Preconceptional factors associated with very low birthweight delivery in East and West Berlin: a case control study

BACKGROUND: Very low birthweight, i.e. a birthweight < 1500 g, is among the strongest determinants of infant mortality and childhood morbidity. To develop primary prevention approaches to VLBW birth and its sequelae, information is needed on the causes of preterm birth, their personal and social antecedents, and on conditions associated with very low birthweight. Despite the growing body of evidence linking sociodemographic variables with preterm delivery, little is known as to how this may be extrapolated to the risk of very low birthweight. METHODS: In 1992, two years after the German unification, we started to recruit two cohorts of very low birthweight infants and controls in East and West Berlin for a long-term neurodevelopmental study. The present analysis was undertaken to compare potential preconceptional risk factors for very low birthweight delivery in a case-control design including 166 mothers (82 East vs. 84 West Berlin) with very low birthweight delivery and 341 control mothers (166 East vs. 175 West). RESULTS: Multivariate logistic regression analysis was used to assess the effects of various dichotomous parental covariates and their interaction with living in East or West Berlin. After backward variable selection, short maternal school education, maternal unemployment, single-room apartment, smoking, previous preterm delivery, and fetal loss emerged as significant main effect variables, together with living in West Berlin as positive effect modificator for single-mother status. CONCLUSION: Very low birthweight has been differentially associated with obstetrical history and indicators of maternal socioeconomic status in East and West Berlin. The ranking of these risk factors is under the influence of the political framework

Acute kidney injury in children

Acute kidney injury (AKI) (previously called acute renal failure) is characterized by a reversible increase in the blood concentration of creatinine and nitrogenous waste products and by the inability of the kidney to regulate fluid and electrolyte homeostasis appropriately. The incidence of AKI in children appears to be increasing, and the etiology of AKI over the past decades has shifted from primary renal disease to multifactorial causes, particularly in hospitalized children. Genetic factors may predispose some children to AKI. Renal injury can be divided into pre-renal failure, intrinsic renal disease including vascular insults, and obstructive uropathies. The pathophysiology of hypoxia/ischemia-induced AKI is not well understood, but significant progress in elucidating the cellular, biochemical and molecular events has been made over the past several years. The history, physical examination, and laboratory studies, including urinalysis and radiographic studies, can establish the likely cause(s) of AKI. Many interventions such as ‘renal-dose dopamine’ and diuretic therapy have been shown not to alter the course of AKI. The prognosis of AKI is highly dependent on the underlying etiology of the AKI. Children who have suffered AKI from any cause are at risk for late development of kidney disease several years after the initial insult. Therapeutic interventions in AKI have been largely disappointing, likely due to the complex nature of the pathophysiology of AKI, the fact that the serum creatinine concentration is an insensitive measure of kidney function, and because of co-morbid factors in treated patients. Improved understanding of the pathophysiology of AKI, early biomarkers of AKI, and better classification of AKI are needed for the development of successful therapeutic strategies for the treatment of AKI

The Evolution of Enzyme Specificity in the Metabolic Replicator Model of Prebiotic Evolution

The chemical machinery of life must have been catalytic from the outset. Models of the chemical origins have attempted to explain the ecological mechanisms maintaining a minimum necessary diversity of prebiotic replicator enzymes, but little attention has been paid so far to the evolutionary initiation of that diversity. We propose a possible first step in this direction: based on our previous model of a surface-bound metabolic replicator system we try to explain how the adaptive specialization of enzymatic replicator populations might have led to more diverse and more efficient communities of cooperating replicators with two different enzyme activities. The key assumptions of the model are that mutations in the replicator population can lead towards a) both of the two different enzyme specificities in separate replicators: efficient “specialists” or b) a “generalist” replicator type with both enzyme specificities working at less efficiency, or c) a fast-replicating, non-enzymatic “parasite”. We show that under realistic trade-off constraints on the phenotypic effects of these mutations the evolved replicator community will be usually composed of both types of specialists and of a limited abundance of parasites, provided that the replicators can slowly migrate on the mineral surface. It is only at very weak trade-offs that generalists take over in a phase-transition-like manner. The parasites do not seriously harm the system but can freely mutate, therefore they can be considered as pre-adaptations to later, useful functions that the metabolic system can adopt to increase its own fitness

Persistent left superior vena cava: Review of the literature, clinical implications, and relevance of alterations in thoracic central venous anatomy as pertaining to the general principles of central venous access device placement and venography in cancer patients

Persistent left superior vena cava (PLSVC) represents the most common congenital venous anomaly of the thoracic systemic venous return, occurring in 0.3% to 0.5% of individuals in the general population, and in up to 12% of individuals with other documented congential heart abnormalities. In this regard, there is very little in the literature that specifically addresses the potential importance of the incidental finding of PLSVC to surgeons, interventional radiologists, and other physicians actively involved in central venous access device placement in cancer patients. In the current review, we have attempted to comprehensively evaluate the available literature regarding PLSVC. Additionally, we have discussed the clinical implications and relevance of such congenital aberrancies, as well as of treatment-induced or disease-induced alterations in the anatomy of the thoracic central venous system, as they pertain to the general principles of successful placement of central venous access devices in cancer patients. Specifically regarding PLSVC, it is critical to recognize its presence during attempted central venous access device placement and to fully characterize the pattern of cardiac venous return (i.e., to the right atrium or to the left atrium) in any patient suspected of PLSVC prior to initiation of use of their central venous access device

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Beyond the Hypercube:Evolutionary Accessibility of Fitness Landscapes with Realistic Mutational Networks

Evolutionary pathways describe trajectories of biological evolution in the space of different variants of organisms (genotypes). The probability of existence and the number of evolutionary pathways that lead from a given genotype to a better-adapted genotype are important measures of accessibility of local fitness optima and the reproducibility of evolution. Both quantities have been studied in simple mathematical models where genotypes are represented as binary sequences of two types of basic units, and the network of permitted mutations between the genotypes is a hypercube graph. However, it is unclear how these results translate to the biologically relevant case in which genotypes are represented by sequences of more than two units, for example four nucleotides (DNA) or 20 amino acids (proteins), and the mutational graph is not the hypercube. Here we investigate accessibility of the best-adapted genotype in the general case of K > 2 units. Using computer generated and experimental fitness landscapes we show that accessibility of the global fitness maximum increases with K and can be much higher than for binary sequences. The increase in accessibility comes from the increase in the number of indirect trajectories exploited by evolution for higher K. As one of the consequences, the fraction of genotypes that are accessible increases by three orders of magnitude when the number of units K increases from 2 to 16 for landscapes of size N ∼ 106 genotypes. This suggests that evolution can follow many different trajectories on such landscapes and the reconstruction of evolutionary pathways from experimental data might be an extremely difficult task