Background: Adrenal myelolipoma (AML) is a nonfunctional benign neoplasm from the adrenal cortex, composed of mature fat and hematopoietic tissue. Usually, patients have no symptoms. However, some patients with hypertension and blood pressure normalize after AML surgery, indicating some connections between AML and hypertension.Materials and Methods: This was a retrospective cohort study of 369 patients diagnosed with AML from September 2008 to December 2018 collected in the Urology Department of West China Hospital, Chengdu, Sichuan, China. We collected clinical records of patients before surgery. Postoperative follow-up was also carried out for those with hypertension and whether patients needed to take antihypertensive drugs and postoperative blood pressure were recorded. We aim to explore the characteristics of both patients with AML having hypertension and having remission of hypertension in 1 year after surgery.Results: There were 369 patients with AML included in the study, 156 men and 213 women, aged 49.86 ± 11.61 years old. Among them, 121 (32.8%) patients presented with hypertension. Body mass index was significantly higher in the hypertension group than that in the nonhypertension group, even after adjusting other variables . Sixty patients were followed up for 1–9 years, with a median follow-up of 52 months. The duration of hypertension in the remission group was shorter than that in the non-remission group (P = 0.020), and the tumor lateralization was significantly different between the two groups (P = 0.005).Conclusions: Nearly one-third of patients with AML suffered from hypertension in our study, and there existed some potential links between AML and hypertension. To be more specific, AML-related hypertension was more likely to result from obesity and renal compression by perirenal fat than from endocrine disorders or blood vessels compression. Patients with AML and with more than 3 years of hypertension might have less possibility to recover. Adrenal myelolipoma (AML) is generally considered a non-functional benign tumor originated from the adrenal cortex, made up of a variable proportion of mature fat and hematopoietic tissues . The numThis was a retrospective cohort study of 369 patients diagnosed with AML from September 2008 to December 2018 collected in the Urology Department of West China Hospital, Chengdu, Sichuan, China. The study was approved by the Ethics Committee of West China Hospital of Sichuan University . Informed consent was exempted because all clinical records were acquired retrospectively from the hospital inpatient system (HIS) when patients were admitted to the hospital for diagnosis and treatment, without revealing the privacy of patients. All patients were treated with surgery.The including criteria of this study were as follows: (1) neoplasm from adrenal presented by computer tomography ; (2) adrThe excluding criteria of this study were as follows: (1) diagnosis of other tumors by postoperative pathological examination, such as pheochromocytoma, primary aldosteronism (PA), and other adrenal diseases that may cause hypertension; (2) diagnosis of congenital adrenal hyperplasia and primary adrenocortical hypofunction.We collected clinical records of patients before surgery, consisting of gender, age, leading complaints , history of diabetes, tumor lateralization, tumor diameter, body mass index (BMI), duration of hypertension, blood pressure, the number of antihypertensive drugs, preoperative plasma renin activity (PRA), and plasma aldosterone concentration (PAC). Postoperative follow-up was also carried out for those with hypertension and whether patients needed to take antihypertensive drugs and postoperative blood pressure were recorded.All eligible patients were divided into two groups: hypertension and non-hypertension, based on the complication of hypertension . Based on the outcomes of follow-up, those with hypertension were divided into two groups: the remission group and the non-remission group. The remission of hypertension after the operation is defined as stopping the use of antihypertensive drugs for 3 months and the blood pressure coming down to normal (<140/90 mmHg), which lasts till the end of follow-up.t-test. The multivariate logistic regression model was utilized to test selective variables in previous univariate analyses. The receiver operation characteristic (ROC) curve was utilized to examine the efficacy of a diagnostic model. P < 0.05 was applied to certify the statistical significance between the two groups of data. The normality of the distribution was tested using the Shapiro–Wilk test.All the procedures were finished on the statistical software, IBM SPSS Statistics 22. Discrete variables were reported as a percentage and continuous variables as mean ± SD or median (quartile). For analysis of categorical variables, Pearson's chi-square test or Fisher's exact test was used. Differences between means were tested with Student's There were 369 eligible patients with AML enrolled in our study, comprising 156 men and 213 women. Around 10 patients were with open surgery. The average age was 49.86 ± 11.61 years, among which 121 patients were complicated with hypertension. The duration of hypertension ranged from 1 day to 30 years, and the mean systolic and diastolic pressures in the hypertension group are 168.89 ± 23.18 and 99.29 ± 14.82 mmHg, respectively. Antihypertensive drugs included nifedipine, amlodipine, captopril, irbesartan, metoprolol, prazosin, furosemide, and so on. The incidence of hypertension in AML is 32.8%.2, P < 0.001 for both univariate and multivariate analyses). In addition, BMI was significantly higher in almost all subgroups, except for subgroups 20–29 and 60–79 . LL, LC, and LZ data analysis and/or interpretation. LL, YZ, and LZ drafting of the manuscript and/or critical revision. YZ and LZ approval of the final version of manuscript. All authors contributed to the article and approved the submitted version.The work was supported by the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZY2016104), the Innovation Spark Project of Sichuan University (2018SCUH0061), and the National Natural Science Fund of China (81800667).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Of the respondents, about 78% were working remotely, and 64% were female. Hierarchical multiple regression analyses were used to analyze the main and moderating  effects. The results indicated that perceived loneliness was directly and positively associated with stress and exhaustion. Further, as hypothesized, personal resilience moderated the relationship between loneliness and stress and exhaustion, and organizational support moderated the relationship between loneliness and stress. Unexpectedly, organizational support did not moderate the loneliness–exhaustion relationship. Moreover, a sense of social belonging was not associated with stress and exhaustion, nor did it moderate loneliness and well-being relationships. The results demonstrate the importance of personal resilience and organizational support in enhancing well-being in organizations during the COVID-19 pandemic. Future research directions and practical ways to promote resilience and to increase organizational support are discussed.The aim of this study is to investigate whether personal, social and organizational level resources can buffer against the negative effects of perceived loneliness on stress and exhaustion. The data was collected from Finnish university employees ( The WHO declared the COVID-19 outbreak a pandemic on 11 March 2020. Since then, the pandemic has dramatically affected the social lives of the world population. In most societies, number of human contacts has been strictly limited by restrictions imposed by authorities, reducing the frequency of social contacts and of face-to-face social interactions at work and leisure ,4. For eLoneliness can be defined as the distressing subjective feeling of lacking a social network or companion, and refers to a perception that one’s social relationships are inadequate in light of one’s preferences . Hence, Loneliness can be seen as an interpersonal stressor giving rise, for instance, to negative emotions and negative physiological reactivity, which in turn is conducive to unhealthy activity of the nervous system . In lineFindings during the COVID-19 pandemic support the view of the detrimental effects of loneliness. For instance, in autumn 2020, greater loneliness among Finnish workers was associated with higher COVID-19 anxiety . FurtherWe conceptualize loneliness as a personal demand with a potentially negative influence on job-related well-being. The construct of personal demands is one of the most recent additions to the Job Demands–Resources (JD-R) model ,21. AlthLoneliness may affect work effort, performance and job-related well-being, because it is linked, for instance, to general stress levels, poorer overall cognitive performance, negative reactivity and mood. It is highly plausible that this kind of negativity may spill over to the work domain and make it difficult to work collaboratively, while poor cognitive performance may influence and impair the quality of work performance . The empHypothesis 1.Perceived loneliness is associated with higher perceived stress.Hypothesis 2.Perceived loneliness is associated with higher emotional exhaustion.Various workplace resources have been shown to be directly associated with better job-related well-being . AccordiIn the present study, resilience is defined as a person’s capacity to bounce back or recover quickly from a significant source of stress ,30. MoreWe conceptualize resilience as a personal resource that encourages adequate adaptation to significant stressors ,30. AccoIn line with the assumptions of the JD-R model, personal resilience has been shown to be directly associated with reduced burnout symptoms, such as lower emotional exhaustion ,34,35. AHypothesis 3.Personal resilience will be directly associated with lower stress and emotional exhaustion.Hypothesis 4.Personal resilience will moderate the relationship between loneliness and stress and exhaustion. That is, the relationship between loneliness and well-being is stronger among those employees with a lower (vs. higher) level of resilience.Humans have a basic need to belong, form and maintain interpersonal relationships , and theFeelings of social support and connection can be an important protective resource against the negative effects of loneliness during the pandemic . As an eHypothesis 5.Social belonging will be directly associated with lower stress and emotional exhaustion.Hypothesis 6.Social belonging will moderate the relationship between loneliness and stress and exhaustion. That is, the relationship between loneliness will be stronger among those employees with lower (vs. higher) levels of social belonging.According to the JD-R model and empirical findings, organizational support is a job resource that is associated with improved job-related well-being and can protect against the harmful effects of demands ,21,28. VDuring the COVID-19 pandemic, many organizations have implemented full-time remote work for their employees in response to the crisis. Consequently, more extensive remote work support was needed in organizations. The perceived organizational remote work task support is employees’ sense that their organization provides them with the necessary resources for remote work, such as information technology support, timely information, relevant work materials and decision-making authority ,52. The Therefore, we expected that:Hypothesis 7.Organizational support will be directly associated with lower stress and emotional exhaustion.Hypothesis 8.Organizational support will moderate the relationship between loneliness and stress and exhaustion. That is, the relationship between loneliness will be stronger among those employees with lower (vs. higher) levels of organizational support.Taken together, the aim of the present study is to explore whether personal, social and organizational level resources can protect against the negative effects of perceived loneliness on stress and exhaustion. The respondents are Finnish university employees who were advised to work remotely due to governmental recommendations. These recommendations came into effect at the beginning of March 2020 and will end in July 2021. At the time of data collection, study participants had been working remotely for about eight months.This study contributes to the literature in different ways. First, we study protective factors against the potential negative effects of loneliness. We consider that this is important, since many studies have focused on the mean levels and antecedents of loneliness during COVID-19 .The data used in this study were collected as a part of the research project “Safely remotely—occupational well-being and its management in telework”, funded by the Finnish Work Environment Fund. The overall aim of the research project was to examine university employees’ experiences of remote work during the COVID-19 pandemic. The data used in this study were collected from a multi-faculty university located in western Finland from 28 September to 11 October 2020. Cross-sectional data were collected using the LimeSurvey tool. The electronic survey was sent to the work email addresses of 3788 university employees via the university’s general mailing list. Participants were informed about the survey before it was sent to them, and one reminder was used. The study was approved by the Institutional Review Board of the University of Turku, date 18 May 2020. All subjects provided their informed consent to inclusion before they participated in the study. In total, 1487 participants completed the survey, resulting in a response rate of 39%.n = 1463) comprises all those who had employment contracts with the university, i.e., grant holders supported by  grants from external funding agencies were excluded from the data. Half of the respondents were academic staff (50%), comprising, for example, full professors, lecturers, (senior) researchers and academic assistants. Of the respondents, 38% belonged to the group of administrative and technical personnel. The rest of the respondents belonged either to the group of managers (6%) or doctoral students with employment contracts (6%). Of the sample, 64% were women (0.7% declined to report their gender and 4.1% preferred not to answer the question), the average age was 45 years (SD = 10.66), 76% had a partner and the majority had either a master’s (46.5%) or doctoral (33%) degree. The vast majority of the participants were working remotely during the study (78%). Compared to the personnel structure of the university in question, the participants in our study were somewhat older (45 years vs. 44 years), and women were slightly over-represented in our data (64% vs. 59%). In 2020, the total number of staff working in 13 Finnish universities administered by the Ministry of Education and Culture was 30,050 [The sample of the present study  ,59. The Organizational support was measured with six new items specifically developed by the research team for the purposes of this study: (1) “The top management of the university have communicated clearly about the current exceptional circumstances”, (2) “My practical questions have been answered quickly enough”, (3) I have received enough instructions on performing my tasks and duties from home”, (4) “I have received support for my work when I have encountered difficulties”, (5) “I have received enough instructions on using the electronic systems and tools ” and (6) “The electronic systems and tools  have worked well technically”. The items were scored on a 5-point rating scale .The single-item measure of stress has been found to adequately capture experience of perceived stress . A ratinExhaustion was measured with three items from the Burnout Assessment Tool , capturiGender , age (a continuous variable) and marital status  were used as covariates in the analyses, as they have been in earlier studies associated with loneliness and well-being ,14.2 change at each step of the analysis was used to determine the variance explained by each antecedent or antecedents set. The standardized beta values reported were used to determine the effect of each variable on stress and exhaustion. Analyses were conducted using SPSS Statistics Version 27 . Basic assumptions of regression analysis on normality and homoscedasticity of residuals were tested and the assumptions were met. Outliers were also checked, and there was no apparent multicollinearity.Hierarchical multiple linear regression analyses were used to examine the main effect of loneliness and the possible buffering  effects of resilience, social belonging and organizational support on loneliness–well-being (perceived stress and exhaustion) relationships. Both stress and exhaustion were regressed on the antecedent sets in six steps, as follows: (1) demographics , (2) loneliness, (3) resilience, (4) social belonging, (5) organizational support, and (6) the interaction terms between loneliness and resiliency, social belonging and organizational support. The magnitude of Rp < 0.001) and exhaustion . Moreover, resilience, social belonging and organizational support all correlated negatively and statistically significantly with perceived stress and exhaustion. Personal, social and organizational resources also correlated with each other , as also did perceived stress and exhaustion . Of the background variables, women   and younger employees  perceived more stress and exhaustion than the others. There was no difference in levels of loneliness between genders (p > 0.05), but single individuals  reported higher levels of loneliness than those in relationships  = 24.77, p < 0.001).Correlations of all the study variables are presented in p < 0.001) and exhaustion  were reported. Consequently, our Hypotheses 1 and 2 were supported. The significant main effects were also evident for resilience  and organizational support  on both stress and exhaustion, respectively (see p < 0.01) and exhaustion , and the interaction terms significantly contributed to the explained variance.The results of the multiple regression analyses are presented in vely see . HypotheGraphical representations of the significant two-way interactions see  were madOrganizational support, moreover, moderated the loneliness–stress relationship, thus lending partial support to our Hypothesis 8 see . Figure Social belonging did not contribute to explaining the variance of perceived stress or exhaustion. Additionally, the interaction terms between loneliness and social belonging turned out to be nonsignificant see . TherefoThe main aim of this study was to investigate whether among Finnish university employees, personal resilience, social belonging and organizational support buffered against the potentially negative effects of perceived loneliness on stress and exhaustion. This study is among the first to study loneliness as a personal demand from the perspective of the JD-R model . It alsoFirst of all, as hypothesized, perceived loneliness was associated with stress (H1) and exhaustion (H2), where the higher the level of loneliness, the more stress and exhaustion were experienced. Taken together, these results support the existing evidence on the detrimental influence of loneliness on well-being in general ,11,13 anOverall, the results support the basic assumptions of the JD-R model ,21, nameOur findings, moreover, indicated that perceived organizational support was directly and negatively associated with exhaustion and stress: the higher the level of perceived organizational support, the less stress and exhaustion were experienced. Organizational support also moderated the loneliness–stress relationship, as hypothesized, but did not moderate the loneliness–exhaustion relationship. Thus, Hypothesis 7 was fully, and Hypothesis 8 partially supported. These findings concur with earlier findings on the importance of organizational support for work well-being in general ,21,28,50Contrary to expectations, social belonging was associated with neither stress nor exhaustion, and did not moderate the relationships between loneliness and well-being. Hypotheses 5 and 6 were therefore not supported by the results. Since social belonging was not associated with well-being, we propose that in the future, other constructs, such as emotional and instrumental social support , could be included in the analyses. Additionally, quality of social support or social interaction should be considered since quality of social interaction has been shown to be relevant for well-being ,45,62. MThe main limitation of this study concerns the fact that the study design was cross-sectional, making it impossible to draw conclusions about the direction of causality, for example, whether loneliness causes stress and exhaustion, or whether stress and exhaustion cause more loneliness to be experienced. Further, it is likely that feelings and experiences have changed and developed during the pandemic due to changing risk levels and restrictions, for example . We therThe respondents in the present study were Finnish university employees surveyed at the turn of September and October 2020. At the time of data collection, most of them had been working remotely for about eight months. We suggest that our findings are likely to be generalizable not only to those working in universities but also to those knowledge workers who work remotely. However, it is worth noting that Finland is a Nordic welfare society, providing reasonable social security benefits and public services, which may be reflected in general stress and exhaustion levels. Further, during COVID-19 in Finland, the number of those working remotely was very high , and mosOur main findings demonstrated the importance of two resources—personal resilience and organizational support—in enhancing well-being during the COVID-19 pandemic. Based on these, we make two practical recommendations for organizations. First, in terms of enhancing personal resilience, one option could be to intentionally promote the ability to focus on positive experiences and to intensify and prolong positive feelings . In prac


The effects of the COVID-19 pandemic have required substantial adjustments in terms of university teaching–learning processes. The aim of this study was to verify whether there were significant differences between the academic year of 2020 and the two preceding years in factors and symptoms and stress. A total of 642 university students (ages 18–25 years) participated by filling out validated self-reports during the months from March to August 2020. Using an ex post facto design, SEM analyses and simple and multiple ANOVAs were performed. Structural results showed that stress factors from the teaching process had a predictive value for the learning process, emotions, and academic burnout, and being a man was a factor predicting negative emotion. In a similar way, inferential results revealed no significant effect of academic year but did show an effect of gender on stress experiences during the pandemic. Aside from certain specific aspects, there was no significant global effect of the year 2020 on factors and symptoms of stress. The results showed that studying in the year of the COVID-19 outbreak did not have a significant effect on stress triggered by the teaching process. From these results, we draw implications for specific guidance interventions with university teachers and students. Numerous health-related studies  and reseThe rapid expansion of the coronavirus pandemic has disrupted life for persons, states, and institutions worldwide. Feelings of great uncertainty and anxiety have been triggered . This siteaching process include maladjusted teaching methodology, poor classroom climate, and irrelevant content; factors related to the learning process include an excess of learning activities (perceived as a heavy workload), student presentations in class, and an assessment system that induces lack of control over one’s achievement  Theory (2017) is a complementary perspective and a valid heuristic for analyzing this reality. Reports of previous evidence have already revealed effects in this direction, in different motivational variables Regulatory teaching–learning. This is the case where teachers have properly planned and designed the teaching–learning process, including a range of technical support that allows the process to proceed adequately and be fitted to the new situation with minimal planning changes. In this scenario, students are less likely to show stress symptoms, negative emotionality, and burnout, and motivational behaviors of engagement can be maintained.2)Nonregulatory teaching–learning. In this case, where teachers have prepared only face-to-face learning without the use of online technologies, their planning is not compatible with the new situation of online teaching. Clear teaching-learning guidelines for the new situation do not exist. Students feel uncertain about the way forward, and external regulation is lacking, thus increasing the likelihood of stress symptoms, negative emotionality, and a certain degree of burnout.3)Dysregulatory teaching–learning. In such cases, teachers follow an irregular pattern; previous planning is lacking, and they make arbitrary decisions about teaching and learning in the new situation. Assessment criteria undergo changes and unexpected new activities are incorporated. Consequently, students feel overwhelmed by the demands, are plagued with uncertainty and negative emotionality, and show greater levels of burnout.The potential psychological and academic impact of confinement during the COVID-19 pandemic, students’ management of stress, and a possible gender modulation, among other factors, are subjects of growing interest in the most recent psychoeducational research . There aAll the foregoing aspects seem to be modulated by the gender variable, with ample supporting evidence already . Recent Academic obstacles related to learning and teaching (or academic stress) affect one’s emotionality toward academic tasks. There is plenty of recent evidence that achievement emotions are a significant correlate of academic well-being and of the degree of satisfaction with the academic experience at university . When thCertain studies highlight differences in the associations between academic stress and academic well-being, burnout, and engagement . Pekrun burnout and engagement have been analyzed profusely in the organizational context  to analyze whether the students’ perception of teaching and learning stress factors predicted significant changes in achievement emotions and motivational state of engagement-burnout and whether the academic year and gender could also predict these emotional changes; (2) to inferentially analyze the specific causal effects that the academic year and gender had on stress factors originating in the teaching–learning process, on negative emotions, and on the state of engagement-burnout of undergraduate students.Based on the above, the hypotheses. (1) The perception of stressors in teaching will positively and significantly predict learning stressors; and these will, in turn, predict negative emotions, as well as students’ state of engagement-burnout. Additionally, this relationship will be predicted by the COVID-19 academic year and by gender. (2) The year and gender factors will have a significant main effect on the level of the teaching-learning factors of stress, negative emotions, and the state of engagement-burnout. These results would be differentiated according to gender, based on prior evidence, with men showing more emotional decline toward burnout and women showing greater engagement and greater test anxiety.We established the following A total of 642 university students (between the ages of 18 and 25) participated in this study. Of these, 201 students participated in 2018, 168 students in 2019, and 305 in 2020. The mean age was 20.42 years (SD = 5.8), and the age range was 19–25 years. Participation was anonymous and voluntary. Lecturers from various departments were invited to participate, and those who agreed then extended the invitation to their students. Participating lecturers and students were awarded a Certificate of Participation. Online questionnaires were applied to assess each specific teaching–learning process. The groups of participating students were different and from different academic subjects. All of them were studying for Degrees in Psychology and Education. Group equivalence was checked using the relevant statistical analyses .p < 0.001; relative chi-square or CMIN/df = 5.11; SRMR = 0.075, CFI = 0.935, TLI = 0.961, IFI = 0.947, RFI = 0.965, NFI = 0.947, RMSEA = 0.057, HOELTER = 0.430 (p < 0.05) and 0.532 (p < 01)]. The proposed model contained 53 items with a seven-factor structure having two dimensions, where one factor differs from the original version. The resulting dimensions and factors were: (1) Dimension of Stress in Learning: Heavy Workload (Factor 2), Lack of Control over Achievement (F3), Social climate (Factor 5), and Test Anxiety (Factor 7); (2) Dimension of Stress in Teaching: Methodology difficulties (Factor 1), Public speaking (Factor 4); Content lacking value (Factor 6). Overall reliability, Alpha = 0.961; part 1, Alpha = 0.932, part 2, Alpha = 0.946, in this study. Some examples of items are as follows: “I get nervous or tense… when they ask me questions in class” and “It worries me…that the subjects we are studying are of little interest.”The Academic Stress Questionnaire, CEA/ASQ . First, We measured achievement emotions with a validated Spanish version  of the AUtrecht Work Engagement Scale for Students . Cronbach alpha in this sample was.900 (14 items), with 0.856 (7 items) and 0.786 (7 items) for the two parts, respectively. A validated Spanish version of the Burnout Scale for Students . Cronbach’s alpha for this sample was Alpha = 0.874 (15 items), with Part 1, Alpha = 0.853 (8 items) and Part 2, Alpha = 0.793 (7 items) for the two parts, respectively. Examples of items are as follows: “I feel happy when I am studying intensively” and “I doubt the significance of my studies.”A validated Spanish version of the Students  was usedStudents  was usedwww.inetas.org) had been designed for this purpose . Both the students and the teachers involved were offered certification of their participation in the Research Project.Researchers from the present project were asked to invite students from their university to complete the questionnaires mentioned above during the months from March to August 2020. Samples of these questionnaires had previously been collected during the same months of 2018 and 2019. We followed the same protocol that was established and approved by the ethics committee of the University of Navarra (ref. 2018.170). Questionnaires were completed online, on a voluntary basis, outside of class time. An automated tool structural predictive hypotheses, confirmatory factor analysis was tested with a Structural Equation Model (SEM) in this sample. Data were aggregated using the determination of factors obtained in the corresponding previous exploratory and confirmatory factor analyses  in order to avoid false positives. We assessed model fit by first examining the ratio of chi-square to degrees of freedom, SRMR, then the Comparative Fit Index (CFI), Normed Fit Index (NFI), Incremental Fit Index (IFI), and Relative Fit Index (RFI). Ideally, these should all be greater than 0.90. Sample size adequacy was checked using the Hoelter Index (For the er Index . AMOS (v2)inferential hypotheses, we carried out ANOVAs and MANOVAs, 3  × 2 (gender = Men and Women), using Pillai’s index. The software package SPSS v. 25 (New York) was used for these analyses.For the Model 1 included the effect of both variables (teaching and learning factors of stress) on positive vs. negative emotions, as well as engagement vs. burnout, but it was not considered positive. Model 2 tested the effect of both variables (teaching and learning factors of stress) only on negative emotions and burnout, with more acceptable, but still insufficient, values. Model 3 tested the effect of the academic variables  on stress factors, negative emotions, and burnout, achieving acceptable values. The values of each model are presented in Several models were tested in order to validate the hypothesis of the year × gender effect. B = –0.90). Stress factors of teaching were also found to positively predict the stress factors of learning (B = 0.943). Stress in learning positively predicted negative emotions (B = 0.692) and to a lesser degree burnout (B = 0.215). However, negative emotions while studying strongly predicted Burnout (B = 0.512). See Direct effects were found, showing that gender (Men = 1 and Women = 2) negatively predicted negative emotions during the study (B = 0.652) and burnout (B = 0.536). Finally, stress factors while learning appeared as indirect predictors of burnout (B = 0.354). See Indirect prediction effects were of particular interest. Gender (M = 1 and F = 2) was a negative but less consistent predictor of burnout (–0.046), of negative emotions while learning, and of the factors of burnout. Stress factors in Teaching appeared as positive indirect predictors—with greater consistency—of negative emotions  = 0.884, p < 0.519]. The ANOVA revealed a single main effect of gender on the level of total stress factors, tending toward women  and no significant interaction effect. Note that the most important effect of gender was found in stressors in the learning process. See values in The previous analyses showed an adequate level of homogeneity of variance  and stress factors of learning . Regarding the MANOVA, there was a single significant main effect, referring to gender, with a higher level of stress factors for women. The partial effects showed an effect of gender in stress factors of the teaching process, and of the learning process. Finally, there was a marginally significant interaction effect of year x gender for stress factors of the learning process. See raw descriptive values and effects in The previous analyses showed an adequate level of homogeneity of variance, both for stress factors of teaching  and stress factors of learning . Regarding the MANOVA, there was a single significant main effect referring to gender, with a higher level of specific stress factors for women. The partial effects showed an effect of gender on the following stress factors: in the teaching process, these were methodological difficulties and heavy workload (this factor with higher power), and in the learning process, these were public speaking and lack of control over achievement. Finally, there were two, marginally significant, year x gender interaction effects, for stress factors of the teaching process: heavy workload and public speaking. See the descriptive values and effects in The previous analyses showed an adequate level of homogeneity of variance, both for stress factors of teaching  and negative emotions . There was no significant main or partial effect of year, gender, or year × gender on total achievement emotions during study.The Levene test, based on the mean, showed an absence of significant differences in errors of variance, for both positive emotions  or burnout . The MANOVA revealed a significant main effect of gender on total engagement-burnout—an effect that was partially maintained for each total score of the motivational states, engagement (in favor of women), and burnout (in favor of men).The analyses of the variance of error revealed no significant differences, whether for gender also appeared in the set of all engagement-burnout factors. The partial effects showed a significant partial effect of gender on cynicism, tending toward men, as well as effects on dedication and absorption, tending toward women. Raw values are shown in A significant main effect of first aim of the present research specifically examines the predictive effect between university students’ perception of academic stress (induced from the teaching and learning process), achievement emotions, and motivational state of engagement-burnout during the period of the COVID-19 outbreak and in the two previous academic years, taking into account gender differences (Hypothesis 1).The In this case, the hypotheses were partially fulfilled. Firstly, consistent positive predictive relationships were found between stress factors during the teaching process  and stress factors in the learning process . In turn, these variables predicted the absence of positive emotions and the presence of negative emotions, as well as academic burnout. These initial results are intrinsically interesting because they show the potential stressful effect that the teaching process had on the learning process. Research on teaching styles has shown that teaching style can be a stress inducer . This evsecond aim was fulfilled because the inferential results corroborated, in greater detail, the initial predictive analysis. Hypothesis 2 was partially fulfilled since the year examined (COVID-19 context) did not have a sufficiently significant impact on the variables, while the gender factor appeared to show significant statistical power to determine the value of the dependent variables analyzed. First, although there was no significant general effect of academic year (during the COVID-19 outbreak), a significant increase in certain stress factors of teaching appeared during the year 2020 (COVID-19 context), such as methodological difficulties and heavy workload. Although this effect has been less analyzed, it was to be expected, concurring with other prior evidence in mental health contexts (The contexts . Howeverregulatory teaching (as effective teaching) is designed in a way that clearly and precisely selects and establishes the times, learning activities, content, technology resources, demands, and assessment systems in order to help students learn in a regulated manner Certain specific decisions in the teaching process were probably hasty and inadequate, causing distress and concern in the students; they did not fit into the design of the original subject syllabus and led to a perceived loss of control, an aspect that tends toward student stress , 2018. P(2)However, these mismatches were not determined by the academic year, rather, determinants of negative academic emotions and the state of burnout were stable despite COVID-19. For this reason, we argue that the teachers’ teaching styles remain stable despite the pandemic and seem to operate as a protective factor against stress .learning process are indirectly triggered through the teaching process (a predictive aspect that has been shown in the results) but are also triggered directly by the student. Prior evidence has shown that the student’s level of regulation determines their stress factors while learning . Therefore, student levels of nonregulation and dysregulation would be accompanied by higher levels of stress factors while learning, thereby explaining the increase in the negative emotions of boredom in men  and in the learning process (loss of control over achievement). Women had higher scores in anxiety, which is consistent with prior evidence reporting gender differences in response to academic stress (dedication to task (a correlate of engagement) in the present pandemic situation, which could be interpreted as a higher level of self-regulation and desire to manage the incongruencies that the pandemic has brought about  and in the motivational state of cynicism, a typical burnout behavior . Prior rFrom a complementary perspective, prior studies have found that the negative deactivating emotion of boredom has greater weight in class and study situations, while the positive deactivating emotion of relief is more relevant in exam situations. However, the relationship persists across class, study, and exam situations, revealing stability in the students’ emotional responses, according to their learning approaches: negative emotions are typical of surface approaches to learning, while positive emotions are typical of deep approaches , e. ThisOne limitation of this study refers to the research methodology used, given that an ex post facto design itself limits the inferences that can be drawn. In addition, since we could not ensure that the academic subjects were the same ones each year, we cannot infer that methodological changes were owing exclusively to the COVID-19 crisis. Academic subjects were not grouped a priori according to regulatory, nonregulatory, or dysregulatory teaching systems, something which would have made it possible to attribute a cause to this factor, nor were students categorized as practicing self-regulated, nonregulated, or dysregulated learning. Nonetheless, recent research has documented this , 2020. FPredictive and explanatory analysis of how different sources of stress affect academic emotions, and how these relate to the motivational states of burnout-engagement, has particular relevance for higher education institutions. Also significant is the role of gender as a modulating variable in the development of different stress profiles at university. This analysis can help identify which stressors in the teaching and learning process may affect university students’ stress levels and, consequently, how to reduce these levels  and ensuThere are several implications for psychoeducational intervention. On one hand, teachers need to be trained in regulatory teaching processes; lack of regulation and improvised changes in activities and/or assessment during the teaching–learning process become stress factors for students . On the It also seems reasonable to train students in stress management competencies for learning at university, given that unforeseen events can easily appear, as in the present situation. We should not assume that nothing will happen in the future; based on experience, it is possible that unexpected life events or academic incidents will occur, and we should be prepared to take them on in a resilient manner . It is tCurrent events are forcing us to make broad behavioral adjustments in the organization of our personal, family, and academic life for the weeks ahead. To make these adjustments smoothly, we must keep in mind different behavioral principles and strategies for coping with the pandemic, something which is under-addressed in university Study Plans at every level. It is essential to design programs and improvement strategies for competency in dealing with the pandemic, from a psychological viewpoint  and from(1)regulatory environmental design that prompts a perception of control and continuity in students:In the subjects you teach, maintain a a.Keep your usual contact hours with students, using appropriate technology media. Direct, online classes allow you to continue with the subject and lessen anxiety in the students.b.Make every adjustment so that all participants perceive normality and a sense of control. It is best to keep up the normal pace of the subject, although with adjustments as the situation requires. It is not a good time to make big, unexpected changes.c.If needed, adjust your assessment system and activities during this period. Make students aware that new situations involve new behavioral challenges and opportunities, for example, the chance to practice online teamwork from home.(2)Apply external regulation to help students in their learning process:a.In case you have not already done so, this is a good time to convert all learning resources to digital formats and encourage students to learn autonomously from home. Keep this material and instructions well-structured because students are dealing with several modules at the same time.b.Plan regular, general messages and aids for your students, so they feel that the teaching–learning process continues with some normality.c.Offer personalized online consultation for students who need it. It is especially important to keep direct contact with the student representative in each class in order to be informed of any possible problems or help that students are needing.d.Regularly reevaluate whether students need adjustments to the material, assignments, etc.e.Pay attention to the emotional state and expectations of your students. Convey calm and assurance with your own behavior. Your students see themselves reflected in you, and in the image that you portray when interacting with them. Become a mentor that supports the process, also emotionally.(1)Self-regulate your own behavior: while homebound, stick to your usual schedule.a.Following Circadian rhythm and keeping up personal habits go far to help maintain one’s sequence of activity, self-regulate, and not lose motivation.b.Give yourself daily doses of positive emotions and rewarding experiences while sheltering at home. It is very important to keep a positive emotional frame of mind. Distress  can be triggered by abrupt changes in one’s daily rhythm, or by a sense of uncertainty and loss of behavioral control.1.Self-regulate your learning behavior during this period:a.Every day, plan objectives, schedules, and action steps, being flexible but also systematic.b.Exercise control over your behavior. Structure your continuous work time to include pauses for rest. Stop and take time for leisure activities (a substitute for outdoor activities). Tell yourself that you are doing the right thing. Use different relaxation techniques to decrease any anxiety.c.It is not a good time to take on complex issues in your life situation, because this may cause even greater stress and loss of situational control. If it is truly necessary, make small, gradual adjustments.d.Take advantage to catch up on matters that are pending, whether personal, family-related, or academic tasks. This is a gift of time.e.Reevaluate your daily behavior at the end of the day and redefine your objectives  for the next few days.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Universidad de Navarra. The patients/participants provided their written informed consent to participate in this study.JF: project IP, design, data analysis and initial writing. MP-B: bibliographic and text review. FS: data collection and review of the English text. MG-T, RA-G, FP-S, PP, and MG: data collection and final text review. All authors contributed to the article 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.


Obesity is a major public health issue. Combining exercise training, nutrition, and therapeutic education in metabolic rehabilitation (MR) is recommended for obesity management. However, evidence from randomized controlled studies is lacking. In addition, MR is associated with poor patient adherence. Mobile health devices improve access to MR components.The aim of this study is to compare the changes in body composition, anthropometric parameters, exercise capacity, and quality of life (QOL) within 12 weeks of patients in the telerehabilitation (TR) program to those of usual care patients with obesity.This was a parallel-design randomized controlled study. In total, 50 patients with obesity (BMI>30 kg/m²) were included in a TR group (TRG) or a usual care group (UCG) for 12 weeks. Patients underwent biometric impedance analyses, metabolic exercise tests, actimetry, and QOL and satisfaction questionnaires. The primary outcome was the change in fat mass at 12 weeks from baseline. Secondary outcomes were changes in body weight, metabolic parameters, exercise capacity, QOL, patients’ adhesion, and satisfaction.P=.48). Compared with the UCG, TRG patients tended to significantly improve their waist to hip ratios  and improved QOL physical impact . Significant time effects were observed for body composition, 6-minute walk test distance, exercise metabolism, sedentary time, and QOL. Adherence (95%) and satisfaction in the TRG were good.A total of 49 patients completed the study. No significant group × time interaction was found for fat mass (TRG: mean 1.7 kg, SD 2.6 kg; UCG: mean 1.2 kg, SD 2.4 kg; In adults with obesity, the TR program was not superior to usual care for improving body composition. However, TR was able to deliver full multidisciplinary rehabilitation to patients with obesity and improve some health outcomes. Given the patients’ adherence and satisfaction, pragmatic programs should consider mobile health devices to improve access to MR. Further studies are warranted to further establish the benefits that TR has over usual care.ClinicalTrials.gov NCT03396666; http://clinicaltrials.gov/ct2/show/NCT03396666 Obesity is a chronic disease defined by a BMI of >30 kg/m² in the context of increased fat mass (FM). It is currently a highly prevalent disorder and a major public health issue . It is aObesity results in an imbalance between energy intake and energy expenditure ,7. CreatHowever, the delivery of MR in the clinical field is a complex issue, and population-based trials have shown poor patient adherence  facilities  improved the delivery of the components of rehabilitation when taken individually . In patiTherefore, we developed a mobile TR solution for patients with obesity and used it in a blended multidisciplinary MR combining exercise training at LIPOXmax intensity, nutritional intervention, and educational tools. The aim of this randomized controlled study is to compare the changes in body composition, anthropometric parameters, exercise capacity, and QOL within 12 weeks of the TR program versus usual care in patients with obesity. In addition, feasibility, patients’ adherence satisfaction, and effects of this TR were assessed in the TR group (TRG).Adults aged 25-65 years with a BMI of ≥30 kg/m² were eligible for participation. The main exclusion criteria were participants with a contraindication for exercise training .This was a 12-week, prospective, parallel-group, randomized controlled trial. Individuals were recruited from consultations of the Physiology Department of the University Hospital of Montpellier (France) and from the general population with media advertisements. After a screening period of 12 months, interested patients were contacted by email or phone and were registered on the Aviitam health platform. They were scheduled for half-day baseline assessments. All participants provided written informed consent. The study was conducted in accordance with the CONSORT  ethical guidelines and the CONSORT of Electronic and mHealth Applications and Online Telehealth checklist . The stuPatients were admitted to the Physiology Department of the University Hospital of Montpellier (France) between January 2018 and November 2018. Baseline assessments included physical examination, bioimpedance, blood test, effort calorimetry, 6-minute walk test (6MWT), and self-questionnaires. Once baseline assessments were completed, participants were randomized to either a 12-week TR program or usual care. The randomization sequence was computer-generated using random blocks in an order unknown to investigators. The list was established by a statistician and was only accessible by the personnel in charge of randomization. Although participants could not be blinded to their treatment, both programs were presented as active interventions.All tests and evaluations of the study were performed at the same place for each group under the same conditions and with the same devices. All assessments made at baseline were realized at the end of follow-up by technicians blinded to group allocation.Patients from both groups had a specialized medical consultation with cardiopulmonary exercise testing and prescription of an adapted PA program at an intensity that elicited maximal lipid oxidation . The patients in the usual care group (UCG) were advised to carry out their sessions independently, focusing on endurance PA sessions such as brisk walking, cycling, or swimming. Moreover, these patients received a booklet with different exercises and tips on PA and nutrition management. All patients were registered on the Aviitam website before and during the trial. Aviitam is a highly secure health record that allows the centralization, protection, and sharing of medical data with doctors. No restrictive diet was prescribed in either group.The TR program is a multicomponent intervention available on smartphones  and the The primary outcome was the modification of the amount of FM, expressed in kilograms between baseline and 12-week follow-up in the TRG versus those in the UCG.The main secondary outcomes were changes from baseline in body composition indexes , anthropometric parameters , and metabolism during exercise . Other outcomes were changes from baseline in PA and sedentary levels, exercise capacity, biological parameters, and self-assessment QOL questionnaires. The usability of the solution and satisfaction with the TR program were also assessed.Body composition; weight; height; and waist, hip, and neck circumferences were measured after 12 hours of fasting.Participants’ body composition was assessed by bioimpedance analysis with a 6 terminal impedance plethysmograph . This device measures the total resistance of the body to an alternating electric current of 50 kHz ,28. BodyHeight was measured to the nearest 0.5 cm using a standardized height gauge. BMI was calculated as weight (kg)/height² (m). Neck, chest, waist, and hip circumference measurements were obtained using standardized procedures. WHR was then calculated.max th) was calculated for all patients using Wasserman equations [max th), with a calculation of carbohydrate and lipid oxidation rates from gas exchange measurements at steady state at the 5th to 6th minute of every step according to the nonprotein respiratory quotient technique [The participants performed an exercise test on an electromagnetically braked cycle ergometer  connected to a breath-by-breath device  for gas exchange measurements. The theoretical maximal aerobic power  and one specific to the population of patients with obesity . The PA level was assessed using the Voorrips questionnaire (modified Baecke questionnaire). The questionnaire scored the past year’s household activities, sports activities, and other physically active leisure time activities and gave an overall PA score. The participants were asked to describe the type of activity, hours per week spent on it, and the period of the year in which the activity was normally performed. All activities were classified according to posture and movement. This questionnaire provides a reliable and valid method for classifying the activity level of older participants as high, medium, or low. With this method, normal participants with scores <9.4 are classified as having low PA.The SF-36 is a generic self-reported measure of health-related QOL comprising 36 questions across 8 domains . Answers to each question are rated on a Likert-type scale and summed to produce a raw score that is transformed to a scale of 0 to 100, with higher scores indicating a better QOL . SF-36 sUsability was evaluated with the System Usability Scale (SUS) . It is aA venous blood sample was obtained in the fasting state to measure the lipid proﬁle, plasma glucose, insulinemia, and C-reactive protein.Participants wore a GT3X accelerometer (ActiGraph) on the nondominant wrist, programmed to collect data from the vertical axis in 15-second epochs and initialized using a normal filter (AGNorm). Accelerometers were worn for 7 days during all waking hours and removed for sleeping and during water-based activities. The minimum wearing criteria was ≥4 days, with ≥8 hours of wearing time each day . In addiThe calculation of the number needed to treat is based on a hypothesis supported by the literature . We expet test or Mann-Whitney U test according to the data distribution. The intra- and intergroup kinetics of changes for the variables under intervention were analyzed with linear mixed effects models including a time effect, a group effect, and the interaction between these effects as a fixed factor and a subject effect as a random factor, using the nmle package in R. In case of significant interaction effect, false discovery rate–adjusted post hoc tests were performed. Linear mixed effects assumptions were tested before each test. Per-group analysis of the effect of the intervention was performed in the TRG group using paired t tests. The effect size was also calculated using Cohen d. Spearman rank order or Pearson correlations, depending on the data distribution, were used to determine associations between continuous variables. The data were analyzed using R software  and plotted using Prism Software. Statistical significance was set at P<.05.The baseline characteristics of the 2 groups were compared using the independent 1-tailed Of the 140 screened patients, 50 were included in the study and underwent randomization. A total of 49 patients completed the study . One patP<.001), meaning that although not different between groups, an improvement in FM occurred in both study groups. An FM decrease was observed in the TRG  and in the in the UCG , with 48% (12/25) of patients improving the FM of >5% of initial values , FFM , muscle mass , crossover point , power at the crossover point and LIPOXmax , SF-36 mental component , and EQVOD physical and psychosocial components  showed statistically significant improvements. Effect sizes ranged from small to moderate according to Cohen d (TR patients who completed the 12-week intervention performed an average of 30.5 (SD 16.7) sessions of PA and connected 283.5 (SD 193.4) times to the app and the website throughout the study. The SUS score averaged 65.3 (SD 15.02). A score of >68, indicating good usability of the system, was found in 46% (11/24) of patients. Only 1 patient found the program with poor usability, with an SUS score of <39. In the TRG, per-group analyses showed an improvement in body composition, exercise metabolism, and health-related QOL after the intervention. The FM . FFM and muscle mass were correlated with the crossover point , LIPOXmax , and MFO . The 6MWT distance was correlated with muscle mass , LIPOXmax , and the SF-36 physical impact . EQVOD’s psychosocial score was correlated with FM  and WHR . In addition, at the end of the 12-week trial, the change in BW was correlated with relative changes in FM (%) and FFM (%) in the TRG and UCG . In the whole population (N=49), BW change was inversely correlated with the 6MWT distance . In contrast, the number of training sessions and changes in WHR or FM  and the number of connections and changes in WHR or FM  were not significantly correlated.At baseline, univariate correlations between parameters were found in all patients with obesity. FM was correlated with BMI , there was a significant advantage regarding the domain of QOL and tendency for the WHR. In addition, per-group analysis indicated that the significant time effects on body composition, exercise capacity, PA behavior, and QOL were mainly because of the TRG. These effects occurred while the TR solution’s adherence and usability were good during the 12-week trial duration.P=.07), which is consistent with previous reports of the effect on WC induced by multidisciplinary rehabilitation [Scientific societies have recommended that patients with obesity should benefit from a multidisciplinary program including exercise, diet, and cognitive behavioral therapy . Howeverlitation  or 12-welitation . This eflitation ,47. As pDetailed, structured PA counseling in daily life was provided to the UCG patients in line with the guidelines for obesity . The intd>0.50) and reached 5% or more in 48% (12/25) of patients in the TRG. These effects appear to be particularly relevant, as FM loss appears to be the best predictor of physical functioning improvement during weight loss in patients with obesity [d was 0.644, which indicates a medium improvement exercise metabolism in patients with obesity. In addition, given that the minimally clinically important difference has been estimated from 2 to 4 points of the SF-36 physical component summary or mental component summary [In contrast, patients in the TRG showed significant improvements in body composition (FM), exercise capacity (6MWT distance), exercise metabolism (intensity at the crossover point and LIPOXmax), health-related QOL (SF-36 and EQVOD), biological parameters , and sedentary time in each group analysis  and 2. A obesity . In addi obesity ,54. The  obesity , but Coh summary ,56, 25% TR has been studied in several conditions such as stroke , chronicOne limitation of the study was the lack of sufficient objective monitoring of the intervention in the TRG. Patients experienced difficulties, mainly secondary to connectivity defects with Bluetooth, to use heart rate monitors, pedometers, and cycle ergometers. Therefore, too few data were collected to monitor the intervention correctly. However, our study was a pragmatic trial, and all analyses were intention-to-treat analyses, which means that the impact of these missing data on the results was limited. Nonetheless, monitoring the intervention would have allowed for a better understanding of the effects of the TRG in patients with obesity, particularly in terms of the patients’ phenotype in response to the intervention. In addition, the information technology firm that codeveloped the TR solution with our team did not have the opportunity to implement push notifications and provide pertinent feedback to patients. Finally, it is probable that patients need a longer and more stimulant intervention. Therefore, long-term intervention and the addition of human support, for example via videoconferencing, could address these limitations and improve the outcomes of TR programs. Altogether, the development of a TR solution remains an issue that must be addressed from the global perspective of our mHealth project.In adults with obesity, our TR program was able to deliver full MR but did not demonstrate superiority to the usual care on body composition. Over a period of 12 weeks, it induced effects on most rehabilitation outcomes in patients with obesity . These effects were not significantly superior to those induced in our UCG. However, the excellent patient adherence to the TR constitutes an answer to the challenge of patient adherence to face-to-face rehabilitation programs. Pragmatic MR programs should consider mHealth devices to deliver interventions. In parallel to the continuous development of technological solutions, large-scale and long-term studies are needed to translate these technological promises into fully efficient interventions in the clinical field.


This study aimed to explore the radiomic features from PET images to detect active cardiac sarcoidosis (CS).U-test and a logistic regression classifier were used to compare the individual features of the study groups.Forty sarcoid patients and twenty-nine controls were scanned using FDG PET-CMR. Five feature classes were compared between the groups. From the PET images alone, two different segmentations were drawn. For segmentation A, a region of interest (ROI) was manually delineated for the patients' myocardium hot regions with standardized uptake value (SUV) higher than 2.5 and the controls' normal myocardium region. A second ROI was drawn in the entire left ventricular myocardium for both study groups, segmentation B. The conventional metrics and radiomic features were then extracted for each ROI. Mann-Whitney min had the highest area under the curve (AUC) and greatest accuracy among the conventional metrics. However, for both segmentations, the AUC and accuracy of the TBRmax were relatively high, >0.85. Twenty-two (from segmentation A) and thirty-five (from segmentation B) of 75 radiomic features fulfilled the criteria: P-value < 0.00061 (after Bonferroni correction), AUC >0.5, and accuracy >0.7. Principal Component Analysis (PCA) was conducted, with five components leading to cumulative variance higher than 90%. Ten machine learning classifiers were then tested and trained. Most of them had AUCs and accuracies ≥0.8. For segmentation A, the AUCs and accuracies of all classifiers are >0.9, but k-neighbors and neural network classifiers were the highest (=1). For segmentation B, there are four classifiers with AUCs and accuracies ≥0.8. However, the gaussian process classifier indicated the highest AUC and accuracy .For segmentation A, the SUVRadiomic analysis of the specific PET data was not proven to be necessary for the detection of CS. However, building an automated procedure will help to accelerate the analysis and potentially lead to more reproducible findings across different scanners and imaging centers and consequently improve standardization procedures that are important for clinical trials and development of more robust diagnostic protocols. Sarcoidosis is a multisystem, granulomatous inflammatory disease of unknown etiology, characterized by the presence of non-caseating granulomas in the involved organs , 2. Sarc18F] Fluorodeoxyglucose Positron Emission Tomography [[18F] FDG PET] have been demonstrated in the literature to improve the identification and treatment of patients with CS. Currently, these imaging tools are critical for early diagnosis, disease prediction and progression, and therapeutic response monitoring.The challenging in diagnosing cardiac sarcoidosis (CS) is due to the probability of involving any organ, leads to variability in clinical presentation . In addi18F] FDG PET, it is important to suppress the use of glucose by normal cardiomyocytes as this improves its specificity. Several approaches have been proposed, including following a ketogenic diet (high fats and low carbohydrates), prolonged fasting, intravenous heparin, and usually, a combination of these methods (max), can identify the highest uptake value within the region of interest (ROI). This can differentiate positive (CS+) and negative (CS−) results; however, in the presence of high physiological uptake, this metric fails to detect sarcoidosis within this region (max) is more robust than SUVmax due to the effective normalization for blood uptake , and all subjects gave written informed consent.Subjects with clinical suspicion of CS based on demonstrated clinical manifestations of extracardiac lesions and/or disease were recruited at Mount Sinai Hospital in New York, to undertake a PET-CMR examination. All subjects were treatment-naïve and had to avoid carbohydrate diet for 24 h before the scan and fast during the last 12 h. The preparation for imaging followed the recent recommendations by Ishida et al. . After t18F] FDG PET on an integrated PET-CMR system  was used in this study. Five MBq/kg of [18F] FDG was injected into the patients intravenously, who then waited for 10 min. Thoracic PET acquisition (one-bed position centered on the heart) took about 90 min but for this study only a late time window (last 60 min) was selected. PET images were reconstructed using the iterative ordinary Poisson ordered subset expectation maximization (OP-OSEM) with three iterations and 21 subsets on a 344 × 344 × 129 image matrix and an isotropic voxel size of 2 mm, followed by an isotropic 4 mm Gaussian post-filtering. The data obtained with PET were not respiratory-gated or ECG-gated and were not corrected for any potential motion artifacts. A 3D breath-hold Dixon-based MR image was used for attenuation correction. Simultaneously with PET imaging, CMR was performed with electrocardiograph triggered; the scan included short-axis T2 mapping and cine images. Approximately 15 min after 0.2 mmol/kg gadolinium injection, inversion-recovery fast gradient-echo LGE sequences were acquired.The simultaneous CMR with LGE and  and the corrected P-value of < 0.00061 was considered to be statistically significant. Logistic regression classifiers were then trained with individual features. Stratified 5-fold cross-validation was used to determine the mean area under the curve (AUC), mean accuracy, and 95% confidence intervals (CIs). Features with a P-value < 0.00061, AUC >0.5, and accuracy >0.7 were retained. In addition, principal component analysis (PCA) was used to identify highly correlated features and reduce feature redundancy. PCA reduces a large number of features into a small number of principal components (PCs). Components that explained 90% of the cumulative variance were retained. Lastly, to find the best machine learning (ML) algorithm, PCs were used as an input to test and train the following ten classifiers: Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, Gaussian Process Classifier, Stochastic Gradient Descent, Perceptron Classifier, Passive Aggressive Classifier, Neural Network Classifier and K-neighbors Classifier with stratified 5-fold cross-validation.Statistical analyses were undertaken using Scikit-learn software (Version 0.23.2) . Mann–WhU-tests on the conventional metrics of the different study groups for each segmentation separately. Predictably, for segmentation A, the SUVmin had the highest AUC and greatest accuracy due to specifying SUV >2.5 as the minimum value for the patient group, while for segmentation B, the highest performance was for TBRmax . This slight difference in TBRmax results between both segmentations came from the difference in the number of participants in the patient group who met the criteria for each segmentation.The results are relatively different by applying the Mann–Whitney Rmax see . HoweverU-tests, for segmentation A: 40 of the 75 radiomic features and for segmentation B: 61 of the 75 showed statistically significant differences between patients and controls, with a P-value < 0.00061. The five best radiomic features based on P-values for both segmentations are shown in P-value < 0.00061, AUC >0.5, and accuracy >0.7. The AUC and accuracy (95% CI for each criterion) with stratified 5-fold cross-validation of the five best-performing radiomic features based on the AUC value are shown in From the Mann–Whitney As the SUV-related metrics tend to overperform, and to study the performance of non-first order features, the SUV-related metrics were excluded from the PCA. By applying PCA, five PCs were retained to explain 90% of the information. These PCs were used to test and train the ML classifiers. Most of them had AUCs and accuracies ≥0.8. For segmentation A, all classifiers showed high performance in terms of AUC (95% CI 0.88–1.00) and accuracy (95% CI 0.87–1.00), with values >0.9. A k-neighbors and neural network classifiers showed the highest AUC and greatest accuracy, with values equal to 1.00, as shown in min showed high performance individually. These results were predictable as they are affected by the distribution of voxel intensities within the ROI, one of the criteria for including the patients at the first place. In addition, these features cannot be relied upon because they are greatly affected by the success of glucose suppression in normal cardiomyocytes. TBRmax was the most reliable metric over other conventional metrics among both segmentations. Although the TBRmax is sensitive to noise and it is not necessarily easy to harmonize across different scanners and imaging centers, types of data, and parameters, this is not the case in this study as datasets originated from a single scanner and institution. Therefore, when comparing TBRmax with those of the five-best performance radiomic features, the superiority of TBRmax over the rest of the features can be clearly seen. This outcome supports any previous studies that utilized TBRmax.This study aimed to explore the diagnostic utility of radiomic features compared to conventional metrics to distinguish between study groups and find the best performance ML classifier to create an automated model. From segmentation A, some conventional metrics like SUVmax. On the other hand, from segmentation B, the best performing radiomic feature was GLDM_Dependence Non-Uniformity with AUC (0.87) and accuracy (0.83). This feature measures the heterogeneity in the ROIs. The values of this feature are higher in sarcoid patients than controls which illustrates more heterogeneous regions in the group of patients. In addition, many other features measure heterogeneity with high AUCs and accuracies. These features look at the spatial relationships rather than voxels values themselves. However, these features had large error bars, unlike the TBRmax which had very small bars regardless of the segmentation approach.From segmentation A, by comparing the diagnostic utility of individual radiomic features, GLSZM-Large Area High Gray Level Emphasis radiomic feature showed the best performance in terms of AUC and accuracy. This feature measures the proportion in the image of the joint distribution of larger size zones with higher gray level values. This means there is a difference in gray level zones between patients and controls. However, it cannot be reliable due to the criteria of this segmentation approach that is based on SUV threshold and TBRmax and SUVmean. They reported, in addition, that the effect of utilizing different attenuation correction methods on radiomic features was not significant. At the same time Yip et al. (min and TBRmax. However, this approach can be influenced by observer experience, especially for cases with very small hotspots. Conversely, segmentation B approach is more robust and efficient.Several studies of different diseases advocated the importance of radiomic analysis to predict outcomes , 23. Howp et al.  had contThis study is subject to some limitations. First, the sample size is relatively small, and more extensive studies are needed to confirm these results. This is of great significance to prevent overfitting and type I errors. Applying a Bonferroni correction and dimensionality reduction techniques resulted in reducing the effect of this issue. In addition, the lack of an automated segmentation, a segmentation reference to compare with, unavailability of an independent clinical gold standard to validate the performance of the model that was trained on initial input data are other limitations for this study. In addition, the selection of only one focal lesion per patient in segmentation A was considered a limitation of this approach. Furthermore, the models proposed in this study should be validated in normal controls showing non-specific physiological uptake. This study showed uncertainty results of radiomic features and expanding the study to test the reproducibility of the results is required. New knowledge gained from this study is that using radiomic analysis does not provide any additional information related to disease activity in these patients. However, building an automated model regardless of the strategies used for glucose suppression and/or observer experience may prove helpful in further studies. Furthermore, in this study, the MRI acquisitions were not utilized, except for providing anatomical information. In this study the main goal was the radiomic features on PET; the designated tool for CS.P-value < 0.00061. However, by measuring AUCs and accuracies, large error bars can weaken the results. TBRmax showed its superiority over all other conventional and radiomic features in both segmentation approaches. This methodology needs to be validated further in normal control subjects showing non-specific physiological uptake.Radiomic analysis of PET data may not be a useful approach to detect CS. Several radiomic features that were not related to first-order tracer uptake showed high AUC and accuracy with The original contributions presented in the study are included in the article/This study was conducted with the approval of the Institutional Review Board at Mount Sinai. The patients/participants provided their written informed consent to participate in this study.NM segmented all the datasets using 3D slicer software, analyzed the data using PyRadiomics, performed the statistical analysis, analyzed the results, and wrote the manuscript. GS shared datasets, reviewed segmentations, and helped in modifying code as well as in the guidance of the project. LD wrote python code and helped to modify it and provide essential guidance on how to perform the optimization of the radiomic analysis, and machine learning approaches. MT facilitated the availability of data. MT, ZF, and PR contributed to reviewing the manuscript and the overall guidance of the project and data. ZF is the PI of the NIH grant. CT supervised the specific study and helped in restructuring and reviewing the manuscript. All authors contributed to the article and approved the submitted version.NM is fully funded by Taif University, Saudi Arabia. GS and PR are supported by NIH grant R01HL071021. LD is fully funded by the EPSRC Centre for Doctoral Training in Tissue Engineering and Regenerative Medicine: Innovation in Medical and Biological Engineering – grant number EP/L014823/1.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.