Endophthalmitis due to Brevibacterium Casei

Endophthalmitis is a serious post-traumatic ocular complication that can lead to loss of vision. We report a case of acute post-traumatic endophthalmitis following a penetrating injury caused by an unusual organism, Brevibacterium casei. The patient was successfully treated with intravitreal antibiotics like ceftazidime and vancomycin, along with topical cefazolin and tobramycin. Brevibacterium casei can be added to the list of rare bacteria causing endophthalmitis and should be kept in mind by clinicians as a potential source of pathology

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

Randomized Clinical Trial of High-Dose Rifampicin With or Without Levofloxacin Versus Standard of Care for Pediatric Tuberculous Meningitis: The TBM-KIDS Trial

Background. Pediatric tuberculous meningitis (TBM) commonly causes death or disability. In adults, high-dose rifampicin may reduce mortality. The role of fluoroquinolones remains unclear. There have been no antimicrobial treatment trials for pediatric TBM. Methods. TBM-KIDS was a phase 2 open-label randomized trial among children with TBM in India and Malawi. Participants received isoniazid and pyrazinamide plus: (i) high-dose rifampicin (30 mg/kg) and ethambutol (R30HZE, arm 1); (ii) high-dose rifampicin and levofloxacin (R30HZL, arm 2); or (iii) standard-dose rifampicin and ethambutol (R15HZE, arm 3) for 8 weeks, followed by 10 months of standard treatment. Functional and neurocognitive outcomes were measured longitudinally using Modified Rankin Scale (MRS) and Mullen Scales of Early Learning (MSEL). Results. Of 2487 children prescreened, 79 were screened and 37 enrolled. Median age was 72 months; 49%, 43%, and 8% had stage I, II, and III disease, respectively. Grade 3 or higher adverse events occurred in 58%, 55%, and 36% of children in arms 1, 2, and 3, with 1 death (arm 1) and 6 early treatment discontinuations (4 in arm 1, 1 each in arms 2 and 3). By week 8, all children recovered to MRS score of 0 or 1. Average MSEL scores were significantly better in arm 1 than arm 3 in fine motor, receptive language, and expressive language domains (P < .01). Conclusions. In a pediatric TBM trial, functional outcomes were excellent overall. The trend toward higher frequency of adverse events but better neurocognitive outcomes in children receiving high-dose rifampicin requires confirmation in a larger trial. Clinical Trials Registration. NCT02958709

SMARThealth Pregnancy: feasibility and acceptability of a complex intervention for high-risk pregnant women in rural India: protocol for a pilot cluster randomised controlled trial

Introduction: India is in the process of a major epidemiological transition towards non-communicable diseases. Cardiovascular disease (CVD) is the leading cause of death in women in India. Predisposing independent risk factors include pregnancy-related conditions, e.g. hypertensive disorders of pregnancy (HDP) and gestational diabetes (GDM) - also associated with significant perinatal mortality and morbidity. Early identification, referral and management of pregnant women at increased risk of future CVD may offer opportunities for prevention. In rural India, Community Health Workers (CHWs) provide most antenatal and postnatal care. Innovative solutions are required to address integrated care for rural women during transitions between antenatal, postnatal and general health services. The George Institute’s SMARThealth Programme has shown that CHWs in rural India screening non-pregnant adults for cardiovascular risk, using a decision support system, is feasible. Building on this, we developed a targeted training programme for CHWs and a complex system-level intervention that uses mobile clinical decision support for CHWs and primary care doctors to screen high-risk pregnant women. In addition to addressing HDP and GDM, the intervention also screens for anaemia in pregnancy. Methods/Design: A pilot study will be undertaken in two diverse rural districts of India: Jhajjar (Haryana) and Guntur (Andhra Pradesh). Two Primary Health Centre clusters will be randomised to intervention or control groups at each study site. The primary objective of this pilot study is to explore the feasibility and acceptability of the SMARThealth Pregnancy intervention. Secondary objectives are to estimate: a)prevalence rates of moderate to severe anaemia, HDPs and GDM at the study sites; b) referral and follow-up rates, and c) mean haemoglobin and blood pressure values at the routine six week postnatal visit. A process evaluation will be conducted to explore the acceptability of the SMARThealth Pregnancy intervention for pregnant women and healthcare workers using qualitative methods. Discussion: It is anticipated that the findings of this pilot study will help determine the feasibility and acceptability of the SMARThealth Pregnancy intervention, and highlight how the intervention might be further developed for evaluation in a larger, cluster randomised controlled trial. Trial registration: ClinicalTrials.gov Identifier: NCT0396895

Overview of SIMS-based experimental studies of tracer diffusion in solids and application to Mg self-diffusion

Tracer diffusivities provide the most fundamental information on diffusion in materials, and are the foundation of robust diffusion databases that enable the use of the Onsager phenomenological formalism with no major assumptions. Compared to traditional radiotracer techniques that utilize radioactive isotopes, the secondary ion mass spectrometry (SIMS)-based thin-film technique for tracer diffusion is based on the use of enriched stable isotopes that can be accurately profiled using SIMS. An overview of the thin-film method for tracer diffusion studies using stable isotopes is provided. Experimental procedures and techniques for the measurement of tracer diffusion coefficients are presented for pure magnesium, which presents some unique challenges due to the ease of oxidation. The development of a modified Shewmon-Rhines diffusion capsule for annealing Mg and an ultra-high vacuum system for sputter deposition of Mg isotopes are discussed. Optimized conditions for accurate SIMS depth profiling in polycrystalline Mg are provided. An automated procedure for correction of heat-up and cool-down times during tracer diffusion annealing is discussed. The non-linear fitting of a SIMS depth profile data using the thin-film Gaussian solution to obtain the tracer diffusivity along with the background tracer concentration and tracer film thickness is demonstrated. An Arrhenius fit of the Mg self-diffusion data obtained using the low-temperature SIMS measurements from this study and the high-temperature radiotracer measurements of Shewmon and Rhines (Trans. AIME 250:1021-1025, 1954) was found to be a good representation of both types of diffusion data over a broad range of temperatures between 250 and 627°C (523 and 900 K)