Assessment of the impact of inpatient infectious events in pediatric patients with newly diagnosed acute leukemia at Dr. Robert Reid Cabral Children's Hospital, Dominican Republic.

Survival rates for pediatric acute leukemia vary dramatically worldwide. Infections are a leading cause of morbidity and mortality, and the impact is amplified in low and middle-income countries. Defining the epidemiology of infection in a specific health care setting is paramount to developing effective interventions. This study aimed to define the epidemiology of and outcomes from infection in children with acute leukemia treated in a large public pediatric hospital in the Dominican Republic. A retrospective cohort was assembled of children newly diagnosed with acute leukemia between July 1, 2015 to June 30, 2017 at Hospital Infantil Dr. Robert Reid Cabral in Santo Domingo. Patients were identified from the Pediatric Oncology Network Database (PONDTM) and hospital admissions from the Oncology admissions logbook. Medical records and microbiology results were reviewed to identify all inpatient invasive infections. Distance from a child's home to the hospital was determined using ArcGIS by Esri. Infection rates were described in discrete time periods after diagnosis and risk factors for invasive infection were explored using negative binomial regression. Overall, invasive infections were common and a prominent source of death in this cohort. Rates were highest in the first 60 days after diagnosis. Gastroenteritis/colitis, cellulitis, and pneumonia were most frequent, with bacteremia common early on. Multidrug resistant bacteria were prevalent among a small number of positive cultures. In a multivariate negative binomial regression model, age ≥ 10 years and distance from the hospital > 100 km were each protective against invasive infection in the first 180 days after diagnosis, findings that were unexpected and warrant further investigation. Over one-third of patient deaths were related to infection. Interventions aimed at reducing infection should target the first 60 days after diagnosis, improved supportive care inside and outside the hospital, and increased antimicrobial stewardship and infection prevention and control measures.

Sedimentation Velocity Analysis with Fluorescence Detection of Mutant Huntingtin Exon 1 Aggregation in Drosophila melanogaster and Caenorhabditis elegans.

At least nine neurodegenerative diseases that are caused by the aggregation induced by long tracts of glutamine sequences have been identified. One such polyglutamine-containing protein is huntingtin, which is the primary factor responsible for Huntington's disease. Sedimentation velocity with fluorescence detection is applied to perform a comparative study of the aggregation of the huntingtin exon 1 protein fragment upon transgenic expression in Drosophila melanogaster and Caenorhabditis elegans. This approach allows the detection of aggregation in complex mixtures under physiologically relevant conditions. Complementary methods used to support this biophysical approach included fluorescence microscopy and semidenaturing detergent agarose gel electrophoresis, as a point of comparison with earlier studies. New analysis tools developed for the analytical ultracentrifuge have made it possible to readily identify a wide range of aggregating species, including the monomer, a set of intermediate aggregates, and insoluble inclusion bodies. Differences in aggregation in the two animal model systems are noted, possibly because of differences in levels of expression of glutamine-rich sequences. An increased level of aggregation is shown to correlate with increased toxicity for both animal models. Co-expression of the human Hsp70 in D. melanogaster showed some mitigation of aggregation and toxicity, correlating best with inclusion body formation. The comparative study emphasizes the value of the analytical ultracentrifuge equipped with fluorescence detection as a useful and rigorous tool for in situ aggregation analysis to assess commonalities in aggregation across animal model systems.