Comparison of SARS-CoV-2 seroprevalence estimates between commercial lab serum specimens and blood donor specimens, United States, September-December 2021.

We estimated monthly cross-sectional seroprevalence rates of anti-nucleocapsid (anti-N) and anti-spike (anti-S) antibodies to severe acute respiratory syndrome coronavirus 2 in two U.S. nationwide studies. The nationwide blood donor seroprevalence (NBDS) study included specimens from blood donors, while the nationwide commercial laboratory seroprevalence (NCLS) study included residual serum specimens tested in commercial laboratories for reasons unrelated to the assessment of coronavirus disease 2019 infection. In September-December 2021, specimens collected from both nationwide studies were tested for anti-N antibodies. In September-October 2021, specimens from both studies within a five-state area were tested for anti-S antibodies. We used raking methods to adjust all seroprevalence estimates by the population distribution of key demographics in included states. Seroprevalence estimates of each antibody type were compared across the two studies for specimens drawn in the same U.S. states during the same time period. Our analysis revealed that over a 4-month period, national NCLS monthly anti-N estimates were 0.5-1.9 percentage points higher than NBDS estimates. In contrast, across five states during a 2-month period, NBDS anti-S estimates were 7.6 and 8.2 percentage points higher than NCLS estimates. The observed differences in seroprevalence estimates between the NBDS and NCLS studies may be attributed to variations in the characteristics of the study sample populations, particularly with respect to health status, health behaviors, and vaccination status. These differences should be considered in the interpretation of seroprevalence study results based on blood donors or commercial lab residual specimens. IMPORTANCE: This study was the first systematic comparison between two nationwide severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) studies which estimated seroprevalence, or the proportion of the population with antibodies to the virus, using differing convenience sample populations. One study tested blood donor specimens; the other study tested specimens left over from clinical blood tests. The seroprevalence of anti-nucleocapsid and anti-spike antibodies was compared in the same states during the same months with statistical adjustments based on state demographics. Similar anti-nucleocapsid antibody seroprevalence estimates produced by two independent studies using differing convenience samples build confidence in the generalizability of their anti-nucleocapsid findings. Due to high blood donor vaccine rates, blood donor SARS-CoV-2 anti-spike antibody estimates might overestimate general population seroprevalence, an important consideration for interpreting national seroprevalence study results. Furthermore, because laboratory residuals and blood donations are two common sources of specimens for seroprevalence studies, study findings may be informative for other respiratory virus seroepidemiology studies.

A Leaky False Pouch: Left Ventricle Pseudoaneurysm with Active Hemopericardium Detected on Cardiac Computed Tomography Angiography.

Pseudoaneurysm is a rare but fatal complication of myocardial infarction (MI). With the advances in cardiovascular disease detection and treatments, fatal structural complications post-MI are now rare. When they occur, advanced diagnostic modalities can be used for early diagnosis, aiding surgical planning, and improving prognosis. In our case, post-MI left ventricle pseudoaneurysm complicated by hemopericardium was diagnosed using cardiac computed tomography angiography (CCTA). Use of attenuation measurement on CCTA helped diagnose active extravasation into the hemopericardium. This case highlights the high index of suspicion needed for rare but fatal complications post-MI and the utility of CCTA in their management.

Modeling the risk of aquatic species invasion spread through boater movements and river connections.

Aquatic invasive species (AIS) are one of the greatest threats to the functioning of aquatic ecosystems worldwide. Once an invasive species has been introduced to a new region, many governments develop management strategies to reduce further spread. Nevertheless, managing AIS in a new region is challenging because of the vast areas that need protection and limited resources. Spatial heterogeneity in invasion risk is driven by environmental suitability and propagule pressure, which can be used to prioritize locations for surveillance and intervention activities. To better understand invasion risk across aquatic landscapes, we developed a simulation model to estimate the likelihood of a waterbody becoming invaded with an AIS. The model included waterbodies connected via a multilayer network that included boater movements and hydrological connections. In a case study of Minnesota, we used zebra mussels (Dreissena polymorpha) and starry stonewort (Nitellopsis obtusa) as model species. We simulated the impacts of management scenarios developed by stakeholders and created a decision-support tool available through an online application provided as part of the AIS Explorer dashboard. Our baseline model revealed that 89% of new zebra mussel invasions and 84% of new starry stonewort invasions occurred through boater movements, establishing it as a primary pathway of spread and offering insights beyond risk estimates generated by traditional environmental suitability models alone. Our results highlight the critical role of interventions applied to boater movements to reduce AIS dispersal.

Modelo del riesgo de la invasión de especies acuáticas dispersadas por movimiento de botes y conexiones entre ríos Resumen Las especies acuáticas invasoras (EAI) son una de las principales amenazas para el funcionamiento de los ecosistemas acuáticos a nivel mundial. Una vez que una especie invasora ha sido introducida a una nueva región, muchos gobiernos desarrollan estrategias de manejo para disminuir la dispersión. Sin embargo, el manejo de las especies acuáticas invasoras en una nueva región se complica debido a las amplias áreas que necesitan protección y los recursos limitados. La heterogeneidad espacial de un riesgo de invasión es causada por la idoneidad ambiental y la presión de propágulo, que puede usarse para priorizar la ubicación de las actividades de vigilancia e intervención. Desarrollamos una simulación para estimar la probabilidad de que un cuerpo de agua sea invadido por EAI para tener un mejor entendimiento del riesgo de invasión en los paisajes acuáticos. El modelo incluyó cuencas conectadas a través de una red multicapa que incluía movimiento de botes y conexiones hidrológicas. Usamos como especies modelo a Dreissena polymorpha y a Nitellopsis obtusa en un estudio de caso en Minnesota. Simulamos el impacto de los escenarios de manejo desarrollado por los actores y creamos una herramienta de decisiones por medio de una aplicación en línea proporcionada como parte del tablero del Explorer de EAI. Nuestro modelo de línea base reveló que el 89% de las invasiones nuevas de D. polymorpha y el 84% de las de N. obtusa ocurrieron debido al movimiento de los botes, lo que lo estableció como una vía primaria de dispersión y nos proporcionó información más allá de las estimaciones de riesgo generadas por los modelos tradicionales de idoneidad ambiental. Nuestros resultados resaltan el papel crítico de las intervenciones aplicadas al movimiento de los botes para reducir la dispersión de especies acuáticas invasoras.

Adaptive COVID-19 Mitigation Strategies: Tradeoffs between Trigger Thresholds, Response Timing, and Effectiveness.

Background. To support proactive decision making during the COVID-19 pandemic, mathematical models have been leveraged to identify surveillance indicator thresholds at which strengthening nonpharmaceutical interventions (NPIs) is necessary to protect health care capacity. Understanding tradeoffs between different adaptive COVID-19 response components is important when designing strategies that balance public preference and public health goals. Methods. We considered 3 components of an adaptive COVID-19 response: 1) the threshold at which to implement the NPI, 2) the time needed to implement the NPI, and 3) the effectiveness of the NPI. Using a compartmental model of SARS-CoV-2 transmission calibrated to Minnesota state data, we evaluated different adaptive policies in terms of the peak number of hospitalizations and the time spent with the NPI in force. Scenarios were compared with a reference strategy, in which an NPI with an 80% contact reduction was triggered when new weekly hospitalizations surpassed 8 per 100,000 population, with a 7-day implementation period. Assumptions were varied in sensitivity analysis. Results. All adaptive response scenarios substantially reduced peak hospitalizations relative to no response. Among adaptive response scenarios, slower NPI implementation resulted in somewhat higher peak hospitalization and a longer time spent under the NPIs than the reference scenario. A stronger NPI response resulted in slightly less time with the NPIs in place and smaller hospitalization peak. A higher trigger threshold resulted in greater peak hospitalizations with little reduction in the length of time under the NPIs. Conclusions. An adaptive NPI response can substantially reduce infection circulation and prevent health care capacity from being exceeded. However, population preferences as well as the feasibility and timeliness of compliance with reenacting NPIs should inform response design. Highlights: This study uses a mathematical model to compare different adaptive nonpharmaceutical intervention (NPI) strategies for COVID-19 management across 3 dimensions: threshold when the NPI should be implemented, time it takes to implement the NPI, and the effectiveness of the NPI.All adaptive NPI response scenarios considered substantially reduced peak hospitalizations compared with no response.Slower NPI implementation results in a somewhat higher peak hospitalization and longer time spent with the NPI in place but may make an adaptive strategy more feasible by allowing the population sufficient time to prepare for changing restrictions.A stronger, more effective NPI response results in a modest reduction in the time spent under the NPIs and slightly lower peak hospitalizations.A higher threshold for triggering the NPI delays the time at which the NPI starts but results in a higher peak hospitalization and does not substantially reduce the time the NPI remains in force.

Better data for decision-making through Bayesian imputation of suppressed provisional COVID-19 death counts.

PURPOSE: To facilitate use of timely, granular, and publicly available data on COVID-19 mortality, we provide a method for imputing suppressed COVID-19 death counts in the National Center for Health Statistic's 2020 provisional mortality data by quarter, county, and age. METHODS: We used a Bayesian approach to impute suppressed COVID-19 death counts by quarter, county, and age in provisional data for 3,138 US counties. Our model accounts for multilevel data structures; numerous zero death counts among persons aged <50 years, rural counties, early quarters in 2020; highly right-skewed distributions; and different levels of data granularity (county, state or locality, and national levels). We compared three models with different prior assumptions of suppressed COVID-19 deaths, including noninformative priors (M1), the same weakly informative priors for all age groups (M2), and weakly informative priors that differ by age (M3) to impute the suppressed death counts. After the imputed suppressed counts were available, we assessed three prior assumptions at the national, state/locality, and county level, respectively. Finally, we compared US counties by two types of COVID-19 death rates, crude (CDR) and age-standardized death rates (ASDR), which can be estimated only through imputing suppressed death counts. RESULTS: Without imputation, the total COVID-19 death counts estimated from the raw data underestimated the reported national COVID-19 deaths by 18.60%. Using imputed data, we overestimated the national COVID-19 deaths by 3.57% (95% CI: 3.37%-3.80%) in model M1, 2.23% (95% CI: 2.04%-2.43%) in model M2, and 2.96% (95% CI: 2.76%-3.16%) in model M3 compared with the national report. The top 20 counties that were most affected by COVID-19 mortality were different between CDR and ASDR. CONCLUSIONS: Bayesian imputation of suppressed county-level, age-specific COVID-19 deaths in US provisional data can improve county ASDR estimates and aid public health officials in identifying disparities in deaths from COVID-19.

Economic burden of skin cancer treatment in the USA: an analysis of the Medical Expenditure Panel Survey Data, 2012-2018.

PURPOSE: We report the prevalence and economic cost of skin cancer treatment compared to other cancers overall in the USA from 2012 to 2018. METHODS: Using the Medical Expenditure Panel Survey full-year consolidated data files and associated medical conditions and medical events files, we estimate the prevalence, total costs, and per-person costs of treatment for melanoma and non-melanoma skin cancer among adults aged ≥ 18 years in the USA. To understand the changes in treatment prevalence and treatment costs of skin cancer in the context of overall cancer treatment, we also estimate the prevalence, total costs, and per-person costs of treatment for non-skin cancer among US adults. RESULTS: During 2012-15 and 2016-18, the average annual number of adults treated for any skin cancer was 5.8 (95% CI: 5.2, 6.4) and 6.1 (95% CI: 5.6, 6.6) million, respectively, while the average annual number of adults treated for non-skin cancers rose from 10.8 (95% CI: 10.0, 11.5) to 11.9 (95% CI: 11.2, 12.6) million, respectively. The overall estimated annual costs rose from $8.0 (in 2012-2015) to $8.9 billion (in 2016-18) for skin cancer treatment and $70.2 to $79.4 billion respectively for non-skin cancer treatment. CONCLUSION: The prevalence and economic cost of skin cancer treatment modestly increased in recent years. Given the substantial cost of skin cancer treatment, continued public health attention to implementing evidence-based sun-safety interventions to reduce skin cancer risk may help prevent skin cancer and the associated treatment costs.

Duration of Behavioral Policy Interventions and Incidence of COVID-19 by Social Vulnerability of US Counties, April-December 2020.

OBJECTIVE: State-issued behavioral policy interventions (BPIs) can limit community spread of COVID-19, but their effects on COVID-19 transmission may vary by level of social vulnerability in the community. We examined the association between the duration of BPIs and the incidence of COVID-19 across levels of social vulnerability in US counties. METHODS: We used COVID-19 case counts from USAFacts and policy data on BPIs (face mask mandates, stay-at-home orders, gathering bans) in place from April through December 2020 and the 2018 Social Vulnerability Index (SVI) from the Centers for Disease Control and Prevention. We conducted multilevel linear regression to estimate the associations between duration of each BPI and monthly incidence of COVID-19 (cases per 100 000 population) by SVI quartiles (grouped as low, moderate low, moderate high, and high social vulnerability) for 3141 US counties. RESULTS: Having a BPI in place for longer durations (ie, ≥2 months) was associated with lower incidence of COVID-19 compared with having a BPI in place for <1 month. Compared with having no BPI in place or a BPI in place for <1 month, differences in marginal mean monthly incidence of COVID-19 per 100 000 population for a BPI in place for ≥2 months ranged from -4 cases in counties with low SVI to -401 cases in counties with high SVI for face mask mandates, from -31 cases in counties with low SVI to -208 cases in counties with high SVI for stay-at-home orders, and from -227 cases in counties with low SVI to -628 cases in counties with high SVI for gathering bans. CONCLUSIONS: Establishing COVID-19 prevention measures for longer durations may help reduce COVID-19 transmission, especially in communities with high levels of social vulnerability.

Impact of Time Out of Intended Storage and Freeze-thaw Rates on the Stability of Adeno-associated Virus 8 and 9.

There are an increasing number of clinical studies evaluating different adeno-associated virus (AAV) serotypes as vectors for gene therapy. Long-term frozen storage can maximize the stability of AAV. Freeze-thaw (F/T) cycles and exposures to room temperature (RT) and refrigerated conditions occur during manufacturing, labeling, and clinical use. In this work we exposed AAV8 and AAV9 at low and high concentrations to five F/T cycles compounded with RT and refrigerated holds in a 'daisy chain' time out of intended storage (TOIS) stability study, which may be a best practice in early development. We also evaluated the impact of 5 F/T cycles for multiple permutations of fast and slow cooling and rewarming rates. The quality attributes of AAV8 and AAV9 remained within acceptable ranges after the daisy chain TOIS and F/T rate studies. Potency and concentration were unchanged within method variability. There was a minor increase in non-encapsidated ('free') DNA released from AAV8 after F/T in a phosphate-buffered saline formulation. DNA release during F/T was minimized in a formulation with a low buffer concentration and was not detected in a formulation containing sucrose. We conclude that AAV8 and AAV9 have stability profiles that are suitable for manufacturing and clinical development.

Development of a stable lyophilized adeno-associated virus gene therapy formulation.

Adeno-associated viruses (AAV) are among the most actively investigated vectors for gene therapy. Supply of early clinical studies with frozen drug product (DP) can accelerate timelines and minimize degradation risks. In the long-term, logistical challenges of frozen DP may limit patient access. In this work, we developed a lyophilized (freeze-dried) formulation of AAV. The mass concentration of AAV is typically low, and AAV also requires a minimum ionic strength to inhibit aggregation. These factors result in a low collapse temperature, which is limiting to lyophilization. Mannitol crystallization was found to cause extensive degradation and potency loss of AAV during the freezing step. With further development, we determined that AAV could be lyophilized in a sucrose and citrate formulation with a more desirable high glass transition temperature of the dried cake. An optimal residual moisture range (1-3%) was found to be critical to maintaining AAV8 stability. Glycerol was found to protect AAV8 from over-drying by preventing capsid damage and genome DNA release. A lyophilized formulation was identified that maintained potency for 24 months at 2-8 °C, indicating the feasibility of a dried formulation for AAV gene therapy.

Quantitation of Trace Levels of DNA Released from Disrupted Adeno-Associated Virus Gene Therapy Vectors.

Adeno-associated virus (AAV) vectors for gene therapy have potential to provide a durable treatment response for a number of diseases with unmet need. DNA is released from AAV capsids at high temperatures. Less is known about DNA release that may occur under conditions relevant to clinical and commercial manufacturing, storage, and distribution. In this work we developed and applied a sensitive fluorescent dye-based method to quantitate trace levels of DNA released from AAV capsids. The method was used to characterize the impact of manufacturing process steps on the increase (up to 1.5%) and removal (down to 0.2%) of free DNA. Free DNA increased by 0.3% per day at 37 °C and by 0.4% per freeze/thaw cycle in a phosphate-buffered saline formulation. When stored for 2 years at different temperatures, free DNA remained low (<0.6%) at both ≤ -60 °C and at 2-8 °C but was higher (2.6%) when the same sample was stored at -20 °C. The dye-based method may be used to further characterize release of free DNA for different processes, formulations, and stress conditions. Overall, release of free DNA was a relatively minor degradation pathway under the conditions studied in this work.

Follow the Sex: Influence of Network Structure on the Effectiveness and Cost-Effectiveness of Partner Management Strategies for Sexually Transmitted Infection Control.

BACKGROUND: It is well established that network structure strongly influences infectious disease dynamics. However, little is known about how the network structure impacts the cost-effectiveness of disease control strategies. We evaluated partner management strategies to address bacterial sexually transmitted infections (STIs) as a case study to explore the influence of the network structure on the optimal disease management strategy. METHODS: We simulated a hypothetical bacterial STI spread through 4 representative network structures: random, community-structured, scale-free, and empirical. We simulated disease outcomes (prevalence, incidence, total infected person-months) and cost-effectiveness of 4 partner management strategies in each network structure: routine STI screening alone (no partner management), partner notification, expedited partner therapy, and contact tracing. We determined the optimal partner management strategy following a cost-effectiveness framework and varied key compliance parameters of partner management in sensitivity analysis. RESULTS: For the same average number of contacts and disease parameters in our setting, community-structured networks had the lowest incidence, prevalence, and total infected person-months, whereas scale-free networks had the highest without partner management. The highly connected individuals were more likely to be reinfected in scale-free networks than in the other network structures. The cost-effective partner management strategy depended on the network structures, the compliance in partner management, the willingness-to-pay threshold, and the rate of external force of infection. CONCLUSIONS: Our findings suggest that contact network structure matters in determining the optimal disease control strategy in infectious diseases. Information on a population's contact network structure may be valuable for informing optimal investment of limited resources.

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling.

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.

Impact of resilience on health in older adults: a cross-sectional analysis from the International Mobility in Aging Study (IMIAS).

OBJECTIVES: Self-rated health (SRH) is a predictor of objective health measures, including mortality and morbidity. The link between resilience and SRH among the elderly is unclear. We aim to examine whether resilience aligns with SRH and, secondarily, whether resilience can override the negative health consequences of adverse childhood experiences (ACE). DESIGN AND SETTING: We use 2012, 2014 and 2016 data from the International Mobility in Aging Study, a longitudinal cohort study that collects survey and biophysical data from Albania, Brazil, Colombia and Canada. The main independent variables were resilience and ACE (social and economic). PARTICIPANTS: Community-dwelling 65-74 year olds (in 2012) were recruited through primary care registers. The sample size of the study was 1506. PRIMARY OUTCOME: The outcome measure was SRH. RESULTS: We found that sex, site, economic ACE, current income sufficiency, current depressive symptoms, current physical function and current resilience were associated with current SRH. In regression analyses, we showed that the association between ACE and SRH disappeared once factors such as sex, site, income, depression, physical health and resilience were considered. CONCLUSIONS: The association between resilience and health poses a compelling argument for building resilience throughout life.

Modeling cost-effectiveness of risk-based bovine tuberculosis surveillance in Minnesota.

In the United States, slaughter surveillance combined with other measures has effectively maintained a very low prevalence of bovine tuberculosis (bTB). However, bTB continues to be sporadically detected, causing substantial economic burden to the government and cattle producers. To detect the infection earlier and reduce sudden economic losses, additional risk-based surveillance of live animals might be more cost-effective than slaughter surveillance alone to detect and prevent bTB infection. The objective of this study was to evaluate alternative risk-based surveillance strategies targeting high-risk herds to complement slaughter surveillance in a region with very low bTB prevalence. We developed an integrated within- and between-herd bTB transmission model with simulated premises-level cattle movements among beef and dairy herds in Minnesota for 10 years. We constructed ten risk-based surveillance strategies for beef herds and dairy herds, and predicted the epidemiological outcomes and costs for each strategy in combination with slaughter surveillance. Our models showed that slaughter surveillance alone resulted in low risk of between-herd transmission with typically small outbreak sizes, and also cost less compared to alternative risk-based surveillance measures. However, risk-based surveillance strategies could reduce the time to detect infection and the time to reach disease freedom by up to 9 months. At a higher initial prevalence, alternative risk-based surveillance could reduce the number of infected herds and shorten the time to disease freedom by almost 3 years (34-35 months). Our findings suggest that risk-based surveillance could detect infection more quickly and allow affected regions to reach disease freedom faster. If the bTB status of the affected regions changes after an outbreak happens, the reduced time to disease freedom could reduce the economic impact on the affected region.

Improved detection of myocardial damage in sarcoidosis using longitudinal strain in patients with preserved left ventricular ejection fraction.

BACKGROUND: Cardiac infiltration is an important cause of death in sarcoidosis. Transthoracic echocardiography (TTE) has limited sensitivity for the detection of cardiac sarcoidosis (CS). Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is used to diagnose CS but has limitations of cost and availability. We sought to determine whether TTE-derived global longitudinal strain (GLS) may be used to identify individuals with CS, despite preserved left ventricular ejection fraction (LVEF), and whether abnormal GLS is associated with major cardiovascular events (MCE). METHODS: We studied 31 patients with biopsy-proven extra-cardiac sarcoidosis, LVEF>50% and LGE on CMR (CS+ group), and 31 patients without LGE (CS- group), matched by age, sex, and severity of lung disease. GLS was measured using vendor-independent speckle tracking software. Parameters of left and right ventricular systolic and diastolic function were also studied. Receiver-operating characteristic curves were used to identify GLS cutoff for CS detection, and Kaplan-Meier plots to determine the ability of GLS to predict MCE. RESULTS: LGE was associated with reduced GLS (-19.6±1.9% in CS- vs -14.7±2.4% in CS+, P<.01) and with reduced E/A ratio (1.1±0.3 vs 0.9±0.3, respectively, P =.01). No differences were noted in other TTE parameters. GLS magnitude inversely correlated with LGE burden (r=-.59). GLS cutoff of -17% showed sensitivity and specificity 94% for detecting CS. Patients who experienced MCE had worse GLS than those who did not (-13.4±0.9% vs -17.7±0.4%, P=.0003). CONCLUSIONS: CS is associated with significantly reduced GLS in the presence of preserved LVEF. GLS measurements may become part of the TTE study performed to screen for CS.

Monitoring Ionizing Radiation Exposure for Cardiotoxic Effects of Breast Cancer Treatment.

Serial assessments of left ventricular ejection fraction (LVEF) are customary in patients with breast cancer receiving trastuzumab. Radionuclide angiography (RNA) is often used; however, a typical monitoring schedule could include 5 scans in a year. We evaluated the proportion of imaging-related ionizing radiation attributable to RNA in 115 patients with breast cancer, from 3 medical centers in the United States, Ireland, and Japan, who completed 12 months of trastuzumab treatment. Estimated radiation dose (ERD) was used to calculate exposure associated with imaging procedures spanning the 18 months before and after trastuzumab therapy. In addition, 20 cardiologists and oncologists from participating centers were surveyed for their opinions regarding the contribution of RNA to overall radiation exposure during trastuzumab treatment. When RNA was used to monitor LVEF, the mean ERD from imaging was substantial (34 ± 24.3 mSv), with the majority attributable solely to RNA (24.7 ± 14.8 mSv, 72.6%). Actual ERD associated with RNA in this population differed significantly from the perception in surveyed cardiologists and oncologists; 70% of respondents believed that RNA typically accounted for 0% to 20% of overall radiation exposure from imaging; RNA actually accounted for more than 70% of ERD. In conclusion, RNA was used to monitor LVEF in most patients in this cohort during and after trastuzumab therapy. This significantly increased ERD and accounted for a greater proportion of radiation than that perceived by surveyed physicians. ERD should be taken into account when choosing a method of LVEF surveillance. Alternative techniques that do not use radiation should be strongly considered.

Prognosis of Myocardial Damage in Sarcoidosis Patients With Preserved Left Ventricular Ejection Fraction: Risk Stratification Using Cardiovascular Magnetic Resonance.

BACKGROUND: Cardiac sarcoidosis is associated with an increased risk of heart failure and sudden death, but its risk in patients with preserved left ventricular ejection fraction is unknown. Using cardiovascular magnetic resonance in patients with extracardiac sarcoidosis and preserved left ventricular ejection fraction, we sought to (1) determine the prevalence of cardiac sarcoidosis or associated myocardial damage, defined by the presence of late gadolinium enhancement (LGE), (2) quantify their risk of death/ventricular tachycardia (VT), and (3) identify imaging-based covariates that predict who is at greatest risk of death/VT. METHODS AND RESULTS: Parameters of left and right ventricular function and LGE burden were measured in 205 patients with left ventricular ejection fraction >50% and extracardiac sarcoidosis who underwent cardiovascular magnetic resonance for LGE evaluation. The association between covariates and death/VT in the entire group and within the LGE+ group was determined using Cox proportional hazard models and time-dependent receiver-operator curves analysis. Forty-one of 205 patients (20%) had LGE; 12 of 205 (6%) died or had VT during follow-up; of these, 10 (83%) were in the LGE+ group. In the LGE+ group (1) the rate of death/VT per year was >20× higher than LGE- (4.9 versus 0.2%, P<0.01); (2) death/VT were associated with a greater burden of LGE (14±11 versus 5±5%, P<0.01) and right ventricular dysfunction (right ventricular EF 45±12 versus 53±28%, P=0.04). LGE burden was the best predictor of death/VT (area under the receiver-operating characteristics curve, 0.80); for every 1% increase of LGE burden, the hazard of death/VT increased by 8%. CONCLUSIONS: Sarcoidosis patients with LGE are at significant risk for death/VT, even with preserved left ventricular ejection fraction. Increased LGE burden and right ventricular dysfunction can identify LGE+ patients at highest risk of death/VT.

Control of androgen receptor signaling in prostate cancer by the cochaperone small glutamine rich tetratricopeptide repeat containing protein alpha.

Although the androgen receptor (AR) is accepted as the major determinant of prostate cancer cell survival throughout disease progression, it is currently unclear how the receptor sustains genomic signaling under conditions of systemic androgen ablation. Here, we show that the evolutionarily conserved Hsp70/Hsp90 cochaperone, small glutamine-rich tetratricopeptide repeat containing protein alpha (alphaSGT), interacts with the hinge region of the human AR in yeast and mammalian cells. Overexpression and RNA interference revealed that alphaSGT acts to (a) promote cytoplasmic compartmentalization of the AR, thereby silencing the receptors basal/ligand-independent transcriptional activity, (b) regulate the sensitivity of receptor signaling by androgens, and (c) limit the capacity of noncanonical ligands to induce AR agonist activity. Immunofluorescence, coactivator, and chromatin immunoprecipitation analyses strongly suggest that these effects of alphaSGT on AR function are mediated by interaction in the cytoplasm and are distinct from the receptors response to classic coregulators. Quantitative immunohistochemical analysis of alphaSGT and AR levels in a cohort of 32 primary and 64 metastatic human prostate cancers revealed dysregulation in the level of both proteins during disease progression. The significantly higher AR/alphaSGT ratio in metastatic samples is consistent with the sensitization of prostate tumor cells to androgen signaling with disease progression, particularly in a low-hormone environment. These findings implicate alphaSGT as a molecular rheostat of in vivo signaling competence by the AR, and provide new insight into the determinants of androgen sensitivity during prostate cancer progression.