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Background In 2015, the United Nations adopted the Sustainable Development Goals (SDGs) as key priorities to improve the global health and international development agenda in an intersectoral manner, highlighting 17 SDGs. Six billion people lack access to safe, timely, and affordable cardiac surgical care due to capacity, geographic, and financial barriers. Nevertheless, cardiac surgery is largely disregarded on the global health agenda. In this review, we explore the intersection between cardiac surgery and the SDGs to delineate potential policy and advocacy avenues for the cardiothoracic surgical community. Main body A narrative review was performed using the PubMed/MEDLINE, Scopus, and WHO databases with variations of the search terms “cardiac surgery,” “cardiovascular diseases,” and keywords extracted from individual SDGs. All SDGs were manually reviewed to define intersectionality with global cardiac surgery. Out of 17 SDGs, 15 are relevant and require additional attention from the cardiovascular community. SDG3, “Good Health and Well-being,” is the most relevant, although the intersection between global cardiac surgery and other SDGs is apparent. A call for interdisciplinary collaboration through increased preventive mechanisms, rigorous, all-inclusive clinical trials, advocacy with relevant legislators, and mobilizing capacity building mechanisms are made. Conclusion Meeting the SDGs will require recognition of cardiovascular disease management, including cardiac surgical care. Cardiac surgeons are essential stakeholders of multidisciplinary collaborations working to improve access to safe, timely, and affordable cardiac surgery for all. Their role as advocates will be vital to establish local, national, regional, and international partnerships and to ensure progress towards SDG attainment.

Within a national funded Brazilian-German cooperation project ASHES, the thermal utilization of bagasse and other residuals from the sugar processing industry was examined. The characterization of the ashes was also performed but it is not in the focus of this paper. To determine the relevant combustion parameters, tests were carried out in a laboratory-scale, fixed bed reactor (KLEAA) at KIT-ITC. Subsequently, Fraunhofer UMSICHT carried out tests in continuously operated combustion plants with different plant sizes of 30 kW, 100 kW, and 440 kW. All relevant compositions were analyzed by CUTEC. The primary objective was to gain knowledge regarding the combustion and emission behavior of the fuels. These findings were used to identify optimization potentials at various points and elaborate concepts for their improvement. In the next steps of the project, optimization of these concepts will be the focus. In cooperation with the Brazilian project partners, several of them should be implemented into practice after the project.

Objectives: To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study. Methods: First, previously extracted CT decision rules were used to obtain initial ordinal (0-3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rule's agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κw) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions. Results: Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κw: 0.81-0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κw: 0.42-0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κw: 0.09-0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was

Objectives: Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. Methods: Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (μg m-3 respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters' homogeneity (defined as >75% with the same estimate) was examined compared to a dichotomized probability estimate (

Promising new drugs are being evaluated for treatment of multiple myeloma (MM), but their impact should be measured against the expected outcome in patients failing current therapies. However, the natural history of relapsed disease in the current era remains unclear. We studied 286 patients with relapsed MM, who were refractory to bortezomib and were relapsed following, refractory to or ineligible to receive, an IMiD (immunomodulatory drug), had measurable disease, and ECOG PS of 0, 1 or 2. The date patients satisfied the entry criteria was defined as time zero (T-0). The median age at diagnosis was 58 years, and time from diagnosis to T-0 was 3.3 years. Following T-0, 213 (74%) patients had a treatment recorded with one or more regimens (median = 1; range 0-8). The first regimen contained bortezomib in 55 (26%) patients and an IMiD in 70 (33%). A minor response or better was seen to at least one therapy after T0 in 94 patients (44%) including >= partial response in 69 (32%). The median overall survival and event-free survival from T-0 were 9 and 5 months, respectively. This study confirms the poor outcome, once patients become refractory to current treatments. The results provide context for interpreting ongoing trials of new drugs. Leukemia (2012) 26, 149-157; doi:10.1038/leu.2011.196; published online 29 July 2011

Thousands of patients with renal disease are on waiting lists for kidney transplant. Survival and quality of life on hemodialysis are much lower than that after renal transplantation. Renal allografts are extremely valuable and worth saving at all costs. Many complications can be seen after organ transplants on short and long term as rejection, vascular compromise, and infection. There are various reports on partial nephrectomy after renal transplant secondary to de novo masses in the renal allograft. Here, we present a case where we used radiofrequency bipolar sealer for partial nephrectomy for necrotic abscess of the renal allograft. We successfully saved the allograft with partial nephrectomy despite parenchymal infection and necrosis.

Candidate gene and genome-wide association studies (GWAS) have identified 11 independent susceptibility loci associated with bladder cancer risk. To discover additional risk variants, we conducted a new GWAS of 2422 bladder cancer cases and 5751 controls, followed by a meta-analysis with two independently published bladder cancer GWAS, resulting in a combined analysis of 6911 cases and 11 814 controls of European descent. TaqMan genotyping of 13 promising single nucleotide polymorphisms with P < 1 x 10(-5) was pursued in a follow-up set of 801 cases and 1307 controls. Two new loci achieved genome-wide statistical significance: rs10936599 on 3q26.2 (P = 4.53 x 10(-9)) and rs907611 on 11p15.5 (P = 4.11 x 10(-8)). Two notable loci were also identified that approached genome-wide statistical significance: rs6104690 on 20p12.2 (P = 7.13 x 10(-7)) and rs4510656 on 6p22.3 (P = 6.98 x 10(-7)); these require further studies for confirmation. In conclusion, our study has identified new susceptibility alleles for bladder cancer risk that require fine-mapping and laboratory investigation, which could further understanding into the biological underpinnings of bladder carcinogenesis.

Objectives: Algorithm-based exposure assessments based on patterns in questionnaire responses and professional judgment can readily apply transparent exposure decision rules to thousands of jobs quickly. However, we need to better understand how algorithms compare to a one-by-one job review by an exposure assessor. We compared algorithm-based estimates of diesel exhaust exposure to those of three independent raters within the New England Bladder Cancer Study, a population-based case-control study, and identified conditions under which disparities occurred in the assessments of the algorithm and the raters.Methods: Occupational diesel exhaust exposure was assessed previously using an algorithm and a single rater for all 14 983 jobs reported by 2631 study participants during personal interviews conducted from 2001 to 2004. Two additional raters independently assessed a random subset of 324 jobs that were selected based on strata defined by the cross-tabulations of the algorithm and the first rater's probability assessments for each job, oversampling their disagreements. The algorithm and each rater assessed the probability, intensity and frequency of occupational diesel exhaust exposure, as well as a confidence rating for each metric. Agreement among the raters, their aggregate rating (average of the three raters' ratings) and the algorithm were evaluated using proportion of agreement, kappa and weighted kappa (κw). Agreement analyses on the subset used inverse probability weighting to extrapolate the subset to estimate agreement for all jobs. Classification and Regression Tree (CART) models were used to identify patterns in questionnaire responses that predicted disparities in exposure status (i.e., unexposed versus exposed) between the first rater and the algorithm-based estimates.Results: For the probability, intensity and frequency exposure metrics, moderate to moderately high agreement was observed among raters (κw = 0.50-0.76) and between the algorithm and the individual raters (κw = 0.58-0.81). For these metrics, the algorithm estimates had consistently higher agreement with the aggregate rating (κw = 0.82) than with the individual raters. For all metrics, the agreement between the algorithm and the aggregate ratings was highest for the unexposed category (90-93%) and was poor to moderate for the exposed categories (9-64%). Lower agreement was observed for jobs with a start year

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participant's reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests ( predictions from many trees) were used to identify the underlying rules from the questionnaire responses, and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity and frequency. Data were split into training (n=10 488 jobs), testing (n=2247) and validation (n=2248) datasets. Results The CART and random forest models' predictions agreed with 92-94% of the expert's binary probability assignments. For ordinal probability, intensity and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86-90% and 57-85%, respectively) than for low or medium exposed jobs (7-71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs, and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent, and creates a mechanism to efficiently replicate exposure decisions in future studies.

Objectives: Professional judgment is necessary to assess occupational exposure in population-based case-control studies; however, the assessments lack transparency and are time-consuming to perform. To improve transparency and efficiency, we systematically applied decision rules to questionnaire responses to assess diesel exhaust exposure in the population-based case-control New England Bladder Cancer Study. Methods: 2631 participants reported 14 983 jobs; 2749 jobs were administered questionnaires ('modules') with diesel-relevant questions. We applied decision rules to assign exposure metrics based either on the occupational history (OH) responses (OH estimates) or on the module responses (module estimates); we then combined the separate OH and module estimates (OH/module estimates). Each job was also reviewed individually to assign exposure (one-by-one review estimates). We evaluated the agreement between the OH, OH/module and one-by-one review estimates. Results: The proportion of exposed jobs was 20-25% for all jobs, depending on approach, and 54-60% for jobs with diesel-relevant modules. The OH/module and one-by- one review estimates had moderately high agreement for all jobs (κw=0.68-0.81) and for jobs with diesel-relevant modules (κw=0.62-0.78) for the probability, intensity and frequency metrics. For exposed subjects, the Spearman correlation statistic was 0.72 between the cumulative OH/module and one-by-one review estimates. Conclusions: The agreement seen here may represent an upper level of agreement because the algorithm and one-by-one review estimates were not fully independent. This study shows that applying decision-based rules can reproduce a one-by-one review, increase transparency and efficiency, and provide a mechanism to replicate exposure decisions in other studies.

Periampullary tumors are one of the most common tumors of the gastrointestinal tract. Despite the increase of 1-5% in five year life expectancy in periampullary tumors prognosis is still poor. The controversies in diagnosis, surgical and adjuvant treatment will be discussed in this article.