Case Report: Stepwise Anti-Inflammatory and Anti-SARS-CoV-2 Effects Following Convalescent Plasma Therapy With Full Clinical Recovery.

In these times of COVID-19 pandemic, concern has been raised about the potential effects of SARS-CoV-2 infection on immunocompromised patients, particularly on those receiving B-cell depleting agents and having therefore a severely depressed humoral response. Convalescent plasma can be a therapeutic option for these patients. Understanding the underlying mechanisms of convalescent plasma is crucial to optimize such therapeutic approach. Here, we describe a COVID-19 patient who was deeply immunosuppressed following rituximab (anti-CD20 monoclonal antibody) and concomitant chemotherapy for chronic lymphoid leukemia. His long-term severe T and B cell lymphopenia allowed to evaluate the treatment effects of convalescent plasma. Therapeutic outcome was monitored at the clinical, biological and radiological level. Moreover, anti-SARS-CoV-2 antibody titers (IgM, IgG and IgA) and neutralizing activity were assessed over time before and after plasma transfusions, alongside to SARS-CoV-2 RNA quantification and virus isolation from the upper respiratory tract. Already after the first cycle of plasma transfusion, the patient experienced rapid improvement of pneumonia, inflammation and blood cell counts, which may be related to the immunomodulatory properties of plasma. Subsequently, the cumulative increase in anti-SARS-CoV-2 neutralizing antibodies due to the three additional plasma transfusions was associated with progressive and finally complete viral clearance, resulting in full clinical recovery. In this case-report, administration of convalescent plasma revealed a stepwise effect with an initial and rapid anti-inflammatory activity followed by the progressive SARS-CoV-2 clearance. These data have potential implications for a more extended use of convalescent plasma and future monoclonal antibodies in the treatment of immunosuppressed COVID-19 patients

Problems recruiting and retaining postnatal women to a pilot randomised controlled trial of a web-delivered weight loss intervention ISRCTN48086713 ISRCTN

Abstract Objective This paper highlights recruitment and retention problems identified during a pilot randomised controlled trial and process evaluation. The pilot trial aimed to evaluate the feasibility and acceptability of a web-delivered weight loss intervention for postnatal women and associated trial protocol. Results General practice database searches revealed low rates of eligible postnatal women per practice. 16 (10%) of the 168 identified women were recruited and randomised, seven to the intervention and nine to the control. 57% (4/7) of the intervention women completed 3 month follow-up measurements in comparison to 56% (5/9) in the control group. By 12 months, retention in the intervention group was 43% (3/7), with 2/7 women active on the website, in comparison to 44% (4/9) of the control group. Interview findings revealed the web as an acceptable method for delivery of the intervention, with the suggestion of an addition of a mobile application. Alternative recruitment strategies, using health visitor appointments, midwifery departments or mother and baby/toddler groups, should be explored. Greater involvement of potential users should enable better recruitment methods to be developed. Trial registration ISRCTN: ISRCTN48086713, Registered 26 October 201

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models

Action to protect the independence and integrity of global health research

Storeng KT, Abimbola S, Balabanova D, et al. Action to protect the independence and integrity of global health research. BMJ GLOBAL HEALTH. 2019;4(3): e001746

Multiple high-reward items can be prioritized in working memory but with greater vulnerability to interference

An emerging literature indicates that working memory and attention interact in determining what is retained over time, though the nature of this relationship and the impacts on performance across different task contexts remain to be mapped out. In the present study, four experiments examined whether participants can prioritize one or more ‘high reward’ items within a four-item target array for the purposes of an immediate cued recall task, and the extent to which this mediates the disruptive impact of a post-display to-be-ignored suffix. All four experiments indicated that endogenous direction of attention towards high-reward items results in their improved recall. Furthermore, increasing the number of high-reward items from 1 to 3 (Experiments 1-3) produces no decline in recall performance for those items, while associating each item in an array with a different reward value results in correspondingly graded levels of recall performance (Experiment 4). These results suggest the ability to exert precise voluntary control in the prioritization of multiple targets. However, in line with recent outcomes drawn from serial visual memory, this endogenously driven focus on high-reward items results in greater susceptibility to exogenous suffix interference, relative to low-reward items. This contrasts with outcomes from cueing paradigms, indicating that different methods of attentional direction may not always result in equivalent outcomes on working memory performance

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis