Data were collected from 2017 to 2020. Parents completed the Social Responsiveness Scale 2 and the Parenting Stress Index—Short Form, while the children’s attachment representations were assessed using the School-age Assessment of Attachment. Parents of children with ASD reported higher stress compared with controls. No differences were found between mothers and fathers. Implicit attachment representations have been found to be associated with parenting stress only in mothers, while the severity of social impairment showed a significant effect on parenting stress in both parents. These findings revealed the potential benefit of adaptive attachment representations not only for children themselves but also for mothers and the family system, suggesting the bidirectional nature of parent–child relationships in the context of ASD. The uniqueness of maternal and paternal parenting experiences should be considered when parenting stress is addressed.Mothers and fathers of autistic children (ASD) tend to report elevated levels of parenting stress. Thus, it is critically important to understand which factors contribute to an imbalance between the perceived demands of parenting and the available psychological resources. To date, little is known about the association between child attachment representations and parenting stress. In this study, we first examined group differences in parenting stress levels based on child diagnosis and parents’ gender. Second, we explored the predictive role of child diagnosis, autism severity, and child attachment representations on parenting stress. The study involved 23 school-age children with ASD (IQ > 70), 27 without ASD (7–13 years), and their mothers ( Parenting stress encompasses a variety of negative feelings and adverse responses arising from the difficulties of balancing personal psychological resources and parenting demands . PreviouSpecifically, ASD is characterized by early-onset and persistent impairments in social communication, as well as restricted and repetitive patterns in behaviors, interests, and activities . Due to Communicative difficulties and impairments in social reciprocity and interpersonal relatedness, including restrictive interests and repetitive behaviors that characterize ASD diagnosis, may increase parental burden and demands, regardless of the level of cognitive functioning . Prior rHowever, only a small number of studies focused on fathers including balanced samples ; thus, fFurthermore, prior research examined the role of specific predictors associated with parenting stress in the context of ASD, distinguishing between parental, child, and environmental factors . Most ofIn addition, further studies showed the contribution of other child dimensions that account for variations in parenting stress, such as executive functions  and behaSince attachment and social motivations share common underlying biobehavioral mechanisms , there wEarly metanalytic findings  revealedIn this regard, it is essential to note that the assessment of attachment during this developmental stage is particularly puzzling, since the attachment system evolves as children develop, reflecting the impact of profound changes that occur at physical, psychological, and interpersonal levels ,30. OldeAlthough it has been assumed that attachment during middle childhood plays a key role in children’s adjustment, little attention has been paid to the contribution of internal representations in the context of ASD . More spTo only study available considering the child perspective  found noThe present case-control study sought to examine the relations between child attachment representations, assessed using a semi-structured interview, and parenting stress in mothers and fathers of school-age children with ASD . The first aim of the study was to examine differences in the level of parenting stress between groups, based on child diagnosis and parents’ gender. We hypothesized that parents of children with ASD showed higher levels of stress compared with those of TD children in line with the previous literature . On the n = 50) and fathers (n = 50). Italian native speakers’ children were included in this study. We did not include children with a history of psychiatric disorder in the TD group, which was based on parent reports at the recruitment stage. For the ASD group, the exclusion criteria were severe impairment of cognitive functioning and language using the Wechsler Intelligence Scale for Children (WISC), 4th edition.This study is part of a project investigating attachment and parenting in school-age children with typical and atypical neurodevelopment . SpecifiIn addition, the parent-reported Social Responsiveness Scale (SRS)  scores wWe reached families of children with ASD through two Italian clinical centers for neurodevelopmental disorders. Data were collected from September 2017 to February 2020 in the Trentino and Campania regions . In the first stage, participation in the study was proposed by clinical psychologists of the clinical centers to parents of children with certified ASD diagnosis who met the inclusion criteria of the study. Clinical psychologists briefly clarified the aims and methodology of the study. Parents who were interested in participating authorized the research staff to contact them by phone to provide detailed information. Families of children with TD were recruited using snowball sampling. For parents who voluntarily decided to receive more information about the research project, an appointment was scheduled. After signing the informed consent, families who agreed to be part of the study were asked to attend a two-hour interview. The semi-structured interview was administered by a trained clinical psychologist, while parents completed the questionnaire in a separate room. This research was accomplished according to the EU General Data Protection Regulation (GDPR) n. 2016/679. The Ethical Committee of the (masked for blind review) approved the study protocol .ASD severity was assessed by using the Social Responsiveness Scale-2 , a widelFor the assessment of children’s internal attachment representations, the School-age Assessment of Attachment  clinicalThe primary coder assessed all the transcripts, whereas the secondary coder examined 25% of the total sample. We examined interrater agreement between the two coders by using Cohen’s k coefficient. A continuous variable (SAArisk) which reflects the degree of risk of attachment representations was used as the main outcome in this study. The SAA psychometric properties were confirmed both in clinical and normative groups ,43.The Parenting Stress Index—Short Form (PSI-SF)  is a widFirstly, we examined whether kurtosis and skewness values were within the range of −1 to +1.Then, differences between ASD and TD groups with respect to the control variables  were tested to check for potential confounders. To this purpose, the t-test and chi-squared test were used, respectively, for continuous and categorical variables. Next, bivariate correlational was used to test the associations among the study variables. With respect to the first study aim, we ran a two-way MANOVA to examine group differences in parenting stress total scores and subscales , testing the main effect of group (ASD and TD), gender (mothers and fathers), and their interaction (gender X group). According to the second aim of the study, two separate hierarchical linear regressions were conducted to assess the effect of specific child predictors on parenting stress (dependent variable) of mothers (2a) and fathers (2b) using the PSI total score. In the first block child diagnosis (ASD vs. TD) was entered, while the second block included ASD symptom severity. In the third block, implicit attachment representations were added to the model. Finally, the interaction between child diagnosis and SAA risk was tested in the fourth block of the regression. Due to the wide age range of the children, we added this variable to the model as a confounding factor. The inclusion of the variables was forced according to our research purposes and statistical analyses were conducted using the SPSS package (25.0 for Windows).p > 0.05). Preliminary analyses revealed significant differences between groups on SAArisk, with children with ASD showing higher levels of at-risk attachment representations (t(49) = −3.32, p = 0.002).Descriptive statistics of parents’ and children’s variables were reported, respectively, in Correlations among the study variables for both study groups are displayed in the p < 0.001; Wilk’s Λ = 0.77, partial η2 = 022), whereas no effect of parent gender  = 0.44, p < 0.77; Wilk’s Λ = 0.98, partial η2 = 0.019) and no interaction between group and gender  = 0.94, p < 0.44; Wilk’s Λ = 0.96, partial η2 = 0.039) were found. Specifically, univariate analysis showed a main effect of group on Parent–Child Dysfunctional Interaction  = 20.83, p < 0.001, partial η2 = 0.18) and Difficult Child subscales  = 18.49, p < 0.001, partial η2 = 0.16) as well as on the Total Stress scale,  = 14.48, p < 0.001, partial η2 = 0.13), with parents of children with ADS reporting higher scores compared with parents of children with TD. No differences based on group emerged on the Parental Distress subscale  = 1.18, p < 0.20, partial η2 = 0.017).With respect to the first study aim, the two-way MANOVA (Group × Gender) showed statistically significant differences between the two groups in parenting stress  = 6.66, p < 0.001), explaining 37% of the variance. In the first step, child diagnosis  significantly predicted parental stress, whereas it was no longer significant in the second block  = 8.17, p < 0.001) when ASD symptom severity  was added to the model. Specifically, higher SRS total scores predicted greater parental stress in mothers. The second block added a significant amount of variance . In the third block, internal attachment representations  did not contribute significantly to the model. The last block, which tested the interaction effect between child diagnosis and SAA risk, added a significant contribution to the model, revealing a significant predictive effect on maternal stress . The relationship between child internal attachment representations and parenting stress was significant for the ASD but not for the TD group (p = 0.01). The control for child age did not change the significance level of the associations in both maternal and paternal hierarchical regression models.According to the second study aim, we tested the effect of specific child predictors  on Total Parenting stress using two separate models for mothers and fathers. The hierarchical linear regression on maternal parenting stress  showed aTD group . Moreovep = 0.002) explaining 31% of the variance . The second block includes ASD symptom severity (β = 0.71 p < 0.001) and added a significant amount of variance , which constituted the best model fit for predicting paternal stress scores F = 8.85, p = 0.001, R2 = 0.28). The third  and fourth blocks  did not contribute significantly to the overall model. Data revealed that the SRS total score remains the only significant child predictor of paternal stress across blocks, maintaining a significant predictive effect in the final model , with higher ASD symptom severity associated with an increase in paternal stress scores.Next, the overall model on paternal parenting stress is statistically significant  = 5.01, variance . The firAlthough the study of parenting stress has received much attention in the ASD literature, research is still dominated by maternal stress since the number of fathers is often relatively small and unbalanced . PreviouConsistent with well-established findings of the ASD literature , parentsFurthermore, in line with our hypothesis, no significant differences were found in parenting stress between mothers and fathers in both study groups. This is consistent with other studies showing similar parenting stress levels in mothers and fathers ,5,10. ThThus, considering their critical influence on child development and family functioning , fathersTo the best of our knowledge, this is the first study to explore the association between child attachment and parenting stress in ASD, considering internal attachment representations. Firstly, at a preliminary level, children with ASD showed higher at-risk attachment internal representations compared with the control group . This fiWith respect to the predictors of parenting stress, ASD symptom severity was found to be associated with higher parenting stress in mothers and fathers. This is consistent with previous findings  documentA pivotal novelty of this study is the application of a semi-structured interview to assess implicit attachment representations in children with ASD. In the current study, at-risk internal attachment representations are associated with higher parenting stress in mothers of children with ASD. This finding contrasts with previous results by Keenan and colleagues  who repoAccording to our results, prior research revealed that at-risk attachment is associated with higher parenting stress in typical development  and may For instance, children’s rigid interpersonal expectations and difficulties in the interpretation of the social environment may lead to more frequent disruptions and less sustained positive engagement in the parent–child relationship . MoreoveTaken together, these distinctive characteristics may explain the way in which at-risk internal attachment representations contribute to stressful and demanding parenting experiences. Since we did not find a similar effect in parents of children with TD, difficulties of children with ASD in interpreting parental goals and intentions as well as in signaling their affective needs may also explain the higher level of at-risk attachment representations and in turn of parenting strain.Nevertheless, no associations between child attachment representations and the parenting stress of fathers were found. It is plausible that other non-ASD score factors instead of attachment can contribute to increased parenting stress in fathers of children with ASD, such as externalizing behaviors ,54.Some limitations of the study should be acknowledged, such as the wide age range of the children, the small sample size, and the lack of a measure assessing parenting style and caregiving roles. The cross-sectional design did not allow us to consider the predictive effects across time, only highlighting associations among the study variables. In addition, we did not consider the potential confounding role of comorbidity in children with a diagnosis of ASD.The current study contributed to the field of family processes in the context of ASD by examining the influence of child attachment representations on parenting stress and by including both mothers and fathers, using a balanced sample. According to previous studies, parents of children with ASD reported higher stress compared with parents of children with TD. No differences were found between mothers and fathers, suggesting that rearing a school-age child with ASD  may constitute a relevant source of stress regardless of parent gender. Implicit attachment representations are associated with parenting stress only in mothers. This suggests the potential benefit of adaptive attachment representations not only for children themselves but also for mothers and the family system since parenting stress levels have been found to be correlated among parents. For this reason, child interpersonal expectations, regulatory strategies, and self-protective organization should be considered in the psychological assessment with the aim of tailoring personalized interventions for families of children with ASD. Moreover, these results confirm the need to use a multimodal assessment to capture different components and processes related to attachment, providing encouraging results concerning the use of the SAA in the context of ASD. Future studies should replicate and extend these findings, using larger sample sizes, longitudinal designs, and sophisticated statistical models to address bidirectional effects between parent and child dimensions. In addition, further research on ASD should consider additional factors that could make the experience of parenting more stressful  by examining the influence of parental stress on maternal and paternal responsiveness, attunement, and sensitivity toward their child.Taken together, our findings suggest that ecological approaches to determinants of parenting stress in families of children with ASD should include specific factors explaining individual psychological functioning, such as child attachment representations. Finally, the different impact of child variables on parenting stress confirms the need to assess the uniqueness of maternal and paternal parenting experiences in the context of ASD, both in clinical and research practice.


Some studies have found associations between the peer network and childhood obesity. The present study aimed to analyze the association of the peer network with obesity-related cognition, behaviors and adiposity indicators, and explore whether peer network influences the effect of a childhood-obesity intervention. Based on DECIDE-Children, 1392 children’s friendship nominations within the class were collected and peer network indicators including the network size, network density, and in- and out-degree centrality were calculated. The linear mixed model was used to analyze the association between peer network indicators and children’s cognition, behaviors and adiposity indicators (body mass index (BMI), BMI z score, the prevalence of overweight and obesity). Children with a higher in-degree centrality had 34.4% (95%CI: 17.4% to 48.1%) lower risk of overweight or obesity. The baseline degree centrality was inversely associated with the BMI and BMI z score at the end of the trial. For each unit increase in in-degree centrality at baseline, the BMI at the end of the trial decreased by 0.047 (95%CI: 0.015 to 0.080), and the BMI z score decreased by 0.015 (95%CI: 0.003 to 0.028). Children’s popularity reflected by centrality in their peer network was associated with cognition, behaviors, and adiposity indicators. Future childhood-obesity intervention research could pay more attention to socially inactive children. Individuals are embedded in thick networks of social relationships and interactions. Social networks represent relatively stable systems composed of relationships among individuals , and reflect an individual’s social environment . In sociTraditional epidemiological research has mainly focused on health-risk behaviors of individuals in a group, while the network theory suggests that individuals’ health and health-related behaviors are affected by the embedded social network . A peer To date, some observational evidence has supported the idea that the social environment is one of major determinants of health and health-related behaviors, and highlighted the role of the peer network in the children’s health. Children’s activeness or popularity in a peer network might affect their health-related behaviors, such as sleep, physical activity and sedentary behaviors ,6. Some Based on a multifaceted intervention program for preventing obesity in primary school children in China, the present study aimed to analyze the association of the peer network with obesity-related cognition, behaviors and adiposity indicators, and explore whether centrality in a peer network influences the effect of a childhood-obesity intervention , in order to provide scientific evidence for the future childhood-obesity intervention strategies.The participants in this study were from a cluster randomized controlled trial named Diet, Exercise and Cardiovascular Health-Children (DECIDE-Children) conducted in 3 socioeconomically distinct regions from the eastern (Beijing), central , and western  parts of China from September 2018 to June 2019. There were 705 grade 4 students (about 10 years old in the Chinese school system) from 12 intervention schools and 687 from 12 control schools. Children of this age have the ability to complete questionnaires. Based on the socio-ecological model, dietary and exercise interventions for children were carried out in the intervention group at the school, family and individual levels. For the school level, we developed school obesity prevention policies including physical education sessions, healthy food environment, and health education sessions for teachers and children. For the individual level, we promoted children’s physical exercise at school and at home and recommended for them not to eat fried food, snacks, fast food, etc. For the family level, we conducted health education sessions for parents. In addition, an application on the mobile phone of the parents (named Eat Wisely and Move Happily) was used to conduct the regular monitoring and feedback of children’s diet and exercise behaviors and adiposity indicators, and promote the family involvement in the childhood-obesity intervention. Details of sampling and interventions can be found in previously published articles ,14. EthiThe children’s obesity-related cognition and behaviors were collected through questionnaires at the baseline and at the end of the trial, which included (1) the children’s correct rate of answering obesity-related questions; (2) dietary habits ; (3) exercise behavior ; and (4) screen behavior (average daily screen time ≤ 2 h). In order to comprehensively evaluate obesity-related cognition and behaviors, the scores of the above 4 dimensions were added to obtain a score of Weight-control-related Cognition and Behavior (WCB), with each dimension scored according to a full score of 5. A higher WCB score indicated healthier obesity-related cognition and behaviors in a child .The adiposity indicators in this study included the body mass index (BMI) and BMI z score. Children’s anthropometric measures at the baseline and at the end of the trial were collected by trained personnel, using identical devices and standardized forms referring to standard methods and procedures. The BMI z score was calculated according to World Health Organization (WHO) standards . OverweiChildren were asked to fill out questionnaires at school at baseline for collecting data on children’s peer networks. Each child was asked to nominate his/her friends within the same class who met at least two of the following five conditions: they (1) play together almost every day during recess, (2) often play or do homework together after school (>3 days/week), (3) often share snacks and toys, (4) often share extracurricular books, or (5) often go to school or go home after school together.The children’s friendship-nomination data were organized into a binary relational matrix, with the number of rows and columns equal to the total number of respondents in the class, and Ucinet 6 software was used to calculate peer network indicators at the individual and group levels. Peer network indicators included the network size, network density (group level) and degree centrality . Specifically, the network size was the number of children in each class, indicating the size of the relationship network within the class. The network density was the existing total number of friendship connections (ties) divided by the total possible number of ties within the same class, indicating the degree to which individuals in a peer network are connected to each other. Degree centrality was the total number of friends one child had. This could act as a measure of popularity or socially activeness in a peer network. Based on the direction of friendship nomination, the degree centrality was classified as out-degree and in-degree centrality in the social network analysis. Specifically, the out-degree centrality was the number of friendship nominations one child sent to others. And the in-degree centrality was the number of relationship nominations one child received from other classmates in their peer network.t test for continuous data, with a Chi-square test for categorical variables. The association of the peer network with WCB and adiposity indicators at baseline was assessed using linear mixed models that included class-level random intercepts to account for the clustering effect of children nested within the same class, with adjustment for age, gender, region, etc. General linear models and linear mixed models were used to explore the association of baseline degree centrality with adiposity indicators at the end of the trial, with adjustment for corresponding adiposity indicators at baseline, group, age, gender, region, etc. For sensitivity analysis, we further adjusted for parenting styles (whether parents limited children’s screen time), psychological factors (self-efficacy in weight loss), family dynamics (family support for the intervention program), and school environments . The results were considered statistically significant at a two-sided p < 0.05. Statistical analysis was carried out using R software (version 4.2.2). Network properties were calculated in Ucinet software (version 6.0).Categorical variables were presented as numbers and percentages (%), and continuous variables with normal distribution were presented as means and standard deviations (SD). Basic characteristics were compared using student’s A total of 1392 children were involved in this study. The control group and intervention group had similar sociodemographic characteristics at baseline. There were no significant differences in the adiposity indicators and degree centrality between the two groups, while the difference in network size and network density was statistically significant .We found that out-degree centrality was positively associated with the WCB and negatively associated with the BMI z score at baseline. For each unit increase in out-degree centrality , the WCB increased by 0.172 (95%CI: 0.093 to 0.250), and the BMI z score decreased by 0.042 (95%CI: 0.001 to 0.083) in the children. The in-degree centrality was positively associated with the WCB and negatively associated with the BMI and BMI z score. For each unit increase in in-degree centrality , the WCB increased by 0.153 (95%CI: 0.064 to 0.241), BMI decreased by 0.184 (95%CI: 0.072 to 0.290), and BMI z score decreased by 0.066 (95%CI: 0.024 to 0.107). In addition, children with higher in-degree centrality had a lower risk of overweight or obesity . And the network size was positively associated with BMI, but the association was no longer statistically significant after adjusting for parental educational level, parental nutritional status, etc. There were no statistically significant associations between the network density and WCB and adiposity indicators, nor between the network size and adiposity indicators See . We perfp = 0.030). The reduction in BMI of the children with higher out-degree centrality (above median) was also larger than those with lower out-degree centrality .The results of the linear mixed models showed that the baseline degree centrality was inversely associated with BMI and BMI z score at the end of the trial. Specifically, for each unit of increase in out-degree centrality at baseline , the BMI at the end of the trial decreased by 0.042 (95%CI: 0.014 to 0.071), and the BMI z score decreased by 0.018 (95%CI: 0.007 to 0.029) in children; for each unit of increase in in-degree centrality at baseline , the BMI at the end of the trial decreased by 0.047 (95%CI: 0.015 to 0.080), and the BMI z score decreased by 0.015 (95%CI: 0.003 to 0.028) in children See . The senThis study found that the children’s degree centrality in their peer network was associated with obesity-related cognition, behaviors and adiposity indicators. Those socially active children in the class tended to have healthy obesity-related cognition, behaviors and adiposity indicators. We also found that in-degree centrality was more closely associated with childhood obesity than out-degree centrality. This may be because a child’s in-degree centrality  could more accurately reflect the popularity of the child within the peer network compared with out-degree centrality. Furthermore, the degree centrality of the children was inversely associated with the adiposity indicators at the end of the trial. In addition, children with a higher degree centrality had a larger reduction in their BMI and BMI z score in the obesity prevention program, indicating that more popular or central children in the class benefited more from the prevention program.The degree centrality in this study reflected the social status of children in the social environment within the class, indicating the degree to which children were welcomed by their peers . This study found out the association between the degree centrality and cognition, behaviors and adiposity indicators in the children, which was consistent with previous observational findings. It was found that there was a significant difference in centrality between the overweight and normal-weight children, with overweight children receiving fewer friendship nominations and less popularity among peers, suggesting the social marginalization of overweight children ,19,20,21p = 0.044) [p < 0.05) [This study identified that the degree centrality played a role on childhood obesity, and found that children with a higher degree-centrality had lower BMI and BMI z score at the end of the trial, indicating that children who were connected to their peers in class benefited more from the obesity intervention. This was consistent with the results of the School-EduSalt trial, which found out that children with more friends tended to have a larger salt intake reduction  , and sug < 0.05) . Another < 0.05) . Further < 0.05) ,25. Howe < 0.05)  and the  < 0.05)  or posts < 0.05) ,27. Chil < 0.05) ,29,30,31 < 0.05) ,33.The strength of this study is that it is the first to explore the role of the peer network on childhood obesity, based on a well-designed randomized controlled trial that successfully decreased children’s BMI and BMI z score. We found that children with higher degrees of centrality had lower BMI or BMI z score at the end of the trial, which was consistent with findings in adult obesity intervention studies and other childhood behavioral interventions and observational studies. In addition, centrality was divided into in-degree and out-degree centrality based on peer network data, and it was revealed that in-degree centrality was more closely associated with children’s cognition, behaviors and adiposity indicators than out-degree centrality. For the association between the peer network and childhood obesity, the present prospective obesity intervention trial with a plausible temporal relationship overcame the possibility of reverse causality found in previous cross-sectional studies. And covariates such as the family support, socioeconomic status, self-efficacy, and class clustering effect were taken into account, making the results more reliable. However, the study had several limitations. First, the information about children’s obesity-related behaviors, including diet, exercise, and screen behaviors, were self-reported, and possibly affected by social desirability bias and recall bias. However, we found that the change in behavioral measures paralleled the changes in objective adiposity indicators, and the peer network indicators were inversely associated with adiposity indicators. Second, we identified friendships within the same classes, and lacked data of children’s other friends out of the class. Future studies are needed to identify a wider range of children’s friends to estimate children’s peer networks more comprehensively. Third, we explored the short-term association between the peer network and adiposity indicators. Future research could further explore the association between the peer network position in childhood and adulthood health outcomes. Lastly, although the research participants in the present study were from three socioeconomically distinct regions in China, from the eastern, central, and western parts, and included schools in rural and urban areas, the study’s applicability to larger populations and diverse cultural settings should also be considered with caution.This study found that children with more friends in their peer networks tended to have healthy cognition and behaviors, and lower BMI. In addition, children who were more active and popular in their peer networks tended to benefit more from the intervention program. Future childhood-obesity intervention research should pay more attention to socially inactive children and explore how to enhance obesity intervention effects among these children.


Furthermore, intention to leave the profession was more frequent among physicians with greater work alienation (p = 0.003). On the contrary, more empathic physicians never considered leaving their profession during the COVID-19 pandemic (p = 0.03). In physicians’ verbatim, cognitive empathy appeared associated to a positive change in doctor–patient relationships. On the contrary, having an overwhelming emotional empathy appeared associated to a negative change in doctor–patient relationships. These findings characterize differences in how physicians cope while working in the frontline of the pandemic.Approximately one out of ten COVID-19 cases in Ecuador was a physician. It has been reported that this situation has led to a serious detriment of physicians’ health and well-being. This study aimed to (i) identify predictors of emotional exhaustion, somatization, and work alienation in Ecuadorian physicians working with COVID-19 patients and (ii) explore the pandemic impact on doctor–patient relationships and on empathy. In 79 Ecuadorian physicians (45 women) who worked with COVID-19 patients, two separate multiple regression models explained the following: 73% of the variability of emotional exhaustion was based on somatization, work alienation, working sector, and passing through a symptomatic infection ( In clinical encounters, the definition of empathy is a group of abilities including communication skills and understanding of the views of the patients, together with their inner experiences and feelings, without getting intensely emotionally involved . Three mBefore the COVID-19 pandemic began in 2020, being a physician in a public institution in low-to-middle income countries (LMICs) implied working in harsh working environments usually with scarce or inadequately distributed resources and with work overload. In Latin America, where this situation has being reported in recent studies ,10,11, cLatin America and the Caribbean (LAC) are among those regions most affected by the COVID-19 pandemic worldwide . The COVFrom all healthcare professionals, those who are in the frontline attending and caring for COVID-19 patients were the ones who experienced the greater emotional overload and the ones more exposed to suffering its impact on their health and well-being at the workplace. This situation may negatively affect their professional performance. Some authors even suggest it as an important cause of physicians’ work abandonment or lack of compassionate care due to the emotional fatigue ,24. In this context, the main objectives of this study were: to characterize factors of influence on the variability of emotional exhaustion, somatization, and work alienation, three indicators of deterioration of physical and psychological occupational well-being, in physicians dedicated to attending COVID-19 patients during the pandemic; and to explore the pandemic impact on doctor–patient relationships and on medical empathy. For the first aim, two research objectives were set: (i) to measure symptoms associated with emotional exhaustion, somatization, and work alienation in a sample of Ecuadorian physicians working with COVID-19 patients; and (ii) to identify factors influencing the self-perception of the above-mentioned symptoms from socio-demographic, working, professional, and COVID-19 exposure variables. For the second aim, another two research objectives were established: (i) to compare the scores on medical empathy according to the above-mentioned variables; and (ii) to explore whether self-perceived changes in doctor–patient relationships are mainly associated with specific features of a cognitive or an emotional empathy. A sample of physicians, working in healthcare institutions of the Province of Pichincha (Ecuador) attending COVID-19 patients during the pandemic, were contacted and asked if they were willing to take part in this study. Healthcare workers from different areas as nursing, physicians not focusing their clinical work on COVID-19 patients, and physicians working with COVID-19 patients in other territories different than the Province of Pichincha were excluded from this study.The study design, which had been authorized by an independent ethical committee (Ref. CEImLAR-PI-252), was performed following the guidelines of the Declaration of Helsinki regarding studies with human subjects. All participants of the study signed a web-based informed consent. The sample was obtained from a database of medical graduates of the Faculty of Medicine at the University of Las Americas, Quito, Ecuador. Participation of respondents was anonymous and voluntary. Currently, because of my work, I feel:” followed by one specific symptom. Symptoms included in the SE are: work overload, emotional exhaustion, physical exhaustion, and mental saturation. The SS measures the following symptoms: digestive disorder, back pain, insomnia, headache, and muscle tension. Finally, the SA evaluates: bad mood, low personal fulfilment, depersonalized treatment, and frustration. The perception of each symptom is answered following a 7-point Likert-type scale reflecting a daily frequency in the last week from 1 (never) to 7 . The original version of the SCE was designed in Spanish language. This instrument has been initially tested in professionals from different disciplines in Spain and in Latin American countries, including Ecuador, showing good psychometric properties [Self-perception of somatization, emotional exhaustion and work alienation were used as main measures of indicators of deterioration of physical and psychological occupational well-being. The Scale of Collateral Effects (SCE) of the Questionnaire of General Labour Well-being was applied as measuring instrument . The SCEoperties . In physoperties ,10,11,26In addition, the Health Professional version of the Jefferson Scale of empathy (JSE-HP)  was usedQuestionnaires were accompanied with a form including socio-demographic variables , professional and working variables , and exposure to COVID-19 variables . Finally, information related to their doctor–patient relationship (DPR) during the pandemic was collected in a semi-structured form. In this form, respondents indicated: (i) the main DPR model they established in their daily work from the four proposed by Ezequiel and Linda Emanuel ; if, durOnly psychometric instruments with alpha coefficients equal to or higher than 0.70 were included in the analyses. Emotional exhaustion, somatization, work alienation, and empathy were used as dependent variables. After normality was assessed, using Pearson’s chi-squared and Lilliefors–Kolmogorov–Smirnov tests, Spearman’s correlation analyses were performed in order to determine statistical associations among emotional exhaustion, somatization, work alienation, empathy, age, and years of professional experience.r) was calculated applying the formula published by Fritz, Morris and Richler [r-value equal to 0.50 was categorized as a large effect size with a key practical importance; equal to 0.30 was categorized as a medium effect size with a moderate practical importance; and equal to 0.10 was categorized as a small effect size with a negligible practical importance. Finally, separate multiple linear regression analyses were completed using emotional exhaustion, somatization, and work alienation as dependent variables, while all the other variables with statistical significance in correlation and comparative analyses were used as potential predictors. A regression model was accepted only if conditions of statistical inference  were met. Effect sizes (Cohen’s-f2) were calculated by each model obtained. A value equal to or greater than 0.02 and smaller than 0.15 was interpreted as a small effect, equal to or greater than 0.15 and smaller than 0.35 as a medium effect, and equal to or greater than 0.35 as a large effect, following the interpretation criteria proposed by Cohen [For categorical variables, comparative analyses using non-parametric Mann–Whitney U-tests were completed. Effect size  years old, ranging between 24 and 66 years old. The average professional experience in the entire sample was 7 years (SD = 8), with a range between 1 and 30 years of medical practice. By specialty, 30 (43.5%) did not have a medical specialty , while the other 49 (56.5%) were distributed in 14 specialties including family medicine, emergency medicine, internal medicine, pneumology, anesthesiology, oncology, or geriatrics. The summary of the descriptive analysis by all variables collected is shown in From the 104 physicians who initially accepted to participate in the study, 79 (45 women) returned fully answered surveys and were included into the analysis. The average age of this sample was 35  and with work alienation  was confirmed. Neither empathy  or age  variables were correlated with exhaustion. However, both variables were closer to the statistical significance. Finally, no correlation was observed between exhaustion and years of professional experience . Mann–Whitney U-tests confirmed a greater exhaustion in physicians who worked in public institutions , suffered symptomatic COVID-19 infection , had the intention to leave their job , and had the intention to leave their profession . Based on these findings, a regression model was created  = 50.6; p < 0.001) with a large effect size (Cohen-f2 = 2.85). According to this model, greater somatization (p < 0.001), greater alienation (p < 0.001), working in a public health institution (p = 0.03), and having suffered a symptomatic infection with COVID-19 (p = 0.04) appeared as predictors of greater emotional exhaustion in Ecuadorian physicians , and a positive correlation with work alienation , were observed. Neither age  or years of professional experience  correlated with somatization. Mann–Whitney U-tests showed a greater somatization in female physicians , in physicians who considered leaving their job , and leaving their profession . Taking into account the findings described, a regression model using multiple linear regression analysis was created, as is presented in 2-adjusted = 0.56; F(1.74) = 50.2; p < 0.001) with a large effect size (Cohen-f2 = 1.36). According to it, women (p = 0.006) and having greater emotional exhaustion (p < 0.001) appeared as predictors of greater somatization in Ecuadorian physicians working with COVID-19 patients , age  nor years of professional experience  showed a correlation with this variable. A Mann–Whitney U-test showed a greater work alienation in physicians who had the intention to leave their profession , and to leave their job , and those who established a doctor–patient relationship different from a paternalistic one during the pandemic . These findings are represented in Finally, in the case of work alienation, neither empathy  with a small effect size (r = 0.25). This finding is shown in Regarding the second aim, exploring the pandemic impact on doctor–patient relationships and on medical empathy, comparative analyses based on Mann–Whitney U-tests showed differences in empathy only by the variable intention to leave the profession , and its components such as, empathic understanding , communication of understanding , or developing a more altruistic behavior , were mentioned in nine cases. In this group, seven physicians associated this change with others aspects not related to empathy . In the second group, six physicians described two specific features of sympathy—being more egoistic and self-centered  and having a greater affective distance —as the main negative change in their consultations with the patients. In this group, another two physicians associated this deterioration in their relationships with the patients with aspects not related neither to empathy or sympathy . The summary of the verbatim of all respondents is shown in Taking into account Cronbach’s alpha coefficients equal or higher than 0.70, generally recommended by guidelines, but also by the American Educational Research Association, all instruments showed adequate psychometric properties. . In studied sample, punctuations on emotional exhaustion, somatization, and work alienation were a little above those previously reported in two studies in healthcare professionals from Spain  as well Regarding the first aim of this study, the findings observed confirm that a greater somatization and a greater work alienation are two main predictors of emotional exhaustion. This is in agreement with results from a different study, also in Latin American healthcare professionals, in which exhaustion, somatization, and work alienation were positively correlated . FurtherRegarding the second aim of this study, the findings reported in this study confirm that less empathetic physicians are in higher risk of leaving their profession or at least of losing their professional motivation. This finding brings new experimental evidence supporting the important role that clinical empathy, as a core component of professionalism in medicine, plays in this matter . FurtherLimitations. The study included a heterogeneous group of physicians working in two very different environments: public and private institutions. In addition, not all questionnaires were fully answered, and the sample corresponded to one specific territory. However, taking into account that the province where this study was performed was one of the two provinces more affected by the pandemic, the authors considered that this limitation was not crucial, and the study sample reflected important aspects of the experiences lived. Another aspect is related to the fact that this study was conducted only with physicians, while other healthcare professionals (such as nurses) were not included. Therefore, its generalizability to other healthcare disciplines different from medicine is limited. However, the authors agree that even if there are some similarities, other important aspects related to the type of work make it necessary to perform a different study specifically focused on nurses and on other healthcare disciplines different from medicine.The results underline the key role of empathy in physicians’ health and welfare and the different roles that cognitive and emotional empathy played during the pandemic. Moreover, the results of this study offer new clues of elements that play as risk factors of greater emotional exhaustion, somatization, and work alienation in physicians who were working with COVID patients. These results also indicate the urgent need to introduce relevant modifications in the organizational culture of healthcare institutions and in the importance of enhancing empathic abilities from the early stages of the medical career with the objective of decreasing the negative effect that work overload has on the health and well-being of physicians.Future lines of research could focus on follow-up studies to confirm the findings reported in this study. Based on the above-mentioned findings, interventional studies focused on the reinforcement and development of emotional regulation, communication abilities, and key mental-processing mechanisms (such as understanding patients’ needs and concerns) could offer a valuable tool not only in the acquisition of greater empathy with the patients but also in the reduction in burnout. In addition, further observational studies with bigger samples could also provide more evidence for clarifying the exact role that clinical empathy plays in this matter, either as a direct factor of influence in the reduction in work distress or as a moderator or mediator of other variables of influence directly involved. Furthermore, the findings reported in this study highlight the urgent need to introduce important changes in the organizational culture of healthcare institutions, especially in those that carried intense clinical work during the pandemic. One area appears as a potential target in future studies: the measurement of the positive impact that the reinforcement of services centered on psychological and occupational support has in the occupational well-being of healthcare professionals more exposed to emotional exhaustion. In this matter, developing targeted training programs focused on the acquisition and enhancement of cognitive aspects related to clinical empathy appears to be a valuable alternative.


Without capnography, on average, 7.5 events of the primary endpoint were observed per 100 procedures and 2.9 with additional capnography monitoring (p < 0.001). A significant reduction was observed for mild oxygen desaturation, with a resulting odds ratio of 0.25 (95% CI 0.14 to 0.46). ASA I patients had the highest difference in combined incidence of any oxygen desaturation of 5.85% in the pre-capnography group and 0.64% in the post-capnography group. Although procedural sedation using propofol is not associated with severe adverse events, the incidence of composite adverse events could be reduced with the addition of capnography monitoring.Endoscopic procedures are routinely applied to cancer screening programs and surveillance. The preferred technique is usually deep sedation with propofol being a convenient agent allowing for a quicker patient recovery while maintaining a similar safety profile compared to traditional agents. However, adverse events, including respiratory depression and consequent undesirable cardiovascular side effects, may occur. The goal of this work is to evaluate the patient safety impact of adding capnography during endoscopic procedures under deep propofol sedation. Data were retrospectively collected from patients undergoing deep, procedural sedation for gastrointestinal (GI) endoscopy in October 2019 to January 2021 in a single Turkish university hospital. Included in the analysis were all adult patients classified by the American Society of Anesthesiologists (ASA) as I–IV, who were scheduled for GI endoscopy utilizing propofol alone or in combination. Data on 1840 patients were collected, of whom 1610 (730 pre- and 880 post-capnography implemention) met inclusion criteria. The primary outcome was a change in the composite incidence of mild oxygen desaturation (SpO Endoscopic procedures are a common part of modern health care provisioning and prevention of death related with gastrointestinal (GI) system cancers. The most prominent use of endoscopy is the utilization for gastric and colorectal cancer (CRC) screening, with both forms of cancer being associated with a high death toll . A surveTo facilitate endoscopic procedures and increase patient comfort, the patients are generally placed under procedural sedation . In a 20An important factor for procedural sedation is the chosen sedative, which should be informed by evidence as well as the target sedation level. Compared to the traditional agents of midazolam and diazepam, propofol was found to be associated with a shorter procedure duration and an earlier discharge time ,11, as wTo prevent cardiopulmonary events, non-invasive procedures, such as blood pressure (NIBP), electrocardiogram (ECG), pulse oximetry, and recently, capnography, are recommended to be utilized for patient surveillance ,6,7,8,9.Given the inconclusive evidence of propofol safety and a lack of solid evidence on the utilization of capnography in the Turkish setting this investigation was aimed to quantify the safety and impact of additional capnography monitoring during endoscopic procedures under deep sedation.Data were collected from all therapeutic and diagnostic endoscopy procedures. The evaluation took place between October 2019 and January 2021 in a large Turkish university hospital. Inclusion criteria were all adults scheduled for an endoscopy procedure with procedural sedation utilizing propofol and who had a risk classification of American Society of Anesthesiologists (ASA I–IV). This evaluation was designed as pre-capnography and post-capnography implementation. We used portable respiratory monitor . In our unit, anesthesiologists and anesthesia technicians monitored the capnography during the procedure. We used the Richmond Agitation Sedation Scale, and we applied propofol dose titration by targeting the −3, −4 level in the RASS score. No changes were made in the pre- and post-capnography implementation periods in terms of equipment used, unit staff, etc.The data collected were selected after the literature review and expert opinion, focusing on respiratory compromise, cardiac events, and severe outcomes, as well as risk factors for the occurrence of such events. The identified parameters were programmed into an offline, Microsoft Excel™-based data collection tool, which was deployed on site. The data were first collected on paper forms and then entered into the tool by a Turkey-based independent third party (EBM research services). No patient identifiers were collected, and all data entered were secured using password protection.This study was approved by the Ethics Committee of Ankara University .Collected parameters were split between three different sections: periprocedural data, adverse events, and interventions. Selected options for periprocedural data were procedure duration, date, procedure type , sedativ2 75–90%) for <60 s, severe oxygen desaturation (SpO2 < 75% at any time or <90% for >60 s), prolonged apnea (>60 s), airway obstruction, bradycardia (<60 bpm), tachycardia (>25% from baseline), cardiovascular shock/collapse, cardiac arrest/absent pulse, and other, together with patient death. Included interventions were chest compressions, tracheal intubation, oral/nasal airway, bag-valve mask/assisted ventilation, laryngeal mask airway, continuous positive airway pressure (CPAP), use of reversal agents, and other. The events, interventions, and event definitions were taken from Mason et al. [The included adverse events were oxygen desaturation , as well as outcomes not covered by the app: time until meaningful patient response to verbal stımuli and time until patient standing/mobile. On site and in the app, data were collected on the occurrence of an adverse event and the use of interventions. As no patient identifiers were stored, it was not possible to cross-reference onsite and app data one-to-one. These data are, therefore, assessed separately.2 75–90% for <60 s), severe oxygen desaturation (SpO2 > 75% anytime or <90% for >60 s), bradycardia (<60 bpm), and tachycardia (>25% from baseline). A 20% reduction in the incidence of the primary outcome was set as the threshold for a meaningful difference in the event occurrence. The patient population required was calculated based on a 20% alpha and 5% beta error, leading to an estimated patient count of 666 for each pre- and post-capnography implementation group.To conclude any clinical significance of the findings, the potential adverse events were combined as a primary outcome. The events were defined after the literature review and expert opinion, identifying those events that are preventable using both pulse oximetry and capnography. This composite outcome included: oxygen desaturation , BMI , ASA level , gender (binary), adverse event occurrence (binary), and capnography (binary) to predict the duration. Linear regression analysis was performed in RStudio   relative reduction of 60.79% was observed in the capnography group.In total, 55 adverse events relevant to the primary outcome were recorded during the 730 non-capnography procedures and 26 after in the 880 procedures with capnography, corresponding with 7.5 and 2.9 events per 100 procedures, respectively . The mosp-value is supplied.Considering patient risk groups, except for bradycardia and the severe oxygen desaturations observed in ASA II patients, all incidences were reduced. The highest difference was observed in ASA I patients, where the combined incidence of the primary outcome was 5.85% during the baseline and 0.64% after the addition of capnography. Given that the patient count is not sufficient to conclude significance, no Except for bradycardia, the events comprising the primary outcome were reduced after the implementation of capnography. For moderate oxygen desaturations, this difference was even estimated to be significant, with an associated odds ratio of 0.25 (95% CI 0.14 to 0.46) . The othThe procedures took on average 20.14 min in the capnography group and 21.87 min in the non-capnography group . CompariConsidering the procedure duration by risk classification and event occurrence, the highest difference between the non-capnography and the capnography group is evident for patients classified as ASA 1 . Here, wAfter the procedure, the time until a meaningful response was 4.34 and 5.38 min in the capnography and non-capnography group, respectively. For time to standing, however, the trend was reversed, with the average time to standing in the non-capnography group being almost 15 min less. A potential confounding parameter could be the change in protocol due to the onset of the SARS-CoV-19 pandemic, which escalated only at the end of the non-capnography group data collection and affected the capnography group in full.p < 0.001) of a statistical relationship between risk factors and procedure duration. The model intercept was calculated at 21.70 min for a male patient younger than 20, who was classified as ASA 1, with a BMI of 26, no adverse event, and no capnography used. Of the parameters included in the model, it was estimated that capnography (a reduction in procedure length by 2.50 min), age >90 years (an increase in procedure length by 9.47 min), and the occurrence of an adverse event (an increase in procedure length by 3.49 min) are significant predictors (p ≤ 0.05) of a change in the procedure duration. For ASA, while not reaching significance, the procedure duration increased with higher classification. The BMI had only a low impact on the procedure length, with every point increase decreasing the procedure length by 3 min and vice versa for each point of decreased BMI.To adjust for the individual risk factors contributing to the procedure duration, a multivariable linear model was created from the used dataset . The modOverall, only two pre-defined interventions, one tracheal intubation and one utilization of reversal agents, each accounting for 0.13% of the baseline procedures, were recorded during the data collection. The vast majority of interventions were classified as other and utilized in 5.06% and 1.93% of non-capnography and capnography procedures, respectively. Other interventions include minor interventions, like oxygen augmentation, head repositioning, and tactile and verbal stimuli to the patient. The utilization of these interventions was in all cases linked to adverse events occurring during the procedure.The addition of the capnography monitor for procedures utilizing procedural sedation with propofol showed significantly increased patient safety in regard to the events as defined in the primary outcome. This was predominantly due to the significant reduction in the odds of a mild oxygen desaturation event . Compared to other Turkish cohorts, the number of events observed during the baseline is lower than in previous publications reporting on propofol sedation. Gurbulak et al. compared the safety of propofol with midazolam/medperidine sedation for colonoscopic procedures, reporting an 8.1% incidence of oxygen desaturation events in the propofol arm . In our A similar study by Uzman et al. compared the same sedation regimen for upper gastrointestinal endoscopy . Here, 7In our finding, patients classified as ASA 1 benefited the most from capnography monitoring, which was surprising given that patients with this classification are generally considered to have an absence of notable risk factors for respiratory complications. The finding, though, is in line with the observations of Vargo et al. and was also observed in a similar evaluation published by Bisschops et al. ,19. ThisA large retrospective chart review of a single hospital by Kilic et al. also investigated the safety of sedation during ERCP but additionally considered endoscopic ultrasound while administering ketamine–propofol as a sedative agent . The maiConsidering the efficacy of capnography monitoring, the results of this work show similar tendencies as the meta-analysis by Saunders et al. . For modGiven the magnitude in the difference in event occurrence between the baseline and the capnography arm, aside from the patient safety benefit, the hospital resource burden could also be reduced. In a payer survey from 2018 for France, Germany, Italy, the UK, and the US, oxygen desaturations were associated with an average cost between USD 53 and USD 130 depending on the severity and country .For the sake of patient satisfaction, a better control over the procedure may also increase the adherence to endoscopy screening protocols, given that skill and adequacy of discomfort were high ranking across patient expectations on endoscopy procedures . In TurkDuring propofol sedation, the addition of a capnography monitor can significantly reduce the occurrence of the events of respiratory compromise as defined within the primary outcome of this investigation. After the implementation of capnography, both a reduction in adverse events as well as a reduction in interventions were recorded, showing the potential to reduce the burden of the procedure to the clinicians.This work was designed as a retrospective cohort evaluation and is, therefore, not comparable with a full study. The patients were not randomized, and healthcare staff were not blinded to the monitor. In addition, due to training on the usage of the capnography monitor, it cannot be excluded that awareness for respiratory compromise was increased. This work was also conducted during the onset of the SARS-CoV-2 pandemic; therefore, a potential bias due to patient selection and recording is possible, but this could not be foreseen and accounted for by the design.Additionally, the high sample size of 1610 patients and the resulting large dataset shows promising results on the utilization of capnography during propofol-based sedation.RTT is an employee of and RS is the owner of Coreva Scientific GmbH & Co KG, which received consultancy fees for performing, analyzing, and communicating the work presented here.


Differential diagnosis of primary intestinal lymphoma (PIL) and Crohn’s disease (CD) is a challenge in clinical diagnosis.To investigate the validity of the nomogram based on clinical and computed tomography (CT) features to identify PIL and CD.This study retrospectively analyzed laboratory parameters, demographic characteristics, clinical manifestations, and CT imaging features of PIL and CD patients from two centers. Univariate logistic analysis was performed for each variable, and laboratory parameter model, clinical model and imaging features model were developed separately. Finally, a nomogram was established. All models were evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA).This study collected data from 121 patients  from Center 1. Data from 43 patients  were collected at Center 2 as an external validation cohort to validate the robustness of the model. Three models and a nomogram were developed to distinguish PIL from CD. Most models performed well from the external validation cohort. The nomogram showed the best performance with an AUC of 0.921 (95% CI: 0.838–1.000) and sensitivities, specificities, and accuracies of 0.945, 0.792, and 0.860, respectively.A nomogram combining clinical data and imaging features was constructed, which can effectively distinguish PIL from CD. PrimaryCrohn’s disease (CD) is an idiopathic inflammatory disease with a slow course, often alternating between relapses and remissions, influenced by genetic, immunological and environmental factors , 5. The It is noteworthy that the treatment of PIL and CD is completely different. PIL is mainly treated with surgery or chemotherapy , 8. HoweTherefore, we retrospectively collected data on clinical features, laboratory parameters, and radiological characteristics of PIL and CD patients. The aim is to develop an effective and simple diagnostic model that would be helpful in differentiating PIL from CD.2.2.1.We searched medical records for 264 patients diagnosed with PIL and CD from January 2011 to December 2022 at the Shandong provincial hospital affiliated to Shandong First Medical University (Center 1). Finally, 121 patients who met the inclusion criteria  were included. We collected 43 patients approved by Qilu Hospital of Shandong University (Center 2) from June 2015 to August 2022. Due to the retrospective nature of this study, the requirement of informed consent was waived. 2.2.Patients with PIL were included according to the Dawson criteria . Patient2.3.Clinical information about the patients including demographic characteristics, laboratory parameters, and clinical manifestations and imaging features was obtained from the electronic medical record.Demographic parameters included gender and age of onset.Laboratory data were recorded as follows: hemoglobin level, platelet count, albumin level, lymphocyte absolute value, neutrophil cell absolute value, eosinophil absolute value, and C-reactive protein (CRP) level.Clinical manifestations include time from onset to diagnosis, abdominal pain, diarrhea, bloating, bloody stools, fever, increased frequency of stools, abdominal mass, tenesmus, and weight loss.All included patients had undergone at least one CT examination and were evaluated by two experienced radiologists. The CT features include intestinal wall thickness, intestinal stenosis, aneurysmal dilatation, enlargement of the abdominal lymph nodes, the enhanced density of the peri-intestinal fat, “comb sign,” the degree and mode of enhancement after enhancement scan, and CT values in each phase . Considehttp://www.itksnap.org)  . The reg2.4.p < 0.05 was considered a statistically significant difference. Missing values were all less than 20%, and missing values were filled using multiple interpolations. Predictive mean matching (PMM) was chosen to interpolate the data 20 times, and then the interpolated results were split and summarized to generate the final dataset.Several scales were designed to analyze information on patients’ demographic, clinical, laboratory, and imaging characteristics. Patients in the PIL and CD group were analyzed using IBM SPSS Statistics (RRID:SCR_019096)  and GraphPad Prism (RRID:SCR_002798) . Continuous variables that were normally distributed were expressed as mean ± SD, otherwise median (upper and lower quartiles) was used. Categorical variables were expressed as frequencies and percentage values. All statistical tests were two-sided. p < 0.05 and AUC ≥0.6 (rounded) were integrated, and the laboratory parameters model, clinical model combining demographic data with clinical symptoms, and imaging features model, respectively, were developed using R Project for Statistical Computing (RRID:SCR_001905) . In addition, covariate diagnostics were performed for each model’s variables. Finally, the indicators with p < 0.05 in the three models were integrated and the nomogram was plotted.First, univariate logistic analysis was performed for each variable. Then, parameters with In addition, receiver operating characteristic (ROC) curves were plotted to assess the discrimination. The DeLong test was used to compare the AUC between models. The Hosmer–Lemeshow goodness-of-fit test was used to determine the goodness-of-fit of the nomogram and calibration curves were plotted to assess the agreement between the predicted and actual results. Sensitivity, specificity, and accuracy were calculated to assess the performance of all models. Finally, clinical decision curves (DCA) were plotted to understand patient benefits. Patients from Center 2 served as an external validation set to demonstrate the robustness of the model. The characteristics of the two centers are shown in 3.3.1.A total of 121 patients from Center 1 (52 CD and 69 PIL) and 43 patients from Center 2 (19 CD and 24 PIL) were included in this study.3.2.p < 0.05).No significant difference was found between PIL and CD patients in terms of gender. However, the age of onset in patients with the PIL group was significantly higher than those in the CD group . In contrast, the incidence of abdominal masses was significantly higher in the PIL group than in the CD (p < 0.05). One-way logistic regression analysis of demographic characteristics of primary intestinal lymphoma and Crohn’s disease are presented in In terms of clinical presentation, the time from onset to diagnosis was significantly longer in the CD group than in the PIL group . On enhancement scans, PIL more often showed homogeneous, mild enhancement, whereas CD tended to have moderate, stratified, or mucosal enhancement (p < 0.05). In addition, the CT values of lesions in the arterial and venous phases were higher in patients with CD compared to PIL (p < 0.05). One-way logistic regression analysis of computed tomography imaging features of primary intestinal lymphoma and Crohn’s disease are listed in CT examination showed that aneurysmal dilatation of the lesion area and enlarged abdominal lymph nodes were more common in the PIL group than in the CD group  but had statistical significance with other models (p < 0.05). The nomogram fit well in the training cohort (p = 0.148) and the external validation cohort (p = 0.660) according to the Hosmer–Lemeshow goodness-of-fit test. The nomogram shows good agreement in the calibration curves of both cohorts . In addition, although the nomogram had a slightly higher AUC than the imaging features model, the difference was not statistically significant (p > 0.05). This indicates that the discrimination efficiency of the imaging features model is not lower than that of the nomogram. However, the indicators of the imaging features model are complex and easily influenced by subjective factors. In contrast, the nomogram includes only five indicators, which are simple and easily accessible. Therefore, we believe that the nomogram can simplify the indicators and improve diagnostic efficiency while maintaining high diagnostic efficacy.Since there were no Compared with Zhang’s study, our nomogram did not incorporate endoscopic features, which may explain the slightly lower AUC of the model. However, the AUC of our nomogram was slightly higher than Yang’s study and had higher sensitivity and accuracy. More importantly, compared to Zhang’s differential model that incorporated nineteen variables, we used five features to obtain a model with similar efficacy, simplifying the process of differential diagnosis. In addition, unlike the calibration curves plotted by Yang et al., we used the Bootstrap method to plot calibration curves after 1,000 sampling of data from Center 1, and the results showed that the calibrated nomogram still had good consistency and stability. Also, in this study, the patient benefit of each model was examined by DCA curves, and the study showed that the nomogram had the highest net benefit of all models in most of the threshold ranges in both cohorts. Finally, our study added data from Center 2 as an external validation cohort to comprehensively evaluate the performance of the nomogram. In the external validation cohort, the nomogram had a sensitivity of 0.945, accuracy and specificity of 0.86 and 0.79, respectively, and an area under the ROC curve of 0.921. The nomogram in this study contains simple and easily accessible indicators and features that may provide greater diagnostic value for rassroots hospital.In clinical practice, PIL and CD may lead to misjudgment by clinicians due to similar clinical symptoms and endoscopic presentation. Kammal et al.  publisheThis study has some limitations. Firstly, the sample size of the study was small, which may be related to the low prevalence of PIL and CD. The nomogram we developed needs further validation and modification. Secondly, this is a retrospective study, and the missing data and the different scanning machines maybe affect the accuracy of the model. In addition, our collection of PIL patients included multiple pathological types. In the future, we look forward to exploring the differences between different subtypes of PIL and CD.In conclusion, we explored a nomogram based on clinical data and CT images to easily and effectively distinguish PIL from CD. It is expected to provide valuable clues for clinical diagnosis and treatment.The original contributions presented in the study are included in the article/The studies involving humans were approved by The Ethics Committee of Shandong Provincial Hospital Affiliated to Shandong First Medical University, China. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.HW was the guarantor. MX was involved in statistical analysis, data collection, and entry. MX, JT, HL, CQ, YM, and HW were involved in study conduct, manuscript draft, and revision. All authors contributed to the article and approved the submitted version.This study was supported by Key R&D Program of Shandong Province, China (2021SFGC0104).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.


Obesity is classified as a low-grade chronic and systemic inflammatory disease and results from complicated interactions between genes and environmental factors, which leads to many diseases and affects the quality of life. There are growing interests in the effectiveness of probiotics as a supplementation to reduce obesity through regulating microbiota host metabolism. Probiotics may influence the interplays among gut, brain, adipose, and liver in a way leading to weight. Since limited studies have been conducted on human subjects, more investigation is needed in this field. Therefore, this study sheds light on the investigation of the anti-obesity effect of probiotic supplementation.Ninety adult Saudi overweight or obese adult will be enrolled in this clinical trial and randomized to receive daily placebo or probiotics “MCP® BCMC® strains” for 12 weeks in a double-blind study. Biochemical markers will be measured through blood samples analyzed. Measurements and samples will be obtained at baseline and by the end of the study, at 12 weeks of treatment.This study expects that the multi-strain probiotic product will induce beneficial changes in gut microbiota (GM) including reduction in weight, especially the visceral fat, which leads to reduction in systemic inflammatory state associated with fat accumulation. At the local level, the latest rapid changes in the pattern of Saudi diet have led to exacerbating obesity and overweight to reach 28.7%, 30.7%, respectively, from 15 years and older. Moreover, according to a cross-sectional study has been conducted among school children aged 6 to 16 years in Riyadh city, the overall prevalence of overweight was 13.4% (14.2% for girls and 12% for boys) while obesity was 18.2% (18% for girls and 18.4% for boys). Adult obesity is a major public health problem in Saudi Arabia, if the observed trends continue raising, the prevalence is expected to increase considerably over the next decade from 12% in 1992 to 41% by 2022 in men, and from 21% to 78% in women. Another study has been performed in Jeddah city including 1419 individuals (667 men and 752 women). The spreading of overweight was 35.1 in men and 30.1% in woman, while obesity was 34.8% in men, and 35.6% in women.The incidence of obesity has increased to be a major health concern in the twenty-first century. According to the World Health Organization, 39% of adults ages 18 years and over were overweight in 2016, and 13% were obese. Generally, the human gut has several types of microbes, involving bacteria, archaea, fungi, eukaryotes, and viruses. All these combined organisms compose gut microbiota (GM), which their genes are known as microbiomes. The diversity of the microbiome is affected by many factors such as diet, environment, genetic background, and early microbial exposure. However, in a healthy individual, bacteria are dominant in GM, specifically of the phyla Bacteroidetes and Firmicutes. However, In obese individuals, Turnbaugh et al noticed a significant reduction of GM diversity, also there was a clear lower ratio of Bacteroidetes and a higher ratio of Actinobacteria and Firmicutes in comparison with lean, this alteration is noticed also in both animal and human studies.Confirmed data in humans have shown an association between obesity and the gut microbial community structure.Based on the above, it is evident that a deep understanding of the GM functions helps to find appropriate solutions to control the increasing weight. In animal studies, physical and biochemical parameters, metabolic and inflammatory markers, and alterations in GM diversity revealed beneficial results against obesity whereas the results in humans are still rare. Alteration of the GM via applying natural or supplementation probiotic is considered as a new and promising therapeutic intervention treating human obesity. The present clinical trial aims to study the anti-obesity effects of multi-strain probiotic supplementation on overweight and obese adults.Study the anti-obesity effect of consuming multi-strain probiotic supplementation on overweight and obese adults.Evaluate the anti-obesity effect of multi-Strain probiotic supplementation.Investigate the effect of probiotics on reducing Lipopolysaccharides LPS.Determine the effect of probiotics on liver functions.Study the association between probiotics and food intake.Evaluate the role of gender difference in response to probiotic.2 and relatively stable body weight in the previous 3 months of the trial.The study is a 12-week, a single center, double-blind, placebo-controlled, randomized, trial to be conducted at the occupational clinical of King Saud University Medical City, Riyadh, Saudi Arabia. Recruitment of subjects will be opened for all students and employees who from King Saud University. The study link will be sent to their email, also they can scan the study code from the banners which are in the active areas at the hospital. A total of 90 overweight or first-class obese subjects, males and females will be recruited and divided equally into 2 groups, a probiotics group, and a placebo group. To be included, a participant should be an adult male or females aged between 19 to 40 years with body mass index (BMI) from 25 to 35 kg/mSubjects who suffer from diseases and are on treatment, such as immune system diseases or thyroid disorders, pregnant women or who plan to be pregnant, those who had gastrointestinal surgery, hormone replacement therapy, on antibiotics and those who consume probiotic or prebiotic supplements regularly will be excluded. Figure The study has been approved by the Institutional Review Board of the College of Medicine in King Saud University (IRB E-20-5503). The study has also been registered in the Saudi Food and Drug Administration registry (SCTR number 21070702) as well as the US clinical trials registry .9 CFU/gram): Lactobacillus acidophilus BCMC®12130, Lactobacillus casei subsp BCMC®12313, Lactobacillus lactis BCMC®12451, Bifidobacterium bifidum BCMC®02290, Bifidobacterium infantis BCMC®02129, and Bifidobacterium longum BCMC®02120. Subjects in the placebo group will receive the placebo consisting of only the carrier of the probiotic product, that is maize starch and maltodextrins.The study is a 12-week, double-blind, randomized, placebo-controlled clinical trial, 90 overweight or first-class obese subjects will be divided equally into 2 groups, a probiotics group, and a placebo group. A hypocaloric diet will be applied for both groups. Interventions will be performed in the first week and then continue to the twelfth week, in which all subjects will receive 3 boxes of either probiotics or placebo, all boxes are identical in color, weight, and shape. All subjects will be instructed to consume 2 sachets per day, first sachet is before the first meal by 10 minutes, and the other one before the last meal by ten minutes. For probiotics sachet, it contains granular powder with 6 microorganism strains  measurement from baseline to end-of-treatment. Other obesity-related outcomes included body weight, WC, and hip circumference. The anthropometrics included:Height will be measured using stadiometer to the nearest 0.5 cm. Subjects should remove shoes, any things cover their head, any cloths that may make it difficult to stand flat against the wall. Standing should be straight, on feet flat on the floor with keeping head, shoulders, and buttocks are touching the wall. The line of sight and chin should be parallel to the floor.Body weight will be recorded before breakfast, using a calibrated column scale to the nearest 0.1 kgWC will be measured at the approximate midpoint between the lower margin of the last palpable rib and the top of the iliac crest to the nearest 0.5 cm. To get an accurate waist size, the WC should be taken in a normal respiration situation, and after overnight fasting to reduce stomach contents which may affect the measurement.Hip circumference will be taken around the widest portion of the buttocks, with the tape parallel to the floor. Measuring waist and hip will be obtained by using 150 cm anthropometric measuring stretch-resistant tape ended with steel Hook. The tape should be held snugly around the body, but not in the constricted way. The subject should stand with closed feet, opened arms, with evenly distributing body weight, and should wear little clothing. Also, the subject should be relaxed to avoid any changing in the tension of the abdominal wall. To insure obtain right measuring, measurement should be repeated twice; if the difference is only 1 cm, the average should be counted. If the difference is more than 1 cm, the measurement should be repeated.Physical activity will be assessed at the beginning and the end of the study using the International Physical Activity Questionnaire . Table The identity of probiotic and control treatments will not be known to investigators, research staff, or subjects. The following study procedures will be in place to ensure double-blind administration of study treatments:•Access to the randomization code will be strictly controlled.•Packaging and labeling of test and control treatments were coded, but shape, color, smell, and weight will be identical.•Due to necessity of evaluate the activity of treatment product, both packages (with different letters) will be sent to microbiology lab to determine the activity of strains, the result will be blind without mention to any letter.Unblinding will be done on completion of the clinical study. During the study, the blind may be broken only in emergencies when knowledge of the subject treatment group is necessary for further subject management. In this case, the investigator should discuss the emergency with the Medical Monitor prior to unblinding. The principal investigator (PI) has a direct number-mobile to contact with the company personnel in charge of blinding. The product will be provided in sachets packed and coded as numbers. All subjects will be allocated in blocks according to their gender, age, and BMI. From those blocks, PI will make a list of pairs, each pair has 2 subjects. This list will be sent later to the collaborative members from inpatient pharmacy, who will allocate subjects (1:1) in 2 different groups. The randomization scheme will be computer generated using Excel sheet. After completing the intervention, a request letter will be sent to the company to unblind the study. Then the company lab will send an unblinding letter to inform us which alphabet is probiotic or placebo.The roles of members in allocation process:First: PI:Give a unique number for each subject.Sign subjects in blocks according to their gender, age, and BMI.Make a list of pairs and send it the inpatient pharmacy.Second: Members from inpatient pharmacy:Allocate subjects (1:1) in 2 different groups.Make the randomization scheme by computer generated via Excel sheet. In this study, to avoid any later drop-off, both groups will be set to have 45 subjects. Data will be analyzed using SPSS . Categorical data will be presented as frequencies and percentages. t tests and Mann–Whitney tests will be used to determine significant differences between groups at baseline. Also, a mixed-method analysis of covariance and a chi-square test will be used to determine within and between-group differences in some parameters. Intervention effects will be presented with a 95% confidence interval. A P value < .05 will be considered statistically significant.The power calculation was based on the results of Gomes and his colleagues who used the G-Power software (version 3.0.10) to calculate the sample size. The primary outcome in that study is the difference in WC between groups. The power calculation requires 17 subjects in each group  to detect a difference in WC.There is a growing interest in the effectiveness of probiotics for treating chronic diseases. This study will evaluate the effects of multi-strain probiotics as an anti-obesity among overweight and obese Saudi adults. Regulating microbiota is considered a potential therapeutic avenue for obesity through many different mechanisms.,12 Although anthropometric measures were obtained in those trials, no discernible findings were observed over time. The present study aims to focus on the effects of multi-strain probiotics on obese participants without diabetes. Serum analysis of biochemical parameters will be done routinely as done in previous studies.,14 Unexpected results will generate new ideas such as adjusting the concentration of strains or looking for new ones. The outcome of this study will be published and will be submitted to the conferences. All participants will receive their results.The strains which have been used in this study are expected to beneficial effect on weight loss, however, any other results will still be interesting. Previous clinical trials in Saudi Arabia yielded positive results on the effects of multi-strain probiotics among patients with diabetes, specifically favorable outcomes in terms of improving insulin sensitivity.The authors acknowledge the research coordinators who are currently supporting the conduct of this trial.Conceptualization: Samira M. Almalki.Data curation: Mariam Eid Al-Juhani.Investigation: Samira M. Almalki, Nasser M. Al-Daghri, Hanan A. Alfawaz.Methodology: Samira M. Almalki.Resources: Mariam Eid Al-Juhani.Supervision: Nasser M. Al-Daghri, Hanan A. Alfawaz.Writing – original draft: Samira M. Almalki.Writing – review & editing: Nasser M. Al-Daghri, Mariam Eid Al-Juhani, Hanan A. Alfawaz.