Retinopathy of prematurity (ROP) is a blinding morbidity of preterm infants, which represents a significant clinical problem, accounting for up to 40% of all childhood blindness. ROP displays a range of severity, though even mild disease may result in life-long visual impairment. This is complicated by the fact that our current treatments have significant ocular and potentially systemic effects. Therefore, disease prevention is desperately needed to mitigate the life-long deleterious effects of ROP for preterm infants. Although ROP demonstrates a delayed onset of retinal disease following preterm birth, representing a potential window for prevention, we have been unable to sufficiently alter the natural disease course and meaningfully prevent ROP. Prevention therapeutics requires knowledge of early ROP molecular changes and risk, occurring prior to clinical retinal disease. While we still have an incomplete understanding of these disease mechanisms, emerging data integrating contributions of maternal/placental pathobiology with ROP are poised to inform novel approaches to prevention. Herein, we review the molecular basis for current prevention strategies and the clinical outcomes of these interventions. We also discuss how insights into early ROP pathophysiology may be gained by a better understanding of maternal and placental factors playing a role in preterm birth.
Racial disparities in opioid use disorder (OUD) management exist, however, and there is limited research on factors that influence opioid cessation in different population groups.
We employed multiple machine learning prediction algorithms least absolute shrinkage and selection operator, random forest, deep neural network, and support vector machine to assess factors associated with ceasing opioid use in a sample of 1,192 African Americans (AAs) and 2,557 individuals of European ancestry (EAs) who met Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria for OUD. Values for nearly 4,000 variables reflecting demographics, alcohol and other drug use, general health, non-drug use behaviors, and diagnoses for other psychiatric disorders, were obtained for each participant from the Semi-Structured Assessment for Drug Dependence and Alcoholism, a detailed semi-structured interview.
Support vector machine models performed marginally better on average than other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression considered the 83 most highly ranked variables across all methods and models and identified less recent cocaine use (AAs: odds ratio (OR) = 1.82, P = 9.19 × 10−5; EAs: OR = 1.91, P = 3.30 × 10−15), shorter duration of opioid use (AAs: OR = 0.55, P = 5.78 × 10−6; EAs: OR = 0.69, P = 3.01 × 10−7), and older age (AAs: OR = 2.44, P = 1.41 × 10−12; EAs: OR = 2.00, P = 5.74 × 10−9) as the strongest independent predictors of opioid cessation in both AAs and EAs. Attending self-help groups for OUD was also an independent predictor (P < 0.05) in both population groups, while less gambling severity (OR = 0.80, P = 3.32 × 10−2) was specific to AAs and post-traumatic stress disorder recovery (OR = 1.93, P = 7.88 × 10−5), recent antisocial behaviors (OR = 0.64, P = 2.69 × 10−3), and atheism (OR = 1.45, P = 1.34 × 10−2) were specific to EAs. Factors related to drug use comprised about half of the significant independent predictors in both AAs and EAs, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics.
These proof-of-concept findings provide avenues for hypothesis-driven analysis, and will lead to further research on strategies to improve OUD management in EAs and AAs.
Chronic kidney disease (CKD) is a disease regularly seen in clinical practice. At present, CKD is described as a change of kidney structure and/or function and it is classified in relation to cause, values of glomerular filtration rate and albuminuria category. Seeing that CKD is closely linked to the development of end-stage renal disease and other comorbidities, the determination of additional independent predictors for CKD is clinically necessary. At present, there is evidence associating non-alcoholic fatty liver disease (NAFLD) with CKD, thereby suggesting that NAFLD patients may require intensive surveillance to reduce their risk of CKD. In 2008, genome-wide association studies documented an association between the variant rs738409 (C > G p.I148M) in the patatin-like phospholipase domain containing 3 (PNPLA3) gene (mainly implicated in the lipid regulation) and the entire spectrum of NAFLD (i.e., liver steatosis, non-alcoholic steatohepatitis, fibrosis, and hepatocellular carcinoma). In the last years, accumulating epidemiological evidence suggests the existence of a relationship between PNPLA3 rs738409 and risk of CKD, indicating that rs738409 may also contribute to the kidney injury. This is of particular scientific interest, as such association may explain, at least in part, the epidemiological association between liver and kidney disease. In this narrative review, we will discuss the accumulating evidence regarding the association between PNPLA3 rs738409 and risk of CKD, the putative biological mechanisms underpinning such relationship, and the possible future perspective.