Advances in Bioactivity of MicroRNAs of Plant-Derived Exosome-Like Nanoparticles and Milk-Derived Extracellular Vesicles

MicroRNA (miRNA) is a class of small noncoding RNA involved in physiological and pathological processes via the regulation of gene expression. Naked miRNAs are unstable and liable to degradation by RNases. Exosome-like nanoparticles (ELNs) secreted by plants and extracellular vesicles (EVs) found in milk are abundant in miRNAs, which can be carried by ELNs and EVs to target cells to exert their bioactivities. In this review, we describe the current understanding of miRNAs in plant ELNs and milk EVs, summarize their important roles in regulation of inflammation, intestinal barrier, tumors, and infantile immunological functions, and also discuss the adverse effect of EV miRNAs on human health. Additionally, we prospect recent challenges centered around ELN and EV miRNAs for interventional applications and provide insights of grain-derived ELNs and miRNAs interventional use in human health. Overall, plant ELNs and milk EVs can transfer miRNAs to mitigate the pathological status of recipient cells by mediating the expression of target genes but may also exert some side effects. More studies are required to elucidate the in-depth understanding of potential interventional effects of ELN and EV miRNAs on human health.

Inequities in Children's Reading Skills: The Role of Home Reading and Preschool Attendance

OBJECTIVE

Children from socioeconomically disadvantaged backgrounds have poorer learning outcomes. These inequities are a significant public health issue, tracking forward to adverse health outcomes in adulthood. We examined the potential to reduce socioeconomic gaps in children's reading skills through increasing home reading and preschool attendance among disadvantaged children.

METHODS

We drew on data from the nationally representative birth cohort of the Longitudinal Study of Australian Children (N = 5107) to examine the impact of socioeconomic disadvantage (0-1 year) on children's reading skills (8-9 years). An interventional effects approach was applied to estimate the extent to which improving the levels of home reading (2-5 years) and preschool attendance (4-5 years) of socioeconomically disadvantaged children to be commensurate with their advantaged peers, could potentially reduce socioeconomic gaps in children's reading skills.

RESULTS

Socioeconomically disadvantaged children had a higher risk of poor reading outcomes compared to more advantaged peers: absolute risk difference = 20.1% (95% confidence interval [CI]: 16.0%-24.2%). Results suggest that improving disadvantaged children's home reading and preschool attendance to the level of their advantaged peers could eliminate 6.5% and 2.1% of socioeconomic gaps in reading skills, respectively. However, large socioeconomic gaps would remain, with disadvantaged children maintaining an 18.3% (95% CI: 14.0%-22.7%) higher risk of poor reading outcomes in absolute terms.

CONCLUSION

There are clear socioeconomic disparities in children's reading skills by late childhood. Findings suggest that interventions that improve home reading and preschool attendance may contribute to reducing these inequities, but alone are unlikely to be sufficient to close the equity gap.

Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators

Many epidemiological questions concern potential interventions to alter the pathways presumed to mediate an association. For example, we consider a study that investigates the benefit of interventions in young adulthood for ameliorating the poorer mid-life psychosocial outcomes of adolescent self-harmers relative to their healthy peers. Two methodological challenges arise. First, mediation methods have hitherto mostly focused on the elusive task of discovering pathways, rather than on the evaluation of mediator interventions. Second, the complexity of such questions is invariably such that there are no well-defined mediator interventions (i.e. actual treatments, programs, etc.) for which data exist on the relevant populations, outcomes and time-spans of interest. Instead, researchers must rely on exposure (non-intervention) data, that is, on mediator measures such as depression symptoms for which the actual interventions that one might implement to alter them are not well defined. We propose a novel framework that addresses these challenges by defining mediation effects that map to a target trial of hypothetical interventions targeting multiple mediators for which we simulate the effects. Specifically, we specify a target trial addressing three policy-relevant questions, regarding the impacts of hypothetical interventions that would shift the mediators' distributions (separately under various interdependence assumptions, jointly or sequentially) to user-specified distributions that can be emulated with the observed data. We then define novel interventional effects that map to this trial, simulating shifts by setting mediators to random draws from those distributions. We show that estimation using a g-computation method is possible under an expanded set of causal assumptions relative to inference with well-defined interventions, which reflects the lower level of evidence that is expected with ill-defined interventions. Application to the self-harm example in the Victorian Adolescent Health Cohort Study illustrates the value of our proposal for informing the design and evaluation of actual interventions in the future.

Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn

The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements-effect definitions with causal interpretation, clarification of assumptions required for effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this article is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions, and using concrete examples for illustration. This presentation differentiates 2 perspectives (or purposes of analysis): the explanatory perspective (aiming to explain the total effect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the article proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types-interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect. This general class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Decomposition Analysis to Identify Intervention Targets for Reducing Disparities

There has been considerable interest in using decomposition methods in epidemiology (mediation analysis) and economics (Oaxaca-Blinder decomposition) to understand how health disparities arise and how they might change upon intervention. It has not been clear when estimates from the Oaxaca-Blinder decomposition can be interpreted causally because its implementation does not explicitly address potential confounding of target variables. While mediation analysis does explicitly adjust for confounders of target variables, it typically does so in a way that effectively entails equalizing confounders across racial groups, which may not reflect the intended intervention. Revisiting prior analyses in the National Longitudinal Survey of Youth on disparities in wages, unemployment, incarceration, and overall health with test scores, taken as a proxy for educational attainment, as a target intervention, we propose and demonstrate a novel decomposition that controls for confounders of test scores (e.g., measures of childhood socioeconomic status [SES]) while leaving their association with race intact. We compare this decomposition with others that use standardization (to equalize childhood SES [the confounders] alone), mediation analysis (to equalize test scores within levels of childhood SES), and one that equalizes both childhood SES and test scores. We also show how these decompositions, including our novel proposals, are equivalent to implementations of the Oaxaca-Blinder decomposition but provide a more formal causal interpretation for these decompositions.