During the COVID-19 pandemic, health care workers (HCWs) have experienced considerable stress and shown great resilience. Multiple organisations advocate for better support for the health workforce, both during pandemics and in routine health care. Effective interventions aimed at supporting HCWs should be based on evidence stemming from HCWs’ coping strategies and support needs. This workshop highlights coping mechanisms and support strategies for HCWs during public health emergencies. It is chaired by the Robert Koch Institute (RKI), Germany's national public health institute, who played a major role in the pandemic response over the last two years, and the EUPHA Health Workforce Research Section. While the workshop's primary geographical focus is Germany, the roundtable participants will explore, from an international perspective, what lessons can be learned from HCWs’ experiences during the COVID-19 pandemic and discuss the relevance to researchers, health professionals, health managers, politicians, and other decision makers.The aims are to: (i) introduce an analytical framework for coping mechanisms and support strategies for HCWs during public health emergencies, (ii) present reported coping mechanisms among, and support strategies provided to, HCWs during the COVID-19 pandemic, and (iii) discuss the coping mechanisms and support strategies that have been reported to be most effective in improving the situation of HCWs in the future.The panel workshop starts with two 10-minute presentations followed by a moderated panel and subsequent discussion with the audience. The presentations introduce the workshop topic using a scoping literature review conducted at RKI on HCWs’ experiences during the COVID-19 pandemic, and a summary of key findings of a mixed-method study on coping strategies and support needs of HCWs during COVID-19 in Germany, highlighting support needed and received and crucial sources of support. The panel highlights the most effective reported coping mechanism, addresses gaps in support strategies, and collects suggestions for how to make use of the suggested solutions. These as well as lessons to be learned for policy and practice will be discussed with panel members and attendees, connecting the perspectives of health policymakers, management. and professionals. The aim of the workshop is to contribute to the wellbeing of HCWs, who represent one of the most important pillars of a health system.• Healthcare workers’ coping mechanisms and support strategies during the COVID-19 pandemic are an important source of health workforce resilience.• Lessons learned from the COVID-19 pandemic on supporting health care workers must be jointly implemented across sectors.Megan EvansRobert Koch Institute, Berlin, GermanyGeorge ValiotisEuropean Health Management Association, Brussels, BelgiumBernhard GibisKassenärztliche Bundesvereinigung, GermanyVéronique S GrazioliCenter for Primary Care and Public Health, University of Lausanne, Lausanne, SwitzerlandBer OomenEuropean Specialist Nurses Organisation, Brussels, Belgium


We revisit several conditionally formulated Gaussian Markov random fields, known as the intrinsic conditional autoregressive model, the proper conditional autoregressive model, and the Leroux et al. conditional autoregressive model, as well as convolution models such as the well known Besag, York and Mollie model, its (adaptive) re-parameterization, and its scaled alternatives, for their roles of modelling underlying spatial risks in Bayesian disease mapping. Analytic and simulation studies, with graphic visualizations, and disease mapping case studies, present insights and critique on these models for their nature and capacities in characterizing spatial dependencies, local influences, and spatial covariance and correlation functions, and in facilitating stabilized and efficient posterior risk prediction and inference. It is illustrated that these models are Gaussian (Markov) random fields of different spatial dependence, local influence, and (covariance) correlation functions and can play different and complementary roles in Bayesian disease mapping applications. To achieve these goals, hierarchically formulated Bayesian GLMM is commonly used to model disease incidence or mortality data and to facilitate stabilized and efficient posterior risk prediction and inference.In the Bayesian disease mapping literature, several conditionally formulated Gaussian Markov random fields (GMRF), also known as conditional autoregressive (CAR) models, have been proposed as spatial prior options for random effects in spatial generalized linear mixed effects (GLMM) models for spatially aggregated areal data.1), proper CAR , and Leroux et al. CAR . We also consider several convolution models, such as the Besag, Yorke, and Mollie (BYM) model1 and its re-parameterization named MBYM in MacNab,6 and their scaled alternatives.8Without essential loss of generality, we consider a typical disease mapping model, a hierarchically formulated Bayesian GLMM of Poisson likelihood for areal data of observed disease incidence or mortality cases, denoted 6 the three CARs, and the BYM and MBYM, were studied together as prior options in Bayesian disease mapping. The present study revisits and extends the topic by researching these models analytically, via graphic visualization, and with comprehensive simulation and case studies. The main objective, contribution, and message of the present paper is to shed light on the risk models discussed herein, not just as competing prior options for spatial smoothing but also as complementary risk models in disease mapping, spatial regression, and related studies. We do so by presenting important insights and critique on these models for their nature and capacities in modelling spatial dependencies, local influences, spatial correlations, and spatially structured heterogeneities, and in facilitating stabilized and efficient posterior risks prediction and inference based on data of extremely rare or rare or more common disease.In MacNab,911) and the widely applicable information criterion (WAIC1315) for model evaluation and comparison via simulation and case studies. In addition, a new proposal of adaptive MBYM is put forward and illustrated in Bayesian disease mapping and spatial regression.Another contribution of the present study is the illustration of a recent proposal of deviance information criterion  must be symmetric and non-negative definite. To fulfil the two requirements, functional characterizations and simplified parameterizations to the CAR conditionals have been proposed (mainly) in the disease mapping literature; and the previously mentioned iCAR, pCAR, and LCAR are the most commonly used GMRFs in disease mapping applications at the present time; see 5 However, shown in MacNab,18 and in the present paper, as we broaden the scope of Bayesian disease mapping to the studies of rare or more common diseases, and non-communicable or communicable diseases, CARs and GMRFs can offer tools not just for borrowing information and spatial smoothing, but for analysis of spatial risk dependencies, local risk influences, spatial risk correlations, and spatial risk heterogeneities.The CARs were initially proposed to facilitate borrowing information and spatial smoothing.2.2influence functions, denoted Influence18 The two CARs share a common characteristic that they postulate asymmetric conditional dependency (i.e. asymmetric direct influence) of direct influence of the area the area 18 this could be an intuitively plausible assumption, consistent with their conditional precision functions  risks smoothing over the map. The two CARs also have iCAR spatial dependencies and heterogeneities.23The scale parameter 2.31structured or clustered heterogeneity.1The one-parameter iCAR model.1 The BYM model is also noted for its excessive parameterization and identification issues.24In disease mapping, the iCAR (7) is commonly used when the 6:In the present paper, we illustrate that, to gain identification, posterior estimation and inference of BYM can be implemented by placing (weakly) informative priors on the BYM scale parameters or by a reparameterization of BYM, named modified BYM or MBYM in MacNabWhile not discussed in the literature, in the present paper we also highlight and critique on the re-parameterization approach to identification by noting that it is equivalent to placing functional constraints on the BYM scale parameters 25 for mapping the iCAR precision 8 put forward a proposal of scaled BYM, also named BYM2 in Riebler et al.,7 which is an equivalent of the scaled MBYM:A scaled iCAR covariance functions are often not available.The CAR conditionals (5), and the resulting sparse precision matrix 6 as illustrated in 29 As we illustrate herein, graphic visualization can shed light on the GMRF spatial correlation or covariance functions and unveil different spatial features and patterns of spatial correlation (covariance) functions for the risk models as Gaussian Markov random fields for iCAR, pCAR and LCAR or Gaussian random field for (M)BYM.However, some of the characteristics of the GMRF covariance matrices, as well as the (M)BYM covariance matrices, can be explored and understood by graphic visualization, such as plots of spatial correlation and covariance functions, as well as the marginal variance functions, for given model parameters. Here, we define spatial correlation or covariance functions as correlationdecreasing correlations between county 1 and its increasingSpecifically, for iCAR, pCAR, LCAR and (M)BYM, ions see .Illustrated in the supplement Figure S1, even for small values of spatial parameters, the pCAR and LCAR model locally clustered spatial correlation functions. It also shows that the differences between pCAR and LCAR correlation functions are consistent with the differences in their influence functions see . For c∈In addition, the supplement Figures S1 and S2 illustrate that the iCAR and (M)BYM (of first-order adjacency-defined neighbourhood map) lead to positive and clustered spatial (correlation) covariances between an area and its first-order neighbours but negative (correlations) covariances between an area and its ‘distance’ Further more, the supplement Figure S2 offers new insight into the (M)BYM partial corrrelation functions: They are spatially varying functions that lead to locally clustered partial correlations when the spatially structure variation exceed the unstructured variation  an adaptive MBYM . In addi3WinBUGS.30 It is worth mentioning that formulating GMRFs via full conditionals facilitates coding the powerful Gibbs sampler as a computational tool for posterior estimation of the spatial random effects with CAR/GMRF priors via MCMC simulations.31We carried out a comprehensive simulation study in the context of hierarchical Bayesian estimation, learning, and inference of GLMM (1)–(4), for options of the non-adaptive CAR or convolution risk models as random effects prior. Posteriors of all unknowns were estimated via Markov chain Monte Carlo (MCMC) simulations implemented in The simulation and computational details are presented in the Supplemental Material (SM) to the paper. Simulated data were generated to represent disease mapping scenarios of extremely small, small, modest, or large sample sizes . Detailed results are also presented in the SM, including seven tables (named the supplement Tables S1 to S7) and 14 figures . Here, the key results are summarized and highlighted. The overall performances of posterior estimation and inference of model parameters and relative risks  MBYM led to comparable performances in terms of posterior estimation of the spatial dependence or weight parameter: Under non-informative prior For all simulation scenarios, the iCAR scale parameter was estimated with near-zero posterior bias and near-the-target coverage rate.Consistent and comparable performances were observed from the posterior estimates of the (M)BYM and scaled (M)BYM model parameters. The  BYM was shown to perform slightly better than the  MBYM, observed from modestly lower posterior bias and rmse from the  BYM; this is particularly evident for data of extremely small sample size, likely as a result that the spatial weight parameter in MBYM is typically underestimated for a large Overall, the CAR and (M)BYM models led to consistent and comparable performances in terms of posterior risk prediction and inference; minor or modest differences were only observed for data of extremely small sample size. For all simulation scenarios, the iCAR performed well in terms of posterior risk prediction and inference. For all models and simulation scenarios, and even for extremely small sample size, the 95% credible intervals for the county-specific relative risks led to near or above 90% coverage rates.For pCAR, LCAR and MBYM, minor or modest posterior risk sensitivities to spatial parameter prior options were mostly observed from the posterior risk standard deviations (posterior risk uncertainties) and the resulting posterior risk coverage rates; posterior risk biases and root mean square errors remained robust; informative spatial parameter priors led to reduced posterior risk standard deviations and improved posterior risk coverage rates.In terms of posterior prediction and inference for relative risks (RRs) and the components number of free parameters defined in MacNab,10 in which the deviance, pD1 and DIC = Dbar+pD1 are invariant to re-parameterization and can facilitate model evaluation and comparison among (multivariate) CAR models, including those with non-identifiable or partially identifiable model parameter(s). Illustrative results of widely applicable information criterion (WAIC) were also presented, where WAIC = -2 lppd (predictive accuracy) + 13 for details. Both the DIC and WAIC were calculated based on conditional likelihood of the Poisson data model.For the five risk models, we present here illustrative simulation results of deviance information criterion, the Dbar (deviance), pD, and DIC scores, where pD = pD1 and pD1 is the The estimated DIC and WAIC statistics (e.g. mean scores and associated standard deviations) are overall comparable across the models, although the effective numbers of parameters in DIC (denoted pD) were consistently lower than those in WAIC (denoted pW); see Overall, when pCAR or LCAR or (M)BYM was the true risk model, the estimated DIC and WAIC scores led to consistent and high rates of favouring correct models when iCAR was the misspecified prior. When iCAR is the true risk models, the WAIC scores led to high rates of favouring pCAR, LCAR, and (M)BYM for both scenarios; this may suggest evidence that the iCAR is not a preferred out-of-sample predictive model among the five.When the (M)BYM was the true data generating risk model, the DIC- and WAIC rates of true model preferred were consistent and comparably high (e.g. favouring the correct model), which suggested evidence that the (M)BYM may be a plausible risk model for both the within- and out-of-sample predictions.428 cancer mortality data for 87 counties of Minnesota (USA). This data set contains county-level death counts (observed and expected) for cancer of oesophagus , previously analyzed in MacNab.18 The analysis presented herein serves as an illustrative example of disease risk mapping based on data of comparably large counts of infection cases, without or with covariates. The case study III also illustrates applications of the adaptive MBYM and its scaled alternative in modelling COVID-19 infection risks without or with covariates.The three case studies are presented herein to illustrate, via results of Bayesian GLMM (1)–(4), Bayesian risk mapping of extremely rare, rare, and more common diseases, represented by areal data of small, modest, or large (expected) counts (see 4.1Posterior estimates (median and standard deviation) of the model parameters for all case studies are presented in the supplement Table S8, where modest to high spatial risk dependencies were suggested from the CAR models . Consistent with the results of the simulation study, modest  posterior sensitivities to spatial parameter prior specifications were observed from the first two case studies; The DIC and WAIC results were overall consistent in each of the three case studies (see the supplement Tables S9 and S10). Of note is the DIC results for Results of the scaled iCAR and (M)BYM were also comparable to those of their unscaled counterparts. 4.2We present results of fitting the COVID-19 data to the spatial GLMM (1)–(4) without and with (five) covariates; see Posterior estimates of the model parameters in spatial GLMM (1)–(4) without or with covariates are presented and compared in 4.3We illustrate the new proposal of adaptive MBYM random fields with different spatial dependence (influence) and correlation (covariance) functions. Consequently, they and their multivariate and adaptive model extensions can play different roles in disease mapping applications of contemporary scope and complexity.Our simulation and case studies, for their scope in illustrating and assessing the iCAR, pCAR, LCAR and (M)BYM risk models together using simulated and real data of extremely small, small, modest and large sample sizes, provided a wealth of important information on the Bayesian posterior estimation, learning, and inference of the model parameters and associated risk prediction and inference, and on the use of DIC and WAIC as tools for evaluations of estimation and out-of-sample predictive models.In addition, a new proposal of adaptive MBYM is presented and illustrated; it illustrates how the existing spatial risk models can be broadened and extended. We discussed and illustrated the various roles the iCAR, pCAR, LCAR and (M)BYM may play in Bayesian disease mapping, for which we summarize here as takeaway messages.23 For risk prediction and inference in the context of mapping spatially correlated disease risks, our analytic, simulation and case studies led to consistent results that the two CARs can approximate each other quite well.The pCAR and LCAR are full rank GMRFs that can play nuanced roles of modelling spatial dependence and local influence functions regulated by their respective spatial parameters. The analytic and simulation results favoured LCAR over pCAR when mapping risks of weak or strong spatial correlations. However, pCAR as a spatial model has the advantage for its rich options of multivariate and adaptive generalizations with flexible  spatial dependence and local influence functions.The iCAR is a singular GMRF and has an unappealing covariance matrix assuming negative correlations between ‘distance areas’, which may be one reason that it was not favoured as an out-of-sample predictive model in the simulation study. Nevertheless, as an ‘a priori’ spatial smoother, it can be used as a spatial risk prior for modelling spatially structured risk heterogeneity in hierarchical Bayesian models. For the purpose of borrowing information for disease risk mapping, our simulation and case studies suggested that it can be the statistically efficient spatial risk smoother among the five when spatially correlated risks of rare diseases are under study.The (M)BYMs have dense precision and covariance matrices that postulate practically unappealing but low negative risks dependencies and correlations between ‘distance’ areas. However, they are full rank Gaussian random fields with spatially clustered correlation and partial correlation functions postulating positive risks dependencies and correlations between neighbouring areas. While the utility of (M)BYM for modelling spatial risk dependencies remains a topic of future research, our study suggested evidence that they can be used as (1) estimation and prediction models and (2) as random effects priors for modelling additive components of spatially and randomly varying effects. Compared to fitting the MBYM for the BYM scale parameters or via re-parameterization for MBYM, the BYM and MBYM can facilitate characterization of risk effects 18 showed, analytically and via a case study, that adaptive extensions of the iCAR, pCAR and LCAR lead to CAR models of different local influence functions; they can be used to model different patterns of locally varying influence functions that characterize local dependencies and spatial discontinuities.The new adaptive MBYM is proposed and illustrated for more flexible posterior risk estimation and inference and for unveiling neighbourhood risks clusters and heterogeneities. In a recent study, MacNab27 our simulation and case studies also suggested that among the commonly used risk priors none was shown to significantly outperform the others in all disease mapping applications. Noted in MacNab,18 and suggested by the results of the current study, Bayesian sensitivity analysis with respect to posterior risks prediction and inference, with goodness-of-fit, predictive accuracy, and model complexity assessments such as the DIC and WAIC scores being evaluated and illustrated herein (or model assessment criterions not discussed herein), is still a viable approach for model evaluation, comparison and selection. More importantly, the risk models discussed herein for their nuanced roles in disease mapping can be used as competing or complementary methods for in-depth analysis of disease mapping data.Consistent with the analytic results presented herein and in MacNab,For data of small or modest sample size, informative hyper-priors for pCAR or LCAR or MBYM spatial parameters can significantly reduce its posterior bias and uncertainty, as illustrated in our simulation and case studies. The present study also showed that both the BYM and MBYM enable (nearly) unbiased posterior estimation of the spatial and non-spatial components Click here for additional data file.Supplemental material, sj-pdf-1-smm-10.1177_09622802221129040 for Revisiting Gaussian Markov random fields and Bayesian disease mapping by Ying C MacNab in Statistical Methods in Medical Research


Caregiving dyads  work as an interdependent emotional system, whereby it is assumed that what happens to one member of the dyad essentially happens to the other. For example, both members of the dyad are involved in care giving and care receiving experiences and therefore major life events, such as a serious illness affect the dyad and not only the individual. Consequently, informal caregiving may be considered an example of dyadic interdependence, which is “the process by which interacting people influence one another’s experience.” This systematic review aimed to synthesize studies of dyadic interdependence, specifically in non-spousal caregiving dyads . Electronic databases  were systematically searched for dyadic studies reporting on interdependence in the emotional and relational wellbeing of non-spousal caregiving dyads. A total of 239 full-text studies were reviewed, of which 14 quantitative and qualitative studies met the inclusion criteria with a majority of dyads consisting of adult daughters caring for their older mothers. A narrative synthesis suggested mutual influences between non-spousal caregiving dyad members based on: (1) associations between intrapersonal  and interpersonal  variables and emotional and relational wellbeing of the dyad; (2) associations between care context variables  and emotional and relational wellbeing of the dyad; and (3) patterns of covariation between caregivers’ and care recipients’ wellbeing. Evidence supporting dyadic interdependence among non-spousal caregiving dyads shed light on the ways dyad members influence each other’s wellbeing while providing and receiving care . Future studies investigating mutual influences in dyads, should differentiate subsamples of caregivers based on relationship type, and adopt dyadic and longitudinal designs.https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42021213147].[ Informal care arises from a communal relationship between an informal caregiver (hereafter referred to as caregiver) and the person in need of care . Evidence suggests that caregiver and care recipient wellbeing is mutually interconnected, and adaptation to disease or aging often involves both members of the caregiving dyad . Using CTo varying degrees, illnesses affect caregiving dyads as a unit, rather than isolated individuals, resulting in dyad members having mutual impact on each other regarding quality of life, psychological health, and relationship functioning . These pThis kind of dyadic interdependence and connectedness is usually seen as a core defining feature of couples, given the strong emotional and physical closeness that often characterizes romantic partners or spouses (hereafter referred to collectively as spouses). Indeed, reviews of research on spouses in the illness context support dyadic interdependence within couples. For example, cancer experiences have been found to be highly interdependent between spouses  and a reThe role of dyadic interdependence within non-spousal dyads is important for a number of additional reasons. First, in most cases, informal care is almost equally directed toward spouses and older parents, with on average 36% of informal caregivers caring for their spouse  and 32% caring for a parent (adult-child caregivers). There is also a relatively high proportion of caregivers who report helping a friend or a neighbor (18%) or taking care of other relatives such as brothers/sisters or aunts/uncles (18%) . MoreoveTheoretical frameworks such as dyadic coping models see  and inteThis systematic review aimed to synthesize three levels of evidence for dyadic interdependence in emotional and relational wellbeing of non-spousal caregiving dyads : (1) first level of evidence for interdependence is whether some characteristics of one member of the dyad are associated with the wellbeing of the other dyad member, and whether the interactions between the two dyad members are associated with the wellbeing of both; (2) second level of evidence for interdependence is in terms of associations between care context variables and wellbeing in both dyad members; and (3) third level of evidence for interdependence is about patterns of covariation between dyads members, that is whether both dyad members report similar wellbeing and emotional states.The current review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . MoreoveA search of three electronic databases  was conducted from database inception to December 2021. The search strategy was developed in consultation with a librarian at the University Medical Center of Groningen and was reviewed following the PRESS peer review guidelines . The seaAdult (≥18 years old) non-spousal caregiver and adult care recipient dyads. Care recipients were community dwelling and had a chronic illness, physical disability, or frailty due to aging. Studies including spousal caregiving dyads in an intimate and romantic relationship, whereby the data was not reported separately for non-spousal dyads, were excluded.Studies were included if predictor and/or outcome variables were measured for both dyad members and if they reported intrapersonal, interpersonal, or context variables possibly impacting or associated with the wellbeing of dyad members. Intrapersonal variables included levels of distress, psychological functioning, and personality traits. Interpersonal variables included communication patterns, exchange of support, dyadic interactions. Care context variables included: socio-demographic variables, health status, care needs, and care tasks. Studies were also included if they reported on covariations between caregivers’ and care recipients’ wellbeing.Qualitative , quantitative  and mixed methods designs were eligible for inclusion. Given the observational nature of this systematic review, randomized controlled trials, quasi-experimental, and case studies were excluded.Study selection was performed in two phases by two independent reviewers  who referred to a third reviewer (MH) if an agreement about inclusion could not be reached. The first reviewer (GF) screened all the studies, while the second reviewer screened 10% of the total amount of studies . In the Data from included studies were extracted with data entered in Microsoft Excel (2016), using a data extraction form developed for this review based on the Cochrane data collection form for intervention reviews on randomized controlled trials (RCTs) and non-RCTs . The firAssessment of study quality was conducted using the Mixed Methods Appraisal Tool (MMAT) . This crn = 14), and given the different theoretical conceptualizations and the heterogeneity in the sample population, a quantitative analysis was not considered appropriate . The second step included tabulation: a general table was created to better define the predictors, outcomes, or correlates. The third step consisted of organizing the included studies into thematic groups depending on patterns (similarities/differences) within and across these studies. Studies were clustered according to the characteristics in the data extraction table . Finally, the fourth step included concept mapping that is creating diagrams or flow charts to visually represent the relationships being explored. This technique aimed at linking evidence extracted from the included studies, highlighting key concepts such as dyadic interdependence between caregivers and care recipients and representing relationships between these factors   .The initial search identified 6,041 studies. After removing duplicates using EndNote, 4,308 studies were screened. Following title and abstract screening, the full-text of 239 studies were screened, resulting in 14 studies suitable for inclusion. n = 14), 11 quantitative , two qualitative, and one mixed-method, are presented in n = 121; n = 369) (The main characteristics of the included studies (n = 369) . Type ofn = 369) . In the n = 369) .Intrapersonal variables include individual-difference factors, such as psychological distress, attachment orientations, personality characteristics, and intellectual abilities of both the dyad members. Interpersonal variables include relationship-oriented factors such as perspective-taking skills, perceived strength of the caregiving relationship, communication patterns between caregivers and care recipients, and dyadic behavioral responses to goal differences in care. Given the heterogeneity of the psychological variables investigated in the included studies, results are presented below grouping first all intrapersonal variables and next interpersonal variables. Overall, regardless of whether the investigated psychological variables pertained to the individual  or to the relationship , psychological variables were found to be associated with different levels of dyad members’ emotional and relational wellbeing in all studies.Seven studies  examined intrapersonal and interpersonal variables associated with emotional and relational wellbeing  of caregiver and care recipient dyads. Findings from a number of studies suggested interdependence in dyad members’ wellbeing by means of associations between care recipients’ intrapersonal variables and caregivers’ wellbeing . For exaSimilarly, findings of a number of studies suggested interdependence in dyad members’ wellbeing by means of associations with caregivers’ and care recipients’ interpersonal variables. All interpersonal variables examined relationship processes and highlighted reciprocity between caregivers and care recipients  resulting in enhanced emotional and relational wellbeing . For exaSeven studies  investigated whether care context variables were associated with levels of emotional and relational wellbeing  of caregiving dyads. Overall, findings suggested interdependence in dyad members’ wellbeing due to shared care and situational factors. Results are synthesized below presenting first, care context variables including care recipients’ health conditions and physical impairments associated with different levels of caregivers’ emotional and relational wellbeing; and second, care context variables defined as situational factors such as perceptions of social support associated with dyad members’ wellbeing. Both care recipients’ health condition and situational factors were found to be associated with emotional and relational wellbeing of dyad members.Findings of a number of studies suggested interdependence by means of associations between care recipients’ health care needs, older age, and presence of multiple chronic health conditions, and lower emotional wellbeing in adult-child caregivers, in terms of lower ratings of control over daily life and social participation , higher With regard to perceptions of social support in the care context, care recipients’ higher satisfaction with the use of social services  was significantly correlated with higher caregivers’ emotional wellbeing   and careIn four studies, interdependence in dyad members’ wellbeing was also identified as patterns of covariation between caregivers’ and care recipients’ outcomes. Results are synthesized below reporting on correlations  and congruent perspectives  between caregivers and care recipients possibly impacting their emotional and relational wellbeing.Findings suggested interdependence by means of correlations between dyad members’ wellbeing  and conglevel of dependence between dyad members, that is the impact of each dyad member on the wellbeing of the other one; the structure that is the shared context of the caregiving situation; and the covariation of interests, as patterns of covariation between caregivers’ and care recipients’ outcomes.Findings synthesized in the current review suggest that there is dyadic interdependence in the emotional and relational wellbeing of non-spousal caregiving dyads . Evidence for dyadic interdependence of non-spousal dyads was found in accordance with interdependence theory . Indeed,In line with the three dimensions of the interdependence theory mentioned above, findings from research on non-spousal caregiving dyads supported dyadic interdependence. On a first level of evidence for interdependence, research on non-spousal caregiving dyads has shown that good psychological functioning of care recipients, and specific personality traits and intellectual abilities of both caregivers and care recipients  might impact reciprocal emotional and relational wellbeing of dyad members . MoreoveOn a second level of evidence for interdependence, research on non-spousal caregiving dyads has confirmed the crucial role of the care context  for both dyad members’ wellbeing. A number of care context and situational variables  were found to be associated with emotional and relational wellbeing of the dyad. Indeed, care recipients’ poor physical health was generally related to lower caregivers’ quality of life , regardless of disease type . This paLastly, on a third level of evidence for interdependence, dyad members’ wellbeing was found to be strongly correlated  and when both caregivers and care recipients reported to have congruent perspectives on the caregiving situation  they also reported enhanced wellbeing . These fn = 14), and findings are heterogeneous underscoring the complexity and multidimensionality of caregiving experiences.Combined, these studies suggest various caregiving difficulties should be addressed at a dyadic level. Findings from this systematic review demonstrate the concept of “linked lives” in non-spousal dyads, referring to how the experiences of individuals in interdependent dyadic relationships affect not only the individual but also the other family member . There iFurther research is needed in this area. First, future studies should differentiate between caregiver subgroups  in order to provide a clearer understanding of dynamics within different relationship type dyads . In our Overall, our findings highlight a need to incorporate the dyadic perspective in caregiving research, whilst also addressing the broader care context. The present review highlighted that studies tend to either examine first level of evidence , or studies examine the second level of evidence . As such, psychological and relational factors are often explored in dyadic studies without addressing the broader care context and, on the other hand, other caregiving studies focus on caregiving factors only , neglecting psychological and relational factors . Further research should combine these perspectives .The majority of included studies focused on interpersonal variables , in line with the existing spousal literature. Certain aspects of communication, such as mutual constructive communication, self-disclosure, partner responsiveness, have been found to be associated with higher levels of intimacy and relationship satisfaction in various couples dealing with illness, especially cancer . HoweverAnother valuable avenue of future research in this area is the design and development of effective dyadic interventions tailored for both the caregiver and the care recipient in close, but non-romantic, relationships . EvidencLastly, on a methodological level, powerful dyadic analytic techniques  have been extensively used in spousal research and they may be integrated also into future non-spousal caregiving research . In our It is important to acknowledge that our review has several limitations: the small number of included studies rendered a statistical meta-analysis impossible to perform; moreover, the 14 studies used diverse variables to measure the constructs of interest and individual study sample sizes were sometimes small. The cross-sectional design of a number of included studies meant that the directionality of associations and influences was not always clear, and more longitudinal studies are needed. Other limitations may be due to the exclusion of non-English studies. Furthermore, the majority of studies were conducted in United States and therefore findings might not be generalizable to other countries and cultures. Further research is needed to investigate whether dyadic interdependence is influenced by socio-contextual and cultural factors. Finally, due to the small number of studies, findings where synthesized across a number of different non-spousal dyad types including adult children taking care of their parents, but also parents taking care of their adult children, siblings, other relatives, or friends. Although these dyads are all non-spousal, many similarities and differences may still exist in the nature of the relationship types  and in the consequences reported . Future research should avoid the risk of treating caregivers as a homogeneous group by taking into account differences in the relational and social context of caregiving.The strengths of our review are reflected, first, in including studies that considered the perspective of both dyad members. Dyadic designs allow researchers to test for interdependence both on a theoretical  and methodological  level . AnotherEvidence supporting dyadic interdependence among non-spousal caregiving dyads informs a growing understanding of mutual influences among dyad members beyond the traditional spousal research. Identification of the different ways wellbeing of one dyad member may depend on the other dyad member and vice versa  has important clinical implications for the development of interventions aimed to improve the outcomes of both caregivers and care recipients. However, the current review identified limited research on interdependence in non-spousal caregiving field, suggesting many potential future avenues of research on interpersonal and relationship processes among non-spousal caregiving dyads. The review did identify some levels of evidence for interdependence between dyad members, which warrants further investigation. Taking into account the type of relationship between caregiver and care recipient  provides an opportunity to examine whether influences of each individual’s functioning on the wellbeing of their companion may also depend on the context of interpersonal relationships and the broader psychosocial environment. In conclusion, understanding the nature and the processes of dyadic relationships offers valuable insights into caregiving as a relational phenomenon also within non-spousal dyads.The original contributions presented in the study are included in the article/GF created the search string with the help of the UMCG librarian and undertook the search in the electronic database for the relevant articles. GF and SD screened the relevant article for eligibility based on the inclusion criteria. GF, SD, JW, and MH conducted the narrative analysis to synthesize the data for both qualitative and quantitative studies. GF and MH drafted the article. SD and JW edited the subsequent drafts. 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.


Complex energy monitoring and control systems have been widely studied as the related topics include different approaches, advanced sensors, and technologies applied to a strongly varying amount of application fields. This paper is a systematic review of what has been done regarding energy metering system issues about (i) sensors, (ii) the choice of their technology and their characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) the setup of energy Key Performance Indicators (KPIs). The paper provides models about KPI estimation, by highlighting design criteria of complex energy networks. The proposed study is carried out to give useful elements to build models and to simulate in detail energy systems for performance prediction purposes. Some examples of energy complex KPIs based on the integration of the Artificial Intelligence (AI) concept and on basic KPIs or variables are provided in order to define innovative formulation criteria depending on the application field. The proposed examples highlight how modeling a complex KPI as a function of basic variables or KPIs is possible, by means of graph models of architectures.  Scientific and industrial research often addresses the formulation of new energy strategies. When a high number of variables is to be considered, the energy system modeling becomes complex. To this purpose, scaling the model to propose a framework suitable for simulations and measurements related to the effective energy scenario becomes an important issue. A scaled model representing the main scenario of the proposed research is sketched in The renewable sources play a very important role in economic and political strategies for energy self-sufficiency of countries. Actually, important technology advances are in the market in terms of biomethane/biogas, photovoltaic, wind, wave, geothermal, hydrogen, thermoelectric, and hydroelectric plants. An important emerging topic for research is the energy harvesting from alternative distributed available sources . The full integration of renewable energy sources in complex grid systems takes into account the implementation of sensor and storage systems, and the possibility to apply innovative methodologies for data processing.Artificial Intelligence (AI) algorithms are increasingly used for data processing, thus providing advanced analytical tools to estimate correlations between variables and predicting different scenarios including energy production, load consumption, and risks. Following the model of In this scenario, different systems are matched with conventional and renewable energy sources, storage devices, and efficient loads. The output results of the KPIs drive decisions and procedures such as ordinary and extraordinary maintenance services and in-grid/off-grid remote operations, thus ensuring reliable power and decreasing energy costs.The paper proposes an overview about energy systems by defining possible variables involved in different energy application fields.(a)following specifications of research projects some topics concerning energy aspects were extracted;(b)Sensors of Measurements, Smart Energy Meters, Advanced Metering Infrastructures, SCADA, Infrared Thermography, Energy Routing, Energy Technologies, Smart Cities, Renewable Energy, Lighting Control, Power Forecasting, Measurement Approaches and Methodologies, Load Balancing, Load Matching, Solar Radiation Estimation, Microgrids, High Voltage, Data Processing Algorithms, System Grids, Energy KPI indicators, Energy and Manufacturing, etc.;keywords to be used for searching were chosen, such as: (c)searching process over the literature was performed by querying the main international journal databases, especially those focused on energy. The Google Scholar engine was used as well. Open datasets concerning the topics of the examined literature and useful to test AI models were found;(d)the searching process was optimized on a two-step basis: after a pre-screening, some main works were filtered with a particular interest in the most recent ones; this refinement process allowed us to group the selected papers into four classes: (i) sensors, (ii) technology characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) energy KPIs; repetitive older papers were neglected;(e)the common basic KPIs related the energy aspects were extracted from the selected papers;(f)criteria were defined to formulate complex KPIs as functions of the basic KPIs or variables.The methodology used in this work is sketched in a, b, …) are either significant basic KPIs or measured variables, a complex KPI can be expressed asThe complex KPIs are important to model energy systems characterized by a large number of variables. If .Energy measurements are required in many application fields both in the civil and industrial sectors. Specifically, applications are in precision agriculture, logistics, buildings, lighting, energy harvesting, wiring, etc. ,31,32,33For what concerns smart buildings, the heating systems are combined with electrical power modules where heating modules could include boilers, cogeneration, heat recovery, and other energy systems to produce heat power. In complex systems, more applications fields are joined, thus increasing the complexity of the model to analyze. In a preliminary study to establish the parameters contributing to the energy behavior of the specific application filed;an interaction analysis of elements in the surrounding environment .Other application fields can be found at different scale dimensions with energy being a variable characterizing processes and physical phenomena. The approach to follow to set up KPIs will involve: The KPIs of complex models can be structured in a multilevel architecture where the KPI of a higher level embeds information of all KPIs of lower levels .Measurement approaches and methodologies ,49,50,51Combining different approaches (for both measuring and processing data) to extract more and new information useful for the definition of new efficient KPIs will be the key concern for researchers in the future.KPIs are fundamental to estimate the energy efficiency of a system and are specific for the application to be considered ,67,68,69The proposed state of the art is quite exhaustive about standard indicators including costs, losses, quality, and pollution. Complex systems, such as sustainable energy systems in a large scale (green economy), could require the use of more of these KPIs which can be furthermore interrelated.Basic KPIs and variables discussed in KPIs of complex energy systems are estimated by processing a big quantity of variables. Distributed energy systems require a high computational cost for data processing. In this trend, quantum computing and related frameworks could support this weakness . For theCO2/year .Pollution monitoring is a research topic too, as carbon dioxide, carbon monoxide, unburned hydrocarbons, particulate matter, sulfur dioxide, and nitrogen oxides emissions have to be counted to match ever higher environmental prescriptions ,79,80. Tyear see  can be aConcerning renewable energy, data of environmental pollution due to energy generation can be analyzed by means of different tools such as drones , acoustFurther important research topics concern the energy storage technologies ,86, and The monitoring of energy consumption in industrial applications can be optimized by the energy manager who manages data processing and processes correlated to the primary energy consumption . A complEnergy Management Systems (EMSs) ,100 reprOther EMS approaches are mainly focused on the analysis of management uncertainties such as fuzzy-based methods, linearization approach, probabilistic method, Monte Carlo method, Gaussian mixture model, estimation distribution and stochastic models . ProbabiAI algorithms are proposed as real-time application optimization control algorithms for energy management strategies for hybrid power engines , thus suThe paper focused on an overview of technologies, KPI investigation and definition, measurement approaches, and data processing methods, spread out over different energy application fields covering civil and industrial scenarios. The specific literature analysis defines many aspects which have to be considered when more complex systems characterized by multilevel KPIs processing different input parameters are addressed. The present review highlights important elements to be considered in real applications modeling advanced energy systems that manage a large number of variables, including the AI concept improving KPIs or defining new ones.Complex KPIs can be modeled by architecture based on nodes linked into a unique graph. Each node can represent a Basic Variable (BV), a Basic KPI (BK) over a Complex KPI (CK) formulated as a combination of a BV and BK as in Equation (1). The nodes representing the CK behave as a “supernode” . Each noa cloud framework;reading signals detected by sensors;processing data by means of AI algorithms predicting daily loads, optimizing energy consumption and loads;switching electric power (as for energy routing applications).A main application field is that of the smart buildings, where energy control and management involve a large number of electric loads and plants, especially if large indoor areas are considered. The formulation of complex KPI systems can define correlated indicators supporting the full energy management process, which can be performed by: KPIs in smart buildings could take into account other important aspects such as wellness/security (gas sensing) and can be matched with home automation applications. The KPI model can be more complex if more buildings are considered in the same system to be analyzed; all the KPIs of all the buildings can be combined to define a unique one for a neighborhood or a whole city. The modularity of the model is then useful to scale the application for wider areas. An example of complex KPIs in smart building is provided in Energy KPI models can also be formulated in particular application fields such as logistics. Actually, logistics applications are commonly characterized by energy aspects. A logistics system can be characterized by different variables contributing directly  or indirectly (such as for the driver behavior which can influence the vehicle consumption). The KPI model will be useful to optimize logistics fluxes based on the energy behavior model of the fleets. More complex systems can be associated with the joined actions of different vehicles involved in the transportation of the same product , and different logistics networks composed of different hubs. An example of complex KPIs in logistics is provided in Concerning renewable energy systems, the KPI model can be characterized by different elements such as renewable sources, local electrical networks , and high-voltage networks. The complexity of the system is increased when different renewable energy fields are considered; the monitoring and control of more PV fields (structured in subfields) transmitting energy to a high-voltage power plant is an example of a complex system. An example of complex KPIs in photovoltaic plants is provided in


Ancient prescriptions of traditional Chinese medicine (TCM) are an important source for innovative drug research and development, which has garnered increasing attention in recent years. Piji Pills, an ancient TCM prescription, has a long history and remarkable clinical efficacy in the treatment of digestive disorders. Thus, the purpose of this study was to explore the origin and development of Piji Pills and to discuss the potential future direction of an ancient TCM prescription.  We analyzed the origin and development of the Piji Pills by reviewing literature records and their evolution in ancient books. We used a full-text database covering 2,090 TCM ancient books and implemented the full-text retrieval function based on Ulysses software. A full-text search was conducted using the keyword “Piji Pills” (“脾积丸” in Chinese). The results generated 128 pieces of literature from 35 ancient TCM books. In order to identify pertinent sections from the generated results, the results were proofread by two independent authors (Fudong Liu and Xiaochen Jiang) who had sufficient experience concerning ancient books. The developmental process of the Piji Pills was divided into early, late, and modern times. With the approach of statistical methods and chronological description, we manually searched, indexed, and transformed 2,090 ancient TCM books.  From the time Piji Pills were first proposed, the records in ancient books became increasingly detailed, providing an in-depth discussion of their composition, dosage, and action mechanisms. In modern times, the research on key drugs found in Piji Pills has made a great contribution to clinical practice. However, the compound research on Piji Pills is still relatively superficial and requires further in-depth study.  In this study, statistical methods were used to chronologically clarify the developmental process of Piji Pills. We found that the Piji Pills were widely used and had a significant advantage in the treatment of digestive system diseases. In-depth knowledge mining of ancient books could potentially promote the theoretical innovation of TCM and the research of new drugs. The unique cognitive system and empirical methodology of traditional Chinese medicine (TCM) have evolved over millennia, and its theoretical system has consistently improved overtime. Ancient books are considered important wisdom carriers, depicting the achievements of TCM. They contain rich connotations that guide the continuous development and progress of TCM. One such example is the discovery of artemisinin by Tu Youyou, a Chinese pharmaceutical chemist and malariologist who was searching for a traditional malaria cure and was inspired by an ancient Chinese medical book (Zhou Hou Bei Ji Fang) and ultimately isolated and extracted artemisinin, an antimalaria drug. This was a breakthrough in twentieth-century medicine and later won the Nobel Prize . Fan et Due to dietary changes, the incidence of digestive system diseases has gradually increased and imposes an enormous economic burden on both the patient and the healthcare system. Chinese and Western medicine combinations have significant advantages in the treatment of digestive system diseases; for example, some experiments have confirmed the efficacy of Chinese medicine in the treatment of elicobacter pylori infection , ulceratβ-elemene, isolated from the Curcuma Rhizoma (Ezhu), for its anticancer effects [Piji Pills have remarkable clinical effects in the treatment of digestive system diseases. Zhai et al. confirmed the potential value of  effects . Zhang e effects .ChiCTR2000032875). It is, therefore, imperative to systematically review the classical medical literature to analyze the origins and development of the Piji Pills and to further explore the sources of innovation and the development of TCM.We are conducting a randomized controlled clinical trial in metastatic pancreatic cancer patients, using prescription medication containing the key compositional compounds found in the Piji Pills, to further validate its efficacy and safety . In order to identify pertinent sections, the results were proofread by two independent authors (Fudong Liu and Xiaochen Jiang) who had sufficient experience concerning ancient books. Thereafter, we manually indexed and classified the pertinent written sections. For data format compatibility , required by the data mining system, we used Microsoft Excel for Mac 16.62 22061100) for data entry purposes. CSV data included eight entries from A to H: A dynasty, B author, C books, D original text, E drugs, F disease, G dose, and H dosage form. Reference WHO international standard terminologies on traditional Chinese medicine regulated TCM standard terminology [00 for daWe reviewed more than 128 pieces of literature from 35 books , generatThe early Piji Pills records in ancient TCM books stretched from the Song dynasty (960–1279 AD) to the Yuan dynasty (1271–1368 AD), during which period the ancient Chinese medical texts not only achieved a break-through starting from scratch but also made great progress in the composition of drugs and the treatment scope of diseases .During the Song dynasty (960-1279 AD), the government attached great importance to the development of medicine. The first record of Piji Pills appeared in Sheng Ji Zong Lu (1117 AD), which was compiled by the government. The book contained nearly 20,000 prescriptions, including prescriptions from ancient medical texts, folk experiences, and prescriptions provided by medical practitioners. Piji Pills recorded in this book were composed of Old Rice (Chen Cang Mi), Citri Reticulatae Pericarpium Viride (Qing Pi), Crotonis Fructus (Ba Dou), Sparganii Rhizoma , Curcumae Rhizoma (E Zhu), and Arecae Semen (Bing Lang). It was mainly used to treat the disease “Masses in the spleen” (Pi Qi), manifested by abdominal swelling, epigastric distension, jaundice, dry mouth, weight loss, reduced diet, chest tightness and intermittent fever. The disease, in terms of symptoms, resembled the digestive systems of malignant tumors and was treated by Piji Pills. However, the absence of a more detailed disease description in the book limited its further application. The Piji Pills were later described in Renzhai Zhizhi Fanglun, 1264 AD, where it was recorded that Piji Pills could potentially be used for the treatment of malaria, dyspepsia, indigestion, and constipation, among others. The prescription composition of Piji Pills had been “updated.” Old Rice (Chen Cang Mi) and Arecae Semen (Bing Lang) were removed; Alpiniae Officinarum rhizoma (Gao Liang Jiang), Gleditsiae Sinensis Fructus (Da Zao Jiao), and Plant Soot (Bai Cao Shuang) were added. Thereafter, most of the ancient books described Piji pills based on this prescription composition.During the Jin and Yuan dynasties, TCM developed rapidly. Four famous Jin and Yuan healers  made a great impact on the development of TCM. During this period, descriptions of Piji Pills were gradually enriched. Four ancient books recorded the composition, dosage, and main symptoms of this prescription. Convenient prescriptions for doctor's cowardice (Yi Fang BianNuo), written by Li Gao, the founder of the “Spleen-Stomach Theory,” further expanded the therapeutic effect of Piji Pills. The book recorded that Piji pills were used to treat indigestion, acid reflux, vomiting, pediatric malnutrition, and abdominal pain in women caused by Qi-blood disharmony, covering internal medicine, pediatrics, and gynecological diseases. Li Gao added a Qi-regulating herb, CaryophylliFlos (Ding Xiang), to the pills, reflecting the academic view that “the spleen and stomach are the pivots of Qi movement.” Influenced by Li Gao, this drug was later widely used by doctors.The development of Chinese medical theory further matured from the Ming dynasty to the Qing dynasty (1368 AD-1644 AD). Especially toward the end of the Qing dynasty, the idea of combining Chinese and Western medicine began to seep into the minds of doctors as Western medicine was introduced into China. During this time, the development and application of Piji Pills became more widespread. Many monographs on pediatrics, internal medicine, and miscellaneous diseases elaborate on the indications for diseases and the medicine dosage.Twenty-two pieces of literature recorded the drug composition of Piji Pills. Based on the analysis of this literature using the Gephi complex network , it was To fully discover the main diseases treated by Piji Pills and to more accurately guide clinical application, we cleaned, transformed, and summarized the data on the diseases (symptoms) recorded in the literature. We then used VBA programs to disaggregate the symptoms and perform descriptive statistics , which sWan Mizhai, a Yuan dynasty physician who was renowned in pediatrics and gynecology, often used the Piji Pills to treat pediatric pox and rash, dyspepsia, indigestion, and infantile convulsion. In You Ke Fa Hui, it was written that “the stomach is known as ‘the sea of water and food', and the intestine is ‘the way of water and food'. Diarrhea is a disease caused by damage to the stomach and intestines. If there is no pathogenic qi in the stomach and intestines, food and water will not become bad, and the person will have no disease. If food retention stays in the stomach and intestines, diarrhea will occur, just like more water in a container will flow out. If diarrhea is to be treated, the source of the disease must be found. The spleen jack pill is the medicine that can get rid of food retention at its source.” This is one of the few discussions in ancient books of TCM on the mechanism of Piji Pills in treating digestive disorders, and it emphasizes the mechanism of Piji Pills with the spleen and stomach as its core. The famous pediatrician Zhou Zhen of the Qing dynasty was also inspired by this and used the Piji Pills to treat diarrhea, abdominal pain, night cries, and other pediatric diseases caused by food accumulation.According to basic TCM theory, the spleen and stomach are the pivots of Qi movement. If Qi movement in the spleen and stomach is not properly regulated, abdominal distension and pain will occur. If there is an imbalance in the ascending and descending of Qi movement, Qi counterflow will lead to nausea, vomiting, acid reflux, and constipation. If the spleen-yang is trapped, diarrhea will follow. If Qi is blocked for a prolonged period of time, it will further develop into tangible accumulation. Therefore, with the use of a large number of herbs that promote the movement of Qi, the Piji Pills have become increasingly effective in the treatment of digestive disorders, especially after Li Gao added Caryophylli Flos (Ding Xiang) to the composition of the medicine.Based on more complete records of Piji Pills in the Ming and Qing dynasties, combined with those from the Song, Jin, and Yuan dynasties, the dosages of Piji Pills' herbs were counted . The anaIn addition, there were some rare units of measure recorded in ancient books, making data conversion difficult. For example, the most frequent record of the amount of Gleditsiae Sinensis Fructus (Da Zao Jiao) was “3 ting,” which was recorded in seven books. We also searched the relevant books and found that Pro. Shi Shenghan, in his commentary on QiMin Yao Shu, believed that the unit of “ting” was equivalent to the modern measurement of “ingot” (one piece). So “3 ingots” were three pieces. At the same time, the dosage of the large soapwort was recorded as “3 qian” (11.19 g) in Dou Zhen Xin Fa and You You Ji Cheng. In summary, we assumed that the dosage of Gleditsiae Sinensis Fructus (Da Zao Jiao) was indicative of approximately15 g. The dosage of Plant Soot (Bai Cao Shuang) was calculated in a similar manner as previously mentioned. Eight books contained a dosage of “3 spoons,” and combined with the dosage of “3 qian” (11.19 g) in four books in the Ming and Qing dynasties, the dosage of Plant Soot (Bai Cao Shuang) was considered to be 12 g.In terms of the maximum amount for each recipe, Sparganii Rhizoma (San Leng) and Curcumae Rhizoma (E Zhu) were the most commonly used. The minimum dosage of each herb was taken from You Ke Yi Xue Zhi Nan, written by Zhou Zhen in the Qing dynasty, which lacked statistical significance. In terms of average dosage, the dosage of Curcumae Rhizoma (E Zhu) was the largest, followed by Sparganii Rhizoma (San Leng), Citri Reticulatae Pericarpium Viride (Qing Pi), Alpiniae Officinarum Rhizom (Gao Liang Jiang), Crotonis Fructus (Ba Dou) and Aucklandiae Radix (Mu Xiang).In regards to the dosage form, we screened 128 pieces of literature documenting Piji Pills and found that the dosage form used in 22 documents were all pills. We also searched for information on pills in ancient texts. The most representative one was Li Gao's comment in his Yong Yao Fa Xiang that “pills are slow, soothing, and healing.” Modern pharmacological studies have also confirmed the ability of solid oral agents to deliver live phages to the gastrointestinal tract . Therefoβ-elemene isolated from Curcumae Rhizoma (E Zhu), had been developed into a drug for the treatment of solid tumors and malignant effusion by intravenous injection, intraperitoneal or peritoneal perfusion. The drug is currently being tested in clinical trials in the United States [Modern times refer to the time period from the beginning of the Republic of China (1912 AD-1949 AD) to the present day. In the early stage of this period, TCM was challenged by western medicine. At the beginning of the twentieth century, the increasing attention to western medicine hindered the development of TCM . Since 1d States . Simultad States . TherefoMachine learning (ML) and artificial intelligence (AI) are developing rapidly in the medical field, which can be beneficial to both medical staff and patients. The IBM Corporation has developed an AI-assisted decision-making system, known as Watson for Oncology (WFO), that is based on cognitive computing and includes medical literature, patents, genomics information, and chemical and pharmacological data. Several studies have confirmed that WFO may be a helpful tool for cancer treatment decision-making –21. MoreHowever, most of these studies were conducted based on modern research and literature. The TCM classics and literature cannot be taken as the object of these studies because the ancient TCM books have accumulated a lot of data over the last millennia since its initial development. A lot of research achievements in prescriptions, herbs, ingredients, and other TCM-related information were dispersedly recorded in books and journals, which hindered the systematic research and application of TCM.In 2018, the State Administration of TCM (SATCM) selected 100 ancient classical prescription records from more than 100,000 prescriptions recorded in 103 medical books. Currently, several documentary studies on classically famous prescriptions are being conducted. TCM has been garnering increasing attention, which has increased the need for TCM-related data resources. Our study had the following strengths. First, our database was extensive; we retrieved the information from a large number of ancient TCM books, including ancient books from the Song, Jin, Yuan, Ming, and Qing dynasties, which eliminated the deviation caused by historical factors and provided greater literature comprehension. Second, we designed an indexing manual and a manual classification strategy for ancient discourse and introduced statistical methods to provide a more objective picture of what was written in ancient texts about Piji Pills, eliminating the bias caused by limited researcher understanding.Despite the aforementioned strengths, our study had some limitations. First, only 128 documents were included in the final review and analysis, and the maneuverability of the indexing manual and classification strategy designed in this study required more testing data. Second, this study used statistical methods to explore the mining of ancient TCM books, which needed to be further combined with ML and AI techniques. Third, this study has only clarified the information on the composition, dosage, and symptoms of Piji Pills; future research on the mechanism of Piji Pills needs to be conducted to promote the modernization of Chinese medicine.Piji Pills, an ancient TCM prescription, has a long history and was inherited and developed in the Ming and Qing dynasties. This study applied statistical methods to extensively investigate and review ancient TCM books, using a chronological approach to sort through the results of the analysis. Based on the study results, we found that the core components of Piji Pills were “Aucklandiae Radix (Mu Xiang), Crotonis Fructus (Ba Dou), Alpiniae Officinarum Rhizoma (Gao Liang Jiang), Sparganii Rhizoma (San Leng), Curcumae Rhizoma (E Zhu), CitriReticulatae PericarpiumViride (Qing Pi), Gleditsiae Sinensis Fructus (Da Zao Jiao), and lPant Soot (Bai Cao Shuang)”, with Sparganii Rhizoma (San Leng) and Curcumae Rhizoma (E Zhu) being the key drugs. Furthermore, we found it was used in clinical practice in the form of pills and had significant advantages in the treatment of digestive disorders. Modern scientific research supports the views expressed in ancient texts. This study concluded that promoting the deep integration of information technology with the content mining of ancient TCM books could facilitate theoretical innovation and the creation of new medicines.


The development and application of next generation sequencing technologies for clinical gastroenterology research has provided evidence that microbial dysbiosis is of relevance for the pathogenesis of gastrointestinal and extra-intestinal diseases. Microbial dysbiosis is characterized as alterations of diversity, function, and density of the intestinal microbes. Emerging evidence suggests that alterations of the gastrointestinal microbiome are important for the pathophysiology of a variety of functional gastrointestinal conditions, e.g., irritable bowel syndrome (IBS) and functional dyspepsia (FD), also known as disorders of brain-gut axis interaction. Clinicians have for many years recognized that small intestinal bacterial overgrowth (SIBO) is typified by a microbial dysbiosis that is underpinned by abnormal bacterial loads in these sites. SIBO presents with symptoms which overlap with symptoms of FD and IBS, point toward the possibility that SIBO is either the cause or the consequence of functional gastrointestinal disorders (FGIDs). More recently, new terms including “intestinal methanogen overgrowth” and “small intestinal fungal overgrowth” have been introduced to emphasize the contribution of methane production by archea and fungi in small intestinal dysbiosis. There is emerging data that targeted antimicrobial treatment of SIBO in patients with FD who simultaneously may or may not have IBS, results in symptom improvement and normalization of positive breath tests. However, the association between SIBO and FGIDs remains controversial, since widely accepted diagnostic tests for SIBO are lacking. Culture of jejunal fluid aspirate has been proposed as the “traditional gold standard” for establishing the diagnosis of SIBO. Utilizing jejunal fluid culture, the results can potentially be affected by cross contamination from oropharyngeal and luminal microbes, and there is controversy regarding the best cut off values for SIBO diagnosis. Thus, it is rarely used in routine clinical settings. These limitations have led to the development of breath tests, which when compared with the “traditional gold standard,” have sub-optimal sensitivity and specificity for SIBO diagnosis. With newer diagnostic approaches–based upon applications of the molecular techniques there is an opportunity to characterize the duodenal and colonic mucosa associated microbiome and associated gut microbiota dysbiosis in patients with various gastrointestinal and extraintestinal diseases. Furthermore, the role of confounders like psychological co-morbidities, medications, dietary practices, and environmental factors on the gastrointestinal microbiome in health and disease also needs to be explored. It is now well established that the human microbiome hosts trillions of microorganisms. These include bacteria, viruses, bacteriophages, and fungi . The mic1 to 103 bacteria cfu/ml in the stomach and duodenum, toward 104 to 107 bacteria cfu/ml in the distal small intestine and reaching 1011 to 1012 cfu/ml in the colon  and density of the microbes colonizing the mucosal lining of the gastrointestinal tract collectively define the gastrointestinal microbiome. It is now acknowledged that soon after birth, the gastrointestinal tract is colonized by microbes from the environment  . The denhe colon . Furtherhe colon . The miche colon . It is ehe colon . The difRelevant changes in the diversity, density or metabolic function of gut microbes is frequently referred to as intestinal “dysbiosis” although the exact cutoff thresholds are poorly defined . There iThe term functional dyspepsia (FD) is used for patients presenting with recurring symptoms of indigestion referred to upper gastrointestinal tract without obvious identifiable cause for these symptoms . Many paThe role of the human microbiome in regulating physiological functions including gastrointestinal motor function, gastric and pancreatic secretion, protection of the epithelial barrier, and the interplay between the gut and the central nervous system potentially explains its contribution to symptoms associated with FGID . Recent Salmonella spp., Escherichia coli O157, Campylobacter jejuni, Giardia lamblia, and Norovirus  and carbon dioxide (CO2) as their only source of energy  by far exceeds the number of host cells. However, the majority of studies characterizing the gastrointestinal microbiome focus on characterizing the “bacterial colonization” of the gastrointestinal tract, and very limited data are available in relation to fungi and viruses. Fungus, and specifically  the gut . Fungal  the gut . Jacobs Rothia, Enterococcus, Streptococcus, Staphylococcus, or the potentially pathogenic species E. coli. While there has been considerable data on stool microbiome (which might be of limited relevance), much less information is available in relation to the link between PPI use and small intestinal bacterial colonization or even the mucosa associated microbiome. In a recent study by 3 CFU/ml) or 16S sequencing. However, a recent systematic review and meta-analysis  of colonic-type bacteria in the culture of the small bowel aspirate is considered as the established criteria for diagnosing SIBO  and methane (CH4) gas in breath samples are the most widely used test for diagnosing SIBO  carbohydrates in the gut (2 and CH4 are quantified as parts per million (ppm) in the exhaled breath using, e.g., gas chromatography (2 within 90 min (after ingestion of the carbohydrate glucose or lactulose) or a level of ≥10 ppm of CH4 is considered a positive test to diagnose SIBO.Breath tests, based on the measurement of hydrogen  and SIBO using LBT, found that the abnormal rise in H2 measured in the LHBT could be due to variations in oro-cecal transit time and not necessarily due to SIBO , expressed mRNA in gastrointestinal tract samples (metatranscriptomics), produced proteins (metaproteomics), and the metabolite profiles (metabolomics). Metagenomics assess genes that could be expressed, while metatranscriptomics focuses on regulatory networks or gene expression and combined with metaproteomics, and metabolomics evaluates the functionality of the microbiota and, therefore, supports work to gain a better understanding of microbial activities in the gut and the potential effects for the host.Culture-based methods have obvious limitations. In particular they do not allow to appropriately determine the microbial diversity of the intestinal microbiota since a large fraction of the microbiota cannot be cultured . Indeed,Prevotella, Veillonella, and Actinomyces compared with control subjects, suggesting that their symptoms may be related to alterations of their microbiome at this site. The findings of the pilot studies  sequencing revealed that SIBO subjects had 4-fold significantly higher relative abundance of Proteobacteria and 1.6-fold significantly lower Firmicutes than non-SIBO subjects. Furthermore, altered Proteobacterial profiles were found that correlated with symptom severity. Furthermore, a study by Enterobacteriaceae) were enriched in many patients classified as having SIBO and high loads of disruptors correlated with a high prevalence of severe gastrointestinal symptoms. This study concluded that SIBO diagnosis via microbial culture should focus on quantification of a specific group of disruptor taxa (Enterobacteriaceae) rather than simply the total microbial load.Another recent elegant study by Microorganisms release many different volatiles also referred to as microbial volatile organic compounds (mVOCs) . These vR which allows to determine the concentrations of gases while the capsule transits through the lumen of the GI tract. While the relative position of the capsule in the gastrointestinal tract is known, the localization of gas production due to localized alterations of composition and density of bacteria in the gastrointestinal tract can be determined. Preliminary studies  but even more importantly by the lack of simple and reliable diagnostic tests. To characterize the potential link between intestinal dysbiosis and FD it is not only required to address the methodological limitations of the traditional tests for SIBO. This includes the poor sensitivity and specificity of breath tests which are commonly used in clinical settings, or culture techniques which are invasive and restricted to microbes that can be cultured. In addition, confounders need to be taken into consideration which include–but not limited to–medications , infectivia ingested capsules and measure concentration within the gastrointestinal tract as these capsules transit though the gastrointestinal tract. However, this indirect approach currently only captures one or two defined bacterial metabolites limiting the utility. Thus, in order to precisely characterize the small intestinal microbiome, there is an opportunity to validate and use novel molecular techniques in the routine clinical setting. It will be important to use these molecular techniques to further characterize the gut microbiome and explore the response to antimicrobial therapy including the links between long lasting symptom resolution and the microbiome. This will pave the path for novel diagnostic and therapeutic approaches that ultimately will allow to define the role of microbes for the pathophysiology of FD and potentially to individualize treatments and ultimately improve patient outcomes.In the clinical setting, glucose or lactulose breath tests are widely used. These tests are based upon the measurement of hydrogen or methane in the breath after oral ingestion of defined amounts of carbohydrate substrates. While the validity and sensitivity and specificity of the diagnostic test for SIBO rely on specific thresholds, direct culture test quantitate colony forming units when duodenal aspirates or biopsies are cultured. However, only a small proportion of microbes colonizing the small intestinal tract can be cultured. The obvious gap relates to diagnostic approaches that allow the characterization of microbes colonizing the mucosal lining of the gastrointestinal tract utilizing molecular techniques. An interesting novel approach might be the use of biosensors that are delivered AS and GH: study idea, concept and design, drafting of the first version of the manuscript, and review of the final manuscript. NT: drafting of the manuscript and review of the final manuscript. All authors contributed to the article and approved the submitted version.NT reports personal fees from Allakos, Aviro Health, Antara Life Sciences, Arlyx, Bayer, Danone, Planet Innovation, Takeda, Viscera Labs, twoXAR, Viscera Labs, Dr. Falk Pharma, Censa, Cadila Pharmaceuticals, Progenity Inc., Sanofi-aventis, Glutagen, ARENA Pharmaceuticals, IsoThrive, BluMaiden, HVN National Science Challenge, non-financial support from HVN National Science Challenge NZ, outside the submitted work. In addition, NT has a patent Biomarkers of IBS licensed (#12735358.9-1405/2710383) and (#12735358.9-1405/2710384), a patent Licensing Questionnaires Talley Bowel Disease Questionnaire licensed to Mayo/NT, a patent Nestec European Patent licensed, and a patent Singapore Provisional Patent NTU Ref: TD/129/17 “Microbiota Modulation of BDNF Tissue Repair Pathway” issued and copyright Nepean Dyspepsia Index (NDI) 1998 and Editorial: Medical Journal of Australia (Editor in Chief), Up to Date (Section Editor), Precision and Future Medicine, Sungkyunkwan University School of Medicine, South Korea, Med . NT participates Committees: Australian Medical Council (AMC) Council Member (2016–2019), MBS Review Taskforce (2016–2020), NHMRC Principal Committee, Research Committee (2016–2021), Asia Pacific Association of Medical Journal Editors (APAME) (current), GESA Board Member (2017–2019). NT Misc: Avant Foundation (judging of research grants) (2019). NT community and patient advocacy groups: Advisory Board, IFFGD . NT holds an NHMRC Investigator grant. GH report to be on the advisory boards Australian Biotherapeutics, Glutagen, Bayer and received research support from Bayer, Abbott, Pfizer, Janssen, Takeda, Allergan. He serves on the Boards of the West Moreton Hospital and Health Service, Queensland, UQ Healthcare, Brisbane, and the Gastro-Liga, Germany. He has a patent for the Brisbane aseptic biopsy device and serves as Editor of the Gastro-Liga Newsletter. The remaining author declares 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. 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The children included were under the age of 7 years; 74 were adopted internationally through legal agencies between October 2010 and December 2016, and the remaining 96 were non-adopted children living with their birth parents  recruited from day-care centers. We used Mary Rothbart’s temperament questionnaires to assess temperament, the Child Behavior Checklist (CBCL) to obtain data on the children’s behavioral/emotional problems and competencies, and the General Health Questionnaire (GHQ) to assess parental psychological distress. The study was approved by the Ethics Committee of the Hospital District of Southwest Finland, and written informed consent was obtained from the parents and the children themselves. (3) Results: The negative affectivities of both mothers and children were associated with the total CBCL and with both internalizing and externalizing problem behaviors after adjusting for age, gender, and adoption status. Both relationships remained significant when tested simultaneously, suggesting additive effects. Maternal negative affect was associated with problem behavior irrespective of child extraversion/surgency. Child extraversion/surgency was associated with lower levels of all internalizing behavioral problems when adjusted for maternal sociability. Child negative affect was associated with all behavioral problem measures irrespective of maternal sociability or maternal psychological distress. Maternal distress was associated with child problem behaviors only in children with low extraversion/surgency. (4) Limitations: The sample size was relatively small, and the information was gathered solely with questionnaires. (5) Conclusions: The results of the study may be clinically significant. Child negative affect, maternal negative affect, and maternal experienced distress, combined with low child extraversion/surgency, may increase the risk of child problem behaviors in both adoptees and non-adoptees.(1) Background: For decades, the temperaments of infants and small children have been a focus of studies in human development and been seen as a potential contributor to children’s developmental patterns. However, less is known about the interplay between the temperamental characteristics of mothers and their children in the context of explaining variations in developmental outcomes. The aim of our study was to explore the associations—with or without genetic links—of the temperaments and psychological distress of mothers and the temperaments of children with behavioral problems in a group of internationally adopted children and their adoptive mothers and in a group of non-adopted children and their mothers. (2) Methods: Data ( Temperament has been described as individual differences in reactivity and self-regulation that are constitutionally based, appear early, and are influenced over time by heredity, maturation, and experience . More reFor decades, temperament in early life has been a focus of studies in human development and psychopathology in children and adolescents . IndividIntergenerational transmission, a process through which an earlier generation influences parenting attitudes and behavior, genetically or otherwise, inevitably results in parent–offspring resemblance ,7. AlthoPrevious research has shown associations between child temperament, parenting, and child adjustment ,12,13, aThe mental well-being of adopted children has been addressed in previous studies; most adopted children are emotionally and behaviorally healthy but more likely than non-adoptees to receive mental health treatment. According to several studies, adopted children are referred for psychiatric treatment at a higher rate than their non-adopted peers ,23, and The aim of our study was to explore the associations between child behavioral problems, the temperaments and psychological distress of mothers, and the temperaments of children. The study’s design, which included adoptees and biological children, enabled us to examine whether these associations depend on genetic links between mothers and children. We decided to focus on two temperamental characteristics, negative affect and extraversion/surgency, as these characteristics have been consistently associated with later psychological problems .This study was part of the ongoing Finnish Adoption (FinAdo) study ,27, whicThe current study was approved by the Ethics committee of the Hospital District of Southwest Finland, and written informed consent was obtained from the parents. To assess child temperament, we used Mary Rothbart’s temperament questionnaires, specifically the Early Childhood Behavior Questionnaire (ECBQ) and the Children’s Behavior Questionnaire (CBQ) .The ECBQ is a parent-report measure of temperament in children aged from 18 months to 3 years that assesses 18 dimensions of temperament , which reliably form the same three factors as the ECBQ. Rothbart et al.  assessedMaternal temperament was measured using the Emotionality–Activity–Sociability Temperament Survey  The quesThe General Health Questionnaire (GHQ) is a self-administered screening questionnaire designed for use in consulting settings to detect individuals with diagnosable psychiatric disorders . The mosTo assess child behavioral problems, we used two CBCL questionnaires: the CBCL for ages 18 months to 5 years, which has 100 questions, and the CBCL for ages 6 to 18 years, which has 113 questions, excluding 5 open-ended questions. These questionnaires produce scores on the same dimensions and are thus comparable and age-adjusted. Each question is answered as (0) not true, (1) somewhat or sometimes true, or (2) very true or often true. The CBCL provides a total score for behavioral characteristics and separate scores for internalizing and externalizing behavioral symptoms. The total CBCL score was calculated by summing up all items. The higher a child’s scores on the CBCL, the more behavioral problems the child has . The CroThe child-related variables included the child’s adoption status , gender, age at adoption (if applicable), and age at the date of completing the questionnaire.t-test. The associations of child problem behaviors with maternal temperamental characteristics, maternal distress, and child temperamental characteristics were examined using linear regression analyses. Maternal temperamental characteristics, maternal distress, and child temperamental characteristics were inserted into the same regression model to test the possible additive nature of the associations. In a separate model, an interaction term between maternal temperamental characteristics or distress and child temperamental characteristics was inserted to examine possible interaction effects. In all analyses, adoption status, gender, and age were used as covariates. The associations are presented as regression coefficients (b) and standard errors (se). All analyses were performed using Stata 17.0. Mediation analyses were conducted using structural equation modeling and Medsem package [Differences in child and maternal temperamental characteristics, child problem behaviors, and maternal distress according to the adoption status were examined using Student’s  package .Descriptive statistics for the study sample are shown in There were no significant differences in the temperamental dimensions between adopted and non-adopted children. Mothers of adopted children reported more externalizing behaviors than mothers of non-adopted children. The effect sizes ranged from small (0.02) to moderate (0.49). These are shown in The coefficients of the correlations between maternal temperament, child temperament, and child problem behaviors are shown in The associations of maternal negative affect and child temperament characteristics with child problem behavior are shown in The simultaneous associations of child problem behaviors with maternal sociability and child temperament characteristics are shown in The associations of child problem behaviors with maternal psychological distress and child temperament characteristics are shown in p-values > 0.05) suggesting that the adoption status did not modify the associations of child and maternal temperamental characteristics and maternal distress with child problem behaviors.Last, in a multivariate model, the associations between child and maternal temperamental characteristics and maternal distress with child problem behaviors were examined . The resz = 2.95, p < 0.01, 26% of total effect mediated) the association between maternal negative affect and child problem behavior . None of the other mediation effects tested were statistically significant.As sensitivity analyses, we tested whether child temperament traits would mediate the associations between maternal traits and child behavioral problems. Child negative affect mediated , but liThe fact that temperamental differences affect psychological development irrespective of a genetic link is of importance to adoptive parents and their children, and the results of our study can thus be considered clinically significant.N = 170). An additional limitation is data collection using questionnaires, which meant that the assessments of behavioral problems, child temperament, maternal temperament, and maternal psychological distress were based solely on maternal reports. Furthermore, as the vast majority of the respondents were mothers, we were unable to assess any potential differences between mothers’ and fathers’ reports. Further studies would benefit from integrating the perspectives of multiple informants .The major limitation of our study is the relatively small sample size  and the ability to consider the genetic links between parents and children. To our knowledge, this is the first study to assess the associations of child and maternal temperament with psychological well-being in both adoptive and non-adoptive families.Our study suggests that certain temperamental features of children and mothers may impact the psychological development and well-being of children. Because adopted children are at a heightened risk of mental health problems, it is important to acknowledge any factors that may affect their psychological development. The results of our study help to identify those children who are at risk to develop behavioral problems. The risk is increased when maternal psychological distress is combined with low extraversion/surgency in the child and in the case of maternal and child negative affect, especially when mother and child share this trait. We are unable to change a child’s temperament, but when there is a risk of externalizing behavior, early interventions, such as Incredible Years, are available. These interventions have been studied and shown to be effective .