Homochirality and the need of energy

The mechanisms for explaining how a stable asymmetric chemical system can be formed from a symmetric chemical system, in the absence of any asymmetric influence other than statistical fluctuations, have been developed during the last decades, focusing on the non-linear kinetic aspects. Besides the absolute necessity of self-amplification processes, the importance of energetic aspects is often underestimated. Going down to the most fundamental aspects, the distinction between a single object -- that can be intrinsically asymmetric -- and a collection of objects -- whose racemic state is the more stable one -- must be emphasized. A system of strongly interacting objects can be described as one single object retaining its individuality and a single asymmetry; weakly or non-interacting objects keep their own individuality, and are prone to racemize towards the equilibrium state. In the presence of energy fluxes, systems can be maintained in an asymmetric non-equilibrium steady-state. Such dynamical systems can retain their asymmetry for times longer than their racemization time.Comment: 8 pages, 7 figures, submitted to Origins of Life and Evolution of Biosphere

Developing a Standard Set of Patient-Centred Outcomes for Inflammatory Bowel Disease—an International, Cross-disciplinary Consensus

Success in delivering value-based healthcare involves measuring outcomes that matter most to patients. Our aim was to develop a minimum Standard Set of patient-centred outcome measures for inflammatory bowel disease (IBD), for use in different healthcare settings.An international working group (n=25) representing patients, patient associations, gastroenterologists, surgeons, specialist nurses, IBD registries and patient-reported outcome measure (PROM) methodologists participated in a series of teleconferences incorporating a modified Delphi process. Systematic review of existing literature, registry data, patient focus groups and open review periods were used to reach consensus on a minimum set of standard outcome measures and risk adjustment variables. Similar methodology has been used in 21 other disease areas (www.ichom.org).A minimum Standard Set of outcomes was developed for patients (aged ≥16) with IBD. Outcome domains included survival and disease control (survival, disease activity/remission, colorectal cancer, anaemia), disutility of care (treatment-related complications), healthcare utilisation (IBD-related admissions, emergency room visits) and patient-reported outcomes (including quality of life, nutritional status and impact of fistulae) measured at baseline and at 6 or 12 month intervals. A single PROM (IBD-Control questionnaire) was recommended in the Standard Set and minimum risk adjustment data collected at baseline and annually were included: demographics, basic clinical information and treatment factors.A Standard Set of outcome measures for IBD has been developed based on evidence, patient input and specialist consensus. It provides an international template for meaningful, comparable and easy-to-interpret measures as a step towards achieving value-based healthcare in IBD

The protective effect of human renal sinus fat on glomerular cells is reversed by the hepatokine fetuin-A

Renal sinus fat (RSF) is a perivascular fat compartment located around renal arteries. In this in vitro and in vivo study we hypothesized that the hepatokine fetuin-A may impair renal function in non alcoholic fatty liver disease (NAFLD) by altering inflammatory signalling in RSF. To study effects of the crosstalk between fetuin-A, RSF and kidney, human renal sinus fat cells (RSFC) were isolated and cocultured with human endothelial cells (EC) or podocytes (PO). RSFC caused downregulation of proinflammatory and upregulation of regenerative factors in cocultured EC and PO, indicating a protective influence of RFSC. However, fetuin-A inverted these benign effects of RSFC from an anti- to a proinflammatory status. RSF was quantified by magnetic resonance imaging and liver fat content by 1H-MR spectroscopy in 449 individuals at risk for type 2 diabetes. Impaired renal function was determined via urinary albumin/creatinine-ratio (uACR). RSF did not correlate with uACR in subjects without NAFLD (n = 212, p = 0.94), but correlated positively in subjects with NAFLD (n = 105, p = 0.0005). Estimated glomerular filtration rate (eGRF) was inversely correlated with RSF, suggesting lower eGFR for subjects with higher RSF (r = 0.24, p < 0.0001). In conclusion, our data suggest that in the presence of NAFLD elevated fetuin-A levels may impair renal function by RSF-induced proinflammatory signalling in glomerular cells

Epidemic Microclusters of Blood-Culture Proven Sepsis in Very-Low-Birth Weight Infants: Experience of the German Neonatal Network

INTRODUCTION: We evaluated blood culture-proven sepsis episodes occurring in microclusters in very-low-birth-weight infants born in the German Neonatal Network (GNN) during 2009-2010. METHODS: Thirty-seven centers participated in GNN; 23 centers enrolled ≥50 VLBW infants in the study period. Data quality was approved by on-site monitoring. Microclusters of sepsis were defined as occurrence of at least two blood-culture proven sepsis events in different patients of one center within 3 months with the same bacterial species. For microcluster analysis, we selected sepsis episodes with typically cross-transmitted bacteria of high clinical significance including gram-negative rods and Enterococcus spp. RESULTS: In our cohort, 12/2110 (0.6%) infants were documented with an early-onset sepsis and 235 late-onset sepsis episodes (≥72 h of age) occurred in 203/2110 (9.6%) VLBW infants. In 182/235 (77.4%) late-onset sepsis episodes gram-positive bacteria were documented, while coagulase negative staphylococci were found to be the most predominant pathogens (48.5%, 95%CI: 42.01-55.01). Candida spp. and gram-negative bacilli caused 10/235 (4.3%, 95%CI: 1.68% -6.83%) and 43/235 (18.5%) late-onset sepsis episodes, respectively. Eleven microclusters of blood-culture proven sepsis were detected in 7 hospitals involving a total 26 infants. 16/26 cluster patients suffered from Klebsiella spp. sepsis. The median time interval between the first patient's Klebsiella spp. sepsis and cluster cases was 14.1 days (interquartile range: 1-27 days). First patients in the cluster, their linked cases and sporadic sepsis events did not show significant differences in short term outcome parameters. DISCUSSION: Microclusters of infection are an important phenomenon for late-onset sepsis. Most gram-negative cluster infections occur within 30 days after the first patient was diagnosed and Klebsiella spp. play a major role. It is essential to monitor epidemic microclusters of sepsis in surveillance networks to adapt clinical practice, inform policy and further improve quality of care

Developing a Standard Set of Patient-Centred Outcomes for inflammatory Bowel Disease-an international, cross-disciplinary consensus

Background and Aims: Success in delivering value-based healthcare involves measuring outcomes that matter most to patients. Our aim was to develop a minimum Standard Set of patient-centred outcome measures for inflammatory bowel disease [IBD], for use in different healthcare settings. Methods: An international working group [n = 25] representing patients, patient associations, gastroenterologists, surgeons, specialist nurses, IBD registries and patient-reported outcome measure [PROM] methodologists participated in a series of teleconferences incorporating a modified Delphi process. Systematic review of existing literature, registry data, patient focus groups and open review periods were used to reach consensus on a minimum set of standard outcome measures and risk adjustment variables. Similar methodology has been used in 21 other disease areas [www.ichom.org]. Results: A minimum Standard Set of outcomes was developed for patients [aged =16] with IBD. Outcome domains included survival and disease control [survival, disease activity/remission, colorectal cancer, anaem

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