Many long-standing theoretical traditions assume that preferences for outgroup members over ingroup members reflect internalized negativity toward the ingroup, especially among members of lower-status groups. Process modeling of implicit outgroup biases suggested otherwise. Among lower-status group members examined here, positivity toward the outgroup contributed more to outgroup biases than negativity toward the ingroup. The pattern of results was mixed among high-status group members with implicit outgroup biases. These findings suggest that members of lower-status groups who prefer higher-status groups over their own groups do not necessarily harbor group self-hatred; instead, they primarily evaluate higher-status outgroups positively. n > 879,000) from Implicit Association Tests  measuring intergroup attitudes across four social domains in exploratory and preregistered confirmatory analyses. Process modeling  was applied to the responses of participants who demonstrated implicit outgroup bias to separately estimate the contributions of negative ingroup and positive outgroup evaluations. The outgroup biases of lower-status group members  consistently reflected greater contributions of positive outgroup evaluations than negative ingroup evaluations. In contrast, the outgroup biases of higher-status group members  reflected a more varied pattern of evaluations. We replicated this pattern of results using explicitly measured intergroup evaluations. Taking these data together, the present research demonstrates a positive–negative asymmetry effect of outgroup bias, primarily among members of lower-status groups.People sometimes prefer groups to which they do not belong (outgroups) over their own groups (ingroups). Many long-standing theoretical perspectives assume that this outgroup favorability bias primarily reflects negative ingroup evaluations rather than positive outgroup evaluations. To examine the contributions of negative ingroup versus positive outgroup evaluations to outgroup bias, we examined participants’ data (total  Intergroup bias can be based on feelings toward the ingroup, feelings toward the outgroup, or both. A wide range of theoretical perspectives converge on the conclusion that ingroup favorability bias primarily reflects positive evaluations of the ingroup rather than negative evaluations of the outgroup .The aim of the present research was to examine the relative contributions of positive and negative evaluations to implicit outgroup bias. To do so, we applied the Quadruple Process model (Quad model)  to respoData, Materials, and Software Availability).Below, we report all sample size determinations, data exclusions, manipulations, and measures . All datWe report descriptive statistics for all participant samples in Participants were American undergraduates who completed an IAT in which they categorized pleasant and unpleasant words along with pictures of Asian and White people. We define East Asian participants as members of the normatively lower-status group, and White participants as members of the normatively higher-status group.Participants were visitors to the North American Project Implicit demonstration website who completed an IAT in which they categorized pleasant and unpleasant words along with pictures of Black and White people. We define Black participants as members of the normatively lower-status group, and White participants as members of the normatively higher-status group.Participants were visitors to the North American Project Implicit demonstration website who completed an IAT in which they categorized pleasant and unpleasant words along with pictures representing gay/lesbian and straight relationships. We define gay and lesbian participants as members of the normatively lower-status group, and straight participants as members of the normatively higher-status group.age = 69.73 y, SDage = 4.62 y, and younger participants’ meanage = 27.72 y, SDage = 5.68 y. In the confirmatory sample, older participants’ meanage = 69.33 y, SDage = 4.41, and younger participants’ meanage = 27.67 y, SDage = 5.85 y. We define older participants as members of the normatively lower-status group, and younger participants as the normatively higher-status group.Participants were visitors to the North American Project Implicit demonstration website who completed an IAT in which they categorized pleasant and unpleasant words along with pictures of old and young people. We operationalized each age group based on the thresholds used by Gonsalkorale et al.  and consAll participants completed an IAT that began with two 20-trial practice blocks, in which they discriminated pleasant from unpleasant words, and the ingroup from the outgroup, respectively. The third and fourth blocks were critical blocks consisting of 20 and 40 trials, respectively. In these blocks, participants pressed one key whenever they saw the ingroup or a pleasant word and another key whenever they saw the outgroup or an unpleasant word. In the fifth 20-trial block, the keys used to categorize the ingroup and outgroup were switched, and participants practiced discriminating the ingroup and outgroup using the new key assignments. The sixth and seventh blocks were critical blocks consisting of 20 and 40 trials, respectively. These blocks were identical to the third and fourth blocks, except participants pressed one key whenever they saw the outgroup or a pleasant word and another key whenever they say the ingroup or an unpleasant word. Group and attribute labels remained on the top left and top right of the screen throughout the task, while stimulus pictures and words appeared at the center of the screen. A red “X” appeared whenever participants made an error, and they were required to correct the error before moving onto the next trial. Latencies were recorded to the correct response, and accuracies recorded the first response. Participants were instructed to make their classifications as quickly and accurately as possible. With the exception of the Asian/White IAT in the exploratory sample, the order of the critical blocks was randomized between participants. The critical blocks of the sexuality IAT for our confirmatory sample included only 20 trials each.Project Implicit hosts three different versions of the sexuality IAT: one consists of stimuli representing gay  and straight relationships; another consists of stimuli representing lesbian  and straight relationships; and a third consists of stimuli representing gay, lesbian, and straight relationships. In the exploratory analyses we did not distinguish among participants' responses to the three types of sexuality IATs, and a puzzling pattern of results emerged. Consequently, in the confirmatory analyses we distinguished participants’ responses among the three types of IATs in an attempt to clarify these findings.The Quad model is a multinomial processing tree model  that estThe structure of the Quad model is depicted as a processing tree in We estimated two AC parameters for each participant.2 for the exploratory sample, and in terms of 1T , and another version specified to reflect ingroup bias . We then computed fit index 1T for each model specification for each participant. We defined participants as demonstrating outgroup bias if 1T indicated better fit for the outgroup-bias model specification than for the ingroup-bias model specification. We excluded participants from further analyses for whom neither model fit, or for whom both models fit equally well.Recognizing the relatively arbitrary nature of the D-scores for each sample. The D-scores of participants in the confirmatory sample were not used to define their direction of bias, but are presented as a validity check.We summarize in In order to determine the extent to which positive outgroup versus negative ingroup evaluations contributed to outgroup bias, we conducted a series of planned contrasts to quantify the difference between the outgroup-pleasant AC parameter and the ingroup-unpleasant AC parameter.2). We applied two additional constraints in order to determine whether each parameter can be reliably distinguished from zero. To do so, we separately constrained each AC parameter to be equal to zero. Without a significant decline in model fit, that constrained AC parameter cannot be inferred to be different from zero.In the exploratory sample, conducting planned contrasts was a multistep process in which we first fit a baseline model in which both AC parameters were estimated freely. Subsequently, for each group of participants we fit a model in which the outgroup-pleasant AC parameter was constrained to be equal to the ingroup-unpleasant AC parameter. Without a significant decline in model fit under this parameter constraint, we cannot infer the two parameters are different from one another. However, if fit significantly declines under this parameter constraint, we can infer that the two parameters are different from one another. The magnitude of the difference between AC parameters is quantified in terms of change in model fit  of the difference does not contain zero, and positive (negative) mean differences indicate that outgroup-pleasant (ingroup-unpleasant) AC estimates are larger than ingroup-unpleasant (outgroup-pleasant) AC estimates. Whereas the maximum-likelihood approach applied to the exploratory sample required model constraint tests to determine whether parameter estimates differed from zero, the hierarchical Bayesian approach applied to the confirmatory sample did not. Instead, parameter estimates in the confirmatory sample can be distinguished from zero if the parameter’s 95% BCI does not contain zero.Because group status plays a prominent role in extant theory about outgroup bias , 8–12, w8In both the exploratory and confirmatory samples, Asian participants’ estimates of White-pleasant associations were larger than their estimates of Asian-unpleasant associations. This difference was reliable in the exploratory but not confirmatory sample. In both samples, White-pleasant and Asian-unpleasant parameter estimates were both different from zero. Thus, Asian participants’ outgroup bias reflected stronger positive outgroup evaluations than negative ingroup evaluations, although positive outgroup and negative ingroup evaluations each contributed to outgroup bias.In both the exploratory and confirmatory samples, Black participants’ estimates of White-pleasant associations were reliably larger than their estimates of Black-unpleasant associations. In both samples, White-pleasant and Black-unpleasant parameter estimates were both different from zero. Thus, Black participants’ outgroup bias reflected stronger positive outgroup evaluations than negative ingroup evaluations, although positive outgroup and negative ingroup evaluations each contributed to outgroup bias.In both the exploratory and confirmatory samples, gay and lesbian participants’ estimates of straight-pleasant associations were larger than estimates of gay/lesbian-unpleasant associations. In both samples, straight-pleasant parameter estimates were different from zero. Gay/lesbian-unpleasant parameter estimates were also different from zero in the exploratory sample, among gay men in the confirmatory sample who completed an IAT with lesbian stimuli, and among lesbian women in the confirmatory sample who completed an IAT with gay stimuli. However, in the confirmatory sample, gay/lesbian-unpleasant parameter estimates were not different from zero among gay men who completed an IAT with gay stimuli, among lesbian women who completed an IAT with lesbian stimuli, and among both gay men and lesbian women who completed an IAT with both gay and lesbian stimuli. Thus, gay and lesbian participants’ outgroup bias reflected stronger positive outgroup evaluations than negative ingroup evaluations, and consistently reflected the contributions of positive outgroup evaluations. Moreover, as the confirmatory analyses reveal, gay and lesbian participants’ outgroup bias did not reflect negative evaluations when their own gender was reflected in the target stimuli in the IAT.In both the exploratory and confirmatory samples, older participants’ estimates of young-pleasant associations were reliably larger than their estimates of old-unpleasant associations. In both samples, young-pleasant and old-unpleasant parameter estimates were both different from zero. Thus, older participants’ outgroup bias reflected stronger positive outgroup evaluations than negative ingroup evaluations, although positive outgroup and negative ingroup evaluations each contributed to outgroup bias.In the confirmatory sample, White participants’ estimates of White-unpleasant associations were reliably larger than their Asian-pleasant associations. Their White-unpleasant parameter estimates were reliably different from zero, but their Asian-pleasant associations were not different from zero. Thus, White participants’ outgroup bias reflected stronger negative ingroup evaluations than positive outgroup evaluations, and only negative ingroup evaluations contributed to outgroup bias.In both the exploratory and confirmatory samples, White participants’ estimates of White-unpleasant associations were reliably larger than their Black-pleasant associations. In both samples, their estimates of White-unpleasant associations were different from zero. In the confirmatory sample, their Black-pleasant associations were different from zero, but were not different from zero in the exploratory sample. Thus, White participants’ outgroup bias reflected stronger negative ingroup evaluations than positive outgroup evaluations, and negative ingroup evaluations consistently contributed to their outgroup bias, but positive outgroup evaluations inconsistently contributed to their bias.In the exploratory sample, straight participants’ estimates of gay/lesbian-pleasant associations were not different from estimates of straight-unpleasant associations, and neither parameter estimate was different from zero. However, in the confirmatory sample, straight participants’ estimates of gay/lesbian-pleasant associations were reliably larger than their straight-unpleasant associations. Their gay/lesbian-pleasant and straight-unpleasant parameter estimates were both different from zero. Thus, straight participants’ outgroup bias reflected stronger positive outgroup evaluations than negative ingroup evaluations, although positive outgroup and negative ingroup evaluations each contributed to outgroup bias, but only in the confirmatory sample, in which we separately modeled the IAT as a function of its stimuli.In both the exploratory and confirmatory samples, younger participants’ estimates of old-pleasant associations were reliably smaller than their estimates of young-unpleasant associations. In both samples, old-pleasant and young-unpleasant parameter estimates were both different from zero. Thus, younger participants’ outgroup bias reflected stronger negative ingroup evaluations than positive outgroup evaluations, although positive outgroup and negative ingroup evaluations each contributed to outgroup bias.Across both exploratory and confirmatory samples, the implicit outgroup biases of lower-status groups  consistently reflected the greater contribution of positive outgroup evaluations than negative ingroup evaluations. However, the implicit outgroup biases of higher-status groups  demonstrated a less consistent pattern of results. The implicit outgroup biases of straight participants reflected the greater contribution of positive outgroup evaluations than negative ingroup evaluations, but the implicit outgroup biases of White and younger participants reflected the greater contribution of negative ingroup evaluations than positive outgroup evaluations. Thus, our findings reveal a positive–negative asymmetry effect for implicit outgroup bias, and primarily in the context of lower-status groups.D-scores of these participants in Given the novelty of our process-modeling approach to investigating the contributions of positive and negative evaluations to implicit outgroup bias, we sought to validate this approach by investigating whether it could also reveal the well-established positive–negative asymmetry effect —an ingroThe specification of the Quad model we applied to the data of participants who demonstrated implicit ingroup bias was largely the same as the specification we applied to the data of participants who demonstrated implicit outgroup bias, with one important difference. Given that the Quad model assumes a compatibility structure to participants’ responses—such that the direction of bias reflects the dominant valences of the underlying associations—we specified one AC parameter to reflect positive evaluations of the ingroup, and another AC parameter to reflect negative evaluations of the outgroup. All other modeling procedures were identical to the outgroup bias analyses. Accounting for sample size, model fit was relatively consistent across datasets, and adequate in all cases . We depiIn order to determine the extent to which positive ingroup versus negative outgroup evaluations contributed to implicit ingroup bias, we conducted a series of planned contrasts to quantify the difference between the ingroup-pleasant AC parameter and the outgroup-unpleasant AC parameter using the same procedures as used with the outgroup bias analyses. We report planned contrasts for the exploratory and confirmatory samples in In the exploratory sample, Asian participants’ estimates of Asian-pleasant associations were reliably larger than their estimates of White-unpleasant associations. Their estimates of Asian-pleasant associations were different from zero, but their estimates of White-unpleasant associations were not different from zero. However, in the confirmatory sample, Asian participants’ Asian-pleasant associations were descriptively (but not reliably) smaller than their estimates of White-unpleasant associations. Their estimates of Asian-pleasant associations and White-unpleasant associations were both different from zero. Thus, Asian participants’ ingroup bias reflected positive ingroup evaluations, but inconsistently reflected negative outgroup evaluations.In the exploratory sample, Black participants’ estimates of Black-pleasant associations were not different from their estimates of White-unpleasant associations. Their estimates of Black-pleasant and White-unpleasant associations were both different from zero. However, in the confirmatory sample, Black participants’ Black-pleasant associations were reliably smaller than their estimates of White-unpleasant associations. Their estimates of Black-pleasant and White-unpleasant associations were both different from zero. Thus, Black participants’ ingroup bias reflected both positive ingroup and negative outgroup evaluations, although the primacy of one type of evaluation was inconsistent across samples.In both exploratory and confirmatory samples, gay and lesbian participants’ estimates of gay/lesbian-pleasant associations were reliably larger than their estimates of straight-unpleasant associations. Their estimates of gay/lesbian-pleasant and straight-unpleasant associations were both different from zero. Thus, gay and lesbian participants’ ingroup bias reflected stronger positive ingroup evaluations than negative outgroup evaluations, although positive ingroup and negative outgroup evaluations each contributed to ingroup bias.In the exploratory sample, older participants’ estimates of older-pleasant associations were reliably larger than their estimates of younger-unpleasant associations. Their estimates of older-pleasant associations were different from zero, but their estimates of younger-unpleasant associations were not different from zero. However, in the confirmatory sample, older participants’ older-pleasant associations were reliably smaller than their estimates of younger-unpleasant associations. Their estimates of older-pleasant associations and younger-unpleasant associations were both different from zero. Thus, older participants’ ingroup bias reflected positive ingroup evaluations, but inconsistently reflected negative outgroup evaluations.In both the exploratory and confirmatory samples, White participants' estimates of White-pleasant associations were reliably larger than their estimates of Asian-unpleasant associations. Their estimates of White-pleasant associations and Asian-unpleasant associations were both different from zero. Thus, White participants’ ingroup bias reflected stronger positive ingroup evaluations than negative outgroup evaluations, although positive ingroup and negative outgroup evaluations each contributed to ingroup bias.In both the exploratory and confirmatory samples, White participants’ estimates of White-pleasant associations were reliably larger than their estimates of Black-unpleasant associations. Their estimates of White-pleasant associations and Black-unpleasant associations were both different from zero. Thus, White participants’ ingroup bias reflected stronger positive ingroup evaluations than negative outgroup evaluations, although positive ingroup and negative outgroup evaluations each contributed to ingroup bias.In both exploratory and confirmatory samples, straight participants’ estimates of straight-pleasant associations were reliably larger than their estimates of gay/lesbian-unpleasant associations. Their estimates of straight-pleasant and gay/lesbian-unpleasant associations were both different from zero. Thus, straight participants’ ingroup bias reflected stronger positive ingroup evaluations than negative outgroup evaluations, although positive ingroup and negative outgroup evaluations each contributed to ingroup bias.In both exploratory and confirmatory samples, younger participants’ estimates of younger-pleasant associations were reliably larger than their estimates of older-unpleasant associations. Their estimates of younger-pleasant and older-unpleasant associations were both different from zero. Thus, younger participants’ ingroup bias reflected stronger positive ingroup evaluations than negative outgroup evaluations, although positive ingroup and negative outgroup evaluations each contributed to ingroup bias.We found evidence for the positive–negative asymmetry effect  in all oThe positive–negative asymmetry effect  was origParticipants in the confirmatory sample each completed two feeling thermometers, one measuring evaluations of their ingroup and another measuring evaluations of the outgroup. Participants responded using an 11-point scale ranging from 0 to 10, such that responses above 5 reflect positive evaluations, responses below 5 reflect negative evaluations, and responses at 5 reflect neutral evaluations. We defined participants as demonstrating outgroup bias when they responded with higher values on the outgroup thermometer than on the ingroup thermometer. We defined participants as demonstrating ingroup bias when they responded with higher values on the ingroup thermometer than on the outgroup thermometer. We did not examine participants who responded with equivalent values on the ingroup and outgroup thermometers. We also did not examine participants in the exploratory samples because we did not measure their explicit biases.We summarize mean responses on each thermometer  in The present research examined the extent to which negative ingroup and positive outgroup evaluations contribute to implicit outgroup bias. In contrast to early intergroup relations theories that assumed internalized inferiority and “group self-hatred” among low-status group members , 8–10, a8Although the outgroup biases of the lower-status group members examined here reflect the greater influence of positive outgroup than negative ingroup evaluations, the present research also indicates that these biases are not devoid of ingroup negativity. Indeed, across most lower-status groups examined here, both positive outgroup and negative ingroup evaluations significantly influence participants’ responses, although gay and lesbian participants’ outgroup biases did not reflect the contributions of negative ingroup evaluations when their own gender was reflected as stimuli in the IAT. This finding both supports and extends social dominance  and systAlthough not the focus of our investigation, our analyses of explicit biases further supported the primacy of positive evaluations in intergroup biases. Across samples and comparisons, explicit biases in favor of both ingroups and outgroups reflected positivity toward the favored group rather than negativity toward the comparison group. However, we observed a more nuanced pattern of results in the context of implicit ingroup bias. The implicit ingroup biases of higher status group members consistently reflected more positivity toward the ingroup than negativity toward the outgroup, but the ingroup biases of lower status groups often reflected contributions of both negative and positive evaluations. This asymmetry in evaluations dovetails with previous work that identifies both lower status and minority status as aggravating factors that increase the relative contribution of negativity to ingroup bias . ConsideThe present research revealed that outgroup bias often reflects positive outgroup evaluations. How might people form positive evaluations of outgroup members that are more positive than their evaluations of ingroup members? One possibility is through contact. People who develop strong ties to an outgroup community may grow to prefer members of that group to their own. However, contact is asymmetrical, such that members of minority groups  often have more contact with members of majority groups  than vice versa, and thus contact may be more likely to explain the positive outgroup evaluations of low-status but not high-status group members. Another possibility is that positive outgroup evaluations are formed through cultural learning; positive depictions of outgroup members in movies, television, and other media would account for the evaluations of both low- and high-status group members. Future research should investigate the relationship between media exposure and outgroup evaluations, which in turn may highlight the value of representation in media.The present research on the evaluations underlying outgroup bias was inspired by research on the evaluations that comprise ingroup bias –5. We teThe most varied outcome that emerged in the present research was among the outgroup biases of higher-status group members. When we separated our analyses by participant gender and IAT stimuli, the outgroup bias of straight participants demonstrated the positive–negative asymmetry effect. However, the outgroup biases of White and younger participants were characterized by more negative ingroup than positive outgroup evaluations. Further research is needed to determine whether this pattern of sexuality evaluations represent the exception or the rule for implicit outgroup biases among higher status group members. Perhaps sexual orientation creates an ingroup that is especially devoid of negative associations: the desire for sexual or romantic relationships that defines group membership is definitively positive. Even straight people who have positive evaluations of gay or lesbian people that outweigh their positive evaluations of other straight people likely have more positive than negative associations with their ingroup, otherwise they would not identify as straight. Other members of higher-status groups with more permeable boundaries than those defined by race and age  may likewise produce outgroup biases that demonstrate the positive–negative asymmetry effect. Given the paucity of research in this area, the outgroup biases of high-status group members represent a potentially fruitful topic for future investigations.The present research is limited in that it only examined outgroup biases in the context of three social identities: race, sexual orientation, and age. Although these identities are highly salient in most intergroup interactions, future research should examine whether the patterns of outgroup bias demonstrated here generalize to other social identities and situations. Outgroup evaluations may be expected to be less positive in contexts with protracted histories of intergroup hostility, such as South Africa, Northern Ireland, and the American South , althougThe present research demonstrates the primacy of positive outgroup over negative ingroup evaluations in the outgroup biases of lower-status group members. In contrast, the outgroup biases of higher-status group members reflect a more varied pattern of positive and negative evaluations. We hope that these findings can be used as a basis for future research that examines the downstream consequences of outgroup bias. Separately studying the positive and negative components of outgroup bias may provide a fruitful means of advancing our understanding of intergroup relations.Supplementary File


Team-based primary care reforms aim to improve care coordination by involving multiple interdisciplinary health professionals in patient care. Team-based primary care may support improved medication management for older adults with polypharmacy and multiple points of contact with the healthcare system. However, little is known about this association. This study compares sociodemographic and prescribing trends among older adults in team-based vs. traditional primary care models in Ontario and Quebec.We constructed two provincial cohorts using population-level health administrative data from 2006–2018. Our primary exposure was enrollment in a team-based model of care. Key endpoints included adverse drug events (ADEs), potentially inappropriate prescriptions (PIPs), and polypharmacy. We plotted prescribing trends across the observation period (stratified by model of care) in each province. We used standardized mean differences to compare characteristics of older adults and providers, as well as prescribing endpoints.Formal patient/physician enrollment increased in both provinces since the time of policy implementation; team-based enrollment among older adults was higher in Quebec (47%) than Ontario (33%) by the end of our observation period. The distribution of sociodemographic characteristics was reasonably comparable between team-based and non-team-based patients in both provinces, aside from a persistently higher share of rural patients in team-based care. Most PIPs assessed either declined or remained relatively steady over time, regardless of model of care and province. Several PIPs were more common among team-based patients than non-team-based patients, particularly in Quebec. We did not detect notable trends in ADEs or polypharmacy in either province.Our findings offer encouraging evidence that many PIPs are declining over time in this population, regardless of patients’ enrollment in team-based care. Rates of decline appear similar across models of care, suggesting these models may not meaningfully influence prescribing endpoints. Additional efforts are needed to understand the impact of team-based care among older adults and improve primary care prescribing practices.The online version contains supplementary material available at 10.1186/s12875-022-01960-z. The proportion of people aged 65 and over has nearly doubled across OECD countries since 1960 . While iTeam-based primary care is one approach that may promote healthy aging by improving the coordination and application of expertise across disciplines providing patient care for older adults , 19, whiTeam-based models are designed to offer a consolidated and coordinated point of care for patients, which could ultimately streamline medication review and prescribing practices . HoweveIt would be informative for policy makers considering primary care model options to understand if older adults in team-based care – and their providers – differ from those who are not in team-based care. An analysis of early adopters in Quebec  reportedOur aim was to describe the characteristics of older adults receiving care in team-based (vs. non-team based) practices in Ontario and Quebec, paying particular attention to within- and between-province temporal trends in sociodemographic composition. We also assess several common indicators of medication management in this population, exploring trends over time and by province/model of care, as well as provider characteristics.We constructed two open cohorts of older adults using population-level health administrative data in Quebec and Ontario, with exclusion criteria and relevant measures harmonized across provinces.In Quebec, we partnered with the Institut national d’excellence en santé et en services sociaux (INESSS) to construct our cohort . In OntaWe assessed our cohorts across a series of repeated cross-sections to examine changes over time. To ensure an adequate number of team-based practices in each year, we focused our analysis on fiscal years 2006–2018. Although several GMFs existed in Quebec prior to the start of this observation period, these practices were unique and served a higher-morbidity (and therefore less representative) population , 29. EliPatients were considered “exposed” to FHT/GMF models based on the affiliation of their family physician in each fiscal year. Our patient/physician linkage strategy joined patients to the family physician with whom they were formally enrolled for at least ¾ of the fiscal year (if applicable). We also constructed a second linkage based on patients’ usual provider of care, or UPC  to examine the robustness of our findings to an alternative definition of patient/provider attachment. As findings were consistently robust to the choice of linkage/attachment definition, we rely on the “formal enrollment” definition throughout this analysis.Patient-level endpoints included three common indicators of medication management in older adults: adverse drug events (ADEs), potentially inappropriate prescriptions (PIPs), and polypharmacy. These endpoints are important and informative indicators of overall medication use and medication appropriateness/related complications in this population –10, 20. The aim of this analysis was to descriptively compare patient/physician characteristics and prescribing trends across the two provinces, and across models of care (team-based vs. non-team-based). We began by harmonizing data across Ontario and Quebec. Challenges emerged in defining our outcome measures given differences in the provincial data holdings: for example, algorithms to create Beer’s List and STOPP/START criteria indicators, two commonly used measures of potentially inappropriate prescribing , 35, werAnother challenge was that was that Ontario and Quebec rely on different drug classification systems  and American Hospital Formulary Service (AHFS) classification systems, respectively), which initially prevented a comparable count of unique medications between provinces, and therefore a comparable measure of polypharmacy. To resolve this issue, we used a crosswalk file in Quebec to map drug identification numbers (DINs), available in both provinces, to ATC codes, and constructed a measure of polypharmacy defined as five or more different drug classes (3rd level ATC) per fiscal year. This approach, which has been employed in other work , 37 yielWe summarized patient/physician characteristics and prescribing measures across repeated cross-sections of our observation period. We also plotted prescribing trends across the observation period, stratified by FHT/GMF status, in each province.Figure Tables Figures Several individual PIPs were persistently more common among FHT/GMF patients than non-FHT/GMF patients ; in contrast, long-acting sulfonylureas were less common. We also observed provincial differences in certain PIPs , which were more prevalent among team-based (vs. non-team-based) patients in Quebec, but not in Ontario. Combinations of PIPs  and the percentage experiencing one or more adverse drug event did not exhibit a clear trend over time in either province Fig. , althougDespite similar FHT/GMF (vs. non-FHT/GMF) sociodemographic profiles within each province, FHT/GMF-enrolled older adults in our sample were more commonly prescribed several of the PIPs we examined. Adverse drug events and polypharmacy were also more common among older adults enrolled in team-based care. Interestingly, the prevalence of polypharmacy remained relatively stable over our observation period even though individual and combination PIPs generally decreased over time.As formal enrollment and FHT/GMF enrollment increased over time among older adults, so too did the preferential reliance on the primary care provider with whom the patient was enrolled, with most older patients in both provinces seeking the bulk of their primary care from the same provider. Our findings suggest that certain patient and provider characteristics – for example, patient rurality, physician gender, and the number of years a physician has been in practice – are positively associated with participation in team-based models of care in both Ontario and Quebec. However, the sociodemographic profile of FHT/GMF patients (vs. non-FHT/GMF patients) is relatively similar, and early differences between these groups have largely dissipated over time.Most adverse endpoints were trending downward in both provinces across both team and non-team-based models of care. PPI prescribing trends were somewhat unique in this analysis as they increased while other PIPs decreased; however, this was likely due to the timing of PPI deprescribing efforts, which occurred relatively late in our observation period (we do see these trends plateau from roughly 2016 onward). The higher prevalence of certain PIPs, ADEs, and polypharmacy among FHT/GMF-enrolled older adults is potentially indicative of important underlying characteristics in this population: for example, patients with chronic conditions require more medications and may be more likely to formally enroll with a provider, which would lead to a higher incidence of  polypharmacy among FHT/GMF enrollees. We therefore expected these trends to diverge early in the observation period (particularly in Quebec), when team-based enrollment was more common among older adults with complex health needs. However, the persistent nature of this difference suggests that, despite our findings that the sociodemographic profiles of these groups were relatively similar, FHT/GMF patients may in fact differ from their non-FHT/GMF counterparts in important ways. Future analyses should consider incorporating measures of morbidity and prescriber characteristics to better understand the observed trends in adverse prescribing outcomes. It is also important to note that our approach does not reflect the complexity of prescribing in this patient population: the physician with whom a patient is enrolled is often not their only prescriber, as illustrated by the number of “unique prescribers” in each province Tables . SimilarOur results are consistent with previous work , despiteOur study had several limitations, many of which were linked to our prescribing measures . First, our ADE definition was based on an established list of ICD-10 codes, but this list is conservative and only captured drug events severe enough to result in hospitalization. Second, given the lack of validated measures of inappropriate prescribing  in Quebec, and in the interest of generating a harmonized measure of inappropriate prescribing, we relied on the substantive knowledge of our team’s pharmacists to generate a list of commonly contraindicated drugs and combinations in this population. This list is not formally validated, but all the included medications appear in one or both of the Beer’s List and STOPP/START criteria. As such, our findings likely underestimate the prevalence of PIPs compared to these established criteria. Another limitation was that Ontario and Quebec rely on different drug classification systems , which initially prevented a comparable count of unique medications between provinces, and therefore a comparable measure of polypharmacy. We resolved this issue by mapping DINs (available in both provinces) to ATC codes to build our harmonized measure of polypharmacy. It is also important to note that our prescribing data reflects dispensations (prescriptions filled and paid for by the public insurer) rather than consumption. Finally, area-level measures of deprivation were only available in Quebec in 2006 and 2016. We used the 2006 indices in the first half of our data period (FY2006–10) and the 2016 indices in the later period (FY2011–18). Some misclassification is inevitable but likely negligible given the high correlation (ranging from .7 to .8) between these area-level deprivation measures over time. Data limitations also limited our ability to describe and adjust for case mix.This descriptive work illustrates both the sociodemographic characteristics of older adults in team-based care (and their non-team-based counterparts) and trends in prescribing outcomes in two Canadian provinces. Our findings offer encouraging evidence that many PIPs are declining over time in this population, regardless of patients’ enrollment in team-based care. Rates of decline appear to be quite similar across models of care, which suggests that these models may not meaningfully influence prescribing endpoints .While our analysis does not quantify the impact of FHTs/GMFs on adverse prescribing endpoints in older adults, our descriptive approach offers valuable insight regarding the composition of these groups and key temporal trends, both of which are essential considerations in estimating the impact of these models of care. Our use of a population-level database allows us to present comprehensive estimates on a key demographic of interest. We also contribute an alternative framework for assessing PIPs in contexts without access to standard measures . Finally, to our knowledge, there have been no comparative studies evaluating how different approaches to implementing team-based care across provinces have affected medication management, nor have there been any attempts to understand the mechanisms by which improvements in medication management and associated outcomes may arise in team-based care. This study is a first step toward these larger goals. Accordingly, this work is useful at face value  and as a catalyst for further rigorous evaluative analyses.Additional file 1: Appendix 1. CIHI list of ADEs. Appendix 2. Trends in patient/provider attachment among older adults in Ontario and Quebec. Appendix 3. Medication trends. Appendix 4. Other PIP trends, by province/enrollment status.


Circular RNAs (circRNAs) are an abundant class of endogenous non-coding RNAs (ncRNAs) generated from exonic, intronic, or untranslated regions of protein-coding genes or intergenic regions. The diverse, stable, and specific expression patterns of circRNAs and their possible functions through cis/trans regulation and protein-coding mechanisms make circRNA a research hotspot in various biological and pathological processes. It also shows practical value as biomarkers, diagnostic indicators, and therapeutic targets. This review summarized the characteristics, classification, biogenesis and elimination, detection and confirmation, and functions of circRNAs. We focused on research advances circRNAs in the mammalian ovary under conditions including ovarian cancer, polycystic ovarian syndrome (PCOS), and maternal aging, as well as during reproductive status, including ovarian follicle development and atresia. The roles of circRNAs in high reproductive traits in domestic animals were also summarized. Finally, we outlined some obstructive factors and prospects to work with circRNA, aiming to provide insights into the functional research interests of circRNAs in the reproduction and gynecology areas. Circular RNAs (circRNAs) are a highly abundant class of endogenous non-coding RNAs (ncRNAs) discovered originally in RNA viruses  and thenAs the name implies, the most critical feature of circRNAs is their covalently closed loop structure without free 5′,3′ ends and poly(A) tail. As a result, circRNAs resist exonucleases (which require free ends) represented by RNase R, while still sensitive to endonucleases such as RNase A . With suDespite being widely expressed in most tissues, circRNAs showed a lower global abundance than their cognate mRNAs with some single-gene level exceptions, in which circRNAs can be 10-fold more abundant than their linear analogues ,12. InteThe evolutionary conservation of circRNAs has been proved by their sequences (originating from conserved exons/introns across eukaryotic species), formation manner, and even regulatory mechanisms ,17. MeanAccording to their distinct sequence composition, circRNAs are classified into three main types: exonic circRNAs (ecircRNAs); intronic circRNAs (ciRNAs); and exon–intron circRNAs (EIciRNAs). Note that three other classifications were also announced recently. tRNA intronic circRNAs (tricRNAs) were so far only reported in archaea and Drosophilid species, in which tRNA introns are highly conserved . InterioThe essential event for most circRNA biogenesis is the back-splicing process that ligates a downstream 5′ splice donor site with an upstream 3′ splice acceptor site to form a closed-loop structure. The detailed circularization may be induced through several non-exclusive pathways, including lariat-driven circularization (exon skipping), direct back splicing (intron-pairing driven circularization), and spatial proximity due to binding and interaction of RNA-binding proteins (RBPs). The varied biogenesis processes were represented nicely by the schematic diagrams in reviews of Huang , Tran 2, and Zan6-methylation of adenosine (m6A) recruited endonucleases [The typical RNA degradation pathways are inapplicable for circRNAs as they have no free ends. Recent studies found that circRNA degradation can be initiated by Nucleases  and the ucleases ,30. Moreucleases , and relucleases ,32.In the cytoplasm, the most straightforward function of circRNA EOC s is based on the complementary binding of miRNAs as sponges, which reduces active miRNA and consequently regulates the expression of its target genes . In otheciRNAs located in the nucleus may have diverse functions in the cis-regulation of genes. The formation of ecRNAs that are derived from exons may compete with the selective splicing of their precursor mRNAs (pre-mRNAs), which may lead to reduced levels of linear mRNAs and changes in their composition due to the missing of specific exons ,39. On tThe detection, validation, and quantification of circRNAs is somehow analogous to studying other types of coding or non-coding RNAs. The main change is to separate circRNA from other RNA species as the majority of their sequence that is generated from the same host gene is shared. The combination of high-throughput sequencing and bioinformatic technology is no doubt the most effective way to explore novel circRNAs. General total RNA library can be used for circRNA identification/prediction with tools such as CircRNA finder , MapSpliThe hybridization-based assays are reliable for the validation of known circRNAs, and PCR-based methods can be used for quantification. Northern blot was considered the gold standard for confirming the size and configuration of circRNAs but was limited by its high requirement of RNA sample amount and time-consuming steps. For in situ detection of circRNAs, fluorescence in situ hybridization (FISH) with probes specific for the back-splice junction sites reveals their subcellular localization . For batThe existing gain and loss of function methods used for gene function study can also be applied for overexpression and depletion of specific circRNA with certain adjustments. When applying RNAi-mediated degradation with small interfering RNA (siRNA) or short hairpin RNA (shRNA), target the back-splicing junction to achieve a circRNA-specific knockdown effect. Plasmids containing circRNA-producing exons and their flanking intronic sequences can be used to introduce circRNAs to cells by transfection. Detailed experimental methods for circRNAs validation were well reviewed and evaluated by Li et al. .The genome-wide profiles of ovarian circRNAs were mainly reported in humans, mice, pigs, and goats. In humans, circRNA studies have been focused on pathological examination of ovarian cancer, PCOS, and ageing. Luckily, studies in animals provided more knowledge regarding ovary growth, changes in estrus, as well as follicle development, and atresia. Comparisons between different reproductive performances and breeds were also reported . Here, wCircRNAs in ovarian dysfunction attracted close attention due to their tight interaction with miRNAs. In 2015, a comprehensive assessment compared circRNA levels across several normal and cancerous tissues, including ovarian cancer, and discovered a global reduction in circular RNA abundance in cancer compared to normal tissues, therefore suggesting a negative correlation between circular RNA abundance and cell proliferation . Almost PCOS is the most common endocrine disorder in women of reproductive age. To reveal the functions of circRNAs in the development of PCOS, circRNA profiles from cumulus cells and follicle fluid were assessed, respectively. Che et al. determined 311 increased and 721 decreased circRNAs in cumulus cells from PCOS compared to control participants who underwent IVF using microarray . With thThe decline of female reproductive capacity with age, termed ovarian senescence, results in a gradual reduction in the quantity and quality of oocytes. Cheng et al. first compared circRNAs in GCs from in vitro fertilization (IVF) patients with young (≤30) and advanced (≥38) ages using human circRNA microarrays. This study revealed 46 up-regulated and 11 down-regulated circRNAs in aged samples. Later, Cai et al. compared circRNA expression profiles between healthy ovarian cortex from young (25–28) and ageing (44–46) groups and identified 194 up-regulated and 207 down-regulated circRNAs enriched in oxidation-reduction, steroid hormone biosynthesis, and insulin secretion pathways, during ageing .CircRNA profiles during ovarian development, estrus cycles, and follicular growth were explored mainly using large animals such as pigs and goats. CircRNA landscape in adult and neonatal ovaries was first examined and compared in mouse ovarian tissue using high-throughput sequencing. Estrogen signaling was found to be the most significant pathway that up-regulates in adult ovaries . In pigsRegarding the estrus cycle, Liu et al. analyzed the circRNA profiles of Yunshang black goat ovarian tissues among high and low fecundity groups in the follicular phase and luteal phase, and conclude that circRNAs play a key role in both the prolificacy trait and transformation of the follicular phase to the luteal phase in the estrus cycle . At the circRNA profiles in healthy and atretic antral follicles were first deep sequenced by Guo et al., and 192 circRNAs were reported to be differentially expressed during the atresia process . Based oTo explore the circRNA functions in reproductive performance, the circRNA function in litter size was investigated in pigs, goats, and sheep. In pigs, circRNA profiles of ovaries from large and small litter sizes groups were performed by Xu et al. , and 56 (1)The detection and validation of circRNAs are less straightforward and accurate quantification and manipulation of circRNAs are more time-consuming and are more likely to be affected by the linear mRNA encoded by the same gene;(2)The conservation of circRNA among species is relatively low, which makes it difficult to make comparisons and transfer discovery of one species to another;(3)The subcellular location and molecular function of circRNAs are varied, which adds complexity to reveal the functional networks of circRNAs. However, precisely because of their versatile and unique structure, circRNAs became a research hotspot of great potential.circRNA-mediated regulation in the ovary has drawn more and more attention recently. Unlike miRNAs, which are well examined in follicular development, atresia, and ovarian disorders , the bioIn the ovarian pathology area, circRNAs show tumor-promoting properties by either increase OC cell proliferation, invasion and migration through PTEN/PI3k/AKT, JAK/STAT, and MAPK signalings or indirectly exert the suppression mechanisms, such as apoptosis, ferroptosis and autophagy pathways through PPARγ, Hippo or Rho GTPase signalings. The involvement of circRNAs also affect ovarian function by contributing in oxidation–reduction and steroid hormone biosynthesis processes. The highly stable nature and cell specificity of circRNAs makes them promising biomarkers for disease as they were detectable in whole blood, plasma, and platelets  when rel(1)circRNAs involved regulatory networks in critical pathways such as steroid hormone biosynthesis, programmed cell death , and oxidative stress response during ovarian physiological processes;(2)circRNAs involved in tumorigenic or suppressive pathways which can be used as therapeutic targets;(3)circRNA serving as stable diagnostic and prognostic biomarkers to assess the reproductive status, disease, and applications in assisted reproductive technology;(4)The roles of exosomal circRNAs in oocyte-granulosa-thecal cell communication;(5)Identification and functional studies of proteins or peptides products of circRNAs;(6)Development of practical and effective techniques for quantitative detection of circRNAs. Although our understanding and application of circRNAs in the mammalian ovary are still in the initial stage, these unique molecules hold immense potential for further research and will pave new avenues for the field.In the female reproduction area, the following aspects may attract more attention in future studies:


Cardiovascular disease remains the leading cause of death worldwide, despite significant advances in medical technology and in our knowledge of the disease and the treatment , 2. OverGiven the complexity of cardiovascular physiology, this field of study must encompass a wide range of research, from basic to translational, and aim to create diagnostic strategies, interventions, and biomedical devices that employ engineering principles and methods for cardiovascular disease. This special issue focuses on recent developments, reviews, and applications in cardiovascular engineering, incorporating topics such as mechanical assist devices, medical imaging, cardiovascular biomechanics, and biomaterials. In this editorial article, we provide a summary of the featured work in this issue.Fang et al. assessed the impact of pulsatility using rotor speed modulation sequences in the HeartMate 3. The authors conducted a comprehensive computational fluid dynamics investigation on the washout performance with a particular focus on slow washout regions as an indicator of elevated risk of in-pump thrombus formation. The findings suggested that artificial pulsatility mostly enhances the washout of the flow separation region distal of the outflow cannula curvature. Furthermore, a physiological data-driven iterative learning controller has been introduced by Magkoutas et al.. Their controller effectively tracks predefined pump flow trajectories, and aims to generate a physiologically relevant, pulsatile, and treatment-driven response for continuous-flow VADs. This is a significant advancement over continuous-flow VADs and speed-based controllers, and opens up new opportunities in the management of continuous-flow VAD therapy since patient-specific preload sensitivity may be chosen directly based on clinical feedback.Several groups focused on mechanical circulatory support (MCS) devices, such as ventricular assist devices (VADs) and total artificial hearts (TAHs) to advance existing knowledge and technological solutions for cardiovascular disease. Two papers shed novel light on the role of artificial pulsatility in continuous-flow support devices. Fox et al. have emphasized the need for effective therapeutic options for infants and children. Their research focused on developing a novel, continuous-flow, magnetically levitated TAH that may be used as a therapeutic option for high-risk pediatric patients with heart failure. This detailed work on a magnetically levitated prototype, which included computational analysis, in vitro hydraulic testing and hemolytic assessment revealed promising hydraulic performance characteristics, supporting the pulmonary and systemic circulation adequately in a hybrid design configuration.Malone et al. developed a new bench top mechanical circulatory loop (MCL) that can model the normal heart function and heart failure with preserved ejection fraction (HFpEF)—a multifactorial type of heart failure with multiple phenotypes. Their MCL features two independently controlled cardiac chambers which can be altered to limit diastolic filling to mimic the severity of HFpEF and can accurately simulate the diastolic hemodynamics of the left atrium and left ventricle. In addition, a patient-specific lumped parameter model of HFpEF was developed and a prototype of a soft robotic extra-aortic counterpulsation device was created to investigate the physiological implications of varying operating conditions by Arduini et al.. Briefly, they investigated the feasibility of patient-specific testing of novel cardiac support devices using lumped parameter computational models based on clinical hemodynamic data. Although the advantages of counter-pulsation therapy are not yet totally elucidated, the framework provided in this study stimulate future research into the efficacy of counter-pulsation therapy as a treatment for patients with HFpEF.Besides MCS, a number of groups demonstrated innovative preclinical disease models as a testbed to drive cardiovascular engineering design, testing, and validation. Kolli et al. created an in vitro experimental flow loop employing patient-specific 3D-printed coronary arteries as a proxy for invasive cardiac catheterization laboratory measurements. In this pilot study, they evaluated coronary artery ischemia using an in vitro method, combination of non-invasive coronary CT angiography and 3D printing based fractional flow reserve (FFR). When compared to invasive FFR approach, their 3D printing based non-invasive environment showed a strong correlation and a consistency, paving the way for a non-invasive diagnostic technique that can eventually substitute invasive FFR.Exarchos et al. describe research supporting the hypothesis that micro-structured surfaces with anisotropic topographies can enhance long-term endothelialization of cardiovascular implants (CVIs). Describing recent efforts to modify the surfaces of various CVIs, including stents, grafts, valves, and VADs, they discuss the associated advances and challenges that have been encountered during their clinical translation. In their original research article, the authors show that anisotropic topographies on micro-structured surfaces could restore the monolayer integrity of senescent endothelial cells, enhances cell alignment, and promote proliferation.Morales et al.. The authors aimed to explore whether DeepStrain may identify asymptomatic dysfunction in young adults with cardiac risk factors such as obesity, hypertension, and type 2 diabetes mellitus. Reference strain values for asymptomatic LV diastolic and systolic dysfunction in various young adult populations have been presented using a fully automatic strain analysis software.Regarding cardiac magnetic resonance imaging (MRI) approaches, a deep learning method for fully automatic strain analysis (termed DeepStrain) based on cine-MRI images was introduced by Huh et al. presented a novel 4D spatiotemporal reconstruction process for dynamically acquired single photon emission computed tomography data. Since cardiac deformation and respiration both reduce the quality of the reconstructed image, the authors developed an algorithm to reconstruct the dynamic sequence of independent respiratory and cardiac phases and tested it using the data generated from a Monte Carlo simulation.What's more, in vitro simulators for pediatric and adult patients. We appreciate the efforts of all authors for helping make this such a fruitful special issue.In summary, this specific Research Topic on cardiovascular engineering provides a platform to exhibit a variety of original research, methods and review articles on recent scientific advances in cardiovascular implants, computational modeling, imaging, and CO drafted the Editorial manuscript. MSD, XZ, ETR, and CTN revised it for interpretation and content. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.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.


We will also discuss limitations hindering the cell therapy field, which include the challenges of performing cell-based clinical trials and the low retention of administered cells, and how future research may overcome them.Myocardial infarction is a leading cause of death globally due to the inability of the adult human heart to regenerate after injury. Cell therapy using cardiac-derived progenitor populations emerged about two decades ago with the aim of replacing cells lost after ischaemic injury. Despite early promise from rodent studies, administration of these populations has not translated to the clinic. We will discuss the need for cardiac regeneration and review the debate surrounding how cardiac progenitor populations exert a therapeutic effect following transplantation into the heart, including their ability to form  Myocardial infarction (MI) is a leading cause of morbidity and mortality worldwide (WHO). With advances in MI treatments, that include pharmacological agents and mechanical assist devices, more patients survive MI and therefore have an increased risk of developing heart failure. Heart transplantation, the only long-term cure available, is limited by an inadequate number of available donor hearts, the risks associated with the procedure, and the need for chronic immunosuppressants. Therefore, the search for an alternative solution is becoming increasingly more urgent.During development, cardiomyocytes arise from early cardiac progenitors or the proliferation of pre-existing cardiomyocytes . ShortlyAstyanax mexicanus cave fish lost the regenerative ability in comparison with their river-dwelling counterparts [However, certain species possess a robust ability to regenerate their hearts, including some teleost fish and neonatal mammals. Resection of the adult zebrafish ventricle results in complete regeneration without fibrotic scar formation , and thiterparts . The neoterparts ,12 whichterparts , and MI terparts .Investigating these differential responses to cardiac injury provides prospects to pinpoint targets in cardiac regeneration. Several factors have been proposed to explain the differences in regenerative capacity, including polyploidisation , endotheAs discussed, the main contribution to cardiomyocyte renewal in the adult mammalian heart is cardiomyocyte proliferation but this occurs at a very low level. However, several cell populations including stromal cells, such as mesenchymal stem cells, and cardiac progenitor cells (CPCs) have been investigated for transplantation with the aim of replacing lost cells following MI. Others have investigated the delivery of terminally differentiated cardiomyocytes or cardiac-committed cells expressing developmental progenitor markers derived from pluripotent stem cells (PSCs). This review will focus on stromal cells, which will be defined and discussed in the following sections.in vitro multipotency, although this plasticity may be the result of the artificial cell culture environment in vitro [The term ‘mesenchymal stem cells’ (MSCs) was first coined in 1991 by US biologist Caplan to describe a population of cells isolated from bone marrow . The celin vitro . MSCs hain vitro , cardiomin vitro , and insin vitro , among oin vitro  and safein vitro . This main vitro .A range of different markers are used to characterise MSCs. In 2014, a study showed that proposals submitted to the US FDA for MSC-based products did not agree on the tissue sources, manufacturing processes, and molecular characterisation of MSCs . MoreoveMoreover, there is controversy about the stemness and functional mechanisms of MSCs due to the lack of evidence of direct regeneration following transplantation. Indeed, Caplan called for a name change from ‘mesenchymal stem cells’ to ‘medicinal signalling cells’ to highlight their paracrine mechanism of action rather than direct contribution to tissue ,42. The de novo cardiomyocytes arise from endogenous CPCs in the adult mammalian heart was, and is, controversial [in vitro to generate sufficient cells for therapy. The resulting populations are all adherent to plastic and express some of the markers used to characterise MSCs  [Fate-mapping studies disputed the endogenous contribution of SCA1after MI ,59, with (cMSCs)  and show (cMSCs) . However+ progenitor population [−). Kit expression has also been described in postnatal cardiomyocytes [CD45 indicating a bone marrow origin [in vivo activation of KIT+ cells in the isoproterenol (ISO)-treated heart. Ablation of dividing KIT+ cells (and other dividing cells) abolished regeneration and functional recovery after ISO.In 2003, the Anversa group described a resident KITpulation  as self-myocytes , adult cmyocytes , coronarmyocytes , and heaw origin  and workw origin  demonstr+ cells has been retracted [+ cells and a combination thereof. The proportion of patients experiencing major adverse cardiac events (HF-MACE) was significantly different across the groups but none of the HF-MACE components were individually significant [The phase I SCIPIO (NCT00474461) clinical trial into the delivery of autologous KITetracted , but a fnificant . Moreove+ cells in the adult heart using Cre recombinase knockin approaches. It was shown that only ∼0.03% of cardiomyocytes were of the KIT lineage [+ cells were consistent with an endothelial phenotype as suggested by their co-expression of CD31, and their localisation within the endocardium and coronary endothelium. Following MI, only a very rare subset co-expressed the cardiogenic markers Nkx2–5 or cardiac troponin T [+ cardiomyocytes rather than cardiomyocytes formed de novo from KIT+ progenitors [+ non-cardiomyocytes and saw no contribution by these cells to de novo cardiomyocytes in homeostasis and after MI. The interpretation of these knockin approaches comes with caveats as they involved disruption of one KIT allele and may therefore lead to under-reporting of cells with low KIT expression [Multiple lineage tracing studies have addressed the cardiomyogenic potential of KIT lineage  and KIT+oponin T ,71 and tgenitors . He et agenitors  used duapression . Other fpression .Isl1 expression is detected in cardiac mesodermal progenitors [+ progenitors have been shown to contribute to the formation of cardiomyocytes, endothelial cells and smooth muscle cells [+ cells give rise to a minor proportion of cardiomyocytes in the postnatal murine heart, although the distribution and numbers of ISL1+ cells are not altered following MI [+ progenitors improved cardiac function following MI demonstrating the validity of transplanting developmental cardiac progenitors with the aim of replacing lost cardiomyocytes [+ cells embedded in a fibrin patch [genitors  and in cgenitors . In the le cells . In 2005le cells  showed towing MI . More remyocytes . The ESCin patch . HoweverKIT, and the mesenchymal markers CD90, CD105 [in vitro but at low maturation levels. This is enhanced using metabolic programming to resemble the metabolic switch from glycolysis to fatty acid oxidation that occurs in cardiomyocytes during cardiac development [in vitro expansion. The trial demonstrated the safety of allogenic transplantation but failed to establish efficacy [In 2004, CDCs isolated from human and murine hearts were described as clonogenic, self-renewing and a heterogeneous population expressing 0, CD105 ,81. CDCselopment . A theraelopment ,83–87. Telopment . The folelopment ,90. In tefficacy .+ CDCs showed pro-angiogenic capabilities, whereas SCA1− CDCs had higher angiogenic potential, mediated by Vegfa and Flt1 interactions. Interestingly, transplantation of SCA1+ CDCs but not SCA1− CDCs improved cardiac function after MI. This was attributed to the secretion of cardioprotective ligands such as Cxcl12 and Hgf from SCA1+ CDCs, demonstrating the therapeutic benefit of the CDC secretome and the previously unappreciated functional differences between CDC subpopulations.More recently, CDCs have been examined at the single cell transcriptomics level  and descDuring development, the epicardium secretes trophic factors required for myocardial maturation, and undergoes EMT to directly contribute precursors of coronary vascular smooth muscle cells and fibroblasts. Epicardium-derived cells (EPDCs) were thought to contribute to the endothelial and cardiomyocyte lineages, but this was later shown to be minimal ,93. Foll+ progenitors transplanted into infarcted hearts formed de novo cardiomyocytes, although the contribution of EPDCs to both vasculature and cardiomyocytes was insufficient for effective myocardial regeneration. However, co-transplantation of hESC-derived epicardial cells and cardiomyocytes resulted in the epicardial cells forming persistent fibroblast grafts that stimulated graft and host vascularisation and increased the size of cardiomyocyte grafts by inducing cell proliferation and maturation [Smart et al.  showed tturation . This suNumerous studies have questioned the differences between the various cardiac stromal cell populations including mesenchymal cells, fibroblasts, pericytes, and CPCs  due to tThe lack of direct differentiation of delivered CPCs into cardiomyocytes has shed doubt on the identity of the proposed CPC populations. In 2017, the Cardiomyocyte Regeneration Consensus Statement noted that: (1) CPCs may contribute to the formation of cardiomyocytes in adult homeostasis, albeit at very low levels and that (2) the mechanism behind cardiomyocyte renewal in the adult mammalian heart is cardiomyocyte proliferation suggesting evolutionary conservation . This is+ and CX3CR1+ macrophages and enhanced cardiac function in a mouse MI model.Mechanisms responsible for the observed beneficial effects following administration of cardiac stromal cells or CPCs in both animal models and clinical trials have therefore been under debate . It has If CPCs are to act as a ‘paracrine factors factory’ following transplantation or to stimulate an immune response, the problem of the extremely low cell retention must be overcome. Following MI, harsh events such as ischaemia, oxidative stress and increased inflammation limit the survival and engraftment of transplanted cells and typically 90% of the cells are lost within the first week . Enhanciin vitro or the method of analysing cardiac function [Since the early days of clinical studies into cardiac cell therapy, trials have produced conflicting results which emphasised the complexity of this approach. In 2005, the REPAIR-AMI trial reported an improvement in cardiac function following infusion of bone marrow cells post MI, whereas the ASTAMI study showed no benefit after transplantation of a comparable cell population in a similar patient cohort .The difffunction . In 2018function . They reSimilarly, although the safety of both autologous (e.g. CADUCEUS and CONCERT-HF) and allogenic (e.g. ALLSTAR and CAREMI) CPC transplantation has been shown, beneficial effects have been limited. The largest CPC trials were ALLSTAR and CONCERT-HF with 134 and 90 patients, respectively ,90. Tylede novo cardiomyocytes. Nevertheless, CPCs entered clinical trials which demonstrated safety, but limited beneficial effect, and phase III data are lacking. More recent evidence has shown that most adult mammalian cardiomyocyte renewal is achieved via cardiomyocyte proliferation rather than a progenitor cell type [Various types of CPCs or cardiac stromal populations have been investigated and shown to be effective in improving cardiac function in animal MI models despite a lack of long-term retention and ability to generate ell type ,104–106 in vitro differentiation into a cardiac phenotype, increased risk of arrythmias, purification to eliminate undifferentiated cells that increase the risk of tumourigenicity, as well as the need for long-term immunosuppression in the case of embryonic stem cells [In cell therapy, the optimal cell population would be a safe and robust population that can exert a therapeutic effect and be isolated and expanded in a time- and cost-effective manner. Cardiac stromal populations have proven to be safe as no adverse effects directly linked to them have been reported in clinical trials of cardiac disease. Although PSC-derived cells have also provided a promising source, their use comes with the added cost of em cells . Moreoveem cells  suggestsem cells ,132–134.em cells  and MSCsem cells , metabolem cells ,137, andem cells  have beeem cells . ClinicaIt is now established that CPCs do not meet the agreed criteria of a true progenitor or stem cell. However, CPC research has been fundamental in further characterising the heterogeneity of the stromal and fibroblast compartments of the heart. Moreover, identifying the mechanisms responsible for regeneration in regenerative animal models and repair after cell transplantation opens prospects for finding alternative ways to induce these mechanisms. CPC populations may potentially be used in combinatorial therapy approaches to aid host tissue revascularisation or enhance the retention and integration of PSC-derived cardiomyocyte grafts. The therapeutic approach will, after all, depend on the patient's clinical presentation as there is no single ideal approach for all MI patients.Unlike the neonatal mouse and zebrafish hearts, the adult mammalian heart cannot regenerate after myocardial infarction. Despite early promise from rodent studies, administration of progenitor cells has not translated to the clinic.Multiple cardiac progenitor cell populations have been proposed, with overlap in their morphology and gene expression. Retention after transplantation is low and therapeutic benefit is thought to result from immunomodulation or release of paracrine factors which stimulate angiogenesis and/or cardiomyocyte proliferation.For cells to act as therapeutic ‘paracrine factories’, it is important to increase donor cell retention and optimise release of key factors to stimulate repair.


This review discusses the approach of network medicine and its application to CVD research.Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of  Cardiovascular disease (CVD) is a leading cause of mortality and morbidity worldwide . In 2018While phenotypically diverse, many CVDs share pathophysiologic intermediaries. For example, atherosclerosis is a chronic inflammatory condition characterized by plaque buildup in the intima of blood vessels. This dominant cause is shared by various vascular diseases through mechanisms, such as (i) thickening of arterial walls in CAD and PAD, (ii) plaque rupture and thrombosis in its onset of acute complications , and (iii) changes to aortic media causing aneurysms. However, components of the immune system underlie atherosclerosis development and aggravation. Dysregulation of innate immune systems may accelerate atherosclerosis through mechanisms such as impaired efferocytosis, sustained macrophage activation, and activation of the NLRP3 inflammasome , 12. SimDespite shared pathologies, individual CVDs that affect different organs and/or tissues naturally display unique sets of symptoms and outcomes. Within each CVD, increasing evidence suggests heterogeneity in clinical manifestations and underlying mechanisms. Diverse clinical presentations of CAD, including asymptotic stenosis due to atheroma, angina pectoris due to thrombosis, and ST-segment elevation MI (STEMI) vs. non-STEMI , are somDisease subtyping or phenotyping, as per nosology, derives from clinical presentations or other observable characteristics without suggesting underlying mechanisms. It is done by clustering subjects with similar observable clinical characteristics. Such phenotypic assessments  helps refine diagnoses and determine treatment strategy. Akin to diverse clinical presentation of the same disease in different patients, heterogeneity also manifests in patients’ drug response, such as seen in HF with either preserved (HFpEF) or reduced (HFrEF) ejection fraction. Traditionally viewed as different stages of the same disease, evidence now indicates that these conditions may be different disorders, with different etiologies and responses to therapy. While they share many common risk factors  , many drHowever, disease endotyping is defined by distinct functions and pathobiological mechanisms as well as altered molecular pathways. Endotyping improves patient stratification, leading to more accurate diagnoses and tailored therapeutic strategies. Proper endotyping requires a framework that can integrate pathways and mechanisms with phenotypic features, molecular measurements, and demographic data –21.Only a few CVDs are monogenic, including hypercholesterolemia, either familial (mutant gene: LDLR) or autosomal recessive (mutant gene: ARH); sitosterolemia (mutant genes: ABCG5/8) ; and sinCardiovascular diseases involve not only genetic, but also various environmental and lifestyle factors. These causal factors determine individuals’ CAD risk and cause multilayer heterogeneity in CVD patients. Along with genetics, data from other omics modalities  provide a way to examine the disruptions and dysregulation caused by disease. While each type of omics data provides helpful information about changes in molecular composition or chromatin states, integrating them into unified models can give valuable insights into mechanisms underpinning disease pathology . The sucNetwork medicine provides a framework that considers different components and constituents in the system and integrates them in an architecture that unravels the disease etiology, facilitates drug target discovery, and enables proper disease endotyping , 29.Networks are analytical tools that allow us to represent complex associations between different entities from complex datasets. Networks may comprise either co-abundance, physical, or regulatory interactions between different biomolecules such as messenger ribonucleic acid (mRNA), proteins, or metabolites. For example, networks may comprise interactions between genes whose expression patterns are correlated or similar. In some cases, networks may represent abstract associations between entities such as genes shared between diseases and correlations shared between clinical features. Network medicine is a subfield of network biology, an area of systems biology that specializes in biomedical applications of network theory, from understanding etiology to discovering drugs and biomarkers , 29–31 .The network medicine approach is often contrasted with the traditional reductionist, “magic-bullet” approach of finding unitary features , but each approach complements the other. Network medicine uses the complex interplay between disparate entities−molecular, biochemical, or cellular−and binds them together into consistent substructures, allowing distillation of relevant biology from the data and providing a glimpse of higher-order organization in the system under study. The major difference between the reductionist approach, which reduces the process into basic units, and the systems-based approach, which considers biological systems as a whole, is that the systems-based approach reveals emergent properties observable only when they are whole and not by their individual constituents . On the Network medicine takes its concepts and terminologies from the mathematical field of graph theory and from complex networks where “nodes” are components such as genes and proteins; the interactions between them are “edges” . Such arThe advent of high throughput technologies, the decreasing cost of sequencing, and willingness in public to allow the use of biomedical data for research through biobanks and consortia has yielded a vast amount of data from different omics platforms as well as clinical and phenotypic information. Network medicine effectively integrates information from multiple omics data types and extracts meaningful information and mechanistic understanding from different omics layers  . SeveralAnother approach to network medicine-based multiomics integration involves simultaneously analyzing different components from different omics layers by incorporating them into unified models. Networks constructed with this approach may have different types of nodes and the nature of edges between different node types may differ. For example, genes, proteins, or epigenetic marks may be connected based on biological principals, signaling pathways, and central dogma, along with correlated abundance levels. The edges may be present among same or different node types such as protein–protein, protein–DNA, or mRNA–protein. Some network methods use mathematical inferences as an additional step to give weights to the interactions . This apNetwork medicine, artificial intelligence (AI), and/or machine learning are at the core of systems biology, which aims to identify the molecular determinants of disease. Using various classifiers, machine learning—supervised or unsupervised—is a major tool for identifying data patterns. Advanced models of machine learning include deep learning models that allow construction of various architectures of neural networks and innovations in loss functions, consequently performing different tasks in a variety of applications. These learning-based approaches are valuable when identifying diagnostic, predictive, prognostic, or therapeutic signatures of disease, including biomarkers. Despite many parallels , significant differences exist between networks- and learning-based approaches; primary among them is their black-box nature and lack of interpretability of the latter. In contrast to linear regression and decision trees, which are easier to understand and have fewer parameters, deep learning algorithms base their decision on intricate neural networks with a huge number of weight parameters. Even knowing all the weights does not allow comprehension of a model’s behavior, leading to its black-box nature.One of the most lucrative outcomes of biomedical big data analysis is the use of multiomics approach to identify new molecular or clinical biomarkers of disease. Such biological signatures could include disease-associated proteins, mutations, deletions, and copy number variation of genes identified by various omics platforms. Biomarker discovery has benefitted greatly from the advent of AI, especially its ability to handle large multivariate and unstructured clinical data . These types of data are not easily amenable for network medicine approaches due to their size, complexity, heterogeneity, incompleteness, and unstructured nature. However, the strength of network medicine lies in leveraging omics data to find important biomolecules that serve as “network biomarkers” . Such biNetwork medicine has grown tremendously over the years, producing many successful applications across various disease areas and pathological landscapes. This growth spurred several excellent review articles and books that discuss its different aspects , 60–62. Many other interesting reviews and books discuss various aspects of network medicine in general and in the context of CVD. While some focused on discussing different omics technologies and how network methods can be leveraged to analyze them, others discussed individual network projects in detail or the philosophical underpinnings of the field, providing a context for understanding the contributions of network medicine to biomedical advances. The present review article is at the cross-roads of these ideas. We do not provide a chart of network methods, but rather try to inculcate an understanding that will empower researchers to select a specific network medicine approach, depending on data type, disease context, and outcome expectation. For this purpose, we rely on our previous review article , which dLarge-scale efforts to collect data have yielded far-reaching effects on biomedical research. For example, the Human Genome Project mapped the human genome and the Encyclopedia of DNA Elements (ENCODE)  providedProtein–protein interactions can be used to study the importance of proteins via networks’ topological properties, such as “hubness”  , and to When combined with phenotype-specific proteomics data, PPI networks also participate in identifying new molecular pathways or biomarkers of CVD. For example, extracting a cardiovascular-specific subnetwork along with mass spectrometry data enabled “knowledge-integrated biomarker discovery” in CVD . As discIntegrating PPIs with gene expression levels, protein abundances, and genotype information may aid the study of disease heterogeneity and enable precision medicine in CVD. Combining patient-specific genotype and clinical data into PPI networks to obtain patient-specific genotype–phenotype relationships is critical for personalized medicine . For exaProteins do not work in isolation; rather, they work together to perform different biological functions. In the PPI network, the presence of a high degree of clustering and topological modules  reflects the formation of such protein complexes , 110. OfProtein–protein interactions networks are undirected and, by design, lack disease context . In mostFurther, PPIs have been incorporated into a larger framework of multilevel networks containing interactions between genes, diseases, proteins, and drugs , and havin vitro mechanistic experiments further demonstrated that OAT/OATP-family transporters and the ubiquitin-proteasome pathway mediate indoxyl sulfate-induced Delta-like ligand 4-Notch signaling and macrophage activation  and IL-4. First, we identified proteins that increased and decreased in pro-inflammatory or in anti-inflammatory macrophages, respectively, and embedded them onto protein interaction networks. Next, we studied the closeness of these proteins to clusters of other diseases and determined that CAD was the closest disease cluster. Again, this approach derives credence from the aspect that disease genes do not act in isolation or randomly, but rather in consonance with similar biological pathologies. Subsequently, we used rophages . The sturophages , 133. Lirophages . They exrophages .As discussed previously, phenotypic context does not come naturally to the interactome or PPI because they are a static map of protein interactions. Disease context must be embedded onto them through seed genes or proteins. By design, co-expression-based networks are built by using measured analytes in the disease or phenotypic context. In our previous review, paradigm 2 discussed different ways to leverage co-abundance information to construct co-expression networks that use a variety of network methods and correlation measures . IdentifIn addition to identifying disease modules and “eigengenes”, researchers frequently pair co-expression networks analysis with PPIs to identify modules’ molecular regulators. Key driver analysis (KDA) is innovative in this regard –162. KDAThis article reviews some exemplary applications in CVD that use co-expression networks. Several studies investigated CAD using co-expression networks and KDGs extensively –170. ForCardiovascular disease shares the same risk factors as cardiometabolic diseases including type 2 diabetes, chronic kidney disease, and non-alcoholic fatty liver disease. Hence, studying these diseases in the context of different tissues allows us to understand their common molecular framework. Notably, Cohain et al. used STARNET’s co-expression modules to investigate the conflicting roles of lipid and glucose metabolism in diabetes and CAD . While mCorrelation network analysis is not restricted to transcriptomics data, and several studies show innovative application to other omics data , 187. FoBecause co-expression networks themselves are symmetrical , directionality pertaining to regulator and effector nodes require inclusion by design. Gene regulatory networks (GRN) comprise interactions between effector nodes, such as TFs, other signaling proteins, and receiver nodes that are genes . While tRegulatory networks centered on transcription factors have played an important role in CVD research. Macrophage activation has an important implication in atherosclerosis. Network analysis of macrophage activation in response to bacterial lipopolysaccharide (LPS) revealed the role of activating transcription factor 3 and NRF-2 during inflammatory response . Using GAn emerging target of CVD therapeutics is non-coding RNAs, especially due to their stability in plasma and confinement in extracellular vesicles. Additionally, miRNAs and long non-coding RNAs (lncRNAs) serve as biomarkers of CVD –213. ParThus far, network-based studies in CVD have identified key molecular protagonists in disease progression. Many studies also identified changes to biological networks as sensors or mediators of the underlying pathobiology. Disease modules and their shared features between different diseases provide insight into a larger organization of molecular networks in different diseases, highlighting both unique and overlapping pathobiologies and instigating new ways to study diseases.In certain areas, network medicine progress can aid CVD research, especially by resolving molecular mechanisms to infer proper endophenotypes. Like most complex diseases, CVD involves multiple common signaling and molecular pathways that are shared between different diseases. Combining these pathways can allow construction of unified disease networks. For example, one study performed endotyping in asthma by combining Ig-E, IL-5, IL-4, and IL-17 pathways . SimilarBy combining network medicine with machine learning and AI, disease endotyping of various CVDs has great potential. Methods such as LIONESS allow construction of sample-specific networks, which permit association of demographic and clinical characteristics with each sample network. When applied to CVD, we can construct gene correlation networks for different subjects in the dataset and compare changes in edge-weights between different groups  to find differentially changed interactions. While these networks differ from reticulotypes, which are genotype driven, sample-specific networks can aid precision medicine approaches in CVD.Additionally, CVD research may benefit from integrative and diverse omics types in network models. Implementing methods such as Multi-omics Factor Analysis (MOFA)  and SimiCombining and extending methods like PANDA and PUMA can explore the regulatory relationship between mRNA–miRNA–lncRNA by modeling different mechanisms of action. Such multi-dimensional networks will facilitate new therapeutic strategy for CVD and help understand its development.Along with the number of technologies that identify diverse omics types, considerable success has been achieved at the cellular level in terms of these measurements. By allowing both hypothesis-driven and unbiased screening of newer cell types in the population, single-cell RNA sequencing technology has become almost a mainstay in various experimental explorations . SimilarOne of the most promising outcomes of the growing application of network medicine approaches in various diseases, including CVD, is the impetus to target discovery and well-designed clinical trials. Network medicine, along with machine learning-based approaches, provides a solid framework for identifying disease mediator biomolecules as drug targets. Moreover, the network view of drug development, which incorporates topological and functional modules of disease subnetworks, will allow repurposing already-approved drugs by finding alternate targets.Despite its roots in graph theory and social networks, use of a network framework in biology formally began around the same time as publication of a draft human genome. In principle, the last 20 years of scientific progress in a variety of technological breakthroughs in data generation have outpaced the development of analytical frameworks required to process the vastness and depth of biomedical big data. Network medicine derives its methodological advancements from innovations in the field of social network analysis and complex network theory in physics. This vast area involves different facets and different types of networks, some elaborated in this review. Often, networks are (mis)used as merely a visualization tool to demonstrate differentially expressed genes or proteins and display functional enrichment analysis results; in other cases, we use complicated mathematical structure to extract information from a network, thus making it an esoteric construct restricted to specialists. Both problems may have limited the allure of network medicine. With increasing interdisciplinarity of scientists in cardiovascular research, network medicine is well poised to study the complexity, ontology, and epistemology of biomolecules in CVD context and to provide translational benefits.AS wrote the initial draft, which was reviewed, edited, and revised by all authors. All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.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.