Prisons as Learning Environments for Nursing and Public Health Practice

Background: Challenges in Securing Community Nursing Rotation Sites Eighteen years of providing clinical placement for Bachelor of Science in Nursing (BSN) students has demonstrated that community-based educational opportunities are shrinking due to: •Increased regulatory requirements •Competing numbers of nursing schools •Increasing student enrollment •Decreasing availability of community resources capable and willing to precept students These issues present challenges to preparing students for nursing practice. A college of nursing at an urban, academic health center found a solution by working with unexpected partners – maximum security prisons and juvenile detention centers. A Novel Solution: Partnerships with Prisons Several factors make prisons an ideal learning environment for nursing students. Prisons serve as microcosms of society, reflecting social determinants of health within confined communities. They allow students to work alongside interprofessional teams experienced in correctional health, mental/behavioral health, infection control, and community health. There is ample opportunity for individual assessment and patient education, as well as population-based care. Finally, working with the diverse inmate population promotes cultural awareness and sensitivity. Poster presented at: Urban Health Symposium, Re-Imagining Health in Cities, From Local to Global. An international symposium hosted by The Drexel Urban Health Collaborative at the Dornsife School of Public Health. Philadelphia, Pa. September 7-8, 2017.https://jdc.jefferson.edu/nursingposters/1012/thumbnail.jp

A Retrospective Analysis of Nursing Students\u27 Clinical Experience in an All-Male Maximum Security Prison.

Prisons provide an ideal learning experience to prepare prelicensure students with the knowledge and skill set needed for practice in the 21st century. Beginning descriptive evidence demonstrates that correctional health is an innovative community resource to educate nursing students in today\u27s changing model of health care delivery and practice. This article shares results from a retrospective analysis of the perceptions and experiences of nursing students during their community clinical rotation in an all-male maximum security prison

Post-Discharge Nutrition Care Instructions for Malnourished Adults

Malnutrition remains an insidious yet common ailment among hospitalized adults, with prevalence estimates ranging from 4-45%. Multiple studies confirm the adverse effects of malnutrition, which include prolonged length of stay, readmissions, higher hospitalization costs, and mortality. Malnutrition is frequently associated with chronic disease. For nutrition interventions to be successful, they must continue to support the long-term nutritional needs of patients beyond the hospital stay. Few studies, however, examine the receipt of recommendations for oral nutrition supplementation (ONS) or basic nutrition care instructions at the time of discharge. There is a need to better understand what post-discharge nutrition care instructions are documented in the electronic medical record (EMR) and how they are communicated to patients once they leave the hospital. This study sought to describe the malnourished adult patient population and the standard of nutrition-focused discharge care they receive at Christiana Hospital in Newark, DE. The investigation revealed that a majority of these patients received inappropriate or inadequate nutrition care instructions at time of discharge. Preliminary survey data suggest that some patients may be unaware of their malnutrition diagnosis, and may receive care instructions that are never documented. Clinician education and redesign of nutrition care options in the EMR may aid in the provision of discharge instructions to treat and prevent malnutrition after patients leave the hospital

Phase I for the Use of TOPEX-Poseidon and Jason-1 Radar Altimetry to Monitor Coastal Wetland Inundation and Sea Level Rise in Coastal Louisiana

The objective of the first phase of this project was to determine the feasibility of applying satellite altimetry data to monitor sea level rise and inundation within coastal Louisiana. Global sea level is rising, and coastal Louisiana is subsiding. Therefore, there is a need to monitor these trends over time for coastal restoration and hazard mitigation efforts. TOPEX/POSEIDON and Jason-data are used for global sea level estimates and have also been demonstrated successfully in water level studies of lakes, river basins, and floodplains throughout the world. To employ TOPEX/POSEIDON and Jason-1 data in coastal regions, the numerous steps involved in processing the data over non-open ocean areas must be assessed. This project outlined the appropriate methodology for processing non-open ocean data, including retracking and atmospheric corrections. It also inventoried the many factors in coastal land loss including subsidence, sea level rise, coastal geomorphology, and salinity levels, among others, through a review of remote sensing and field methods. In addition, the project analyzed the socioeconomic factors within the Coastal Zone as compared to the rest of Louisiana. While sensor data uncertainty must be addressed, it was determined that it is feasible to apply radar altimetry data from TOPEX/POSEIDON and Jason 1 to see trends in change within Coastal Louisiana sinc

Growing old together: What we know about the influence of diet and exercise on the aging host's gut microbiome

The immune system is critical in defending against infection from pathogenic microorganisms. Individuals with weakened immune systems, such as the elderly, are more susceptible to infections and developing autoimmune and inflammatory diseases. The gut microbiome contains a plethora of bacteria and other microorganisms, which collectively plays a significant role in immune function and homeostasis. Gut microbiota are considered to be highly influential on host health and immune function. Therefore, dysbiosis of the microbiota could be a major contributor to the elevated incidence of multiple age-related pathologies. While there seems to be a general consensus that the composition of gut microbiota changes with age, very little is known about how diet and exercise might influence the aging microbiome. Here, we examine the current state of the literature regarding alterations to the gut microbiome as hosts age, drawing particular attention to the knowledge gaps in addressing how diet and exercise influence the aging microbiome. Further, we will demonstrate the need for more controlled studies to investigate the roles that diet and exercise play driving the composition, diversity, and function of the microbiome in an aging population

Compound driven differences in N2 and N2O emission from soil; the role of substrate use efficiency and the microbial community

Organic C is an important control on the process of denitrification, a process that can result in the production and reduction of the potent greenhouse gas nitrous oxide (N2O). This study identified the influence of different low molecular weight C (LMW-C) compounds on the production of nitrous oxide (N2O) and dinitrogen (N2) and the associated role of the size and structure of the microbial community. We examined this following application of glucose, glutamine or citric acid (250 mg C kg−1 dry soil) and 15N-KNO3 (100 mg N kg−1 dry soil) to a sandy loam soil and measured the production of N2 and N2O by denitrifiers using 15N labeling techniques, changes in the bacterial community as measured by T-RFLP on 16SrDNA fragments and changes in the gene copy number of 16SrDNA, nirK, nirS and nosZ over 144 h. Addition of glucose, citric acid and glutamine all increased emissions of 15N-N2 above that found in the control (P < 0.05) while the addition of glucose and glutamine resulted in higher emissions of 14+15N-N2O (P < 0.001) than the addition of citric acid, resulting in a lower 15N-N2O to 15N-N2 ratio in the citric acid treatment. The 16SrDNA gene copy number increased after addition of citric acid and glutamine, whilst 16SrDNA showed significant shifts in community composition in all C treatments although over different time periods. The gene copy number of nosZ only significantly increased at 120 h in the glutamine treatment (P < 0.05) and nirS at 120 h in the citric acid and glutamine treatments (P < 0.05). This suggests that where C is added as a single input, differences in N2 and N2O emissions between LMW-C compounds were not caused by selection for denitrifiers but likely driven by differences in substrate use efficiency and subsequent differences in C partitioning between growth and respiration. The differing influence of the three selected C compounds on denitrification indicates the potential for lowering net N2O emissions through regulation of C compound availability

Understanding the Value of Tumor Markers in Pediatric Ovarian Neoplasms

Purpose The purpose of this study was to determine the diagnostic accuracy of tumor markers for malignancy in girls with ovarian neoplasms. Methods A retrospective review of girls 2–21 years who presented for surgical management of an ovarian neoplasm across 10 children's hospitals between 2010 and 2016 was performed. Patients who had at least one concerning feature on imaging and had tumor marker testing were included in the study. Sensitivity, specificity, and negative and positive predictive values (PPV) of tumor markers were calculated. Results Our cohort included 401 patients; 22.4% had a malignancy. Testing for tumor markers was inconsistent. AFP had high specificity (98%) and low sensitivity (42%) with a PPV of 86%. The sensitivity, specificity, and PPV of beta-hCG was 44%, 76%, and 32%, respectively. LDH had high sensitivity (95%) and Inhibin A and Inhibin B had high specificity (97% and 92%, respectively). Conclusions Tumor marker testing is helpful in preoperative risk stratification of ovarian neoplasms for malignancy. Given the variety of potential tumor types, no single marker provides enough reliability, and therefore a panel of tumor marker testing is recommended if there is concern for malignancy. Prospective studies may help further elucidate the predictive value of tumor markers in a pediatric ovarian neoplasm population

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

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects