N = 106) was analyzed using individual difference measures of age, experiential knowledge, fitness, personality traits, and sex. Of particular note, it was found that for participants who reported any experiential knowledge about slant, estimates decreased  as conscientiousness increased, suggesting that more conscientious individuals were more deliberate about taking their experiential knowledge (rather than perception) into account. Effects of fitness were limited to those without experiential knowledge, suggesting that they, too, may be cognitive rather than perceptual. The observed effects of age, which tended to produce lower, more accurate estimates of hill slant, provide more evidence that older adults do not see hills as steeper. The main effect of age was to lower slant estimates; such effects may be due to implicit experiential knowledge acquired over a lifetime. The results indicate the impact of cognitive, rather than perceptual factors on individual differences in slant estimation.Several individual differences including age have been suggested to affect the perception of slant. A cross-sectional study of outdoor hill estimation ( The perception of the slants of hills has been the focus of a great deal of research in the past 20 years. Very large distortions are evident in the conscious perception of slant . A hill There has been controversy over whether there exists a separate accurate representation of slant that can be measured by motor actions , 2011. ASome of the most puzzling results concerning slant perception are the individual differences that have been reported based on sex  and age One of the concerns that has complicated the interpretation of individual differences in slant perception is that the results of estimation tasks can be biased by subtle or not-so-subtle instructional manipulations see . For exaSimilarly, studies involving fitness, fatigue, or burden manipulations  may alsoagreeableness, for example, is sex-linked, such that females tend to be higher than males on this trait  hill.To maximize sensitivity to population variables , our study focused primarily on a single hill, which was assessed with manual, visual, and verbal measures. All participants were first tested with this hill. In a classic study reporting sex differences, One-hundred and six adults , naïve to the purpose of the study, were successfully recruited to participate in our study from the student body at Swarthmore College or from the surrounding community. Forty-eight of the participants were nonstudent community members, ranging in age from 18 to 72 years old ; 58 of the participants were Swarthmore College students, ranging in age from 18 to 22 . Recruitment materials indicated that participants were to wear corrective lenses if they needed them, that participants must be able to speak and read English, and that they must be able to stand while making their judgments. Visual acuity was measured using a Snellen chart for all but one participant (with whatever corrective lenses they wore during the experiment); only two participants had (corrected) vision worse than 20/40 . The study methods were approved by the local institutional review board.Three hills were tested in the same order for each participant. The primary hill, shown in Three measures were used for the primary (9°) hill. The measures were collected in a fixed order designed to minimize the risk of measure-to-measure bias. Participants were first asked to indicate the slant of the hill by holding their hand parallel to the hill . They wore a lightweight inclinometer on their hand so that we could measure the orientation of the back of their hand. We also measured the angular thickness of their hand when they placed it on a horizontal level surface after holding it up. The calculated orientation of the midline axis of their hand relative to horizontal was used as measure of their intended orientation see . We callThe second measure used was based on the visual measure developed by Finally, a verbal estimate was requested in degrees relative to horizontal. This measure was collected last so that it would be less likely to influence the nonverbal measures. The reasoning for this was that if a participant first gave a verbal response, they might be likely to try to match their visual and manual responses to the verbal response, which would be easier to remember than would be the manual and visual matches. We call this the numeric estimation response (NER). Note that for the 22.5° and 4.5° hills only an NER was collected.To avoid influencing slant judgments, all demographic information and other measures of individual differences were collected after the slant judgments were completed. An assessment of visual acuity was first performed using a Snellen eye chart viewed from 6.1 m. After this, all of the individual difference information was collected on computer by a custom program implemented in E-Prime.not at all (1) to extremely (9). Sample items for agreeableness, conscientiousness, and emotional stability are (a) “helpful,” (b) “practical,” and (c) “moody” (reverse coded), respectively. Note that a positivity bias might be expected for such scales, but such a bias would predict high correlations across all three scales, and this was not observed.The personality variables were tested first. These were three scales of the Big Five Inventory that are known to be sex-linked : Female 1 This rendered its value as a measure of spatial ability questionable; it was not used for analysis.We intended to measure spatial ability by including a computerized mental rotation task with Participants were next asked whether they had prior experiences or special knowledge that was relevant to the evaluation of hill slant ; 36 participants listed some form of such experience. We also asked participants whether they used any particular strategies in making their judgment, though this data were intended simply to screen for unusual approaches to the task and were not included in statistical analyses.SR). BMI has been used previously as a marker of fitness by proponents of embodied perception  between age and emotional stability, (p < .0001).The correlations among our various predictor variables are shown in Once participants arrived at the lab, they signed a consent form and were paid in advance for their time. All participants were first involved in a separate study of distance perception and height perception in a field near the Psychology building . Followir104 = 0.50, p < .001; MMR and NER, r104 = 0.29, p = .003; VMR and NER, r104 = 0.54, p < .001. The three types of measure were therefore averaged to produce a single estimate of perceived hill slant for the 9° hill for each participant . We then used simultaneous multiple linear regression modeling to look at all of the data from the first hill using the individual difference scores as predictors.As has been reported previously , all thrSR as predictors, as well as all two-way interactions between them . This analysis showed main effects of knowledge, sex, and BMISR: Those reporting some knowledge gave lower (more accurate) estimates, β = −5.12°, t(74) = 2.52, p = .014, R2 = 0.05; female participants gave higher slant estimates, β = 4.15°, t(74) = 2.32, p = .023, R2 = 0.04; and participants with higher BMISR gave higher slant estimates, β = 0.50°, t(74) = 2.35, p = .022, R2 = 0.06. However, the analysis also showed that knowledge interacted both reliably with conscientiousness, t(74) = 2.77, p = .007, and marginally with BMISR, t(74) = 1.83, p = .072. We therefore split the data according to whether the participants reported having knowledge relevant to slant (N = 39) or not (N = 67).In previous studies e.g., , prior eSR as predictors) is shown in β = −3.53, t(30) = 4.00, p < .001, R2 = 0.23. Although this result was not anticipated, this pattern is consistent with the idea that knowledgeable people scoring higher in conscientiousness were more likely to take the trouble to use their knowledge  when asked to provide a slant estimate. A plot depicting the interaction between conscientiousness and knowledge is shown in A simultaneous multiple linear regression model of the responses of those reporting any experiential knowledge relevant to slant , a simultaneous multiple linear regression model, shown in SR. We will consider each of these observations in turn.For those who reported no particular knowledge about slant  = 2.93, p = .005, R2 = 0.084. The direction of this effect differs from the claims of As shown in We note that measured visual acuity was, of course, correlated with age, but the age effect found here remained even when visual acuity was included in the model as a covariate.t(59) = 2.03, p = .047, R2 = 0.034. The present data suggest that these effects are not due to sex-linked personality differences (which were included in the model), nor by the forms of experiential knowledge measured by our probe. Note that excluding sex as a factor in this model did not unmask any effects of sex-linked personality traits.Consistent with previous reports, female participants gave estimates that were 4.2° higher than male participants, SR gave higher slant estimates, β = 0.69, t(59) = 3.37, p = .001, R2 = 0.132. This appears to lend support to the claim that behavioral potential  affects slant perception. However, a closer look at the data here shows that the effect is limited to obese women, which may support the alternative perspective that knowingly having a particularly high BMI may influence how participants make their estimate.As shown in SR effect: If we eliminate the seven obese participants (without knowledge) from our analysis, not only does BMISR cease to be predictive, t(52) = 0.13, but sex is no longer predictive either, t(52) = 1.10.Of the 67 people in the no-knowledge condition, seven can be classified as obese, and it is a subset of these seven that drive the BMIM = 38.3°) than did the three males (M = 18.6°), Welch t(6.3) = 4.08, p = .006. If the influence of obesity on slant judgment is sex-linked, this would seem consistent with the idea that effects of obesity are based on sociocognitive factors rather than perceptual effects.Thus it seems possible that greater salience of body image among females is responsible for at least a portion of the sex differences found in previous studies. Among the nine obese participants in our overall sample, the six females gave reliably higher average estimates of the 9° hill  = 2.02, p = .052, R2 = 0.115. Although only a marginal result, statistically, this is exactly the sort of pattern (an interaction of knowledge and agreeableness) that one would predict of participants who might be trying to co-operate with what they expect the experiment to be about . In fact, among those reporting some knowledge of slant, manual estimates tended to increase with agreeableness, be about . Insofarβ = −7.06°, t(30) = 4.46, p < .001, R2 = 0.258. That is we see evidence that both agreeableness and conscientiousness interact with the presence of some knowledge about hill slant overestimation in determining responses.This evidence that agreeable participants were tempted to “help” initially, is not inconsistent with the further observation, that, when finally asked to give a verbal estimate, the conscientious participants (among those who had knowledge) now gave reliably lower verbal estimates, Because the order in which the different types of estimates were collected was fixed, we cannot draw strong conclusions about whether this is indeed an order effect or an effect of the manual nature of the measure. However, the present data are consistent with the idea that personality variables can interact with background knowledge of participants both to elevate estimates  and to rein them in.β = −2.81°, t(74) = 1.83, p = .071, and a reliable interaction between knowledge and conscientiousness, t(74) = 2.02, p = .048. A separate analysis of the participants reporting some knowledge about hill slant indicated a marginal effect of conscientiousness, β = −4.76°/unit, t(30) = 1.93, p = .064, consistent with the results for the primary hill. The analysis of those without knowledge  = 2.16, p = .035, found for the primary hill, as well as  the higher estimates of females than males, β = 7.26°, t(59) = 1.99, p = .051. However, the effect of BMISR was not reliable for this hill, β = 0.34°, t(59) = 0.98, p = .332. Given that the same participants gave judgments at the primary hill first, the replication of most of the patterns is reassuring, but perhaps unsurprising since participants were likely trying to remain consistent with their previous estimate.For the 22.5° embankment, a preliminary analysis of verbal estimates showed a similar pattern to the data from the primary hill. There was a marginal effect of knowledge, nowledge  replicatt(74) = 2.34, p = .022. However, a separate analysis of the participants reporting some knowledge about hill slant did not detect any reliable main effects. The analysis of those who reported no knowledge showed a marginal effect of BMISR, β = 0.34°/unit, t(59) = 1.86, p = .068, while no other main effects were reliable. It seems likely that individual difference effects from the previous analyses were not detectable at this hill simply because the mean estimates were compressed relative to the variance.For the 4.5° path, analysis of verbal estimates again showed a reliable interaction between knowledge and conscientiousness, F = 3.69, p < .001, and also much higher than the 0.38 CoV of the combined estimates for the 9° hill, F = 3.37, p < .001.One way of expressing differential proportional variance is the coefficient of variation (CoV), which is the standard deviation of a measure divided by the mean. For the verbal estimates of the 4.5° hill, the CoV was 0.69, which was substantially greater than the 0.36 CoV for verbal estimates of the 22.5° hill, Note that CoVs of the individual measures for the 9° hill were somewhat higher than those of the combined response: They ranged from 0.43 for the visual matches to 0.52 for the verbal estimates, whereas combining these correlated measures effectively produced a more sensitive measure by reducing proportional variance. N = 11) as well as for participants younger than 23 years old (N = 70). These data are shown in M = 6.5°, 14.6°, 31.4°) are less than the estimates of the younger participants , Welch t(16.1) = 2.30, p = .035, Welch t(16.0) = 3.57, p = .003, Welch t(13.7) = 3.08, p = .008, respectively.For comparison with their results, we computed average verbal estimates at each of the three hills we tested for all participants older than 59 years old  estimates than younger adults for all the hills we tested including a very steep embankment. Note that there is evidence that older adults are also more accurate at estimating egocentric distance  and exocOur study sought to evaluate individual differences in slant estimation in order to differentiate between cognitive  their estimates both for the steep path and for the steep embankment. This observation helps to show that cognitive effects of experience play an important role.We found only circumstantial evidence that sex differences in slant estimation might be due to personality differences in our study. First, for those with some knowledge, agreeableness seemed to elevate initial manual slant estimates. This is consistent with the idea that participants who suspected that we wanted to get overestimates tried to oblige us to the extent that they were agreeable. Although agreeableness was not strongly correlated with sex in our study, it is generally regarded as a sex-linked trait, such that females tend to score higher on measures of agreeableness than males. A combined role of agreeableness and knowledge might be a pathway whereby females would end up giving higher estimates than males even though their perceptual experience might not differ on average.A second hypothesis for explaining sex-differences in hill slant estimation was suggested by our findings related to obesity. Obesity was a predictor of slant estimates among those who did not report having any special knowledge of slant. However, this effect was limited to female participants. Future investigations may want to consider whether obese female participants are more likely to provide particularly high estimates for social reasons or because they actually perceive hills as geometrically steeper. That is, it seems unlikely that these participants really meant that the 9° hill appeared to be tilted half way to vertical; perhaps their exaggerated estimates reflect stereotype threat or consciously taking imagined effort into consideration .Overall, our data suggest that age, knowledge, and personality differences produced cognitive rather than perceptual effects in this study. The effect of age, in particular, went contrary to the widely cited claims of embodied perception. We have suggested that those claims probably ought not to have been made in the first place given that the original data for older participants were collected under such different circumstances than the data of younger participants to which it was compared. The effect of experiential knowledge, by seeming to remove age and fitness effects, strongly suggests that the age effects we have documented are more cognitive than perceptual. That is, experiential knowledge did not simply act as an additional factor, nor did it produce particularly accurate estimates on average, but it seemed to replace these other factors rather than combine with them.Although there may additionally be individual differences in the perceived slant of hills, it is hard to measure such individual differences because cognitive factors can easily overwhelm perceptual variation. The subject-wise correlations across measures we replicated here might mean that participants are reporting their individual and unique percept by each of the measures. However, correlations could also emerge as the result of individual differences in the cognitive translation processes from perception to a reportable impression.More striking to us than these possible individual differences is the success of recent quantitative models of hill perception, such as the angular scale expansion model proposed for hill perception , 2012. TThe present findings regarding effects of age, knowledge, and personality suggest that a complete model of cognitive processes of slant estimation might profitably include a variety of postperceptual transformations and adjustments. These postperceptual processes clearly involve cognitive effects of experience and knowledge that appear to interact with personality variables such as conscientiousness and agreeableness. Depending on context, estimation may additionally be susceptible to social and cognitive biases including anchoring, stereotype threat, and experimental demand characteristics—which may also interact with personality variables.


A subset of patients with Parkinson’s disease (PD) experiences problems with impulse control, characterized by a loss of voluntary control over impulses, drives, or temptations regarding excessive hedonic behavior. The present study aimed to better understand the neural basis of such impulse control disorders (ICDs) in PD. We collected resting-state functional connectivity and structural MRI data from 21 PD patients with ICDs and 30 patients without such disorders. To assess impulsivity, all patients completed the Barratt Impulsiveness Scale and performed an information-gathering task. MRI results demonstrated substantial differences in neural characteristics between PD patients with and without ICDs. Results showed that impulsivity was linked to alterations in affective basal ganglia circuitries. Specifically, reduced frontal–striatal connectivity and GPe volume were associated with more impulsivity. We suggest that these changes affect decision making and result in a preference for risky or inappropriate actions. Results further showed that impulsivity was linked to alterations in sensorimotor striatal networks. Enhanced connectivity within this network and larger putamen volume were associated with more impulsivity. We propose that these changes affect sensorimotor processing such that patients have a greater propensity to act. Our findings suggest that the two mechanisms jointly contribute to impulsive behaviors in PD. Approximately 6–15.5% of Parkinson’s disease (PD) patients experience problems with impulse control . ImpulsePD patients with ICDs compared to those without have a reduced reward circuitry functional connectivity between the striatum and anterior cingulate cortex (ACC) , 9. AtypAt the behavioral level, PD patients with ICDs gather less information before making decisions than patients without ICDs . Previoun = 1), compulsive sexual behaviors (n = 9), compulsive buying (n = 7), compulsive eating (n = 11), or other compulsive behaviors . All patients provided written informed consent in accordance with the Declaration of Helsinki. The study was approved by the medical institutional review board of the University of Michigan.Fifty-one mild to moderate-stage PD patients  particiPatients were tested while their symptoms were being well controlled by dopamine replacement medication. The Unified Parkinson’s Disease Rating Scale (UPDRS) motor subscale was used to assess motor symptoms. Patients completed the Barratt Impulsiveness Scale (BIS) questionnaire, for which we determined a total score as well as separate scores for the three factors: attentional impulsiveness, motor impulsiveness, and non-planning impulsiveness , 24. We M = $29.03, SE = $0.79).As illustrated in Figure http://www.vislab.ucl.ac.uk/cogent.php) running in the Matlab environment. Patients responded via an MRI-compatible claw, with separate buttons designated for choosing the blue urn, the green urn, or drawing another bead. For each patient, structural and rs-fcMRI scans were obtained prior to completion of the task in the scanner; task-based fMRI results will be presented elsewhere. All patients performed the experimental task and other assessments in a single test session which lasted about 90–120 min.Stimulus presentation, timing, and behavioral data registration were controlled by Cogent software  was 220 mm × 220 mm with a 64 × 64 × 43 matrix resulting in an in-plane voxel resolution of 3.44 mm × 3.44 mm × 3.00 mm . The repeat time to accomplish a full volume (TR) was 2,000 ms, the echo time (TE) was 30 ms, and the flip angle was 90°. The slices were collected in an interleaved multi-slice mode (no slice gap), covering the whole brain (scan duration of ~8 min). Structural images were acquired using a T1-weighted spin-echo pulse sequence  with an FOV of 220 mm × 220 mm and with a 256 × 256 × 124 matrix, resulting in an in-plane voxel resolution of 1.0156 mm × 1.0156 mm × 1.20 mm (scan duration of ~10 min).web.mit.edu/swg/software.htm)]. None of the patients showed head motion (translation and rotation about each of the axes) greater than 3 mm during the experiment.We used Statistical Parametric Mapping software version 12  running in the Matlab R2015b environment  for slice timing and motion correction. Slice timing correction to the first slice was performed using SPM’s sinc interpolation. Head motion correction was performed by co-registering each image to the mean EPI image. To examine outliers due to spiking and motion, and additionally to estimate Euclidian motion, we used the Artifact Detection Tool software package . WeWe used the CONN toolbox [version 16.a ] with det-test was applied to evaluate group differences. We used a one-sample t-test to examine associations between behavioral performance and connectivity between the ROIs and the rest of the brain. We entered the average number of draw choices and the BIS score  as predictors and included age as a covariate of no interest. All effects were evaluated using a statistical threshold of p < 0.0005 (uncorrected for multiple comparisons) and a minimum cluster size of 10 voxels; a few effects were significant at a family-wise error (FWE) corrected p < 0.05, as indicated in the tables and text. We used the Harvard-Oxford Cortical and Subcortical Structural Atlases ; again, a few effects were significant at FWE-corrected p < 0.05 as reported in the text and tables.We used voxel-based morphometry (toolbox in SPM5) to evaluate group differences in structural properties and associations with behavior. Each subject’s SPGR scan was segmented into GM, WM, CSF, and other nonbrain partitions and warped to MNI space. Warped images were modulated to allow for tests of GM volumes and were then smoothed using a 10-mm FWHM Gaussian kernel. To evaluate differences in striatal GM volume between the ICD+ and ICD− groups, we performed a two-sample ps > 0.19). The ICD+ group scored significantly higher on the BIS questionnaire than the ICD− group, t(49) = 2.32, p = 0.025. The BIS scores are consistent with those previously reported for PD patients with and without impulsivity (p = 0.17). NART-R scores indicated that IQ estimates were within the normal range and did not differ significantly between groups (p = 0.89).Table ulsivity , and scoulsivity . When diF = 99.48, p < 0.001, ηp2 = 0.67. They also drew more beads during $10 loss than $0 loss trials (2.33 vs. 1.90 draws), F = 29.79, p < 0.001, ηp2 = 0.38. Results showed no significant effects of group (ps > 0.17). Participants more often chose the correct urn during sequences with 80/20 than with 60/40 probability (0.89 vs. 0.72), F = 121.51, p < 0.001, ηp2 = 0.71. Again, we observed no significant group effects (ps > 0.33).Participants drew more beads during sequences with 60/40 compared to 80/20 probability (2.65 vs. 1.59 draws), Results revealed an increased connectivity between the left STN and the left parietal operculum in the ICD+ group that was significant at an FWE-corrected threshold. Furthermore, patients with higher BIS scores  exhibited a weaker connectivity between the left putamen and the right inferior temporal gyrus. At the conservative uncorrected threshold, connectivity differed significantly between the ICD+ and ICD− groups for all ROIs  exhibited a smaller putamen GM volume than those who made fewer draw choices and thus collected less evidence. When evaluating results at the uncorrected threshold, we further observed that patients in the ICD+ group showed a reduced GM volume in the right GPe compared to patients in the ICD− group  and frontal cortical areas was reduced in PD patients with ICDs. In addition, patients with ICDs showed a reduced GPe GM volume. Extending previous work, we also used behavioral assessments to examine whether individual differences in brain properties were associated with behavioral impulsivity in PD. Across all patients, we observed that Our findings in combination with the literature suggest that there may be two mechanisms underlying impulsivity in PD patients. First, weaker connectivity in a frontal–striatal network may lead to impaired assessment of the reward value of actions and more risk-taking. This is in line with previous findings that ICDs in PD were associated with a reduced connectivity in cognitive and affective corticostriatal pathways . DysreguWe further observed that ICD+ patients showed stronger striatal–cerebellar connectivity but reduced parietal–cerebellar connectivity. The cerebellum has traditionally been associated with motor functions, but cerebellar involvement in non-motor functions mediated by (among others) parietal areas has also been recognized . For exaOur results showed no behavioral group differences in beads task performance, which contrasts with earlier findings that PD patients with ICDs drew fewer beads than patients without ICDs . Comparep < 0.0005 and p < 0.005 for the connectivity and structural analyses, respectively). As we compared two groups of PD patients, it seems reasonable that group differences are not as strong as those found when comparing patients to control subjects—especially since the two patient groups did not differ significantly in terms of current age, age of PD onset, disease duration, medication dose, and scores on the MoCA, NART-R, and UPDRS motor subscale. In addition, relative to previous studies, we used a large sample size . Our fiAll patients provided written informed consent in accordance with the Declaration of Helsinki. The study was approved by the medical institutional review board of the University of Michigan.BA, KC, and RS designed the study. TW organized and performed the study. MR, VK, and RS performed the data analyses. MR and RS wrote the first draft of the manuscript. All authors reviewed and edited the manuscript, and read and approved the submitted version.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.


As the first literature‐based online resource for EC, ECGene serves as a useful gateway for researchers to explore EC genetics.Endometrial cancer (EC) ranks as the sixth common cancer for women worldwide. To better distinguish cancer subtypes and identify effective early diagnostic biomarkers, we need improved understanding of the biological mechanisms associated with EC dysregulated genes. Although there is a wealth of clinical and molecular information relevant to EC in the literature, there has been no systematic summary of EC‐implicated genes. In this study, we developed a literature‐based database ECGene  with comprehensive annotations. ECGene features manual curation of 414 genes from thousands of publications, results from eight EC gene expression datasets, precomputation of coexpressed long noncoding RNAs, and an EC‐implicated gene interactome. In the current release, we generated and comprehensively annotated a list of 458 EC‐implicated genes. We found the top‐ranked EC‐implicated genes are frequently mutated in The Cancer Genome Atlas (TCGA) tumor samples. Furthermore, systematic analysis of coexpressed lncRNAs provided insight into the important roles of lncRNA in EC development. ECGene has a user‐friendly Web interface and is freely available at  Endometrial cancer  is the sixth most commonly diagnosed cancer in women worldwide [Ferlay et al., Despite the distinct morphologic and clinical features of the two EC subtypes, most EC patients are treated with the same chemotherapy, without an effective screening strategy in place to distinguish EC subtypes. Similar to other cancers, EC is remarkably heterogeneous at the cellular and molecular levels. The uncontrolled cell growth of EC results from promoter methylation   database was performed on Apr 5, 2015 to identify additional EC‐implicated genes and relevant publications. GeneRif is a collection of short descriptions about gene function in the Entrez Gene database . As shown in Supp. Figure S1, the majority of EC‐implicated genes were identified from our literature curation.To understand the biological function of the EC‐implicated genes, functional annotations for each gene was collected. Basic gene information such as gene names and DNA/protein sequences were integrated from the Entrez gene database . The expression correlation between each of the 458 EC‐implicated genes and all 17,250 lncRNAs was assessed using Spearman's correlation scores.To provide coexpression patterns of EC‐implicated genes with long noncoding RNAs (lncRNAs), we downloaded lncRNA expression data for The Cancer Genome Atlas (TCGA) EC patients , and a false discovery rate (FDR) was applied to correct for multiple testing. For all EC‐implicated gene and lncRNA pairs, we required expression correlation scores greater than 0.3 and FDR‐adjusted P values less than 0.01.The expression correlation scores and corresponding P values were corrected for multiple testing using Benjamini–Hochberg adjustments. Only human pathways with a corrected P value less than 0.01 for any gene set were considered significant.The statistically representative pathways from KEGG and Reactome for each gene set were identified by ToppFun . However, other top‐ranked genes were only supported by a single publication regarding their abnormal expression in EC samples: MUC8  and “p53 pathway feedback loops 2”  (Supp. Table S4). Interestingly, the most significantly enriched pathway is “Signaling by SCF‐KIT” , a system that has been associated with carcinogenesis of the female genital tract ∼k, where P(k) is the probability that a gene interacts with other k genes and b is an exponent with an estimated value of 1.3. This means that our EC pathway map is more closely connected compared with all interactions in the human interactome, where most of nodes were sparsely connected with exponent b given as 2.9 , MYC (37), CTNNB1 (33), AKT1 (23), NFKB1 (23), AR (22), ESR1 (22), HDAC1 (22), FOS (21), PIK3CA (21), and PIK3R1 (20) (Supp. Table S7). All 11 genes were present in ECGene. Annotation of these eleven genes found they are enriched in “cellular response to endogenous stimulus” and “enzyme‐linked receptor protein signaling pathway”  (Supp. Table S8). Moreover, 10 genes are involved in KEGG “Pathway in cancer” , “Colorectal cancer” , “Prostate cancer” , and “Endometrial cancer” . In summary, our reconstructed map not only discovers multiple cancer pathways but also reveals a dense modular structure of previously unconnected EC signaling pathways.Network topological analyses indicate the majority of genes are closely connected in the reconstructed map. This not only supports the accuracy of our data, but also implies that EC‐implicated genes can form a high‐density cellular module. As shown in Figure P value = 1.97×10−10), associated with cell cycle , and chromosome organization . For example, TERF1 was detected to be positively coexpressed with 539 lncRNAs in TCGA EC samples. The full name of this gene is Telomeric Repeat Binding Factor (NIMA‐Interacting) 1, which is present at telomeres throughout the cell cycle, inhibiting telomerase to interrupt the elongation of individual chromosome ends. These preliminary results imply coexpressed lncRNAs may be critical for tumorigenesis processes, such as cell cycle.To explore the role of lncRNAs in EC, we assessed the coexpression between 423 protein‐coding EC‐implicated genes with all 17,250 lncRNA transcripts from the MiTranscriptome database  by systematic data integration and literature curation. A user‐friendly Web interface was developed to access all gene, annotation, and literature information in our database, ECGene. We will continue to collect EC‐implicated genes from literatures in the future. In addition, we also plan to develop more tools that allow users to custom browse and search the Website.Disclaimer: Supplementary materials have been peer‐reviewed but not copyedited.Supp. Figures S1 and S2.Click here for additional data file.Supp. Table S1. The basic annotations of curated 458 curated EC‐implicated genes.Click here for additional data file.Supp. Table S2. The eight genome‐wide expression datasets integrated in ECGene.Click here for additional data file.Supp. Table S3. The gene ranking results of EC‐implicated genes.Click here for additional data file.Supp. Table S4. The biological pathways enriched for EC‐implicated genes.Click here for additional data file.Supp. Table S5. The pan‐cancer mutational frequency for the top 99 ranked EC‐implicated genes.Click here for additional data file.Supp. Table S6. The mutational frequency for the top 100 ranked EC‐implicated genes in TCGA endometrial cancer samples.Click here for additional data file.Supp. Table S7. The network topological properties for the reconstructed EC‐implicated interactome.Click here for additional data file.Supp. Table S8. The enriched functional terms of 11 hub genes from the EC‐implicated interactome.Click here for additional data file.Supp. Table S9. The enriched functional terms of 43 EC‐implicated genes with 100 or more positively co‐expressed lncRNAs.Click here for additional data file.


LPS)‐induced endotoxemic in vitro and in vivo. SD rats were randomly divided into five groups: control group, LPS group (15 mg/kg), LPS plus dexamethasone (2 mg/kg), LPS plus Sal groups with different Sal doses . Hemodynamic measurement and haematoxylin and eosin staining were performed. Serum levels of creatine kinase (CK), lactate dehydrogenase, the activities of the antioxidant enzymes catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GSH‐px), glutathione, tumour necrosis factor‐α (TNF‐α), interleukin‐6 (IL‐6), and interleukin‐1β (IL‐1β) were measured after the rats were killed. iNOS, COX‐2, NF‐κB and PI3K/Akt/mTOR pathway proteins were detected by Western blot. In vitro, we evaluated the protective effect of Sal on rat embryonic heart‐derived myogenic cell line H9c2 induced by LPS. Reactive oxygen species (ROS) in H9c2 cells was measured by flow cytometry, and the activities of the antioxidant enzymes CAT, SOD, GSH‐px, glutathione‐S‐transferase, TNF‐α, IL‐6 and IL‐1β in cellular supernatant were measured. PI3K/Akt/mTOR signalling was examined by Western blot. As a result, Sal significantly attenuated the above indices. In addition, Sal exerts pronounced cardioprotective effect in rats subjected to LPS possibly through inhibiting the iNOS, COX‐2, NF‐κB and PI3K/Akt/mTOR pathway in vivo. Furthermore, the pharmacological effect of Sal associated with the ROS‐mediated PI3K/Akt/mTOR pathway was proved by the use of ROS scavenger, N‐acetyl‐l‐cysteine, in LPS‐stimulated H9C2 cells. Our results indicated that Sal could be a potential therapeutic agent for the treatment of cardiovascular disease.The purpose of the present study was to investigate the effect of salidroside  on myocardial injury in lipopolysaccharide ( Sepsis is a complex syndrome with multi‐organ dysfunction especially cardiovascular disease. Myocardial dysfunction and cardiac diastolic commonly occur in patients with severe sepsis. It should be noted that cardiovascular diseases remain a leading cause of morbidity and mortality around the world The phosphoinositide 3‐kinase/protein kinase B/mammalian target of rapamycin mTOR (PI3K/Akt/mTOR) pathway is a critical cellular cascade in the cellular response to extracellular stimuli. Accumulating evidence indicated that PI3K/Akt/mTOR pathway participated in the cellular proliferation, differentiation, metabolism, cytoskeletal reorganization and apoptosis Rhodiola rosea is a long‐standing herbal used to relieve high altitude sickness and protect erythrocytes against oxidative stress p‐hydroxyphenylethyl‐O‐β‐d‐glucopyranoside) has been reported to have various pharmacological properties, such as anti‐depressive via the ROS‐mediated PI3K/Akt/mTOR pathway.Traditional Chinese medicine, widely used for centuries around the world, is still acknowledged as a main source of medicine N‐acetyl‐l‐cysteine (NAC) were provided by Sigma‐Aldrich . The ELISA kits for determinations of interleukin‐6 (IL‐6), IL‐1β and tumour necrosis factor‐α (TNF‐α) were produced by Nanjing KeyGEN Biotech. CO., Ltd . All biochemical kits were supplied by the Institute of Jiancheng Bioengineering . iNOS, COX‐2, p‐NF‐κBP65, NF‐κBP65, PI3K, P‐PI3k, P‐Akt, Akt, P‐mTOR and mTOR antibodies were purchased from Abcam .Sal (purity: 99%) was purchased from National Institutes for Food and Drug Control . Lipopolysaccharides and 2 in DMEM containing 10% foetal bovine serum  with 100 IU/ml streptomycin and 100 IU/ml penicillin . The H9C2 cells were passaged regularly and subcultured to 80% confluence before the experiments.A rat cardiomyoblast H9C2 cell line, obtained from the Shanghai Institute of Biochemistry and Cell Biology , was cultured at 37°C under 5% CO5 cells/ml on 96‐well culture plates for 24 h. Then, various concentrations of Sal were added to the wells. After a 2‐h treatment, the cells were treated with LPS (4 μg/ml) for 24 h, 50 μl of MTT solution (5 mg/ml) (Sigma‐Aldrich) was added to each well and incubated at 37°C for 3 h. After that, each well was replaced by 150 μl DMSO and MTT‐formazan crystals were dissolved. Finally, the absorbance was measured at 570 nm using a microplate reader .The cells were seeded at 2 × 105 cells/ml on 96‐well culture plates for 24 h and then treated with various concentrations of Sal. Two hours later, the cells were stimulated with LPS (4 μg/ml). After 24‐h incubation, the cells were harvested for ROS detection. As the ROS scavenger, NAC (2 mM) was added prior to drugs. The concentration of NAC was chosen in according to the previous literature The cells were seeded at 2 × 10Intracellular ROS production was measured with a ROS assay kit . 2′,7′‐Dichlorofluorescein‐diacetate (DCFH‐DA) readily diffuses through the cell membrane and is enzymatically hydrolyzed by intracellular esterase to form non‐fluorescent DCFH, which is then rapidly oxidized to form highly fluorescent 2′,7′‐dichlorofluorescein (DCF) in the presence of ROS. The fluorescence intensity is proportional to ROS production. After incubated for 30 min, cells were washed with cold PBS three times and then the fluorescent intensity was assayed by the flow cytometry with excitation wavelength at 488 nm and emission wavelengths at 525 nm, respectively.ad libitum. All the experimental procedures were carried out in accordance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals.SD rats  used in all experiments were obtained from Jiangning Qinglongshan Animal Cultivation Farm . The animals were housed in a constant temperature environment with a regular 12‐h light/dark cycle and provided with standard chow and water Escherichia coli 055: B5). The control rats were intraperitoneally received autoclaved PBS. After 6‐h treatment of LPS, the animals were killed by Nembutal . A schematic diagram of the treatment schedule is shown in Fig. SD rats were randomly divided into five groups with 10 rats in each group: (1) control group, (2) LPS group, (3) LPS + dexamethasone , (4) LPS + Sal Sal, 20 mg/kg), (5) LPS + Sal . The doses of Sal were chosen in accordance with the previous literatures  mg/kg, (max) and maximum LVP decrease rate (LV−dp/dtmax) with a BL‐420s Biologic Function Experiment system .A standard limb lead II electrocardiogram was monitored continuously. The right carotid artery was cannulated with a polyethylene 90 catheter filled with heparin saline (500 U/ml) advanced to the lumen of the left ventricle. The cardiac LV function was evaluated by the left ventricular systolic pressure (LVSP), left ventricular end‐diastolic pressure (LVEDP), maximum LVP increase rate  were immediately harvested and weighed after blotting with filter paper . The HW index (HWI) was computed as HWI = HW/BW.The levels of CK, lactate dehydrogenase (LDH) and the activities of the antioxidant enzymes catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GSH‐px) and glutathione (GSH) were determined according to the manufacturer's protocol Serum and cellular supernatant levels of IL‐6 and TNF‐α were measured by ELISA according to the manufacturer's instructions . All measurements were performed in triplicate.Immediately after the rats were killed, the hearts were excised and fixed in 10% formalin solution for 48 h. Then the heart tissue was processed for sectioning and staining by standard histological methods. Sections from the left ventricle were stained with haematoxylin and eosin and examined by light microscopy .5 cells/ml on 96‐well culture plates for 24 h and then treated with various concentrations of Sal. Two hours later, the cells were stimulated with LPS (4 μg/ml). After 24‐h incubation, the cells were harvested for Western blot analysis. As the ROS scavenger, N‐acetyl‐l‐cysteine  was added prior to drugs.The cells were seeded at 2 × 10The heart tissues and cells were homogenized, washed with PBS and lysed in a RIPA buffer (Beyotime). The protein concentrations were determined with a BCA protein assay (Beyotime). The samples were loaded on 10% sodium dodecyl sulphate polyacrylamide gels and were electrotransferred to nitrocellulose membranes. The membrane was blocked with 5% skim milk in Tris buffer and incubated with the appropriate specific antibodies. After washing, the blots were incubated with horseradish peroxidase‐conjugated second antibodies. The quantification of protein expression was normalized to GAPDH using a densitometer (Imaging System).anova with Tukey's multiple comparison test. P values <0.05 were considered to reflect a significant difference.All data were normally distributed and were expressed as mean ± SD. Results were analysed by one‐way To exclude the possibility that the pharmacological effect of Sal were because of its cytotoxity, we carried out MTT experiment after incubating with H9C2 cells. As expected, the concentrations of 10–40 μM Sal did not affect the cell viability in this study. Therefore, the inhibitory effect were not caused by the cytotoxicity of Sal  and Sal (40 mg/kg) treatments. Our data indicated that Sal could attenuate the LV function in LPS‐induced myocardial injury.Electrocardiographic patterns of control and experimental animals were depicted in Fig. Heart weight index were greater in the LPS‐induced rats than that in the control group rats. With the pre‐treatment of Sal, HWI significantly decreased compared with that of LPS‐induced rats Fig. .To detect myocardial injury marker enzymes, the levels of CK‐MB and LDH were measured. The levels of LDH in serum and cellular supernatant were dramatically increased and the levels of CK in serum were significantly increased after LPS administration. Of note, treatment with Sal effectively decreased the levels of LDH, CK in serum and LDH activity in cellular supernatant Fig. .Lipid peroxidation in serum and cellular supernatant were determined by measuring the generations of CAT, SOD, GSH‐px and GSH. LPS stimulation significantly declined the SOD, CAT, GSH‐px, GSH activities and GSH content, respectively, while Sal treatment effectively restored these levels in serum and cellular supernatant. The analytical results suggested that Sal was capable of ameliorating oxidative stress in LPS‐stimulated rats Fig. .Next, we evaluated the generation of inflammatory cytokines in serum and cellular supernatant. The levels of the cytokines IL‐6 and TNF‐α in serum and cellular supernatant were dramatically increased after LPS administration. By contrast, Sal  administration appeared to down‐regulate the IL‐6 and TNF‐α contents in dose‐dependent manners Fig. .To explore the potential mechanism of Sal on LPS‐induced myocardial injury, we investigated the protein expressions of iNOS, COX‐2 and NF‐κB in heart tissues. In response to LPS, the levels of iNOS, COX‐2 and p‐NF‐κB were evidently up‐regulated. As expected, Sal dramatically inhibited the expressions of iNOS, COX‐2 and phosphorylated NF‐κB. Fig. .As illustrated in Fig. in vitro. Interestingly, after NAC incubation, the alterations were remarkably abrogated. These experimental data indicated the critical role of ROS in the regulation of PI3K/Akt/mTOR signalling , hydroxyl radical (HO−) and hydrogen peroxide (H2O2), play paradoxical roles in cellular environments ROS, including superoxide  family and is reported to be related to ROS in vivo and in vitro, possibly through the inhibition of the ROS‐mediated PI3K/AKT/mTOR pathway. Therefore, our results indicated that Sal could be a potential therapeutic agent for the treatment of cardiovascular disease. Further studies are warranted to explore the clinical application of Sal.In conclusion, the present study demonstrated that the Sal administration improved cardiac function after LPS‐stimulated endotoxemic in rats. The cardioprotective effect of Sal might be attributed to its ability of suppressing myocardial lipid peroxidation and inhibiting inflammatory cytokines both The authors declare that they have no competing interests.Li Chen and Peng Liu, Xin Feng, Chunhua Ma conceived of the study, carried out the work, participated in its design and coordination. Chunhua Ma and Lvyi Chen participated in the design of the study and financially supported the study. All authors read and approved the final manuscript.


D50 = 104% prescribed dose). Furthermore, satisfying D50 reduced the probability of also satisfying D98, constituting a negative correlation of these goals. On the other hand, satisfying D50 increased the probability of satisfying D2, suggesting a positive correlation. A positive correlation is also observed between the PTV V105 and V110. Similarly, a positive correlation between the brainstem V45 and V50 is measured by an increase in the conditional median of V45, when V50 is violated. Despite the imposed institutional and international recommendations, significant variations amongst DVH points can occur. Even though DVH aims are evaluated independently, sizable correlations amongst them are possible, indicating that some goals cannot be satisfied concurrently, calling for unbiased plan criteria.The purpose was to study correlations amongst IMRT DVH evaluation points and how their relaxation impacts the overall plan. 100 head‐and‐neck cancer cases, using the Eclipse treatment planning system with the same protocol, are statistically analyzed for PTV, brainstem, and spinal cord. To measure variations amongst the plans, we use (i) interquartile range (IQR) of volume as a function of dose, (ii) interquartile range of dose as a function of volume, and (iii) dose falloff. To determine correlations for institutional and ICRU goals, conditional probabilities and medians are computed. We observe that most plans exceed the median PTV dose (average PACS number(s): 87.55.dk, 87.53.Bn, 87.55.Qr, 87.55.de. Treatment plan optimization in intensity‐modulated radiation therapy (IMRT) often leads to unsatisfactory solutions due to constraints imposed to achieve clinical goals. To overcome infeasibilities, constraints are relaxed and competing objectives between tumor volume(s) and surrounding structures are formed. Competing objectives lead to trade‐offs for clinical use that are visually inspected and approved. The subjectivity of a visual inspection for IMRT planning decisions introduces biases and tendencies, consequently resulting in plans to become nonreproducible. In fact, Das et al.2 of overlapping area. This reveals the need for more detailed constraints in addition to clinical experience in order to restrict such preferences. Fogliata et al.D50 to PTV that ranged from 43.6 to 51.2 Gy, and the volume that received 20 Gy or higher, V20, ranged from 12.2% to 18.9% over the four institutions. Dx is the minimum dose to the hottest x% of the volume and Vx is the fractional volume that receives at least x% of the prescription dose.In general, such preferences may be introduced from a variety of sources, including delineation of anatomical structures, treatment planning system (TPS) variability, and institutional guidelines, but most importantly treatment planner's preferences. Accurately delineating tumors and organs is heavily dependent on both the quality of CT images and the experience of clinicians. Cazzaniga et al.In principle, clinical dose and volume constraints and guidelines are used to overcome the aforementioned sources of variations, as well as to assess the quality of plans.D2, D50, D95, and D98. They may also directly serve as DVH constraints in plan optimization, as suggested by Cambria et al.The DVH constraints are often imposed through specific dose‐volume points as a comparison metric, such as Variations amongst plans, measured by median and interquartile range.Correlations between planning aims, computed via conditional probability and median.In this work, we study the dependence amongst these DVH control points. Since we cannot assume the DVH variations amongst cases to follow a Gaussian distribution, nonparametric statistical tools are employed in two main steps:We observe significant variations surrounding the DVH constraints even though most of the clinical goals were satisfied. It is shown that the current clinical DVH constraints require prioritizing amongst them, which limits the reproducibility and standardized plan quality. Furthermore, we demonstrate trends in increased dose delivery, suggesting dissension in evaluating treatment plans. The details of materials used in the study are discussed in the Materials & Methods section. Three statistical measures are detailed in subsection A.1 to demonstrate the variation in dose, volume, and DVH falloff along the DVH. To show the efficacy of clinical goals, statistical comparisons between plans that violate and those that satisfy the constraints are presented in subsection B.One hundred head‐and‐neck cancer cases from Indiana University, Melvin and Bren Simon Cancer Center are selected based on their anatomical comparability. The corresponding treatments for all cases are planned using the same TPS , following identical institutional dosimetric criteria. Besides anatomical differences, possible variations stem from plans that were produced by different planners. Based on clinical advice, brainstem and spinal cord are identified as the highest priority OAR to be spared. For brevity, we shall refer to spinal cord as spine. In many cases when the lesion was at or adjacent to the skin, the patient was scanned with bolus. Consequently, the PTV is deliniated to be within 2–3 mm of the skin surface.D95 ≥ 95% and D2 ≤ 107% of the prescribed dose, for spine Dmax ≤ 45 Gy, and for brainstem Dmax ≤ 54 Gy, following the institutional guidelines. All DVHs are collected from the TPS with a resolution of 0.1 cGy dose. Furthermore, the DVHs for PTV are collected using relative volume and dose points, allowing effective comparisons. Note that the median absolute prescribed dose over all patients is 70 Gy, with an interquartile range of 10.3 Gy. Moreover, in order to compare specific dose‐volume points, DVH values are linearly extrapolated by binning remaining doses with 0% volume.These plans were optimized based on dosimetric constraints for PTV of The quantitative analysis is divided into two parts: variations amongst plans and correlations between DVH control points. For the first part, we use three statistical metrics that are well suited for such nonsymmetrically distributed data, namely the interquartile range of dose for changing volume, interquartile range of volume for changing dose, and the approximate DVH gradient to study variation in the falloff. For the second part, we use conditional measures for institutional and for internationally recommended DVH goals. Note that the methods and the results section will follow this two‐part structure. The details of our analytical method are as follows.(Q3‐Q1) of a distribution. It is inherently robust to changes in scale, hence uninfluenced by outliers.The interquartile range is an estimator of spread, computed as the difference of the upper and the lower quartile IQRvol(D) is a statistical estimator of Vx distribution at a dose value D and quantifies directly the spread. In this study, IQRvol(D) is recorded over the entire dose range in increments of 0.1% relative dose (tumor) or 0.1Gy (OAR).IQRdose(V) measures the spread of Dx distribution and is recorded over the entire volume range at V = 0.1% increments. Note that IQRdose(V) and IQRvol(D) are estimators of two different and orthogonal distributions and, hence, independent of each other. and  via finite differences∇ serves as a measure for the DVH falloff. It is determined at two neighboring DVH point pairs D2‐D1 = 0.1%. V2 and V1 are the corresponding volumes. The schematic setting is illustrated in where ∇(D), for all patients as function of dose. Additionally, the interquartile range of the falloff, IQR∇, is determined in order to observe potential dissension in decision‐making favoring a slow or rather a rapid DVH falloff.In order to observe such variations and identify trends amongst past decisions, we compute the median DVH falloff, The iterative treatment planning process aims to satisfy DVH constraints determined by both institutional and internationally recommended aims. In practice, however, it is often not feasible to satisfy all constraints, which in part justifies the existence of institutional guidelines which, in some cases, deviates from the recommended aims. As a result, treatment planners choose to trade off some constraints. The underlying assumption of this pursuit is that it is admissible to violate one or more of the aims, while still providing an overall satisfactory and clinically acceptable plan. To analyze the dependence amongst the constraints, we determine statistical correlations for PTV, brainstem, and spine constraints.Yi) and those that violate it (subset Yi¯) with Yi∩Y¯i=∅. To establish correlations, we use conditioning for two differing control points i and j asFor each DVH control point i, we divide the set of the plans into those that satisfy the constraint i (subset P(⋅|⋅) denotes the conditional probability. In other words, if violating the constraint j reduces the probability of constraint i to be met, these two constraints are positively correlated and negative otherwise. Here, probability reflects the relative number of plans in the subset.where Additionally, we quantify the rate of change by computing conditional medians and derive conclusions analogous to IQRvol(D) ≤ 5%, we denote the corresponding (constrained) dose as Dcon. Conversely, neighboring unconstrained DVH points (within ± 5%) with significant deviation ≫5%) are denoted as Dunc (unconstrained dose). Such constrained and unconstrained points subsequently serve to qualify additional subsets. The analysis is divided for DVH constraints that stem from institutional protocols and those recommended by the ICRU‐83.In addition to these imposed goals, we also consider empirical control points to measure the correlations of the imposed goals to their surrounding DVH sections. To this end, when the plans exhibit unanimous agreement at a particular high dose by displaying a marginal spread, defined as D95 ≥ 95% and D2 ≤ 107% of the prescribed dose, for spine Dmax ≤ 45 Gy, and for brainstem Dmax ≤ 54 Gy. These control points are used to establish subsets of satisfying or violating plans, along with their neighboring points. The corresponding Dcon and Dunc are data‐specific and will be discussed in the Results section below.The institutional dosimetric constraints for PTV are D2 ≤ 107%, median dose D50 = 100%, and near‐min dose D98 ≥ 95% constraints for PTV, and Dmax for the surrounding OARs.D50 goal, we extend the definition to encompass D50 = 100% ± 2%.ICRU guidelines recommend for head‐and‐neck cases, near‐max dose Despite consistent planning protocols and the normalizing nature of DVH, sizable variations amongst the 100 head‐and‐neck cases are observed for PTV, brainstem, and spine in IQR > 5% at a DVH goal to indicate a significant variation amongst plans.As observed in V95 on tumor).Vx over the patients. The spread of this distribution can be measured by IQRvol(D) and is illustrated in In clinical practice, volume constraints are often imposed at specific points, to achieve desired dose distributions  until 90% prescribed dose (IQRvol = 0%). The maximum deviation is reached at IQRvol(104) = 38%, and vanishes again at IQRvol(111) = 0%, depicting the DVH falloff region. The observed peak at D = 104% indicates an increased spread at D = 104%, which can stem from relaxed PTV constraints or how the objectives are specified, amongst others. Note that the recommendations of the Radiation Therapy Oncology Group allow for up to 20% of the tumor volume to receive more than 110% prescribed dose, which supports the observed tendency to tumor excess dose.IQRvol(D) of PTV reflects the higher importance assigned to PTV in comparison to brainstem and spine.IQRvol(4) = 51%, followed by a second peak at IQRvol(17) = 43% with a subsequent steady decrease and vanishes at IQRvol(50) = 0%. One possible explanation would be that treatment planners prefer to relax constraints on brainstem at lower dose values, but tend to much tighter control at higher dose values, supported by the observed narrower spread. For spine, IQRvol(10) = 30% to IQRvol(32) = 65%, followed by a sharp decrease to IQRvol(45) = 0%. This result indicates tight constraints on higher doses for spine, whereas the large spread for D =  Gy suggests relaxation. However, a tighter control is not necessary because the probability of myelopathy vanishes for D < 40 Gy to the cord.Dmax constraints on brainstems and spines, as common in head‐and‐neck cases.Dmax thresholds lead to V = 0% at D ≥ Dmax, resulting in narrower IQRvol.IQRdose(V). For PTV, (IQRdose = 3%) for V ≤ 99%, with a steep increase to IQRdose(100) = 11%. This suggests that tight Dx constraints are imposed on PTVs until V ≤ 95% followed by relaxed control for higher volumes, which is in agreement with the recommendations.In addition to the volume constraints, a set of constraints are imposed at specific dose values. Similarly, they may lead to variations in the surrounding DVH regions which we measure with IQRdose at higher brainstem volumes. This is in agreement with the recommendations that strict Dmax constraints are essential to maintain functionality of such critical organs.D60‐D80, indicating a lack of D50 constraint and a need for tighter control at higher doses.(D98) and near‐maximum dose (D2) constraints are imposed.IQR∇ are shown in To achieve the goal of conformal target dose, intensity modulation is leveraged to deliver heterogeneous distributions, allowing for a sharp dose falloff on DVHs.IQR∇ surrounding the prescribed dose, which is typically a result of employing multiple constraints. For brainstem and spine, however, the significant IQR∇ is spread over multiple regions, demonstrating variabilities in decision‐making as a result of employing single maximum dose constraints. More specifically, ∇(D) for D ≤ 95%, followed by a drop to median ∇(104) = ‐13. This implies that a dose change of 1% results in a ‐13% volume change. In other words, the falloff region V = % extends over a median dose interval of 7.7%(= 100/13), exhibiting a narrow falloff region through tighter control. This is followed by a rise to ∇(110) = 0. This change in median ∇(D) for D = % coincides with the region surrounding the prescribed dose. Noticeably, the sharpest falloff occurs at D = 104% instead of D = 100%, further suggesting planners’ preference towards higher dose, as also reported by Eisbruch et al.(D = 2 Gy), demonstrating tight dose constraints. Unlike brainstem, the median ∇(D) for spine in D = 2 Gy and at D = 40 Gy. The first DVH falloff is attributed to an artifact of the patient geometry . On the other hand, the turning point at D = 40 Gy is a direct result of maximum dose constraints for brainstem and spine, which are typically imposed in head‐and‐neck cancer cases.IQR∇ in Figs 5(a) to (c). For PTV, negligible spread is observed for D ≤ 90%, followed by narrow spread for D = % region, which is typically a result of strict constraints.D ≥ 110%. For brainstem, the largest IQR∇ is recorded at lower dose values, followed by a narrow deviation at higher dose points. This further suggests multiple constraints imposed at higher dose for this critical organ. Similarly for spine, beyond the low‐dose spread, a significant spread is observable only between D = 30 Gy and D = 45 Gy. This demonstrates both the impact of maximum dose constraints and a lack of median dose constraints. Since the maximum dose constraints are typically rigorously enforced on both PTV and critical organs, competing dose‐volume constraints are relaxed to generate feasible plans.The spread around the median is shown via D85 (shown in Appendix A), which is not observable with median ∇ and IQR∇ in Note that the use of statistical estimators for symmetric distributions would misrepresent the actual distribution here. For example, the use of mean and standard deviation for ∇ reveals a substantial ripple at Since all the studied plans were clinically approved, it is justified to expect broad agreement at dose‐volume constraints enforced in the study, while variations may exist at other constraints which were relaxed. To establish possible consequences of such goal relaxation, we included empirical constraints in the vicinity of the DVH goal points. This allows for a guide to gauge potential risks for the overall plan that are associated with violating some constraints. We first focused the analysis on the institutional DVH goals that were enforced for the studied cases. We then analyze the cases based on additional constraints recommended by the ICRU‐83,IQRvol(D) ≤ 5%), we denote the corresponding (constrained) dose as Dcon. We observed a PTV constrained dose point with IQRvol(D) ≤ 5% at Dcon = 110%. For brainstem, Dcon = 50 Gy and for spine, Dcon = 45 Gy. This corresponds to maximum dose constraints that are usually imposed on all structures, often being the only constraint for brainstem or spine.V110, V50, or V45 are negligible (≤ 2%), we hypothesize them to be a consequence of maximum dose constraints, and otherwise (> 2%) independent. On the other hand, we observe a neighboring (within ± 5%) unconstrained point with IQRvol(D)≫5% at Dunc = 105% for PTV, Dunc = 45 Gy for brainstem, and Dunc = 40 Gy for spine. Note that, due to the nonincreasing nature of cumulative DVHs, V105 ≥ V110, V45 ≥ V50, and V40 ≥ V45 for PTV, brainstem, and spine, respectively. This implies that an equal or larger spread (from V ≤ 2% objective) is expected at Dunc when compared to Dcon, since the constraints are found at higher volumes. To establish deviations, we observed two clusters of plans, namely those satisfying V105, V45, or V40 by being less or equal to 2% and those which were significantly different at a neighboring (± 5%) point Dunc. The two groups maximally separate, when the volumes at Dunc exceeded 45%. Therefore, the hypothesis is that such violations (> 45%) are related to meeting other DVH constraints, and otherwise (≤ 45%) independent. To summarize these relationships, When the plans exhibit unanimous agreement at a particular high dose by displaying a marginal spread  in comparison to V110, where violations are negligible (on average ≤ 2%). This implies substantial volume portions received D > 100%, supporting aforementioned observation of tendencies to deliver higher doses to PTV. More importantly, this result shows larger violations at Dunc for cases that also have slight violations at Dcon. This result is more evident when evaluating the conditional median. In particular, the conditional median of V105 given a violation at V110 is Median(V105 | V110 > 2%) = 50% compared to Median(V105 | V110 ≤ 2%) = 19% for PTV.Significant overdose tendencies are observed for PTV. For brainstem, minimal deviations in comparison to spine is apparent, which has higher percentage of constraint violations. This demonstrates the increased planning importance for brainstem. Specifically, D95 = 95% constraints (denoted as Vcon) were imposed on PTV, which is a common institutional choice.Vunc . Patients with significant deviations (D < 70%) at D100 also show deviations from the established D95 constraints (not shown). The conditional Median(D110 | D95 < 95%) = 62% versus Median(D110 | D95 ≥ 95%) = 90% shows improvement in neighboring areas when constraints are satisfied.Similarly, Dunc for brainstem. Even though violations are observed, they are not due to decision tendencies but rather due to the nonincreasing nature of cumulative DVHs and the already existing violations at Dcon. When comparing the conditional medians for brainstem, we observe a difference between Median(V45 | V50 > 2%) = 33% and Median(V45 | V50 ≤ 2%) = 0%. For spine, Dunc propagated with violations at Dcon. There is also a sizable difference between Median(V45 | V50 > 2%) = 45% and Median((V45 | V50 ≤ 2%) = 9% for spine. This result indicates that marginal deviations from DVH constraints can (in median) lead to significant degradation in neighboring unconstrained points and, hence, limit the plan quality. Overall, Dcon = 110% for PTV and Dcon = 50 Gy, and 45 Gy for brainstem, and spine, respectively. We hypothesize that Dcon satisfaction is correlated to Dmax constraints, or otherwise (> 2%) independent. Additionally for PTV, we evaluate Dcon = 95%, for which we hypothesize that V95 ≥ 95% if satisfied, or V95 < 95% if violated. For correlation analysis, we divide all plans into subsets defined in In practice, not all clinical goals can be satisfied concurrently, resulting in constraint relaxation. To measure the risk (probability) that violating some constraints may have on other goals, we evaluate the plans at Dmax goal, leading to V ≤ 2% at Dcon;Subset A: plans satisfying V95 goal, leading to V ≥ 95% at Dcon;Subset B: plans satisfying C¯: plans violating low volume at high‐dose goal, leading to V ≥ 45% at Dunc.Subset A&(B&). For PTV, V110 violation (subset A¯) leads to negligible excess dose. However, V95 violation (subset B¯) has a greater impact through significant underdose, as observed by the broader IQR that is asymmetric towards lower volumes and dose. Note that the IQR limits depict plans that significantly degrade due to V95 violation .V50 (subset A). In fact, the IQR of A and A¯ do not overlap, suggesting that significant improvements are possible, if the constraints are satisfied. For spine, moderate reduction in dose is observed for plans that satisfy the V45 constraint (subset A), suggesting a similar potential for improvements. For brainstem, a substantial improvement is observed for plans that satisfy (Dunc) when conditioned on violating (or meeting) the goals at Dcon. In other words, we compare the conditional probability P(C¯–|A¯–)(at Dcon) to P(C¯–|A) (at Dcon)). Therefore, the impact of not meeting PTV's constraints is observed through the comparison ofThis impact can also be accessed by comparing the probabilities of delivering high doses to larger volume portions in neighboring and unconstrained DVH points andV105 and V110 for the PTV, namely violating one increases the probability of violating the other as well.This demonstrates a positive correlation between Pbrainstem = 0.33 and for spine Pspine = 0.60. In comparison when Dcon are met, Pbrainstem = 0 and Pspine = 0.08. The two proportion z‐tests, P reveals significantly larger than P for all structures with a p‐value less than 0.0001.Similarly for brainstem, the probabilities for degrading the plans are P(D100 < 70% | D95 < 95%) = 0.69 is significantly larger than P(D100 < 70% | D95 ≥ 95%) = 0.01. Furthermore, the two proportions z‐tests confirm this significant difference . Therefore, all these observations support the conclusion that significantly higher risks for degrading outcomes are expected when DVH constraints are violated. These results can serve to quantitatively inform the decision‐making process in treatment planning.Additionally for PTV, Similarly, we analyze the correlations amongst the noninstitutional aims, as defined in section B of Material and Methods. These aims may also become competing , hence, compelling planners to relax or even ignore some of them. To measure correlations among the ICRU constraints, we similarly group patients into subsets as discussed in Materials and Methods section B:D2 ≤ 107%;Subset E: plans that satisfy 98% ≤ D50 ≤ 102%;Subset F: plans that satisfy D98 ≥ 95%.Subset G: plans that satisfy E¯ and the underdose violation of subset G¯ in F¯ in D50 violation is attributed to an increased dose, confirming the overall overdose tendency observed through this analysis. This is also reflected in an elevated dose to larger volumes, hence satisfying D98, as demonstrated in D50 is satisfied by reduced dose to larger volumes, which leads to violating D98 and constituting G¯. In other words, plans in subset F have an increased likelihood to also reside in G¯. Similarly, those in F¯ are more probable to also be in G. Therefore, the set of analyzed plans clearly suggests a negative correlation between the recommended aims of D50 and D98, indicating competing goals that are unlikely to be met simultaneously.The impact of violating the ICRU‐83 recommendations is shown in D50 (subset F¯), the probabilities of F conditioned on E or G are marginal. However, when conditioning on F, a sizable positive correlation is observed for the subset E, since the corresponding probability decreases upon complementing F. In other words, these plans suggest that satisfying D50 increases the probability of satisfying D2 as well. On the other hand, a negative correlation is observed when G is conditioned on F, since the probability increases when conditioning on F¯. This means that satisfying D50 reduces the probability of satisfying D98. This observed competing nature of these two recommended aims demonstrates that the likelihood of meeting them simultaneously is low.The pairwise conditional probabilities for all goals are summarized in D95, as prevalent amongst institutions. At the same time, we observe that these institutional goals lead to trends, such as excess dose tendencies, that potentially jeopardize both plan quality and their comparability across institutions.Since these plans were designed and delivered to the best ability and knowledge of experts, the result that not all ICRU‐83 recommended aims could be satisfied supports the clinical need for institutional guidelines, such as In treatment planning, point‐wise dose‐volume goals are not only used to enforce clinical objectives, or to directly optimize treatment plans, but also to choose between alternative plans. The analysis presented in this work seeks to identify areas of plan variation and constraint correlation. Therefore, a possible extension of the presented study includes developing and integrating unbiased constraints and estimators directly into treatment planning to mitigate variability.To limit dissension in decision‐making, this study reveals the need for constraints or comparison estimators that can be enforced for extended DVH regions. The incorporation of an unbiased estimator for treatment plan evaluation was suggested by Loveless et al.It needs to be noted that this analysis was confined to head‐and‐neck cases only, warranting comparable anatomies. Unique geometries for some patients still may contribute to deviations in received dose on certain structures. These variations are patient‐specific and can occur in any treatment. Therefore, we chose robust estimators to immunize the conclusions against potential uncertainties. Furthermore, since the main goal of this study was to establish correlations, the resulting probabilities and risk measures are indented to inform future treatment planning, but cannot yield causations. The understanding of the nature of these effects, potentially confounded by the geometry, treatment planning system, dosimetric models, and other factors, is beyond the scope of this study. Nevertheless, these results have the potential to quantitatively augment the treatment planning, confining the trial‐and‐error nature of the decision‐making process and, hence, reducing time and ambiguity.The impact of trading off clinically employed DVH goals for treatment planning is demonstrated using three robust statistical estimators on 100 past head‐and‐neck cases. Significant plan deviations are observed on the interquartile ranges of dose and volume, as well as DVH falloff, especially on areas neighboring the constraints. This analysis shows that, when these constraints are even marginally violated, larger deviations are expected across the entire DVH. These results extend conventional notions of a “trade‐off” between clinical goals to a quantitative risk measure that relates each constraint violation to degradation of the overall plan. In fact, some of the internationally recommended aims were identified to be negatively correlated, hence, competing. This study also identifies DVH goals of unanimous agreement amongst planners, as well as areas of dissension and deviations, calling for novel clinical constraints that go beyond the established dose‐volume constraints and address extended areas of the DVH. Since these results relied on standard dosimetric criteria, they are of general nature and, hence, applicable to a wide range of IMRT planning settings.We would like to thank S. Srivastava for technical support in data extraction and M. Langer for insightful discussions. O. Nohadani thanks the support from the National Science Foundation under Grant No. CMMI 1463489.This work is licensed under a Creative Commons Attribution 3.0 Unported License.D85 for PTV suggests an overall phenomenon for this control point. A further analysis of the data reveals that the standard deviation was impacted by only four outlier plans for which specific tumor geometries required deviating arrangements. This ripple, however, does not speak for the cohort of the plans. Similar ripples are observable for brainstem and spine, all representing nonglobal uncertainties. On the other hand, robust estimators, such as the median and IQR, are immune to these pertubations, as exhibited in When the distribution of data is symmetric and in the absence of outliers, conventional statistical estimators, such as sample mean and standard deviation, provide reliable analytical tools for the observations. However, when the distribution is not symmetric and outliers are prevalent, this is no longer the case. Figure A.1 demonstrates the use of mean and standard deviation for the dose falloff ∇ of all three structures. The observed ripple at (D < 70%) at D100 also show some deviations from the established D95 constraint. This analysis demonstrates that when the institutional constraint at D95 is relaxed, the dose point D100 is sizably degraded for most cases. On the other hand, when D95 is met, the value at D100 is also satisfactory.Figure A.2(left) shows that patients with sizable violations EUD=(∑iviDiα)1/α, where vi is the i‐th partial volume receiving the dose Di and an exponent of α = ‐10 for the tumor.Equivalent uniform dose (EUD) is computed as Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.


Heart rate variability (HRV) and ventricular ectopy (VE) were evaluated over 12 months. VE did not change in any group following MSC implantation. However, in patients with ≥ 1 VE run (defined as ≥ 3 consecutive premature ventricular complexes in 24 hours) at baseline, there was a decrease in VE runs at 12 months in the MSC group (p = .01), but not in the placebo group . In a subset of the MSC group, HRV measures of standard deviation of normal intervals was 75 ± 30 msec at baseline and increased to 87 ± 32 msec (p =.02) at 12 months, and root mean square of intervals between successive complexes was 36 ± 30 msec and increased to 58.2 ± 50 msec (p = .01) at 12 months. In patients receiving MSCs, there was no evidence for ventricular proarrhythmia, manifested by sustained or nonsustained ventricular ectopy or worsened HRV. Signals of improvement in ventricular arrhythmias and HRV in the MSC group suggest a need for further studies of the antiarrhythmic potential of MSCs. Stem Cells Translational Medicine2017;6:1366–1372Transendocardial stem cell injection in patients with ischemic cardiomyopathy (ICM) improves left ventricular function and structure but has ill‐defined effects on ventricular arrhythmias. We hypothesized that mesenchymal stem cell (MSC) implantation is not proarrhythmic. Post hoc analyses were performed on ambulatory ECGs collected from the POSEIDON and TAC‐HFT trials. Eighty‐eight subjects (mean age 61 ± 10 years) with ICM (mean EF 32.2% ± 9.8%) received treatment with MSC ( Our study is a novel post hoc analysis which examines potential pro‐ and anti‐arrhythmic effects from transendocardial injection of mesenchymal stem cells (MSCs) in patients with ischemic cardiomyopathy. By combining patient populations from two prospective, randomized controlled trials (POSEIDON and TAC‐HFT) and examining ventricular ectopy and heart rate variability, we were able to study a group of patients who received MSCs and compared them to groups who received placebo and bone marrow‐derived mononuclear cells. The safety and efficacy of stem cell implantation has been explored in prior studies, but the potential arrhythmogenicity of this MSC therapy and how it compares to placebo injections have not previously been studied on this scale. Our study did not show any pro‐arrhythmia with MSC implantation, though there were important trends demonstrated between improvement of arrhythmias and this stem cell population in comparison to placebo or bone marrow derived mononuclear cells. These findings are important in that they beg the need for further large prospective studies to evaluate the anti‐arrhythmic potential of mesenchymal and other newer cell‐based therapies.Heart failure remains an epidemic with prevalence rates estimated between 0.4 and 2% in Europe and the U.S. Randomized controlled studies have demonstrated improvement in structural and hemodynamic parameters using different delivery strategies for a variety of cell types Our hypotheses were that MSC implantation was not associated with an increase in ventricular ectopy (VE) nor worsening of an important marker of ventricular arrhythmias such as heart rate variability (HRV). A secondary objective was to determine whether there were any signals of antiarrhythmic effect from MSC implant in the heart.POSEIDON  and TAC‐HFT  were Phase I/II transendocardial MSC trials performed in patients with ICM 2, serious radiographic contrast allergy, clinical requirement for coronary revascularization, history of a life‐threatening arrhythmia in the absence of an implanted defibrillator, or recent sustained ventricular arrhythmias requiring defibrillator therapy. Patients with persistent atrial fibrillation were also excluded.The enrollment criteria for either trial included LV dysfunction (ejection fraction < 50%) and history of ICM. Exclusion criteria included limited life expectancy (< 1 year), glomerular filtration rate of less than 50 ml/min/1.73 mn = 48), Placebo (n = 21), and BMC (n = 19). Patients in both trials had been optimized in terms of evidence‐based medical and device therapy. One of the patients from the POSEIDON trial was excluded from this analysis because the patient had an intraventricular thrombus and thus never received any stem cell therapy. Another patient from POSEIDON was excluded as the raw data from the Johns Hopkins University were not available.In POSEIDON, patients were randomized to injections of various doses  of allogeneic or autologous bone marrow‐derived MSCs. In the TAC‐HFT study, patients were randomly assigned into 3 groups: autologous bone marrow mononuclear cells (BMCs), bone marrow‐derived autologous MSCs (200 million cells), or placebo. The stem cells or placebo were delivered by transendocardial injection to 10 sites in the border zone of infarcted myocardial territory as previously described Patients underwent 24 to 48 hours ambulatory ECG monitoring at baseline after achieving optimal medical therapy, 4 continuous days after injection, 2 weeks post‐injection, monthly up to 6 months, and at 12 months of follow‐up. The ECG recordings were downloaded and analyzed using the Impresario Holter Analysis System by Del Mar Reynolds Medical, Inc. (Copyright 2001). Two cardiologists from the University of Miami Health System verified all tracings by visual inspection for accuracy of software interpretation of paced beats, ectopic beats, and artifact. A separate third cardiologist (R.D.M.) adjudicated any differences in rhythm interpretation between the first two readers.HRV analysis was performed solely on patients from the POSEIDON trial, as raw data from the Impresario Holter System were available only in these patients. Of the 31 patients who were originally enrolled in the POSEIDON trial, 1 patient was excluded due to a left ventricular thrombus, and 6 were excluded due to unavailability of raw data. Predominant atrial pacing in 2 subjects and excessive noise/artifact in 1 subject precluded accurate HRV analysis. Therefore, 21 patients with complete and authenticated monitoring data were included in the HRV analysis. The ambulatory ECGs were analyzed utilizing the Impresario software for HRV parameters, focusing on the following time domain methods The indices of HRV were compared with functional and anatomic markers, such as ejection fraction, end‐diastolic volume, end‐systolic volume, and sphericity index Eighty‐eight patients with usable ambulatory ECG monitors were included in VE analysis. We defined VE as the number of premature ventricular complexes (PVCs) recorded by an automated counter and normalized to a 24‐hour period. A VE run was defined as three or more consecutive PVCs. A subgroup analysis of patients with greater than or equal to 1 VE run in a 24‐hour period was also performed. Both POSEIDON and TAC‐HFT showed a significant decrease in scar size at 12‐month follow‐up in the MSC group, respectively, which may be related to change in VE t tests and repeated measures ANOVA. p values less than .05 were considered significant. Because of the large variability within the ventricular ectopy data over several orders of magnitude between patients, logarithmic transformation was utilized in an effort to normalize the data. However, as the data remained skewed post‐transformation, medians and IQR were used to summarize the nonparametric distributions. ANOVA and χ2 tests were used to determine differences in baseline characteristics between the 3 groups . Wilcoxon Signed Rank tests were used to determine statistical significance of change in ventricular ectopy from baseline to 12‐month follow up, and Kruskal–Wallis tests were used for intergroup comparisons.The autologous and allogeneic MSC groups were combined for HRV analysis. The data for HRV were represented using means and standard deviations. Determination of statistical significance was performed using Student's p = .01), and the MSC group had higher median VE runs at baseline . No patients had ICD shocks and/or clinical VT events within 30 days prior to the implant procedure.Eighty‐eight patients (mean age 60.9 ± 9.9 years) with ICM (mean ejection fraction (EF) 32.2% ± 9.8%) from the POSEIDON and TAC‐HFT trials were included in our post hoc analysis. There were no differences in baseline gender, age, ejection fraction, mean heart rate, or use of antiarrhythmic drugs Table . Howeverp = .08 comparing ICD shocks between BMC and MSC group; NS between all other groups). During the actual implant procedure, an additional 1 patient had an episode of VT requiring cardioversion which was attributed to nonspecific response to catheter manipulation within the left ventricle.There were two deaths, both of them attributable to sudden death. One patient had received MSCs, also had severe COPD and recent pneumonia and had presented with acute respiratory distress prior to PEA arrest. The other patient had been in the placebo group and died suddenly at home. There were appropriate ICD shocks for ventricular tachyarrhythmias in 4 patients and an inappropriate ICD shock in 1 patient for atrial fibrillation: 3 (16%) of the patients in the BMC group were shocked compared with 1 patient (2.1%) in the MSC group, and 1 (4.8%) patient in the placebo group (p = .01). Nevertheless, there was no significant short‐term increase or decrease in ventricular ectopy in any group  and no significant change in the placebo group  . However, mean SDNN was 75 ± 30 msec and increased by 16% to 87 ± 32 msec after 12 months (p = .024). Mean RMSSD was 36.3 ± 30 msec at baseline and increased by 60% to 58.2 ± 50 msec at 12 months (p = .014) . There was a significant correlation of percentage change in scar size to percentage change in ventricular ectopy in the MSC group  whereas there was no correlation found in the BMC group  or in the Placebo group   compared with 23.1%  in the BMC group and 15.3%  in the placebo group at 12 months  as a result of the injury or long‐term (up to 1 year). Furthermore, there was no evidence that MSC implant increased ventricular arrhythmias or worsened markers of ventricular arrhythmias. Although the changes observed in our post hoc analysis for ventricular ectopy in the MSC group were not statistically significant, there was a consistent trend toward improvement. This may relate to the small sample size and the overall low burden of ventricular arrhythmias. Further studies are needed to better address this question.Moreover, MSC implantation was associated with scar size reduction The limitations of this study are important to consider, as this was a post hoc analysis of two separate, randomized clinical trials. Therefore, the patient populations and therapies differed. Patients in both trials were medically optimized with evidence‐based therapies , counseled regularly about lifestyle choices, and followed closely during the 1‐year period, and these therapies alone could have contributed to clinical improvement. Also, both trials were Phase I/II studies to examine the safety and efficacy of different types  and doses of MSC, and only one study had a placebo control and a BMC group. Furthermore, the patients in these studies had relatively low VE burden and no recent VT episodes. Therefore, it would be difficult to prove an antiarrhythmic effect from MSC implant in a group that was not having significant or sufficient ventricular ectopy or tachyarrhythmias.In conclusion, transendocardial injection of MSCs to the border zone of infarcted myocardium in patients with ICM was not associated with short‐term (days) or long‐term (one year) ventricular proarrhythmia. In a group of patients selected with low ventricular ectopy burden, there were signals of improvement in either ventricular arrhythmias or markers of ventricular arrhythmias. Prospective studies may clarify the role of TESI with MSCs to reduce ventricular arrhythmias.A.R. and C.R.B.: conception and design, collection and/or assembly of data, data analysis and interpretation, manuscript writing; A.M.M. and J.J.G.: data analysis and interpretation, final approval of manuscript; D.L.D.: administrative support, provision of study material or patients, final approval of manuscript; C.H., V.G., and Y.A.: collection and/or assembly of data, final approval of manuscript; J.O.C. and J.F.V.‐G.: final approval of manuscript; A.W.H.: provision of study material or patients, final approval of manuscript; R.J.M.: conception and design, final approval of manuscript; J.M.H.: financial support, administrative support, provision of study material or patients, final approval of manuscript; R.D.M.: conception and design, administrative support, collection and/or assembly of data, data analysis and interpretation, manuscript writing, final approval of manuscript.A.M.M.: employed by Emmes Corporation; D.L.D: consulting fees from Biocardia, Inc., BDS, and Longeveron LLC; A.W.H.—Ownership interest in Vestion, Inc.; J.M.H.—Ownership interest in Vestion, Inc. The other authors indicated no potential conflicts of interest.