacterial DNA was present in such samples, thus ensuring their negativity for Bdellovibrio.The present study was aimed to evaluate, by PCR technique, the presence of B. bacteriovorus-specific PCR was achieved through a human β-globin gene amplification for each sample, as described previously 5′-CAACTTCATCCACGTTCACC-3′; reverse primer, 5′-GAAGAGCCAAGGACAGGTAC-3′. Amplification reactions were carried out in a 50-µl volume containing 1× PCR buffer II , 3 mM magnesium chloride, 200 µM each deoxynucleoside triphosphate, 50 pmol each primer and 1.5 U of AmpliTaq Gold polymerase (Applied Biosystems). The PCR was carried out under the following conditions: 1 cycle of 95°C for 7 min, 35 cycles of 94°C for 1 min, 55°C for 1 min and 72°C for 1 min and 1 cycle of 72°C for 7 min. Twenty microliters of each PCR were run on a 1% agarose gel containing EtBr 0.5 µg/ml, and photographed with DigiDoc-It system .A first-step assessment of DNA suitability for subsequent B. bacteriovorus specific primers Bd529F (5′-GGTAAGACGAGGGATCCT-3′) and Bd1007R (5′-TCTTCCAGTACATGTCAAG-3′) were used to amplify a 481-bp trait of the 16S rDNA gene hit locus, specific for B. bacteriovorus5′-GACAGATGGGATTACTGTCTTCC-3′) and Hit_RW (5′-GTGTGATGACGACTGTGAACGG-3′). We used these two sets of primers because they are reported to be specific for B. bacteriovorus PCR amplification from total DNA, and we decided to use them both to strength the results. PCR was performed with Taq DNA-polymerase . PCR reaction (25 µl) contained 1X buffer for PCR, 300 µg/mL bovine serum albumin , 2.5 mM MgCl2, 200 µM for each dNTP, 0.5 µM of each primer, 1.25 U of Taq polymerase and 100 ng of total DNA (from biopsies or faeces). For 16S rDNA trait, sample DNA was amplified under the following conditions: 95°C for 5 min, 20 cycles of 95°C for 1′, 53°C for 1′, 72°C for 1′, and a final step of 72°C for 10 minutes. For hit locus, sample DNA was amplified under the following conditions: 95°C for 5 minutes, 20 cycles of 95°C for 1′, 60°C for 1′, 72°C for 1′, and a final step of 72°C for 10 minutes. Appropriate positive (B. bacteriovorus strain HD100) and negative (water) controls were employed.B. bacteriovorus in samples, we performed three individual PCR reactions for each sample. The individual PCR reactions were unified, analysed by electrophoresis on 2% agarose gels containing ethidium bromide to determine their size (481 bp), and concentrated with SpeedVac  to reach a final volume approximately equal to 1/3 of the original. The unified PCR reactions were titrated using two different methods: firstly, twenty-five microliters of each concentrated PCR were loaded on a 1% agarose gel containing EtBr 0.5 µg/ml, run for 1 hour at 80 V, photographed with DigiDoc-It system , and analysed for densitometry with Phoretix 1D software ; secondly, measure of DNA density was performed by NanoDrop spectrophotometer  at 260 nm, using one microliter of unified PCR. The results obtained by such measures were in agreement one each other and, upon normalization, were used for subsequent analyses.In order to minimize the PCR bias, and to avoid the problem due to the expected low levels of http://rdp.cme.msu.edu/seqmatch/seqmatch_intro.jsp). The obtained sequences were also compared to gene sequences of known bacterial identities available in GenBank by means of the Basic Local Alignment Search Tool (BLAST) present at National Centre for Biotechnology Information (NCBI) website (http://www.ncbi.nlm.nih.gov).All positive PCR products were sequenced using the BigDyeTM terminator chemistry  and the sequencing mixture was analysed on a DNA sequence analyser ABI3730 . Sequencing was performed in both directions and sequences were analysed with SEQ MATCH at the Ribosomal Database Project II website , while it harbours only one hit locus on the reverse strand (from 96861 bp to 97770 bp). The information so obtained was used to properly normalize the relative abundances of B. bacteriovorus.To ensure that both primer pairs used, for 16S rDNA trait and B. bacteriovorus PCR-negative samples, detecting the presence of bacterial DNA. Universal primers U968 (5′-GAA CGC GAA GAA CCT TAC-3′) and L1401 (5′-GCG TGT GTA CAA GAC CC-3′) were used to amplify the V6–V8 region of bacterial gene coding for 16S rRNA. PCR was performed with Taq DNA-polymerase . PCR reaction (25 µl) contained 1X buffer per PCR, 2.5 mM MgCl2, 200 µM for each dNTP, 0.5 µM of U968-GC and L1401 oligos, 1.25 U of Taq polymerase and 100 ng of total DNA. The samples were amplified under the following conditions: 95°C for 5 min, cycles at 94°C for 45″, 53°C for 45″, 72°C for one min and 10″, and a final step of 72°C for 30 min. To rule out non-specific products, a ‘touchdown PCR’ was performed with a starting annealing temperature of 58°C and decreasing it by 0.5°C each cycle to reach 53°C, followed by 30 cycles at 53°C. In order to minimize the PCR bias, three ‘touchdown PCR’ reactions were performed for each sample and subsequently pooled. To minimize hetero-duplex formation and single-stranded DNA (ssDNA) contamination during PCR amplification, 5 additional cycles of ‘reconditioning PCR’ were performed, taking 1/10 of the previous pooled PCR volume as template in a new reaction. In order to minimize the PCR bias, three ‘reconditioning PCR’ reaction were done for each sample and subsequently pooled. Successful reaction and DNA concentration was quantified by spectrophotometer measurements at 260 nm and DNA integrity checked through 1% agarose gel electrophoresis containing EtBr 0.5 µg/ml.In order to confirm the significance of results, a further 16S rDNA PCR was performed on all B. bacteriovorus in our samples. Primers and hydrolysis probe used were taken from the literature B. bacteriovorus-specific region of 16S rDNA. Briefly, primers used were Bd347F (3′-GGAGGCAGCAGTAGGGAATA-5′) and Bd549R (5′-GCTAGGATCCCTCGTCTTACC-3′), while the probe was Bb396P (5′FAM-TTCATCACTCACGCGGCGTC-TAMRA3′). The qPCR reaction mix was made up with 10 µl of ‘2X qPCR ProbesMaster’ from Jena Bioscience GmbH  2SO4 and MgCl2, ROX, stabilizers), 900 nM for both of the primers, 50 nM for the probe, and 5 µl of template . The final reaction volume was adjusted to 20 µl with PCR-grade distilled water. Thermal cycling conditions were: 2 min at 95°C , followed by 50 repeats of 15 s at 95°C (denaturation) and 1 min at 60°C . Data were collected during the annealing phase. Three different replicates were performed for each sample. In each run no template controls (NTC) were included, along with positive controls. To construct the standard curve, a plasmid (pUC57) containing one copy of the target was provided by Biofab Research , quantified by spectroscopy at 260 nm, and 10-fold serially diluted in the range of 109-100 copies B. bacteriovorus genome We performed a quantitative PCR (qPCR) approach with standard curve in order to assay the relative abundances of B. bacteriovorus in subjects and samples, we used the χ2 test. To assess significant differences among the aforementioned groups in terms of relative abundance of B. bacteriovorus in samples, a Mann-Whitney U test was employed. For both tests, a P value less than or equal to 0.05 was considered significant.To assess putative differences among and within the patients’ groups  in terms of prevalence of B. bacteriovorus in the aforementioned patients. All end-point PCR-positive reactions were sequenced and submitted to SEQ MATCH and BLAST, finding a percentage similarity of 100% against B. bacteriovorus strain HD100 for all the sequences. Specifically, the percentage similarity was 100% across 481 bp for 16S rDNA locus, while it was 100% across 910 bp for hit locus. We found reliable results among the two sets of primers used.We analysed overall 87 intestinal biopsies from healthy controls (n = 8) and patients affected by Crohn’s (n = 9), Ulcerative colitis (n = 6) and Celiac disease (n = 10), and 51 faecal samples from healthy controls (n = 16) and Cystic fibrosis patients (n = 35). Species-specific end-point PCRs and qPCR were employed to detect the presence of B. bacteriovorus prevalence. Within the ‘IBD group’, B. bacteriovorus was significantly found in 8/8 (100.0%) of control subjects, in 6/6 (100%) of UC patients, and in 1/9 (11.1%) of CD patients. Within the ‘Celiac disease group’, B. bacteriovorus was found in 8/8 (100%) of control subjects, and in 8/10 (80.0%) of Celiac patients. Within the ‘CF group’, it was found in 2/16 (12.5%) of controls, and in 4/35 (11.4%) of CF patients, with no significant difference. These results, together with P values, are summarized in We had reliable results among the two approaches used to detect B. bacteriovorus prevalence in biopsy samples of the ‘IBD group’, taken within a single subject from different districts, were reported in B. bacteriovorus was assessed taking into account both end-point and qPCR data. We observed a preponderant prevalence of B. bacteriovorus in ileum, colon, and rectum of control biopsies  in respect to CD . No difference was found among overall UC  and control samples P = 0.6760). It is noteworthy that all ‘IBD group’ biopsy samples  were treated with the reducing agent dithiothreitol (DTT), in order to remove the mucus layer and the luminal flora, as reported in B. bacteriovorus.Results of B. bacteriovorus abundance were collected by qPCR. Within-cohort mean values, expressed as number of copies/mg of sample (biopsies or faeces), were compared after a proper correction for the number of 16S operons, namely two in B. bacteriovorus genome, as assessed by in silico PCR. Standard curves gave a mean slope of −3.3392±0.0405, a mean intercept of 39.4547±0.3976, and a mean R2 equal to 0.9970±0.0007 ((−1/slope), was 99.4% ±1.6%. The lowest reproducible detection level of the qPCR was 10 plasmids per reaction, each containing one target sequence.Data about 0±0.0007 . The meavs Celiac; 23±6 copies/mg, 2±2 copies/mg, and 9±2 copies/mg for controls, CD and UC. In faecal samples of ‘CF group’, B. bacteriovorus abundance was always under the limit of detection in both controls and CF patients. We found that relative abundance of B. bacteriovorus was significantly higher in control bioptic samples of both ‘IBD group’ and ‘Celiac group’ (vs CD (P<0.0001), UC vs CD (P<0.0001), and controls vs Celiac (P = 0.00053). In UC, even though B. bacteriovorus is always present (B. bacteriovorus (80%) similar to controls (100%), but, contrarily, the 93.5% drop in its relative abundance was highly significant (P<0.0005)  .B. bacteriovorus in different intestinal districts, we compared qPCR results obtained from ileum, colon, and rectum biopsy samples, and from faeces encompassing luminal flora. Results from controls clearly showed a decreasing trend of B. bacteriovorus relative abundance along the intestine (B. bacteriovorus levels in duodenum (P = 0.0012), and the lowest in rectum, while it was under the lower limit of qPCR detection in faeces. Furthermore, results in bacteriovorus abundances, recognizing three different levels: starting from 102 copies/mg in duodenum, 101 in ileum/colon, we arrived at an order of 100 in rectum.In order to analyse the relative abundance of B. bacteriovorus-negative . Comparing control and disease statuses , we observed a significant difference only in duodenum (P = 0.00053) and ileum districts (P = 0.0434) (P = 0.7721) or rectum (P = 0.2432) districts.In order to compare biopsy samples from controls and diseased subjects, mostly UC samples were taken into account, because CD samples resulted all  0.0434) . We didnB. bacteriovorus presence in humans, both in healthy and disease status. A precedent study B. bacteriovorus presence in one human faecal sample. The occurrence of these predatory bacteria in the intestinal tract raises questions about their role in this habitat. Predation is an important mechanism in nature to keep bacterial populations under control and plays a major role in the cycling of nutrients through the microbial loop B. bacteriovorus. We assessed B. bacteriovorus presence by end-point and qPCR. Our results showed that B. bacteriovorus is significantly present in paediatric healthy subjects and almost completely absent in CD and Celiac patients. Moreover, we found a significant difference among CD and UC for B. bacteriovorus prevalence in subjects (B. bacteriovorus relative abundance (59.1%) in respect to controls. These last results could underscore the difference between CD and UC patients in terms of microbiota composition, and it could support the idea that UC patients are closer to controls in terms of intestinal habitat, in agreement with already reported studies This is the first extensive survey of subjects , while UB. bacteriovorus levels along the intestine in controls. The preferential duodenal colonization could reflect its predatory nature, or the existence of particular habitat conditions . Duodenal district is usually colonized by a bacterial load in the order of 102 CFU/ml, and our finding of a triple-level abundance of B. bacteriovorus along the gut, with a 10-fold drop in ileo-colonic districts, should reflect the suitable conditions for prey/predator equilibrium, as stated by Lotka-Volterra equation B. bacteriovorus could strive also in ileo-colonic region at mucosal level, a low-pO2 district.As reported in Bdellovibrio could survive in anaerobic conditions 2. Interestingly, disease status affected B. bacteriovorus relative abundance mainly in upper GI districts, such as duodenum and ileum. Future studies should describe the B. bacteriovorus distribution in the human gut in a fine-tune fashion, to better understand its role within the bacterial community. In this study, for the first time, we showed the presence of B. bacteriovorus at the intestinal mucosal level, indicating that this predatory bacterium could be a permanent inhabitant of the gut ecosystem. We didn’t find it in faecal samples at significant prevalence or abundance. We cannot exclude that treatment with DTT, a reducing agent that loosen the disulphide bonds among mucin proteins, ultimately eliminating the mucus layer, should have removed a portion of the B. bacteriovorus population from biopsies. Using DTT, we left only the mucosa-attached microbiota, along with the predators inside their gram-negative preys, and, possibly, with a little percentage of free-swimming B. bacteriovorus. The intestinal mucus layer is 40–240 µm thick B. bacteriovorus to chase prey: its long polar flagellum B. bacteriovorus. In this view, future experiments should address the free-swimming/intra-periplasmic ratio of B. bacteriovorus population within a specific sample, both with and without DTT treatment, to deepen the knowledge about its relative abundances along the intestinal tract.It was found how B. bacteriovorus in the gut microbial community is exerted through its selective gram-negative predatory activity, as found in terrestrial and aquatic ecosystems B. bacteriovorus in the intestinal mucosa of IBD and Celiac patients could bring to an uncontrolled bacterial overgrowth, as already reported in these subjects, where an increased number of mucosa-associated bacteria was found B. bacteriovorus at mucosal level, in both IBD and Celiac paediatric patients, could support the idea that loss in microbiota biodiversity could also involve species, such as predatory Bdellovibrio, that are normally underrepresented, and that exert a role in regulating the bacterial population levels. A recent study found that all BALOs species accounts for 0.3%–0.7% of the bacterial soil community, with Bdellovibrio genus representing around 80% of BALOs The mucosa-associated microbiota, due to its close proximity to the intestinal epithelium and the underlying mucosal immune system, is believed to play a main role in maintaining the homeostasis in healthy gut and in the inflammatory response in diseases. The key role of B. bacteriovorus from the environment. In the future, it will be interesting to check the B. bacteriovorus presence also in adult controls and patients affected by dysbiotic diseases. Thus, B. bacteriovorus could be a good candidate for new therapy strategies, such as shown in birds Further studies will be necessary to improve our results, along with an increase in number of subjects to be enrolled. The present study, performed on naïve paediatric patients and controls, enabled us to investigate an untouched gut ecosystem, with no previous endoscopic assessments or pharmacological treatments, thus supporting the idea of an early gut colonization by Figure S1Standard curve for qPCR. Standard curves gave a mean slope of −3.3392±0.0405, a mean intercept of 39.4547±0.3976, and a mean R2 equal to 0.9970±0.0007. As an example, in figure is the standard curve relative to UC samples. On x axis, log of ‘number of plasmid copies’; on y axis, cycle threshold (Ct).(TIF)Click here for additional data file.


Z-Score (HI(I)T: −2.06 ± 1.31, P = .001 versus MICE: −1.60 ± 1.77, P = .001) and (relative) VO2max (HI(I)T: 15.6 ± 9.3%, P = .001 versus MICE: 10.6 ± 9.6%, P = .001) compared with the sedentary control group. In conclusion, both exercise programs were comparably effective for improving cardiometabolic indices and cardiorespiratory fitness in untrained middle-aged males.Aerobic exercise positively impacts cardiometabolic risk factors and diseases; however, the most effective exercise training strategies have yet to be identified. To determine the effect of high intensity  training (HI(I)T) versus moderate intensity continuous exercise (MICE) training on cardiometabolic risk factors and cardiorespiratory fitness we conducted a 16-week crossover RCT with partial blinding. Eighty-one healthy untrained middle-aged males were randomly assigned to two study arms: (1) a HI(I)T-group and (2) a sedentary control/MICE-group that started their MICE protocol after their control status. HI(I)T focused on interval training (90 sec to 12 min >85–97.5% HRmax) intermitted by active recovery (1–3 min at 65–70% HRmax), while MICE consisted of continuous running at 65–75% HRmax. Both exercise groups progressively performed 2–4 running sessions/week of 35 to 90 min/session; however, protocols were adjusted to attain similar total work . With respect to cardiometabolic risk factors and cardiorespiratory fitness both exercise groups demonstrated similar significant positive effects on MetS- Serum samples were centrifuged at 3000 RPM for 20 min. Total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides, glucose, and uric acid  were determined. During another visit, blood pressure was determined in a lying position after assessment of resting metabolic rate over 30 min, using an automatic oscillometric device . Subjects refrained from coffee and/or tea for at least 12 h before testing and with more than 12 h passing since the last relevant physical exertion or exercise session.2 was continuously determined breath by breath using an Oxycon mobile open spirometric system . For more details the reader is kindly referred to another publication [2 and RER-assessment considering the individual aerobic threshold.During a stepwise treadmill test  up to a voluntary maximum, VOlication . Energy lication  is basedRunning economy (RE) was assessed for the last 30 sec of the second stage of the test (5:30 to 6:00 min), which was consistently kept below the anaerobic threshold .Two sessions per week were consequently supervised by instructors who randomly checked exercise intensity by monitoring the HR watches  which were provided for each participant.≈65–70% HRmax) were prescribed between the intervals. Besides these intervals, continuous high intensity running sessions ranging from 25 to 45 min directly performed at the IAT (≈85% HRmax) were also part of the HI(I)T program. However 25% of total exercise volume of the HIIT group was performed at low exercise intensities (70–82.5% IAT-HR) during warm-up, cool-down, and the active rest periods ( periods .With slight exceptions (IAT-HR tests), exercise intensity of the MICE intervention was prescribed consistently low to moderate (70–82.5% IAT-HR) . Duratioβ) to detect a 5 ± 7.5% difference between the groups was 85%. All the subjects who took part in the follow-up measurements were included in the analysis .The sample size calculation was based on another study endpoint (“left ventricular end-diastolic volume”) that is not presented here. Based on the sample size of 40 subjects per group and a Type 1 error of 5%, the statistical power  were calculated using Cohen's d. All statistical procedures were calculated with SPSS 21.0  statistical procedures.Baseline and follow-up data are reported as mean values and standard deviations. Changes between baseline and follow-up in HI(I)T and MICE were reported as absolute  and perclculated . All tesn = 41) were assessed. In this context, three subjects of the HI(I)T and four subjects of the MICE group quit the study due to injuries related to running exercise. One participant per group withdrew due to injuries related to the training protocol, two subjects of the HI(I)T and one subject of the MICE group due to injuries or diseases unrelated to exercise. Three subjects of the MICE and one subject of the HI(I)T group lost interest and cancelled their participation or did not start MICE after the control phase (n = 2).Seven subjects of the HI(I)T (17.5%) and nine subjects of the MICE group were lost to follow-up (21%), while all the subjects of the CG (P = .012), total work was comparable between both groups (MICE: 30479 ± 6566 versus HI(I)T: 28966 ± 5228 kcal, P = .317).Due to the comparable attendance rate of the HI(I)T  and MICE group  and the approach to compensate the higher exercise intensity of the HI(I)T group by higher exercise volume applied among the MICE group (2303 ± 352 min versus HI(I)T: 2092 ± 298 min; P = .004 versus MICE: −2.5 ± 2.4 kg, P = .001); however, the reduction was significantly higher (P = .046) among the MICE participants. Significant weight gain , resulting in significant differences (P = .001) compared to MICE and HI(I)T, was observed for the CG. Lean body mass was maintained in the HI(I)T-  and CG  but significantly dropped in the MICE group . Body fat mass similarly (P = .261) decreased in both exercise groups (HI(I)T: −4.9 ± 9.0%, P = 0.010 versus MICE: −9.5 ± 10.4%, P < 0.001) but increased in the CG .Weight significantly decreased in both exercise groups (HI(I)T: −1.3 ± 2.3 kg, Z-Score and number of MetS parameters of the HI(I)T group were significantly higher compared with MICE and control (At baseline MetS- control .P = .001) in both exercise groups (HI(I)T: −2.06 ± 1.31 versus MICE: −1.60 ± 1.77). Although changes of the control group were also significant  changes of both exercise groups were significantly more favorable (P = .001) (P = .001) in both exercise groups (HI(I)T: 21.9 ± 25.6% versus MICE: 43.8 ± 31.5%; P = .336) and was unchanged in the CG . Changes of both exercise groups for this parameter significantly differed (P = .001) from the CG Metabolic Syndrome score according to Johnson et al.  signific = .001) . At the Z-Score and the number of criteria of the MetS clearly has to be rejected.Thus the primary hypothesis that HIIT is significantly more effective compared with MICE to impact the Metabolic Syndrome (MetS) P = .720), while a nonsignificant rise (P = .083) was observed for the CG . At the same time, significant reductions of MAP (P = .001) were observed for both exercise groups (HI(I)T: −4.9 ± 4.0% versus MICE: −5.9 ± 4.2; P = .318) that significantly differed from the result of the CG .With respect to underlying mechanisms, P = .001 versus MICE: −4.6 ± 28.8%, P = .361 versus CG: −6.8 ± 33.9%, P = .208). HDL-C also changed favorably and reached significance level for all groups (HI(I)T: 8.8 ± 5.3%, P = .001 versus MICE: 2.3 ± 4.8%, P = .014 versus CG: 2.9 ± 4.3%, P = .001). However, changes between the HI(I)T and the MICE as well as between HI(I)T and CG were significant (P = .001), while no difference was observed between MICE and CG (P = .912). Furthermore, no group (HI(I)T: −1.4 ± 10.0% versus MICE: −0.8 ± 6.6% versus CG: −3.5 ± 16.7%) showed significant changes (P < .212) of fasting glucose. Lastly, no between group differences were determined for this parameter (P < .637).After adjusting for baseline values, no intergroup differences were determined for triglycerides that declined in all groups (HI(I)T: −19.7 ± 24.8%, 2max (mL∗min−1∗kg−1) significantly (P = .001) increased in both exercise groups (HI(I)T: 15.6 ± 9.3% versus MICE: 10.6 ± 9.6%) with no significant difference between HIIT and MICE (P = .121). Relative VO2max did not relevantly change in the CG , thus, changes of both exercise groups significantly differed (P = .001) from the control. Of interest, nonweight adjusted absolute VO2max (mL/min−1) differed significantly (P = .002) between both exercise groups in favor of HIIT ; however due to the significantly more distinct reductions of weight (but not LBM) in MICE this finding disappeared after calculating relative VO2max changes ((Relative) VO changes .Although these results make it difficult to generate a clear statement, for reasons discussed below, we also reject the secondary hypothesis that HIIT was significantly more effective to increase “cardiorespiratory fitness” compared with MICE.P < .05 versus MICE: −37% (P = .13) and the number of MetS criteria  compared with MICE. However, applying continuous running exercise over 6 months for overweight subjects with “mild-to-moderate dyslipidemia”  Johnson et al. [2peak; 128 min or 19 km/week) compared with MICE . In contrast, although nonsignificant, the authors reported more favorable changes of MetS-Z-Score and number of MetS criteria according to NCEP-ATP III among the MICE subgroup. However, with respect to the general effectiveness of these exercise protocols on MetS parameters, both MICE and HI(I)T significantly outperformed the corresponding control group(s). Earnest et al. [≈40 min/week with 50–70% VO2max) subjects were randomly allocated to further 6 weeks of continuous running exercise at 50–70% VO2max or a HIIT-protocol including up to 8 cycles of 2 min at 90–95% VO2max intermitted by 2 min of active recovery (50% VO2max). MetS-Z-Score according to the American Heart Association [−1∗kg−1 or 2.8 versus 3.1 min during treadmill test). Of relevance, since body mass decreased in both groups (−1.3 versus −2.3 kg), absolute VO2max per se was not significantly affected (0.25 versus 0.13 L∗min−1) by the running protocols.Summarizing the results, this study failed to detect a superiority of a HI(I)T compared with a MICE protocol on the Metabolic Syndrome (MetS) as a sensitive cluster of metabolic and cardiac risk factors even under the premise of comparable total “work”  in both protocols . Howevern et al.  did not t et al.  in theirRevisiting the issue of whether some cardiometabolic MetS parameters are more sensitive to HIIT or to MICE, one might think that the large number of comparative trials, reviews, and meta-analysis that focus on this topic e.g., , 33–37) –37 33–372max changes generated by the HIIT protocol of the present study was observed, which is mainly confirmed by comparative trials with untrained subjects with MetS or CHD/CVD [2max changes actually represented higher changes of cardiorespiratory fitness. In fact, this and other studies  by their MICE protocols, although VO2max changes may be more distinct in the HI(I)T group. At first sight this result may appear contradictory, since VO2max is an important predictor of endurance capacity [2max) and economization (RE). Hence, although we think that VO2max may in general be a valid characteristic for cardiorespiratory fitness, a conclusion whether or not HIIT protocols are superior for impacting cardiorespiratory fitness should not be based solely on this parameter.With respect to “cardiorespiratory fitness”, a strong tendency for higher VO CHD/CVD , 19, 20.capacity . Howevercapacity , 46. In Z-Score, a continuous score that is based on individual subjects (raw) data, which may be more appropriate to properly assess the effect of the study intervention [≈20%). Independent of the exercise intensity, half of these subjects cited orthopedic problems for their withdrawal. Furthermore, a slight majority (HIIT: n = 16 versus MICE: n = 15) of the remaining males reported musculoskeletal problems and complaints longer than 7 days. These adverse effects may be related to the high body weight in this cohort and the abruptly increased mechanical impact due to the unaccustomed running exercise and the speedy progression of the running program. (5) With respect to exercise (intensity) compliance the authors did not analyze all the heart rate watches after the session but randomly selected 15–20 subjects per session  for this procedure.Some limitations and features of the RUSH study may complicate a direct comparison with most HIIT protocols cited above: (1) due to the study protocol the exercise groups were not evaluated in parallel but consecutively . Thus, srvention . (4) The(6) In order to adequately focus on the effect of exercise intensity, unlike most other studies with healthy untrained persons, comparable work load for both groups was prescribed. However, the authors are aware that time effectiveness is an important benefit of HIIT protocols that largely account for its attractiveness .2max changes were significantly higher in the HI(I)T group, based on the slightly higher MICE effect on endurance capacity, we do not share the enthusiasm of other researchers in recommending pure HIIT protocols for increasing “cardiorespiratory fitness”. We would rather favor a skilled combination of higher and lower exercise intensity (and volume) that may optimally affect performance parameters related to both economy and maximization.In summary, both protocols favorably impacted cardiometabolic risk factors; however the superiority of HIIT protocols for positively impacting MetS as a cluster of relevant metabolic and cardiac risk factors cannot be confirmed, at least for this cohort of untrained middle-aged males. Furthermore, although VO≈176.000 exercise training hours at all) too low to detect differences between groups, a recent study of Rognmo et al. [Addressing the strengths and limitations of HIIT protocols in the field of primary and secondary prevention, time efficiency along with effectiveness is a clear pro for HIIT, taking into account the sedentary lifestyle and low affinity to exercise of most middle-aged subjects . At the o et al.  listed eThus, although we cannot confirm the general superiority of HIIT, with respect to efficiency and adherence, we strongly recommend HIIT as a reasonable component of endurance exercise protocols for prevention and rehabilitation of cardiometabolic diseases.