Analysis of the Relationship Between Body Mass Index (BMI) and Dento-Skeletal Maturation: A Cross-Sectional Case-Control Study.

The aim of this cross-sectional study was to establish if there is a relationship between body mass index (BMI) and skeletodental development in young obese patients in comparison with normal-weight patients. The sample consisted of 178 individuals (115 normal weight, 37 overweight and 26 obese), aged 6 to 16 years, with a mean biological age of 11.96 ± 2.

Determining Targets for Antiretroviral Drug Concentrations: A Causal Framework Illustrated With Pediatric Efavirenz Data From the CHAPAS-3 Trial.

Background: Determining a therapeutic window for maintaining antiretroviral drug concentrations within an appropriate range is required for identifying effective dosing regimens. The limits of this window are typically calculated using predictive models. We propose that target concentrations should instead be calculated based on counterfactual probabilities of relevant outcomes and describe a counterfactual framework for this.

Causal Inference for Continuous Multiple Time Point Interventions.

There are limited options to estimate the treatment effects of variables which are continuous and measured at multiple time points, particularly if the true dose-response curve should be estimated as closely as possible. However, these situations may be of relevance: in pharmacology, one may be interested in how outcomes of people living with-and treated for-HIV, such as viral failure, would vary for time-varying interventions such as different drug concentration trajectories. A challenge for doing causal inference with continuous interventions is that the positivity assumption is typically violated.

Studying Continuous, Time-varying, and/or Complex Exposures Using Longitudinal Modified Treatment Policies.

This tutorial discusses a methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and estimation of many novel parameters and mathematically generalizes many commonly used parameters, such as the average treatment effect. Longitudinal modified treatment policies apply to a wide variety of exposures, including binary, multivariate, and continuous, and can accommodate time-varying treatments and confounders, competing risks, loss to follow-up, as well as survival, binary, or continuous outcomes.

Pain Management Treatments and Opioid Use Disorder Risk in Medicaid Patients.

Introduction: People with chronic pain are at increased risk of opioid misuse. Less is known about the unique risk conferred by each pain management treatment, as treatments are typically implemented together, confounding their independent effects. This study estimated the extent to which pain management treatments were associated with risk of opioid use disorder (OUD) for those with chronic pain, controlling for baseline demographic and clinical confounding variables and holding other pain management treatments at their observed levels.

Learning optimal dynamic treatment regimes from longitudinal data.

Investigators often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR).

Causal Inference for Social Network Data.

We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties.

Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders.

Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders.

Using instrumental variables to address unmeasured confounding in causal mediation analysis.

Mediation analysis is a strategy for understanding the mechanisms by which interventions affect later outcomes. However, unobserved confounding concerns may be compounded in mediation analyses, as there may be unobserved exposure-outcome, exposure-mediator, and mediator-outcome confounders. Instrumental variables (IVs) are a popular identification strategy in the presence of unobserved confounding.

Sensitivity analysis for causality in observational studies for regulatory science.

Objective: The United States Congress passed the 21st Century Cures Act mandating the development of Food and Drug Administration guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data conducted a meeting with various stakeholder groups to build consensus around best practices for the use of real-world data (RWD) to support regulatory science. Our companion paper describes in detail the context and discussion of the meeting, which includes a recommendation to use a causal roadmap for study designs using RWD.

Effect of Paxlovid Treatment During Acute COVID-19 on Long COVID Onset: An EHR-Based Target Trial Emulation from the N3C and RECOVER Consortia.

Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19.

Second generation of pyrimidin-quinolone hybrids obtained from virtual screening acting as sphingosine kinase 1 inhibitors and potential anticancer agents.

We report here the virtual screening design, synthesis and activity of eight new inhibitors of SphK1. For this study we used a pre-trained Graph Convolutional Network (GCN) combined with docking calculations. This exploratory analysis proposed nine compounds from which eight displayed significant inhibitory effect against sphingosine kinase 1 (SphK1) demonstrating a high level of efficacy for this approach.

The application of target trials with longitudinal targeted maximum likelihood estimation to assess the effect of alcohol consumption in adolescence on depressive symptoms in adulthood.

Time-varying confounding is a common challenge for causal inference in observational studies with time-varying treatments, long follow-up periods, and participant dropout. Confounder adjustment using traditional approaches can be limited by data sparsity, weight instability, and computational issues. The Nicotine Dependence in Teens Study is a prospective cohort study, and we used data from 21 data collection cycles carried out from 1999 to 2008 among 1294 students recruited from 10 high schools in Montreal, Quebec, Canada, including follow-up into adulthood.

Independent and joint contributions of physical disability and chronic pain to incident opioid use disorder and opioid overdose among Medicaid patients.

Background: Chronic pain has been extensively explored as a risk factor for opioid misuse, resulting in increased focus on opioid prescribing practices for individuals with such conditions. Physical disability sometimes co-occurs with chronic pain but may also represent an independent risk factor for opioid misuse. However, previous research has not disentangled whether disability contributes to risk independent of chronic pain.

A causal roadmap for generating high-quality real-world evidence.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations.

Mature iPSC-derived astrocytes of an ALS/FTD patient carrying the TDP43 mutation display a mild reactive state and release polyP toxic to motoneurons.

Astrocytes play a critical role in the maintenance of a healthy central nervous system and astrocyte dysfunction has been implicated in various neurodegenerative disorders, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). There is compelling evidence that mouse and human ALS and ALS/FTD astrocytes can reduce the number of healthy wild-type motoneurons (MNs) in co-cultures or after treatment with astrocyte conditioned media (ACM), independently of their genotype. A growing number of studies have shown that soluble toxic factor(s) in the ACM cause non-cell autonomous MN death, including our recent identification of inorganic polyphosphate (polyP) that is excessively released from mouse primary astrocytes (, , and ) and human induced pluripotent stem cells (iPSC)-derived astrocytes () to kill MNs.

Protection provided by a commercial modified-live porcine reproductive and respiratory syndrome virus (PRRSV) 1 vaccine (PRRSV1-MLV) against a Japanese PRRSV2 field strain.

Background: Porcine reproductive and respiratory syndrome virus (PRRSV) vaccines do not provide full cross-protection, mainly due to the virus genetic variability. Despite this, vaccines based on modified-live PRRSV (PRRSV-MLV) reduce the disease impact.

Objectives: To assess the efficacy of two commercial vaccines-one based on PRRSV1 (PRRSV1-MLV) and another on PRRSV2 (PRRSV2-MLV)-against a Japanese PRRSV2 field strain.

Causal survival analysis under competing risks using longitudinal modified treatment policies.

Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest.

When the Ends do not Justify the Means: Learning Who is Predicted to Have Harmful Indirect Effects.

There is a growing literature on finding rules by which to assign treatment based on an individual's characteristics such that a desired outcome under the intervention is maximized. A related goal entails identifying a subpopulation of individuals predicted to have a harmful indirect effect (the effect of treatment on an outcome through mediators), perhaps even in the presence of a predicted beneficial total treatment effect. In some cases, the implications of a likely harmful indirect effect may outweigh an anticipated beneficial total treatment effect, and would motivate further discussion of whether to treat identified individuals.

Detection and genetic characterization of enteric viruses in diarrhoea outbreaks from swine farms in Spain.

Background: The aim of this work was to study the prevalence and distribution of Porcine astrovirus (PAstV), Porcine kobuvirus (PKoV), Porcine torovirus (PToV), Mammalian orthoreovirus (MRV) and Porcine mastadenovirus (PAdV) as well as their association with widely recognized virus that cause diarrhoea in swine such as coronavirus (CoVs) and rotavirus (RVs) in diarrhoea outbreaks from Spanish swine farms. Furthermore, a selection of the viral strains was genetically characterized.

Results: PAstV, PKoV, PToV, MRV and PAdV were frequently detected.

Effect of Sepsis on Death as Modified by Solid Organ Transplantation.

Background: Patients who have undergone solid organ transplants (SOT) have an increased risk for sepsis compared with the general population. Paradoxically, studies suggest that SOT patients with sepsis may experience better outcomes compared with those without a SOT. However, these analyses used previous definitions of sepsis.