Averting HIV Infections in New York City: A Modeling Approach Estimating the Future Impact of Additional Behavioral and Biomedical HIV Prevention Strategies

Background:New York City (NYC) remains an epicenter of the HIV epidemic in the United States. Given the variety of evidence-based HIV prevention strategies available and the significant resources required to implement each of them, comparative studies are needed to identify how to maximize the number of HIV cases prevented most economically.Methods:A new model of HIV disease transmission was developed integrating information from a previously validated micro-simulation HIV disease progression model. Specification and parameterization of the model and its inputs, including the intervention portfolio, intervention effects and costs were conducted through a collaborative process between the academic modeling team and the NYC Department of Health and Mental Hygiene. The model projects the impact of different prevention strategies, or portfolios of prevention strategies, on the HIV epidemic in NYC.Results:Ten unique interventions were able to provide a prevention benefit at an annual program cost of less than $360,000, the threshold for consideration as a cost-saving intervention (because of offsets by future HIV treatment costs averted). An optimized portfolio of these specific interventions could result in up to a 34$106,378; the total cost was in excess of $2 billion (over the 20 year period, or approximately$100 million per year, on average). The cost-savings of prevented infections was estimated at more than $5 billion (or approximately$250 million per year, on average).Conclusions:Optimal implementation of a portfolio of evidence-based interventions can have a substantial, favorable impact on the ongoing HIV epidemic in NYC and provide future cost-saving despite significant initial costs. © 2013 Kessler et al

Averting HIV Infections in New York City: A Modeling Approach Estimating the Future Impact of Additional Behavioral and Biomedical HIV Prevention Strategies

Background: New York City (NYC) remains an epicenter of the HIV epidemic in the United States. Given the variety of evidence-based HIV prevention strategies available and the significant resources required to implement each of them, comparative studies are needed to identify how to maximize the number of HIV cases prevented most economically. Methods: A new model of HIV disease transmission was developed integrating information from a previously validated micro-simulation HIV disease progression model. Specification and parameterization of the model and its inputs, including the intervention portfolio, intervention effects and costs were conducted through a collaborative process between the academic modeling team and the NYC Department of Health and Mental Hygiene. The model projects the impact of different prevention strategies, or portfolios of prevention strategies, on the HIV epidemic in NYC. Results: Ten unique interventions were able to provide a prevention benefit at an annual program cost of less than$360,000, the threshold for consideration as a cost-saving intervention (because of offsets by future HIV treatment costs averted). An optimized portfolio of these specific interventions could result in up to a 34$106,378; the total cost was in excess of $2 billion (over the 20 year period, or approximately$100 million per year, on average). The cost-savings of prevented infections was estimated at more than $5 billion (or approximately$250 million per year, on average). Conclusions: Optimal implementation of a portfolio of evidence-based interventions can have a substantial, favorable impact on the ongoing HIV epidemic in NYC and provide future cost-saving despite significant initial costs

Airborne Measurements in Support of the NASA Atmospheric Carbon and Transport - America (ACT-America) Mission

NASA announced the research opportunity Earth Venture Suborbital -2 (EVS-2) mission in support of the NASA's science strategic goals and objectives in 2013. Penn State University, NASA Langley Research Center (LaRC), and other academic institutions, government agencies, and industrial companies together formulated and proposed the Atmospheric Carbon and Transport -America (ACT -America) suborbital mission, which was subsequently selected for implementation. The airborne measurements that are part of ACT-America will provide a unique set of remote and in-situ measurements of CO2 over North America at spatial and temporal scales not previously available to the science community and this will greatly enhance our understanding of the carbon cycle. ACT -America will consist of five airborne campaigns, covering all four seasons, to measure regional atmospheric carbon distributions and to evaluate the accuracy of atmospheric transport models used to assess carbon sinks and sources under fair and stormy weather conditions. This coordinated mission will measure atmospheric carbon in the three most important regions of the continental US carbon balance: Northeast, Midwest, and South. Data will be collected using 2 airborne platforms (NASA Wallops' C-130 and NASA Langley's B-200) with both in-situ and lidar instruments, along with instrumented ground towers and under flights of the Orbiting Carbon Observatory (OCO-2) satellite. This presentation provides an overview of the ACT-America instruments, with particular emphasis on the airborne CO2and backscatter lidars, and the, rationale, approach, and anticipated results from this mission

Predictors of Limb Fat Gain in HIV Positive Patients Following a Change to Tenofovir-Emtricitabine or Abacavir-Lamivudine

Background Antiretroviral treatment (cART) in HIV causes lipoatrophy. We examined predictors of anthropometric outcomes over 96 weeks in HIV-infected, lipoatrophic adults receiving stable cART randomised to tenofovir-emtricitabine (TDF-FTC) or abacavir-lamivudine (ABC-3TC) fixed dose combinations. Methodology/Principal Findings The STEAL study was a prospective trial of virologically suppressed participants randomised to either TDF-FTC (n = 178) or ABC-3TC (n = 179). Anthropometric assessment was conducted at baseline, weeks 48 and 96. The analysis population included those with baseline and week 96 data remaining on randomised therapy. Distribution of limb fat change was divided into four categories (≤0%, \u3e0-10%, \u3e10-20%, \u3e20%). Baseline characteristics [demographics, medical history, metabolic and cardiovascular biomarkers] were assessed as potential predictors of change in percent subcutaneous limb fat using linear regression. 303 participants (85% of STEAL population) were included. Baseline characteristics were: mean (±SD) age 45 (±8) years; thymidine analogue nucleoside reverse transcriptase inhibitor (tNRTI) duration 4 (±3) years; limb fat 5.4 (±3.0)kg; body mass index 24.7 (±3.5) kg/m2. Mean (SD) limb fat gain to week 48 and 96 was 7.6% (±22.4) and 13.2% (±27.3), respectively, with no significant difference between groups. 51.5% of all participants had \u3e10% gain in limb fat. Predictors of greater limb fat gain at week 96 were baseline tNRTI (10.3, p = 0.001), glucose \u3e6 mmol/L (16.1, p = 0.04), higher interleukin 6 (IL-6) (2.8, p = 0.004) and lower baseline limb fat (3.8-6.4 kg - 11.2; \u3e6.4 kg - 15.7, p trend\u3c0.001). Conclusions/Significance Modest peripheral fat gain occurred with both TDF-FTC and ABC-3TC. Baseline factors associated with more severe lipodystrophy (lipoatrophy, baseline tNRTI, raised IL6, and glucose) predicted greater limb fat recovery at 96 weeks

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Galectin 3 induces a distinctive pattern of cytokine and chemokine production in rheumatoid synovial fibroblasts via selective signaling pathways

OBJECTIVE: High expression of galectin 3 at sites of joint destruction in rheumatoid arthritis (RA) suggests that galectin 3 plays a role in RA pathogenesis. Previous studies have demonstrated the effects of galectins on immune cells, such as lymphocytes and macrophages. This study was undertaken to investigate the hypothesis that galectin 3 induces proinflammatory effects in RA by modulating the pattern of cytokine and chemokine production in synovial fibroblasts. METHODS: Matched samples of RA synovial and skin fibroblasts were pretreated with galectin 3 or tumor necrosis factor α (TNFα), and the levels of a panel of cytokines, chemokines, and matrix metalloproteinases (MMPs) were determined using enzyme-linked immunosorbent assays and multiplex assays. Specific inhibitors were used to dissect signaling pathways, which were confirmed by Western blotting and NF-κB activation assay. RESULTS: Galectin 3 induced secretion of interleukin-6 (IL-6), granulocyte–macrophage colony-stimulating factor, CXCL8, and MMP-3 in both synovial and skin fibroblasts. By contrast, galectin 3–induced secretion of TNFα, CCL2, CCL3, and CCL5 was significantly greater in synovial fibroblasts than in skin fibroblasts. TNFα blockade ruled out autocrine TNFα-stimulated induction of chemokines. The MAPKs p38, JNK, and ERK were necessary for IL-6 production, but phosphatidylinositol 3-kinase (PI 3-kinase) was required for selective CCL5 induction. NF-κB activation was required for production of both IL-6 and CCL5. CONCLUSION: Our findings indicate that galectin 3 promotes proinflammatory cytokine secretion by tissue fibroblasts. However, galectin 3 induces the production of mononuclear cell–recruiting chemokines uniquely from synovial fibroblasts, but not matched skin fibroblasts, via a PI 3-kinase signaling pathway. These data provide further evidence of the role of synovial fibroblasts in regulating the pattern and persistence of the inflammatory infiltrate in RA and suggest a new and important functional consequence of the observed high expression of galectin 3 in the rheumatoid synovium

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease