A Retrospective Analysis of the Haemodynamic and Metabolic Effects of Fluid Resuscitation in Vietnamese Adults with Severe Falciparum Malaria

BACKGROUND: Optimising the fluid resuscitation of patients with severe malaria is a simple and potentially cost-effective intervention. Current WHO guidelines recommend central venous pressure (CVP) guided, crystalloid based, resuscitation in adults. METHODS: Prospectively collected haemodynamic data from intervention trials in Vietnamese adults with severe malaria were analysed retrospectively to assess the responses to fluid resuscitation. RESULTS: 43 patients were studied of whom 24 received a fluid load. The fluid load resulted in an increase in cardiac index (mean increase: 0.75 L/min/m(2) (95% Confidence interval (CI): 0.41 to 1.1)), but no significant change in acid-base status post resuscitation (mean increase base deficit 0.6 mmol/L (95% CI: -0.1 to 1.3). The CVP and PAoP (pulmonary artery occlusion pressure) were highly inter-correlated (r(s) = 0.7, p<0.0001), but neither were correlated with acid-base status (arterial pH, serum bicarbonate, base deficit) or respiratory status (PaO(2)/FiO(2) ratio). There was no correlation between the oxygen delivery (DO(2)) and base deficit at the 63 time-points where they were assessed simultaneously (r(s) = -0.09, p = 0.46). CONCLUSIONS: In adults with severe falciparum malaria there was no observed improvement in patient outcomes or acid-base status with fluid loading. Neither CVP nor PAoP correlated with markers of end-organ perfusion or respiratory status, suggesting these measures are poor predictors of their fluid resuscitation needs

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

Age-related immune response heterogeneity to SARS-CoV-2 vaccine BNT162b2

Abstract: Although two-dose mRNA vaccination provides excellent protection against SARS-CoV-2, there is little information about vaccine efficacy against variants of concern (VOC) in individuals above eighty years of age1. Here we analysed immune responses following vaccination with the BNT162b2 mRNA vaccine2 in elderly participants and younger healthcare workers. Serum neutralization and levels of binding IgG or IgA after the first vaccine dose were lower in older individuals, with a marked drop in participants over eighty years old. Sera from participants above eighty showed lower neutralization potency against the B.1.1.7 (Alpha), B.1.351 (Beta) and P.1. (Gamma) VOC than against the wild-type virus and were more likely to lack any neutralization against VOC following the first dose. However, following the second dose, neutralization against VOC was detectable regardless of age. The frequency of SARS-CoV-2 spike-specific memory B cells was higher in elderly responders (whose serum showed neutralization activity) than in non-responders after the first dose. Elderly participants showed a clear reduction in somatic hypermutation of class-switched cells. The production of interferon-γ and interleukin-2 by SARS-CoV-2 spike-specific T cells was lower in older participants, and both cytokines were secreted primarily by CD4 T cells. We conclude that the elderly are a high-risk population and that specific measures to boost vaccine responses in this population are warranted, particularly where variants of concern are circulating

A Feature-based Approach for Determining Dense Long Range Correspondences

Planar motion models can provide gross motion estimation and good segmentation for image pairs with large inter-frame disparity. However, as the disparity becomes larger, the resulting dense correspondences will become increasingly inaccurate for everything but purely planar objects. Flexible motion models, on the other hand, tend to overfit and thus make partitioning difficult. For this reason, to achieve dense optical flow for image sequences with large inter-frame disparity, we propose a two stage process in which a planar model is used to get an approximation for the segmentation and the gross motion, and then a spline is used to refine the fit. We present experimental results for dense optical flow estimation on image pairs with large inter-frame disparity that are beyond the scope of existing approaches.Pre-2018 CSE ID: CS2003-076

Structure from Periodic Motion

We show how to exploit temporal periodicity of moving objects to perform 3D reconstruction. The collection of period-separated frames serve as a surrogate for multiple rigid views of a particular pose of the moving target, thus allowing the use of standard techniques of multiview geometry. We motivate our approach using human motion capture data, for which the true 3D positions of the markers are known. We next apply our approach to image sequences of pedestrians captured with a camcorder. Applications of our proposed approach include 3D motion capture of natural and manmade periodic moving targets from monocular video sequences.Pre-2018 CSE ID: CS2003-076