Exposure-Response Estimates for Diesel Engine Exhaust and Lung Cancer Mortality Based on Data from Three Occupational Cohorts

Background: Diesel engine exhaust (DEE) has recently been classified as a known human carcinogen. Objective: We derived a meta-exposure–response curve (ERC) for DEE and lung cancer mortality and estimated lifetime excess risks (ELRs) of lung cancer mortality based on assumed occupational and environmental exposure scenarios. Methods: We conducted a meta-regression of lung cancer mortality and cumulative exposure to elemental carbon (EC), a proxy measure of DEE, based on relative risk (RR) estimates reported by three large occupational cohort studies (including two studies of workers in the trucking industry and one study of miners). Based on the derived risk function, we calculated ELRs for several lifetime occupational and environmental exposure scenarios and also calculated the fractions of annual lung cancer deaths attributable to DEE. Results: We estimated a lnRR of 0.00098 (95% CI: 0.00055, 0.0014) for lung cancer mortality with each 1-μg/m3-year increase in cumulative EC based on a linear meta-regression model. Corresponding lnRRs for the individual studies ranged from 0.00061 to 0.0012. Estimated numbers of excess lung cancer deaths through 80 years of age for lifetime occupational exposures of 1, 10, and 25 μg/m3 EC were 17, 200, and 689 per 10,000, respectively. For lifetime environmental exposure to 0.8 μg/m3 EC, we estimated 21 excess lung cancer deaths per 10,000. Based on broad assumptions regarding past occupational and environmental exposures, we estimated that approximately 6% of annual lung cancer deaths may be due to DEE exposure. Conclusions: Combined data from three U.S. occupational cohort studies suggest that DEE at levels common in the workplace and in outdoor air appear to pose substantial excess lifetime risks of lung cancer, above the usually acceptable limits in the United States and Europe, which are generally set at 1/1,000 and 1/100,000 based on lifetime exposure for the occupational and general population, respectively. Citation: Vermeulen R, Silverman DT, Garshick E, Vlaanderen J, Portengen L, Steenland K. 2014. Exposure-response estimates for diesel engine exhaust and lung cancer mortality based on data from three occupational cohorts. Environ Health Perspect 122:172–177; http://dx.doi.org/10.1289/ehp.130688

An approach to estimating prognosis using fractional polynomials in metastatic renal carcinoma

We present a prognostic model for metastatic renal cell carcinoma based on fractional polynomials. We retrospectively analysed 425 metastatic renal cell carcinoma patients treated with subcutaneous recombinant cytokine-based home therapies in consecutive trials. In our approach, we categorised a continuous prognostic index produced by the multivariable fractional polynomial (MFP) algorithm, using a strategy in which continuous predictors are kept continuous. The MFP algorithm selected five prognostic factors as significant at the 5% level in a multivariable model: lymph node metastases, liver metastases, bone metastases, age, C-reactive protein and neutrophils. The MFP model allowed us to divide patients into four risk groups achieving median overall survivals of 38 months (low risk), 23 months (low intermediate risk), 15 months (high intermediate risk) and 5.6 months (high risk). Our approach, based on categorising a continuous prognostic index produced by the MFP algorithm, allowed more flexibility in the determination of risk groups than traditional approaches

Content and timing of feedback and reflection: A multi-center qualitative study of experienced bedside teachers

Background: Competency-based medical education increasingly recognizes the importance of observation, feedback, and reflection for trainee development. Although bedside rounds provide opportunities for authentic workplace-based implementation of feedback and team-based reflection strategies, this relationship has not been well described. The authors sought to understand the content and timing of feedback and team-based reflection provided by bedside teachers in the context of patient-centered bedside rounds

Independent prognostic value of angiogenesis and the level of plasminogen activator inhibitor type 1 in breast cancer patients

Tumour angiogenesis and the levels of plasminogen activator inhibitor type I (PAI-I) are both informative prognostic markers in breast cancer. In cell cultures and in animal model systems, PAI-I has a proangiogenic effect. To evaluate the interrelationship of angiogenesis and the PAI-I level in breast cancer, we have evaluated the prognostic value of those factors in a total of 228 patients with primary, unilateral, invasive breast cancer, evaluated at a median follow-up time of 12 years. Microvessels were immunohistochemically stained by antibodies against CD34 and quantitated by the Chalkley counting technique. The levels of PAI-I and its target proteinase uPA in tumour extracts were analysed by ELISA. The Chalkley count was not correlated with the levels of uPA or PAI-I. High values of uPA, PAI-I, and Chalkley count were all significantly correlated with a shorter recurrence-free survival and overall survival. In the multivariate analysis, the uPA level did not show independent prognostic impact for any of the analysed end points. In contrast, the risk of recurrence was independently and significantly predicted by both the PAI-I level and the Chalkley count, with a hazard ratio (95% CI) of 1.6 (1.01-2.69) and 1.4 (1.02-1.81), respectively. For overall survival, the Chalkley count, but not PAI-I, was of significant independent prognostic value. The risk of death was 1.7 (1,30-2.15) for Chalkley counts in the upper tertile compared to the lower one. We conclude that the PAI-I level and the Chalkley count are independent prognostic markers for recurrence-free survival in patients with primary breast cancer, suggesting that the prognostic impact of PAI-I is not only based on its involvement in angiogenesis. (C) 2003 Cancer Research UK

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

Work-related allergy in medical doctors: atopy, exposure to domestic animals, eczema induced by common chemicals and membership of the surgical profession as potential risk factors

Purpose To investigate the risk factors associated with work-related allergy-like symptoms in medical doctors. Methods Self-administered questionnaire survey and CAP test were conducted among medical school students in the 4th grade of their 6-year medical course in 1993–1996 and 1999–2001. Follow-up questionnaires were sent in 2004 to the graduates. These questionnaires enquired into personal and family history of allergic diseases, lifestyle, history of allergy-like symptoms including work-relatedness and occupational history as medical doctors. Relationships between allergy-like symptoms and relevant factors were evaluated by multivariate logistic regression analysis. Results Of 261 respondents at the follow-up survey, 139 (53.3%) and 54 (20.7%) had a history of any allergy-like symptoms and any work-related allergy-like symptoms, respectively. Female gender and family history of allergic diseases were signiWcantly associated with any allergy-like symptoms. Personal history of allergic disease, exposure to domestic animals, eczema caused by rubber gloves, metallic accessories, or cosmetics during schooling days, and membership of the surgical profession were signiW- cant risk factors for work-related allergy-like symptoms. On the contrary, to work-related allergy-like symptoms, gender, age, and smoking status were not signiWcantly related, and consumption of prepared foods was inversely related. Conclusions Personal history of atopy and eczema induced by common goods and the history of keeping domestic animals may be predictors of work-related allergy-like symptoms in doctors. After graduation from medical school, physicians start with exposure to various allergens and irritants at work, which relate to work-related allergy-like symptoms, especially for surgeons

DNA hypermethylation markers of poor outcome in laryngeal cancer

This study examined molecular (DNA hypermethylation), clinical, histopathological, demographical, smoking, and alcohol variables to assess diagnosis (early versus late stage) and prognosis (survival) outcomes in a retrospective primary laryngeal squamous cell carcinoma (LSCC) cohort. The study cohort of 79 primary LSCC was drawn from a multi-ethnic (37% African American), primary care patient population, diagnosed by surgical biopsies in the Henry Ford Health System from 1991 to 2004 and followed from 5 to 18 years (through 2009). Of the 41 variables, univariate risk factors of p < 0.10 were tested in multivariate models (logistic regression (diagnosis) and Cox (survival) models (p < 0.05)). Aberrant methylation of estrogen receptor 1 (ESR1; p = 0.01), race as African American (p = 0.04), and tumor necrosis (extensive; p = 0.02) were independent predictors of late stage LSCC. Independent predictors of poor survival included presence of vascular invasion (p = 0.0009), late stage disease (p = 0.03), and methylation of the hypermethylated in cancer 1 (HIC1) gene (p = 0.0002). Aberrant methylation of ESR1 and HIC1 signified independent markers of poorer outcome. In this multi-ethnic, primary LSCC cohort, race remained a predictor of late stage disease supporting disparate diagnosis outcomes for African American patients with LSCC