In psychiatry, recent years have seen a change of focus from a clinician- to a patient-centered perspective that emphasizes quality of life as a treatment target. As a complex construct, quality of life is composed of multiple dimensions that interact with one-another . Here, we used data from the REHABase cohort, which includes N = 2180 patients from 15 psychosocial rehabilitation centers in France, to explore networks of quality-of-life dimensions among six psychiatric disorders: schizophrenia, neurodevelopmental, bipolar, depressive, anxiety, and personality disorders. Stronger connections (edges) involved the Self-Esteem dimension, such as Self-Esteem–Physical Well-Being, Self-Esteem–Autonomy, Self-Esteem–Psychological Well-Being, and Self-Esteem–Resilience. Self-esteem was also consistently retrieved as the most central node (the dimension with the most connections within each network). Between-group tests did not reveal any differences regarding network structure, overall connectivity, edge-weights, and nodes’ centrality. Despite presenting with different symptom profiles, various psychiatric disorders may demonstrate similar inter-relationships among quality-of-life dimensions. In particular, self-esteem may have a crucial inter-connecting role in patients’ quality of life. Our findings could support treatment programmes that specifically target self-esteem to improve patients’ quality of life in a cost-effective way. These analyses however are relatively restrictive because, as clinical and scientific observations have demonstrated, quality of life is not solely the product of mental symptoms, but also likely impacts clinical and socio-demographic characteristics14. Moreover, quality of life is itself a complex construct made of multiple dimensions, which encompass physical and emotional well-being, social functioning and relationships, resilience, autonomy, and self-esteem15. Crucially, these dimensions interact with each other16. For instance, a patient with depression might theoretically improve his/her psychological well-being if he/she places strong emphasis on his/her physical well-being, via the restoration of his/her self-esteem.To date, health services research on quality of life has mostly focused on how socio-demographic and clinical factors, including the duration and intensity of symptoms, would best predict the quality of life of those with psychiatric disordersA full shift towards a patient-centered outcomes perspective could be enabled by understanding how quality-of-life dimensions interact; whether their connections with one-another are strong; which dimensions are specifically stronger; and whether some dimensions are overall more connected than others. Such information could be of high significance if, for instance, one dimension was to be retrieved at the core of a system of quality-of-life dimensions: one would hypothesize that focussing treatment on this specific dimension could substantially improve patients’ quality of life and functioning in a cost-effective way. Clearly, this requires another change of gears, this time from a research perspective, towards systems rather than pure analytical science. Unfortunately however, such knowledge is currently lacking.17. This conceptualization usually allows a more realistic and practical description of psychological suffering thereby reframed under the terminology of “psychological networks”19. Psychological networks analysis usually involves estimating a statistical model on data, which is a weighted network representing strengths of relationships between dimensions of interest. Subsequently, one would be interested in running statistical tests on these networks (within- and between-networks), for instance on their global structure, global and local connection strengths, but also by pointing out which dimensions are core to the systems of interactions.In fact, these insights match well with a recent conceptualization of psychological problems as being the result of networks of interacting components  rather than an underlying entity 20, which has been previously used in observational descriptive and associational studies on mental health services research26. The database contains observations across multiple psychosocial rehabilitation centres in France and presents the notable advantage of being both large and real-life.In the current study, we aimed at using these techniques to explore networks of quality-of-life dimensions among various psychiatric disorders, specifically: schizophrenia, neurodevelopmental, bipolar, depressive, anxiety, and personality disorders. We were interested in gathering information on the global structure of each disorder’s network, as well as its overall connectivity, the strength of each connection between two dimensions, and the relative importance of each dimension. We extracted data from the REHABase cohort database20. Clinically stabilized patients are referred to the centers by public mental health services, private psychiatrists, general practitioners, or are self-referred. They undergo a standardized clinical, functional and cognitive evaluation performed by a multidisciplinary team  in order to subsequently benefit from a personalized rehabilitation care plan over a 1-year period . A standardized electronic case report form is used to collect demographic, clinical, functioning, and cognitive data. Regular group meetings are held to monitor quality control and ensure good inter-rater reliability20.Our dataset consists of patients enrolled in the REHABase cohort. Briefly, the cohort includes patients with serious mental illness referred to 15 centers of a French psychosocial rehabilitation network—described in27. Note that we also performed similar analysis without any diagnosis restrictions.Our analysis was restricted to patients included in the REHABase cohort from January 2016 to September 2021. For our main analysis, each patient presented with a DSM-5 diagnosis of schizophrenia spectrum disorder; autism spectrum disorder; bipolar disorder; depressive disorder; anxiety disorder; post-traumatic stress disorder; obsessive compulsive disorder (the latter three disorders were further regrouped under the umbrella of anxiety disorder); or personality disorder, based upon clinical interview performed by a psychiatrist28, a self-administered questionnaire of 18 items, which are then regrouped under the following quality-of-life (QoL) dimensions : self-esteem (SEL), romantic life (ROM), resilience (RES), psychological well-being (PSY), physical well-being (PHY), relationships with friends (FRI), family relationships (FAM), autonomy (AUT). The SQoL-18 has many convenient properties: (1) it is a short and easy-to-use instrument; (2) it has satisfactory psychometric properties, with a high degree of comparability with a longer, well-validated version of 41 items28; and (3) it covers eight key aspects of quality of life.In the current study, we used data collected on the Schizophrenia Quality of Life-18 (S-QoL18)Our initial sample consisted of 3948 patients, of whom N = 2180 were fully screened for quality of life. Details of the sample, including socio-demographic and clinical characteristics, can be found in Table 18. Such networks contain nodes  and edges . Here, network structures of QoL scores were estimated separately for each of the six mental disorders mentioned above.A network conceptualizes the eight items of the S-QoL as a system of mutually interacting dimensions29. Our network model of QoL dimensions contained a relatively high number of potential edges (36 edges). As is often the case in psychological research, fully saturated parametric regressions aiming to quantify these statistical relationships would quickly break down, because of a limited number of observations given the relatively high number of parameters to be estimated. To palliate for this so-called “curse of multi-dimensionality”, psychological networks make use of the graphical least absolute shrinkage and selection operator procedure (glasso31). This procedure limits the total sum of absolute parameter values and shrinks partial correlations such that very small edges (likely due to noise) are pushed to zero and thus removed from the network. This encourages the selection of simple, sparse models (i.e. with fewer edges). The amount of shrinkage is defined by a tuning parameter lambda used in the glasso procedure.One possible way of representing a psychological network is to use the concept of Gaussian graphical model, where nodes represent observed variables, and edges are partial correlation coefficients between two nodes, while controlling for all other nodes in the network32), which penalizes maximum likelihood estimation by taking into account both the number of edges and the complexity (size) of the model space, is then selected. The strength of the latter penalty depends on the value of a so-called hyperparameter gamma, which we set to 0.5 as recommended in psychological network studies with continuous variables33. Again, this procedure aims at selecting the optimal sparse network model.Technically, the glasso is run for 100 values of the tuning parameter, logarithmically spaced between its maximal value at which all edges are zero. The graph with the minimal extended Bayesian information criterion  vs. absent . Network estimation also provides a way to visually assess the local strength of each edge (the strength of the relationship between two nodes), and the global strength of the network .35. Briefly, node strength quantifies how well a node is directly connected to other nodes; closeness quantifies how well a node is indirectly connected to other nodes; and betweenness quantifies how important a node is in the average path between two other nodes .To gain more insight on the importance of individual QoL dimensions in the networks, we measured so-called centrality indices for each node in each network. Three centrality measures are typically used in network analysis: node strength, closeness and betweennessAmong those three measures, only node strength satisfied our accuracy tests criteria consistently across diagnosis groups see “”. Theref19. To do so, we took the difference between bootstrapped values of one edge-weight or centrality and another edge-weight or centrality, and constructed a bootstrapped confidence interval (CI) around those difference scores. We concluded that two edge-weights or two centrality indices were not significantly different if zero was in the bootstrapped CI19.For each network, we tested for significant differences between edge-weights (connections between nodes) and centrality indices (of each node) using bootstrapping procedure37. This 2-tailed permutation test randomly regroups individuals from each diagnosis group repeatedly (1000 times) and calculates the differences in each of these measures between those samples. The resulting distribution under the null hypothesis that samples are equal is used to test the observed difference of the original samples against a significance level of 0.05, which we corrected to account for multiple comparisons using the Holm procedure.We assessed between-group differences by testing whether the following four measures were different among the six diagnosis groups: network structure; absolute sum of edge-weights ; single edge-weight; centrality of each node. We checked for such differences by means of the network comparison test (NCT) developed by van Borkulo et al.19.We ran the following accuracy analysis to obtain confidence intervals around edge-weights and confirm the stability of our centrality measures, and therefore improve the robustness of our interpretations19.First, we estimated 95% confidence intervals to assess the variability of each edge-weight by means of the non-parametric bootstrap. Second, we also used bootstrapping procedures to determine the stability of each centrality measure. Briefly, with stability we indicate if the order of centrality indices remains the same after re-estimating the network with fewer cases. To quantify the stability of centrality indices using subset bootstraps, we assess the maximum proportion of cases that can be dropped, such that with 95% probability the correlation between original centrality indices and centrality of networks based on subsets is not below 0.7. Under this criteria, we defined stable centrality indices as those for which at least 50% of cases can be removedqgraph, glasso, bootnet, and NetworkComparisonTest.Analyses were conducted with R version 4.0 and packages The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The REHABase database obtained the authorizations required under French legislation . Written informed consent to have their data included in the database was obtained from participants.For each disorder investigated in the current study, we first aimed to estimate a network of eight quality-of-life dimensions (nodes) as measured by the Schizophrenia Quality of Life-18 questionnaire (S-QoL18): self-esteem (SEL), romantic life (ROM), resilience (RES), psychological well-being (PSY), physical well-being (PHY), relationships with friends (FRI), relationships with family (FAM), autonomy (AUT). Second, our centrality analysis provided a measure of the importance that each node had in the network, in terms of its relationships with other nodes . Third, we ran within-group tests aiming to compare, for each network, edge-weights (measuring the strength of the relationship between two nodes) and nodes’ strength. Fourth, between-group tests aimed at comparing, between diagnosis groups, the structure of the networks, their overall connectivity, local edge-weights, and node strength.In terms of network structure and overall connectivity, psychological networks looked relatively similar across diagnosis groups Fig. . Visual Across diagnosis groups, node strength was the most reliable of the three centrality indices measured in the current study . Therefore, we chose to only report results on node strength for our centrality analysis. Centrality plots looked relatively similar across diagnosis groups Fig. . In partFor each network, we found that edges involving self-esteem (SEL), including SEL–PHY, SEL–AUT, SEL–PSY, SEL–RES were consistently and significantly stronger than most other edges Fig.  & 3.FiguIn general, node strength was the most stable of all centrality measures across all diagnosis groups  and node strength . Only four tests demonstrated a small level of significance . Two of these tests compared between-networks overall connectivity . The remaining two tests that were significant compared edge-weights  , in conjunction with psychological networks theory18, to investigate the relationships between quality-of-life dimensions in six relatively common and severe psychiatric disorders. Psychological networks provide an alternative, perhaps more ecological way of representing psychological suffering, where the investigation focuses on a set of interacting dimensions, rather than a specific one (or sum of ones). Psychological networks conceptualize dimensions as nodes that have more or less connectivity with other nodes in the network—such local connectivity is called edge-weight. A crucial step in a psychological network analysis is to run accuracy tests which investigate the reliability of the results19. In addition to its innovative design, one strength of the current study is that our accuracy tests all provided strong evidence towards the robustness of our results. Below, we discuss two important findings of our study: (1) networks were comparable across disorders in terms of their overall structure and connectivity, as well as specific edge-weights and node strength; (2) as a quality-of-life dimension, self-esteem was consistently retrieved as the strongest node within each network, and was involved in the most important edges.In the current study, we used a French multicentric database of psychiatric patients , self-esteem is core to one’s own actions, emotional and physical fulfilment, coping with daily life, and to start and maintain satisfying relationships. Moreover, in a world where norms place a strong emphasis on the individual, his/her level of joy and self-image52. Low self-esteem is also considered a transdiagnostic risk and maintenance factor for mental disorders55. The fact that we did not observe any significant difference in network structure between diagnosis groups does not mean, however, that self-esteem has no discriminatory power among mental disorders. For instance, previous studies have shown that low self-esteem was more pronounced in individuals with major depression and less so in patients with bipolar disorder in the manic phase52. Discrepancies with our findings could stem from the fact that patients from the REHABase cohort may be more clinically stable than those who participated in other studies (e.g. stable enough to attend rehab programmes). Other differences with previous research might result from the specificities of our analysis, which investigates the links between self-esteem and other quality-of-life dimensions, rather than self-esteem merely as an outcome. Finally, our analysis did not investigate the mechanisms by which low self-esteem is core to the quality of life of those with mental problems, which may be very different among disorders. Future studies should explore these mechanisms, which may span a broad spectrum of issues, from low income56, to internal or external stigmatization57, and even high intellectual standing58.Taken as a broad concept related to negative self-evaluation, low self-esteem is core to most mental disorders, either as a formal diagnostic criterion (e.g. in depressive disorders), or as an associated feature (e.g. in schizophrenia)59, and Competitive Memory Training (COMET), which aims at enhancing the retrievability of a patient’s positive self-knowledge60. As a central quality-of-life dimension, self-esteem could be considered a fundamental treatment target in psychosocial rehabilitation programmes, in order to improve quality of life in a cost-effective way61. Future studies should confirm this hypothesis and test whether psychological work directly targeting self-esteem vs. other dimensions , improves quality of life, functioning and clinical symptoms.Our results could also have interesting therapeutic implications. Even though most treatment guidelines focus primarily on psychiatric disorders rather than on underlying processes or quality-of-life dimensions, many interventions have been developed to specifically target low self-esteem. Among them, it is worth mentioning cognitive-behavioural approaches which aim to identify, challenge and modify dysfunctional automatic thoughts and underlying schemasOur study presents several limitations. First, we were only interested in psychological networks of quality-of-life dimensions. Yet, a more ecological representation would probably include relationships with other types of issues, such as clinical symptoms, but also potentially genetic features, past symptomatic history, socio-demographic characteristics, and so on. Clearly, knowledge is lacking on how to best model a bio-psycho-social network of multi-level features among psychiatric disorders.Second, while the SQoL-18 is a very practical tool to measure quality of life in patients with mental disorders, we acknowledge that its construct and content validity may be limited. This is probably inevitable when one wishes to collect data on such a complex and subjective concept.Third, to represent our psychological networks, we aggregated individuals with similar diagnosis profiles. Potentially, this could represent a case for the so-called “ecological fallacy”, where characteristics at the group level would bias an individual analysis. As discussed above, our psychological network analysis would benefit from an investigation across various socio-demographic strata. That said, and taking a health services research perspective, our results should be interpreted just as we aimed to design our study: as (broadly) ecologically relevant.62).Fourth, as gaussian graphical models, our psychological networks made no assumptions about the direction of connectivity between nodes. Gathering longitudinal information would have been interesting to help us make causal inferences, and further, test the effects of specific treatment programmes. This could be the subject of further studies but would require a different modelling strategy . Finally, in simulation studies centrality tests have demonstrated a lower level of power in rejecting the null hypothesis when two centralities differ from one-another19. Clearly, these caveats need to be taken into account as our analysis may have suffered from a slight lack of sensitivity and specificity.Fifth, we acknowledge that in our study, valid statistical inference is challenged by a set of potential issuesDespite these limitations, which are balanced by a relatively large number of observations and the reliability of our accuracy tests, our study shows that networks of quality-of-life dimensions are in general not significantly different across serious mental illnesses. In addition, our analysis shows that within each group, stronger connections are consistently those that involve self-esteem as a core quality-of-life dimension.64. We hope that future studies will continue to use systems science to better describe inter-relations between various features of a patient’s suffering, and test the effectiveness of treatment programmes in an ecological, practical and realistic way.Our study also exemplifies how system thinking techniques can be used to explore dysfunctional psychological dimensions that interact with one-another. Interestingly, these techniques may be more representative of psychological suffering than more analytical tools typically applied in mental healthSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.


The desire to escape from pressures/anxiety represents an important motivation for problematic engagement with short-term rewarding behaviors that could contribute to the development of recognized and candidate behavioral addictions, including problematic shopping, a prevalent condition among youth in the U.S.A. characterized by excessive shopping cognitions/behaviors that lead to distress/impairment. However, to date, the specific correlates of shopping to relieve anxiety or tension have yet to be evaluated. We aimed at addressing this gap by analyzing data (N = 2556) from a high-school survey from Connecticut in an exploratory fashion. Adolescents who acknowledged experiencing a growing tension or anxiety that could only be relieved by shopping were classified as having negative-reinforcement shopping and compared to the remaining students. Data were analyzed in chi-square and logistic regression models to examine negative-reinforcement shopping in relation to socio-demographics, health correlates, gambling-related perceptions/attitudes, and problem-gambling severity/gambling behaviors. Negative-reinforcement shopping was more frequent in female and Hispanic students, was linked to more permissive gambling attitudes and at-risk/problematic gambling, and was associated with the use of alcohol, tobacco, marijuana, and other drugs, dysphoria/depression, and weapon-carrying and physical fighting. Taken together, these findings highlight adverse measures of health and functioning linked to negative-reinforcement shopping that may be particularly relevant to girls and Hispanic youth. Additional efforts to prevent negative outcomes are warranted. The term ‘behavioral addictions’ has been used to describe patterns of engagement with short-term rewarding activities other than psychoactive-substance use which are characterized by core elements of addiction  ,2. AlthoA potentially fruitful approach in the investigation of recognized and candidate behavioral addictions is to consider underlying motivations for the engagement in the related short-term rewarding behaviors. Motivational characteristics are important components of theoretical models of addiction ,11, and Problematic shopping (PS) is a candidate behavioral addiction characterized by excessive shopping cognitions and buying behaviors that lead to distress and/or impairment . PS is cPS during adolescence is of particular concern because it has been associated with several negative health correlates such as substance use , depression, antisocial behaviors , and engagement in non-suicidal self-injury ,22,23. NConceptual models ,26 and eAvailable evidence suggests that individuals who shop to relieve anxiety/tension could be at risk of engagement in other addictive behaviors, e.g., substance use and problem gambling. In a previous study of the data from the high-school survey on which the current analyses are based, adolescents with at-risk/problem gambling were more likely to report negative-reinforcement shopping , suggestThis study aims at addressing gaps in knowledge by evaluating, in an exploratory fashion, socio-demographics, health measures, gambling perceptions and problem-gambling severity/gambling behaviors in adolescent high-school students from Connecticut stratified by negative-reinforcement shopping status. We hypothesized that negative-reinforcement shopping, operationalized as shopping to relieve tension or anxiety, would be associated with substance use, dysphoria/depression, and aggressive/violent behaviors; more permissive attitudes towards gambling; and at-risk/problem gambling. We also explored different preferred gambling locations  and types of gambling  in relation to negative-reinforcement shopping.Data were drawn from a cross-sectional survey of high-school students from the state of Connecticut. The recruitment and data-collection procedures have been previously described in detail in other publications ,34. BrieFor schools interested in participating, additional permission was obtained from school boards and/or superintendents as necessary. Passive parental consent and student assent procedures were approved by the Yale School of Medicine Institutional Review Board and data collection sites. Parents were mailed letters detailing the study and notified to contact the school should they wish to decline their children’s participation. All procedures were performed in accordance with the Declaration of Helsinki and its amendments. Answers were anonymous and confidential, and students were reminded that their participation was voluntary. The refusal rate was low (<1%).The surveys were administered at each school in a single day. The survey consisted of 154 questions that evaluated a range of characteristics such as socio-demographic information, health correlates, including substance use and other risk behaviors, and gambling measures. The survey included validated measures such as the Massachusetts Gambling Screen (MAGS) , and novSocio-demographic variables included age, gender, race/ethnicity, grade level in school, and family structure .2), ‘normal’ weight (18.6–24.9 kg/m2) and overweight/obese (BMI ≥ 25 kg/m2). Lastly, depression/dysphoria was assessed dichotomously (yes/no) by asking “During the past 12 months, did you ever feel so sad or hopeless almost every day for two weeks or more in a row that you stopped doing some usual activities”?Adolescents responded to questions investigating involvement in extracurricular activities (yes/no); grade average ; lifetime tobacco smoking , marijuana use (yes/no), other drug use (yes/no), and alcohol use (yes/no); past 30-days alcohol use  and caffeine use ; past 30-days weapon-carrying  (yes/no), not going to school because of feeling unsafe (yes/no) and being threatened by a weapon (yes/no); past 12-months involvement in a physical fight (yes/no) and getting injured in a physical fight requiring the treatment of a doctor or nurse (yes/no). Adolescents were queried about bodyweight, which was categorized as underweight (body mass index [BMI] ≤ 18.5 kg/mAdolescents responded to questions investigating the perceived importance (important vs. not important) of gambling-prevention approaches. Participants were also queried on perceptions of their parents’ attitudes towards their gambling (disapprove/neither approve nor disapprove/approve) and whether they had any concerns about the gambling behavior of a family member (yes/no).Problem-gambling severity was assessed using the MAGS, a self-report instrument based on DSM-IV criteria for pathological gambling that has shown adequate reliability and predictive and construct validity . Particin = 373, 14.59%) were classified as having negative-reinforcement shopping, and those who answered “no”  were classified as not having negative-reinforcement shopping. Of these 2556 students, 2252 provided dichotomous (yes/no) information on past-year gambling and were included in gambling-related analyses.Of the 4523 adolescents who participated in the survey, 2556 students provided an answer to the question “Have you ever experienced a growing tension or anxiety that can only be relieved by shopping?” Students who answered “yes”  with a 95% confidence interval (CI). All regression models included sociodemographic characteristics  as covariates to adjust for potential confounding effects. Significance for regression models was set at p < 0.05. Statistical analyses were conducted with IBM SPSS 27.Data were double-entered and randomly spot-checked to maintain accuracy. Between-group differences in socio-demographics, health correlates, gambling perceptions and problem-gambling severity/gambling behaviors between groups (with negative-reinforcement shopping versus without) were examined using Chi-square tests. Bonferroni correction for multiple comparisons was applied such that 2 = 38.01, p < 0.001), with a larger proportion of females in the negative-reinforcement-shopping group (82.1% vs. 65.9%), and for ethnicity , with a larger proportion of Hispanic youth in the negative-reinforcement-shopping group (20.9% vs. 13%).The frequencies and chi-square results of socio-demographics stratified by negative-reinforcement-shopping status are described in p < 0.001) and regular  smoking; lifetime use of alcohol , marijuana , and other drugs ; moderate alcohol drinking ; drinking three or more cups of caffeine per day ; dysphoria/depression ; weapon-carrying ; feeling unsafe at school ; having been threatened with a weapon ; and having been injured in a physical fight requiring treatment by a doctor or nurse .The frequencies and chi-square results and odds ratios of health correlates stratified by negative-reinforcement-shopping status are described in 2 = 22.69, p < 0.001; 23.1% vs. 13.3%); fear of losing valuable possessions, close friends, and relatives ; and learning about the risks of gambling from parents .The frequencies and chi-square results of gambling perceptions stratified by negative-reinforcement-shopping status are described in p = 0.02), PG , and GD ; non-strategic gambling ; gambling in a casino ; experiencing anxiety prior to gambling ; earlier age at onset of gambling—9–11 years , 12–14 years , and ≥15 years ; gambling alone  and with strangers .The frequencies and chi-square results and odds ratios of problem-gambling severity/gambling behaviors stratified by negative-reinforcement-shopping status are described in The present study investigated, in an exploratory fashion, the socio-demographics, health correlates, gambling perceptions and problem-gambling severity/gambling behaviors associated with negative-reinforcement shopping in a large sample of high-school students in Connecticut. We found that individuals who acknowledged experiencing a growing tension or anxiety that could only be relieved by shopping  were more likely to be female and Hispanic. We also found that those individuals were more likely to report lifetime  and current substance use , dysphoria/depression, and having experienced violence . Additionally, we found that negative-reinforcement shopping was partially linked to more permissive gambling attitudes as reflected by, for example, classifying learning about gambling risks from parents as unimportant. Lastly, we found that negative-reinforcement shopping was associated with problematic gambling behavior, anxiety as a trigger for gambling, non-strategic gambling, gambling in casinos, and gambling alone or with strangers. Taken together, these findings highlight adolescent negative-reinforcement shopping to be linked to multiple adverse measures of health and functioning and gambling beliefs and behaviors.Our finding of girls being more likely to endorse negative-reinforcement shopping is in line with research on gender-related differences in coping strategies. Coping refers to the cognitive and behavioral processes adopted to manage external and/or internal demands that are perceived as exceeding the person’s usual resources ,44,45. AWe also found that Hispanic youth were more likely to report negative-reinforcement shopping. To the best of our knowledge, this association has not been previously described and suggests the importance of considering ethnicity when considering shopping behaviors, as with other behaviors. For example, research on food marketing has suggested that marketing for Hispanic individuals may be less likely to promote healthy eating, which may contribute to obesity in Latinos , and it Our hypothesis that negative-reinforcement shopping would be associated with substance use, dysphoria/depression, and violence-related measures was confirmed by our results. Previous research has indicated individuals who adopt emotion-focused coping strategies are more likely to experience higher levels of psychopathology, including depression, substance use and aggressive/violent behaviors ,54,55. SOur hypothesis that individuals with negative-reinforcement shopping would display more permissive attitudes towards gambling was partially confirmed. In line with our hypothesis, we found that a larger proportion of individuals with negative-reinforcement shopping classified learning about the risks of gambling with their parents as unimportant, which could indicate the presence of communication barriers between parents and youth with negative-reinforcement shopping. Decreased communication between parents and children may result in increased problem-gambling severity, and effective parental communication may contribute to decreased problems arising from gambling and other risk-taking behaviors, e.g., alcohol use ,57. In tHowever, contrary to our hypothesis, we did not observe a relationship with perceived parental permissiveness towards gambling. Thus, our findings are in line with previous research which has demonstrated associations between perceived parental permissiveness and positive reinforcement motivations  and related constructs such as impulsivity ,58,59, aOur hypothesis that negative-reinforcement shopping would be associated with ARPG and GD was confirmed. Clinically, previous research has demonstrated that the frequencies of PS and GD among treatment-seeking individuals with the other condition are higher than the prevalence estimates of each condition in the community ,64,65. OWe hypothesize that the identification of at-risk youth could be improved by considering reward-based motivations for engagement with short-term rewarding behaviors  because individuals may engage in several such behaviors for similar reasons. Our findings regarding gambling behaviors may be partially supportive of this hypothesis. Although we did not find a direct association between negative-reinforcement shopping and gambling to escape/relieve dysphoria, youth with negative-reinforcement shopping were more likely to report anxiety as a trigger for gambling. Additionally, individuals who gamble as an emotion-focused coping strategy may be more likely to prefer non-strategic forms of gambling because of a dissociative-like attention-absorbing state (‘dark flow’) . DissociDespite our large sample size and the representativeness of our sample, our study has limitations that should be noted. Only 56% of the surveyed adolescents provided an answer to the question querying about negative-reinforcement shopping. It is possible that individuals who did not provide answers may show weaker/stronger associations between negative-reinforcement shopping and socio-demographics, health correlates, gambling perceptions, and problem-gambling severity/gambling behaviors. However, most sociodemographic variables assessed did not show differences between individuals completing versus not completing the negative-reinforcement shopping measure with the exception of male gender. However, sociodemographic variables (including gender) were included as covariates in all regression models to adjust for their potentially confounding effects. The number of youths with negative-reinforcement shopping was relatively small which precluded further examination of health correlates and gambling behaviors stratified by problem-gambling severity. It is possible, for example, that the negative-reinforcement-shopping status mediates stronger/weaker associations between ARPG and health correlates or gambling behaviors, but we were unable to address this question. Second, we used self-reported answers to only one question to classify individuals as having (or not) negative-reinforcement shopping. Future studies should consider a more detailed examination of underlying motivations to engage in shopping. Nonetheless, our study is helpful in demonstrating that a simple question might be helpful in identifying at-risk youth, although the potential utility and validity of screening adolescents in this manner also requires direct examination by future research. Third, our study was cross-sectional and consequently, we are not capable of inferring on longitudinal patterns of the associations observed in this study. Fourth, data were collected approximately 15 years ago, and while they may provide an important historical comparator, more recent studies are warranted to examine negative-reinforcement shopping and its correlates in a current environment. This may be particularly important given the ease of internet-based shopping and other potentially risky behaviors such as gambling now as compared to 15 years ago.This study evaluated the socio-demographics, health correlates, gambling perceptions, and problem-gambling severity/gambling behaviors of adolescents who acknowledged engaging in shopping to relieve tension or anxiety. We found that youth with negative-reinforcement shopping were more likely to be female and Hispanic; report the use of alcohol, tobacco, marijuana, and other drugs; describe more permissive attitudes towards gambling; meet criteria for GD; gamble in casinos, alone and with strangers; experience dysphoria/depression; and acknowledge aggressive/violent behaviors such as weapon-carrying and physical fighting. Taken together, these findings highlight adolescent negative-reinforcement shopping as being linked to multiple youth risk behaviors. Additional research aimed at replicating and extending the current findings is warranted. Likewise, additional efforts to identify and prevent negative outcomes among these at-risk adolescents are warranted. The findings suggest that certain groups  may be particularly vulnerable to experiencing negative-reinforcement shopping and may be particularly important to consider in efforts targeting this behavior. The finding of negative-reinforcement mechanisms motivating engagement in potentially addictive behaviors in females more so than males is consistent with the wider literature . The cur


The advancements in nanotechnology and nanomedicine are projected to solve many glitches in medicine, especially in the fields of cancer and infectious diseases, which are ranked in the top five most dangerous deadly diseases worldwide by the WHO. There is great concern to eradicate these problems with accurate diagnosis and therapies. Among many developed therapeutic models, near infra-red mediated phototherapy is a non-invasive technique used to invade many persistent tumors and bacterial infections with less inflammation compared with traditional therapeutic models such as radiation therapy, chemotherapy, and surgeries. Herein, we firstly summarize the up-to-date research on graphene phototheranostics for a better understanding of this field of research. We discuss the preparation and functionalization of graphene nanomaterials with various biocompatible components, such as metals, metal oxides, polymers, photosensitizers, and drugs, through covalent and noncovalent approaches. The multifunctional nanographene is used to diagnose the disease with confocal laser scanning microscopy, magnetic resonance imaging computed tomography, positron emission tomography, photoacoustic imaging, Raman, and ToF-SMIS to visualize inside the biological system for imaging-guided therapy are discussed. Further, treatment of disease by photothermal and photodynamic therapies against different cancers and bacterial infections are carefully conferred herein along with challenges and future perspectives. Humankind have faced many threats, especially from cancer and infectious diseases, in the past and in the current times. These problems have remained persistent for many decades. Science has provided remedies alongside many religious beliefs, especially during the pandemic times. This scenario increased the need for non-invasive, economic, therapeutic models to fight cancer, Alzheimer’s disease, cardiovascular disease, influenza, COVID-19, and other microbial infections, and existing diseases ,3,4. SciTheranostics is an emerging field in nanomedicine which may provide simple, economic diagnoses and therapy solutions to many cancers and infectious diseases. Rather than rely on single diagnosis and therapy models, multiple practices are important to provide accurate results of disease confirmation and cure. Nanomaterials with multiple diagnosis and therapeutic characteristics are highly desired in nanomedicine ,11. The In recent years, phototherapy has become emerging research topic in nanomedicine to treat cancer and bacterial infections . PhotothGraphene is an allotropic form of carbon where the carbons are arranged in a 2D hexagonal chicken-net-like network which can offer high surface area, better electrical and thermal conductivity with optical transparency, and tuneable surface functionality with the olefin carbon network ,32. The 2 and sp3 bonded carbon atoms. Graphene nanomaterials have benefits such as high mechanical strength, Young’s modulus, surface area, conductivity, and carrier mobility, making them a perfect nanomaterial for various applications . M. M76]. MNanomaterials-based theranostics are the future of personalized medicine as a single nanoplatform can provide multiple imaging and therapies in a short time by simplifying the cost and the amount of the drug required for multiple diseases . Multimo1O2) generation to destruct the cellular components, it is called PDT. If the nanomaterial inherently cannot generate ROS and 1O2, it has to functionalize with PS [Phototherapy (PT) involves light interaction (Vis-NIR) with nanomaterials to generate heat or reactive oxygen to destruct cancer and bacterial infection. If the therapy process involves generation of heat from nanomaterials which can suppress or burn the tumor/bacteria is called PTT. If the PT involves reactive oxygen species (ROS) and singlet oxygen -based imaging has drawn the attention of nanomedicine scientists due to its tunable size and variable colors with stable fluorescence emissions . On the 4) to offer MFG-SiNC4 and the synergistic effects of PTT/PDT and PTT, as shown in 2 for 10 min [We prepared a multifunctional graphene (MFG) by functionalization with polyacrylic acid, FeNPs, and fluorescein ortho-methacrylate to impart both magnetic and fluorescence properties for CLSM imaging in HeLa cells and in zebrafish whole-body imaging . The MFGr 10 min . Irrespe3+ for bright contrast and superparamagnetic iron oxide NPs for dark contrast are used [Among all imaging techniques, MRI and CT scans are clinically versatile and best used so far for imaging-based diagnosis purpose. These techniques do not rely on any fictionalization of fluorophores and QDs. MRI works based on the radio waves and magnetic field to identify damaged (cancer) tissue from the healthy tissues based on activating the local proton environment. It has the great advantage of ease of use for X-ray imaging techniques. However, it has the limitations of poor sensitivity and lengthy signal recording times. The proton magnetic moment of tissue is environment-dependent and the T1 and T2 times may not produce a better image, hence some external contrast agents are frequently used. The well-known Gdare used .2+ ions from KMnO4 are responsible for better proton relaxivity and displayed very good T1- and T2-weighted MRI contrast images [99mTcI and Gd-based pegylated ultrasmall nano GO (99mTc− and Gd-usNGO-PEG) were prepared for the multimodal MRI and SPECT/CT imaging of lymph nodes. The preparation approach is claimed to be chelator free, and the final product has been utilized for multimodal purposes [Graphene-related nanomaterial magnetic composites have a great advantage in helping these imaging guided therapy processes to improve the signals or contrasts to provide enhanced resolution in the final images during disease identification . GO has t images . Moreovet images . A nonmat images . Apart ft images . Moreovet images . In addit images . Furtherpurposes .4 with excellent superperamagnetic properties, fluorescence, biocompatibility, and water dispersability for in vitro MRI to serve as a good contrast agent, as shown in In brief, many authors have reported that GO- and RGO-based iron oxide nanocomposites are excellent for MR imaging and guided therapy of cancer . As discApart from these, many researchers have synthesized GO/RGO based magnetic nanocomposite for MR imaging ,110,111.CT scan is an alternative 3D imaging technique whch works based on the X-ray attenuation of molecular tissues to identify tumors at lower cost than an MRI. It can also provide good spatial–temporal resolution of cancer tissues, and the image recording time is shorter than MRI. Usually, barium sulphate and iodine-based contrast agents are used when CT lacks the necessary sensitivity to image some soft, low-density tissues. The literature on various nanomaterials for CT scan is available. Carbon-based nanomaterials have a unique role due to their biocompatibility and high contrast agent’s immobilization capability. Among the few nanomaterials reported for CT scanning, Bi nanomaterial is exclusively studied, though CuS and some of its composites are also explored for CT and MR imaging together .4 and reduced with HCl before being further functionalized with iodine, and have shown good CT and MR imaging contrast [5 decorated GO-PEG was prepared with good biocompatibility, and was well demonstrated as a CT and MR imaging agent followed by PTT therapy [2O4 and upconversion luminescence nanoparticles (UCNPs) have been well demonstrated for CT scanning along with MRI, PAI, and fluorescence imaging guided phototherapy as a unique multi diagnosis platform [However, the available literature is limited regarding GNCs for CT scanning along with MR imaging. GNCs are synthesized from the oxidation of graphite with KMnOcontrast . It has contrast . GO funccontrast . A nanoc therapy . GO decoplatform .Recently, GO decorated with AuNPs, SPIONPs, along with DOX-loaded 1-tetradecanol (TD) was prepared  and successfully demonstrated for CT- and MRI-guided controlled chemo-phototherapy of cancer in vitro and in vivo. The advantage of this material is that the Au NPs on GO can act as a CT contrast agent and provide better emission of X-rays due to its high atomic number A with goApart from these three  well used techniques PET, PAI, and ToF-SIMS, and combined MRI/CT, CT/PEI, MRI/CT/PL/PAI, and MRI/CT/EPR, could also be important tools to the earlier diagnosis. These are currently of great interest to nanomedicine researchers.198,199Au, 64Cu, 66Ga, 111In, and 121I. In this regard of PET imaging, for the first time GO-based targeting and non-targeting imaging agents such as 64Cu-NOTA functionalized GO-PEG (64Cu-NOTA-GO) and 64Cu-NOTA-GO conjugated with TRC105 (64Cu-NOTA-GO-TRC105) for targeting a CD105 (endogline) has been synthesized. 64Cu was linked with GO-PEG via 1,4,7-triazacyclo nonane-1,4,7 triacetic acid . As shown in 64Cu-NOTA-GO-TRC105 has very good biodistribution and targeting ability towards 4T1 tumor-bearing mice compared to non-targeting 64Cu-NOTA-GO, pre injected TRC105 blocking dosed mice, and CD105-negative MCF7 human breast cancer cells. The combined CT and PET images also can be seen in the images [Radio labelling techniques such as PET and CT have great sensitivity due to the low background, requirement of low signal amplification, and good penetration depth in vivo. As said earlier, nanographene-based scaffolds have a greater importance in radio imaging technology with the functionalization of radioactive elements such as e images .66Ga was functionalized to GO and obtained the same result with similar strategy of PET imaging of 4T1 cells in vitro, in vivo and ex vivo with good distribution in the body without toxicity [64Cu to the rGO with the same synthetic adaption to prepare 64Cu-NOTA-rGO-TRC105 for targeting 4T1 murine breast cancer cells and obtained successful results than with non-targeting rGO nanodrug [124I)-labelled GO nanocomposite has been reported for PET imaging and boron delivery inside mice. The images reveal that time-dependent biodistribution in the liver, spleen, stomach, and heart for long-time circulation inside the body, up to 48 h. The in vitro studies of C. elegans confirmed that the 124I-GO does not show any significant toxicity. Hence 124I-GO could be a better candidate for boron neutron capture therapy of cancer [After that toxicity . Later, nanodrug . Very ref cancer .PAI is another non-invasive imaging model to monitor the tumor environment with greater resolution and high tissue penetration depth. The PA signal production involves the following process. The light energy absorbed by the material converts into heat and increases the temperature, followed by thermoelastic expansion which causes the generation of acoustic waves (AWs). The AW generates an image contrast respective to the concentration of the absorbing material. In this regard, light absorbing nanomaterials have an advantage of converting photo energy into thermal energy and generation of AWs for better PAI imaging ,125. AmoA dual modal PAI and photothermal imaging probe rGADA nanocomposite was fabricated by the rGO functionalized with AuNS (gold nanostars), bilayered lipids, FA (rGADA), and K-Ras gene plasmid (KrasI) rGADA-KrasI, for targeted imaging guided photothermal and gene therapy of pancreatic cancer. The Raman is a spectroscopic technique named by its inventor Sir. C. V. Raman in 1928 who proposed the Raman Effect. The phenomenon is based on the inelastic and elastic scattering of light from the vibrations of objects such as nanomaterials, drugs, and other biological molecules. When this technique is coupled with microscopy it is called Raman microscopy, and can be used to visualize in vitro and in vivo biological components, internalized nanomaterials, and drugs with high specificity and sensitivity at workable spatial resolutions. However, high sample concentration is required for better resolution and fast image acquisition due to the weak scattering signals. To overcome this hurdle, Resonance Raman (RR), Surface-Enhanced Raman Spectroscopy (SERS), and Coherent Raman spectroscopy (CRS) were developed. Scientists have mostly adopted SERS in nanomedicine due to its signal enhancement in the presence of rough surface nanomaterials such as Au, Ag, and Cu in the sample system .A GO@Au and fluorescent tag functionalized dual modal luminescent and Raman imaging has been reported . MoreoveRecently, a SERS-guided multi modal chemo, gene, and PT of cancer with Au@GO-NP-NACs was reported, where the NP stands for nanoparticles and NACs for nucleic acid components ex. BCL2 mRNA. 3+, Arn+, and C60+) with the sample surface, and the secondary ions generated from the sample will be collected according to their flight times and its mass/charge. The chemical compositions can be predicted based on their respective masses in comparison with the reference library. This technique is unique in imaging single cells, human tissues, and skin and cancer cells, and could be an important label-free tool to diagnosis the cancer cells from healthy normal cells. It is helpful in studying the toxicity of nanomaterials, drug internalization into the cells, apoptosis, and to predict other cellular killing mechanisms by collecting and imaging the cellular components’ mass/time values [ToF-SIMS imaging is one of the most surface-sensitive techniques to analyze chemical compositions of materials and biological systems containing chemical components. It has great capability to map low molecular weight components (<500 KD) and submicron resolution. ToF-SIMS involves sputtering the primary ion beam  because of more drug loading capability with colloidal stability. Further, rGO-PLL is labeled with anti-HER2 to form a bond with HER2 receptors to detect breast cancer cells .2 and ~100 µg/mL were the most suitable parameters for phototheranostics. In the case of bacteria, the same parameters are good for photodisinfection. However, according to us and to Liang et al., experiments with very low powers and GNCs dosage have also shown great therapeutic effects [According to the above research discussion, every author made their contribution towards this field. Dai et al., for the first time, introduced the GO and RGO to the theranostic applications, thereby extensively contributing to this important research. Later, other researchers gave their insights to fuel the graphene nanomedicine. For instance, we have prepared MFG to impart long-range absorption of graphene in both biological window I and II, along with demonstrating the CLSM/MRI and both single light induced PTT/PDT ,95,96. C effects ,158. As  effects ,163,164. effects ,166,167.We summarized the recent progress of the general preparation and functionalization of GO, RGO, and GNCs as theranostic materials to provide simple and advanced imaging-guided therapeutic drugs to invade malignant tumors and bacterial infections. The water solubility, low toxicity, and high surface area of GO made a very good nanoplatform to carry many therapeutic organic drugs and to load different imaging probes. However, its low NIR absorption is unlikely, and not very favorable to the phototherapy of cancer and bacteria. Hence, RGO or functionalized nanocomposites of graphene-related materials provide a better solution to overcome the difficulties where GO cannot. The multi-modal imaging and PS functionalized nanographene composite provide a very accurate diagnostic confidence to proceed with the therapy of combined PTT/PDT, which may require in less time and smaller drug concentrations. Among the nanotherapies reported, phototherapy has good results, with less intensive time and energy, and without any side effects and damage to healthy tissues.Graphene/GO/GQDs can offer diversified chemistry for self-acting luminescent for CLSM, magnetic for MRI, surface plasmonic state for SERS and ToF-SIMS signal enhancement, PAI imaging, and inherent PTT, PDT agent. It has great potential to carry many chemical drugs and genes for chemo- and gene therapies with very good biocompatibility. However, much research is required to move GNCs towards clinical implementation, as their size, shape, no of carbons, layers, number of oxygen functional groups, accurate mass, and photo yield to generate ROS and heat have to be optimized precisely. In perspective of PT, the biological windows must be explored in NIR-I and NIR-II. Overall, nanotechnology scientists could use flexible GNCs in whatever they want to fabricate.


Therapeutic water-only fasting and whole-plant-food refeeding was previously shown to improve markers of cardiometabolic risk and may be an effective preventative treatment but sustained outcomes are unknown. We conducted a single-arm, open-label, observational study with a six-week post-treatment follow-up visit to assess the effects of water-only fasting and refeeding on markers of cardiometabolic risk. (2) Methods: Patients who had voluntarily elected and were approved to complete a water-only fast were recruited from a single-center residential medical facility. The primary endpoint was to describe changes to Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) scores between the end-of-refeed visit and the six-week follow-up visit. Additionally, we report on changes in anthropometric measures, blood lipids, high-sensitivity C-reactive protein (hsCRP), and fatty liver index (FLI). Observations were made at baseline, end-of-fast (EOF), end-of-refeed (EOR), and six-week follow-up (FU). (3) Results: The study enrolled 40 overweight/obese non-diabetic participants, of which 33 completed the full study protocol. Median fasting, refeeding, and follow-up lengths were 14, 6, and 45 days, respectively. At the FU visit, body weight (BW), body mass index (BMI), abdominal circumference (AC), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), low-density lipoprotein (LDL), hsCRP, and FLI were significantly decreased from baseline. Triglycerides (TG) and HOMA-IR scores, which had increased at EOR, returned to baseline values at the FU visit. (4) Conclusion: Water-only fasting and whole-plant-food refeeding demonstrate potential for long-term improvements in markers of cardiovascular risk including BW, BMI, AC, SBP, DBP, blood lipids, FLI, and hsCRP. The burden of chronic metabolic diseases, such as cardiovascular disease (CVD), hypertension (HTN), type 2 diabetes mellitus (T2D), and non-alcoholic fatty liver disease (NAFLD) is a growing global public health concern because they may result in diminished quality of life, disability, and risk of premature death . ObesityFasting is the partial or complete abstinence of caloric intake for a defined period of time. Research into intermittent fasting, the fasting mimicking diet, and prolonged fasting methods have demonstrated the potential of these therapies to improve overall health and promote immunity and longevity . ProlongWe recently reported that at least 10 days of water-only fasting and five days of refeeding on an exclusively whole-plant-food diet free of added salt, oil, and sugar (SOS-Free Diet) had mixed effects on markers of cardiovascular and metabolic health at the end of refeed in a non-diabetic, overweight/obese population . We obseThis study (NCT04514146) was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the TrueNorth Health Foundation (TNHF-2020-2VAT).2 and fasting glucose <7 mmol/L and/or hemoglobin A1c <7% who were approved by a physician to water-only fast for at least 10 consecutive days followed by the standard refeeding period of no less than half the duration of the fast. Exclusions were active malignancy, active inflammatory disorders including classic autoimmune connective tissue disorders, multiple sclerosis, inflammatory bowel disorders, and stroke or heart attack within the last 90 days. Participation continued until six-week post-treatment data collection was completed. Of the 40 eligible participants, 17 consenting participants were concurrently enrolled in a study assessing the safety and feasibility of prolonged water-only fasting in the treatment of stage I and II hypertension (NCT04515095).We enrolled 40 non-diabetic, overweight and obese participants who had elected to undergo a medically supervised, water-only fast at a residential fasting facility prior to recruitment. Eligibility included males and females aged 40–70 years with a body mass index (BMI) between 25–40 kg/mOf the 40 participants enrolled, 38 completed the on-site fasting and refeeding treatment protocol and 33 completed the full study protocol including the six- week follow-up (FU) visit. Of the 33 who completed the full protocol, four were unable to return for the on-site FU visit and were provided the necessary materials and education to collect and report data remotely . Two participants did not complete the minimally required fasting length due to treatment emergent adverse events including one grade 1 nausea event and one grade 2 dyspepsia event. There were two participants whose water-only fast was temporarily interrupted. In one case, the fast was interrupted after day four of water-only fasting with two days of vegetable and fruit juice due to one episode of emesis. In the other case, the fast was interrupted after day six of water-only fasting with one day of cooked zucchini/squash blend to alleviate symptoms of emesis. In both cases, water-only fasting resumed once symptoms subsided. All reported adverse events were mild (grade 1) to moderate (grade 2) events, consistent with previously reported water-only fasting safety data [The medically supervised, water-only fasting and refeeding protocol was implemented by non-research medical personnel at a residential, medical facility according to the facility’s standard protocol as previously reported . BrieflyBMI, AC, TG, and GGT were used to calculate Fatty Liver Index (FLI) via the previously established formula [Eligible and consenting participants attended study visits at baseline, end of fast (EOF), end of refeed (EOR), and six-week FU. At each visit, 18 mL of blood were collected, and clinical measurements, which included resting systolic and diastolic blood pressure (SBP and DBP), BW, and AC, were taken. After collection, blood samples were sent to Labcorp for measurements of fasting blood glucose, insulin, hsCRP, blood lipids, and gamma-glutamyl-transferase (GGT). Measured values of glucose and insulin were used to calculate HOMA-IR score according to the equation (fasting insulin (microU/L) × fasting glucose (nmol/L)/22.5) . Measure formula :FLI= was measured using a digital wall-mounted stadiometer  Participants were positioned with backs against the stadiometer, with legs, backs, neck, and head straight. Once in position, the head piece was lowered to touch participants’ crown of the head.2).BW (kg) was measured using digital body weight scale . Participants stepped on the scale and stood still until the measurement was produced and recorded. The participants who elected to complete their FU visit remotely were provided a Conair digital glass scale (WW26 model). BMI was calculated from height and weight using the formula: weight (kg) ÷ height (mAC (cm) was measured at the narrowest part of participants’ midsection below the lowest palpable rib cage yet above the top of the hip/pelvic bones. Tape was placed horizontally and measurements were collected using a tension-sensitive, non-elastic tape . Participants who elected to complete their FU visit remotely were provided a soft retractable cloth measuring tape.Resting SBP and DBP (mmHg) were measured in the morning after blood collection. Participants rested for five minutes in the seated position and blood pressure was measured using a digital blood pressue device  Participants who completed their FU visit remotely were provided a digital blood pressure device with adjustable cuff size .g, 10 min, 4 °C. The serum was separated, refrigerated at 4 °C, and samples were sent to a commercial lab  for analysis [Blood was collected in the seated position by a certified phlebotomist in the morning prior to any caloric food or drink consumption. Participants were instructed to drink 1–2 glasses of water prior to collection. Blood was drawn into vacutainer tubes . Red top tubes were incubated for 30 min at room temperature before being centrifuged at 1500× analysis .The SOS-Free Diet Screener is a 27-question dietary assessment tool that we developed to measure adherence to an exclusively whole-plant-food diet free of added salt, oil, and sugar . For all clinical parameters, regression coefficients were presented with 95% credible intervals (CI). In this report, a regression coefficient is considered significant when the CI does not include zero. The Bayesian framework was used for the statistical analysis due to the small sample size and the availability of baseline, EOF, and EOR data from a different population in a previous study . This prThe primary goal for each clinical parameter was to estimate the difference between values at each time point  and determine which differences were significant. This was investigated using a random intercept mixed effect model with the clinical parameter as the dependent variable and participant ID as the grouping variable. The main fixed effect of interest was the study visit time point . Age, sex, and a prior data indicator were used in the models as fixed effect control variables. For each parameter in the model, the Markov Chain Monte Carlo (MCMC) convergence was assessed using Rhat with a threshold of 1.01 . MoreoveWe enrolled 40 participants, of which 38 completed the EOF and EOR visits and 33 completed the FU visit . At baseline, the median (IQR) non-adherence score was 10  points. At FU, the non-adherence score had decreased slightly to 6  points . Partici2 , and −6.58 cm , respectively (There were clinically meaningful reductions in median BW, BMI, and AC by the EOF visit which were sustained at the EOR and FU visits . Differeectively .There were also clinically meaningful reductions in median SBP and DBP that were sustained at the FU visit . DiffereThere were no differences in median TC nor LDL between the EOF and baseline visits but there was a decrease at the EOR and FU visits compared to the baseline visit . At the There was a decrease in median glucose, insulin, and HOMA-IR at the EOF visit and a subsequent increase at the EOR visit . The estWe also assessed inflammation by measuring hsCRP and calculating FLI with an equation using BMI, AC, TG, and GGT. Median hsCRP increased between the EOF and baseline visits and decreased at the EOR and FU visits compared to the baseline visit . At the We previously found that prolonged water-only fasting followed by an exclusively whole-plant-food refeeding diet improved several biomarkers correlated with increased risk of cardiometabolic disease but also increased insulin resistance in non-diabetic, overweight/obese participants . The resWe found that approximately two weeks of combined water-only fasting and refeeding resulted in an estimated BW reduction of 7.5 kg. Accordingly, median baseline BMI dropped from the obese to the overweight category. Additionally, we have forthcoming data from the same population that the 6% decrease in AC may be associated with a loss of visceral adipose tissue, which is associated with a reduction in whole-body insulin resistance. Methods of fasting or calorie restriction, including water-only fasting, have been criticized for producing unsustainable changes in BW . HoweverOur results support our previous findings that median TC, LDL, VLDL, and TG did not change immediately after fasting, but there was a decrease in median TC and LDL and an increase in median VLDL and TG after refeeding. Additionally, we observed that the decreases in TC and LDL were sustained while the increase in VLDL and TG decreased to within normal clinical range at the six-week FU . The metHOMA-IR is calculated using fasting glucose and insulin and used to approximate insulin sensitivity, with values above 2.0 indicating insulin resistance . As prevFasting causes a well-defined transition from glucose metabolism to fatty acid oxidation ,31 with FLI is another strong predictor of metabolic disease and indicates an increased risk of developing T2D independent of insulin resistance . The useThe participants in this study were educated and instructed to follow an SOS-Free Diet during the refeeding and FU period. Dietary adherence was assessed at baseline and FU using a dietary screener that was specifically designed to measure adherence to the recommended diet but has not yet been validated. This diet is purported to promote health and reverse disease, and may potentiate or prolong the results achieved by fasting alone. Diets predominant in plant foods decrease obesity-related inflammatory markers, such as CRP and IL-6, which are associated with disease . FurtherIt is widely reported that humans have practiced therapeutic water-only fasting for more than two thousand years and there is more than a century of published literature on the physiological and clinical effects of water-only fasting in humans. Over the past decade, clinical research into the beneficial and adverse health effects of water-only fasting has progressed with a level of scientific rigor not previously reported ,10,26,36This open-label, observational study has several limitations including that it occurred at a single fasting center and had a small sample size. Due to the small sample size, the group was not differentiated into any demographic categories such as age, gender, or ethnicity. Another limitation is that the study lacked normal-weight, metabolically “healthy” and diabetic control groups, which are necessary to better understand the effects of body weight and dysregulated glucose metabolism on insulin sensitivity following prolonged fasting. Furthermore, the study lacked a dietary intervention group, which is necessary to assess the effect of the SOS-Free Diet on cardiometabolic health markers and to determine if the sustained effects are attributable to diet and/or the hormetic effects of water-only fasting. An additional limitation is that the SOS-Free Dietary screener has not been validated. Further development and validation of this screener is currently underway.Metabolic diseases, particularly CVD and T2D, are a global public health concern and there is increased interest in developing and implementing cost-effective methods of prevention and reversal. The pathophysiology of metabolic disease is complex, and there is variability in how obesity impacts insulin resistance and lipid metabolism, as some obese people remain insulin sensitive without associated cardiometabolic disorders ,28. Neve


To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC).Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI.In the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions . The diagnostic performance of TLS was not affected by the menstrual state, molecular subtype, or contrast agent type . Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility.An AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC. Lymphovascular invasion (LVI) is a well-recognized risk factor for disease recurrence and shorter survival in patients with invasive breast cancer (IBC), especially those with negative lymph nodes –3. FurthMagnetic resonance imaging (MRI), which can characterize the entire lesion with high spatial resolution, is increasingly studied with LVI assessment in IBC –9. HowevRadiomics is a promising tool for the characterization of breast cancer by extracting quantitative features, but the main drawbacks of using traditional radiomics analysis are time-consuming lesion segmentation and hard-coded feature extraction . CompareHence, the purpose of this study was to evaluate the performance of an AB-MRI-based TL algorithm and compare it with that of radiomics for LVI prediction in patients with clinically node-negative IBC.A schematic illustration of the study design is presented in The ethics committee approved this retrospective study of two participating centers with a waiver for informed consent.in situ or special types of invasive breast carcinoma at final pathologic evaluation (n = 69). Finally, a total of 233 patients  were included. Patients from center 1 were assigned as the training cohort (37 LVI-positive and 93 LVI-negative), and patients from center 2 were the external validation cohort (25 LVI-positive and 78 LVI-negative).Between November 2017 and October 2020, the study enrolled consecutive women with new IBC detected by AB-MRI and clinically lymph node-negative in the study. These patients underwent AB-MRI for breast cancer screening or problem resolving. The inclusion criteria were as follows: (a) underwent AB-MRI and have enhanced lesions on MR image; (b) lesions diagnosed as invasive ductal carcinoma based on pathologic evaluation of surgical specimens; (c) time interval between surgery (mastectomy or lumpectomy) and MRI examination: <2 weeks. The exclusion criteria were as follows: (a) biopsy performed before AB-MRI (n = 58); (b) received neoadjuvant chemotherapy (n = 30) or radiotherapy (n = 55) before MRI examination; (c) node-positive diagnosed on preoperative ultrasonography (n = 37): rounded hypoechoic node, complete or partial effacement of the fatty hilum, focal or diffuse cortical thickening (≥5 mm), complete or partial replacement of the node with an ill-defined or irregular mass, extra-hilar blood vessel flow on color Doppler images, or microcalcifications in the node; (d) MR image with obvious artifacts (n = 15); (e) diagnosed as benign tumors, high-risk lesions, pure ductal carcinoma The AB-MRI protocol included a pre- and early postcontrast phase using a 3D T1 gradient echo with a fat saturation sequence. Detailed AB-MRI parameters are listed in All of the surgical specimens were examined by two senior pathologists with 16 and 13 years of experience in breast pathology, and the pathologic evaluations are provided in U test was used to compare the between-group differences of each radiomics feature in the LVI-positive and LVI-negative groups, and intra-class correlation coefficients (ICCs) were used to evaluate the reproducibility and stability of the radiomics features. The specific process is presented in An RS was developed with the following steps: region of interest (ROI) acquisition, feature extraction, feature selection, and model construction. The ROI was manually segmented by a professional radiologist . Reader 2  randomly chose 30 patients from the training cohort and performed tumor segmentation for inter-reader agreement analysis. Based on the ROI, 10,402 radiomics features were extracted using the in-house software developed with MATLAB 2016 , including first-order, shape-based, and texture features. Then, the Mann–Whitney A least absolute shrinkage and selection operator (LASSO) logistic regression was used to build an RS using a linear combination of features based on the selected features. The features with nonzero coefficients were considered valuable predictors for predicting the LVI status, and the tuning parameter was selected by 10-fold cross-validation for the radiomics method. Finally, the output of the RS was labeled as the radiomics score (R-score).The development of a TLS consisted of two steps: TL features extraction and classification layer training. The first step is the training of feature extraction network. In order to avoid an overfitting of the model, the TL strategy  was usedU test. The second step is the classification layer training based on the LASSO logistic regression; the training process was similar to that of RS.Based on the TL feature, the differences of the transfer learning signature (TLS) between the LVI-positive and LVI-negative groups were assessed using the Mann–Whitney For investigating the interpretability of the TLS, the convolutional filter was visualized with gradient-weighted class activation mapping (Grad-CAM) , which cGiven an ROI image, each convolutional filter generated a response map showing all the corresponding feature patterns extracted from the lesion. A valuable convolutional filter should have different responses to different types of lesions. Thus, the visualization of the response map for convolutional filters in different lesion groups was helpful to understand the TLS.We compared the TLS with the RS to comprehensively evaluate the performances of the models.ROC analysis was performed for the training cohort and external validation cohort to evaluate the diagnostic performance of the TLS and RS. The following parameters were calculated: the area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). The AUCs of the TLS and RS were compared using the DeLong test. In addition, to compare the classification ability of the DLS and RS, the net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated. Decision curve analysis (DCA) was used to estimate the clinical utility of the TLS and RS. Moreover, a stratified analysis was performed on the menstrual state, molecular subtype, and contrast agent type.http://www.rproject.org). All radiomics features were extracted with Matlab 2016, and the TL features were extracted with Python 3.6. LASSO was performed using the “glmnet” package, and the ROC curve analysis was performed using the “pROC”. Clinicopathologic characteristics between the LVI-positive and LVI-negative groups were compared using chi-squared test or Mann–Whitney U test. p Values <0.05 were considered indicative of a statistically significant difference.All statistical tests were performed using R3.0.1  and intraclass correlation coefficient (ICC) values greater than 0.75 were used in the LASSO logistic regression. In the LASSO logistic regression, 14 features with nonzero coefficients  was poor in the validation cohort Table 2,There were several limitations in our study. First, the sample size was relatively small, especially the training cohort, which did not meet the traditional CNN modeling requirement. To overcome this shortage, we pretrained the network with TL. The model based on a transfer learning strategy can avoid overfitting in a training dataset, which reduces the amount of data required for modeling. Nevertheless, the generalization of a TLS still needs to be validated in other centers. Second, multiparametric MRI-based TLS performance was not investigated as the purpose of the study is to investigate whether LVI status can be assessed simultaneously when IBC is detected on AB-MRI. The included patients only underwent AB-MRI for breast cancer screening or lesion diagnosis. However, full diagnostic protocol should be compared with an AB-MRI for LVI prediction in prospective studies. Finally, our study was based on the construction of a two-dimensional slice feature model, and the performance of the three-dimensional features remains to be further studied.In summary, the TLS was superior to the RS for LVI prediction in IBC patients with clinically negative nodes. The proposed AB-MRI-based TLS could potentially serve as an easy-to-access and easy-to-use approach to assist individual breast cancer treatments.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Jiangmen Central Hospital and Nanfang Hospital. The patients/participants provided their written informed consent to participate in this study.WL and LZ designed the research. XL, XC, and RL collected the data. BF, YL, YC, HZ, and TY contributed data analysis tools and performed the analysis; BF also acquired the funding. ZL and BF wrote the paper. WL, EC, and LZ supervised the study. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China , the National Natural Science Foundation of Guangxi (2021GXNSFAA075037), the Guilin University of Aerospace Technology Foundation (XJ21KT17), the Guangdong Basic and Applied Basic Research Foundation (2019A1515011773), the Pearl River S&T Nova Program of Guangzhou under Grant (201906010043), Elite Young Scholars Program of Jiangmen Central Hospital (J201904), and Medical Scientific Research Foundation of Guangdong Province of China (A2020622).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.The reviewer WL declared a shared affiliation with several of the authors, TY and LZ, to the handling editor at time of review.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. 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