Occupational pesticide exposure and the risk of death in patients with Parkinson’s disease : an observational study in southern Brazil

Background: Multiple studies have suggested that various pesticides are associated with a higher risk of developing Parkinson’s disease (PD) and may influence the progression of the disease. However, the evidence regarding the impact of pesticide exposure on mortality among patients with PD is equivocal. This study examines whether pesticide exposure influences the risk of mortality among patients with PD in Southern Brazil. Methods: A total of 150 patients with idiopathic PD were enrolled from 2008 to 2013 and followed until 2019. In addition to undergoing a detailed neurologic evaluation, patients completed surveys regarding socioeconomic status and environmental exposures. Results: Twenty patients (13.3%) reported a history of occupational pesticide exposure with a median duration of exposure of 10 years (mean = 13.1, SD = 11.2). Patients with a history of occupational pesticide exposure had higher UPDRS-III scores, though there were no significant differences in regards to age, sex, disease duration, Charlson Comorbidity Index, and age at symptom onset. Patients with occupational pesticide exposure were more than twice as likely to die than their unexposed PD counterparts (HR = 2.32, 95% CI [1.15, 4.66], p = 0.018). Occupational pesticide exposure was also a significant predictor of death in a cox-proportional hazards model which included smoking and caffeine intake history (HR = 2.23, 95% CI [1.09, 4.59], p = 0.03)) and another which included several measures of socioeconomic status (HR = 3.91, 95% CI [1.32, 11.58], p = 0.01). Conclusion: In this prospective cohort study, we found an increased all-cause mortality risk in PD patients with occupational exposure to pesticides. More studies are needed to further analyze this topic with longer follow-up periods, more detailed exposure information, and more specific causes of mortality

Development and validation of the RCOS prognostic index: A bedside multivariable logistic regression model to predict hypoxaemia or death in patients with SARS-CoV-2 infection

Introduction: Previous COVID-19 prognostic models have been developed in hospital settings and are not applicable to COVID-19 cases in the general population. There is an urgent need for prognostic scores aimed to identify patients at high risk of complications at the time of COVID-19 diagnosis. Methods: The RDT COVID-19 Observational Study (RCOS) collected clinical data from patients with COVID-19 admitted regardless of the severity of their symptoms in a general hospital in India. We aimed to develop and validate a simple bedside prognostic score to predict the risk of hypoxaemia or death. Results: 4035 patients were included in the development cohort and 2046 in the validation cohort. The primary outcome occurred in 961 (23.8%) and 548 (26.8%) patients in the development and validation cohorts, respectively. The final model included 12 variables: age, systolic blood pressure, heart rate, respiratory rate, aspartate transaminase, lactate dehydrogenase, urea, C-reactive protein, sodium, lymphocyte count, neutrophil count, and neutrophil/lymphocyte ratio. In the validation cohort, the area under the receiver operating characteristic curve (AUROCC) was 0.907 (95% CI, 0.892-0.922), and the Brier Score was 0.098. The decision curve analysis showed good clinical utility in hypothetical scenarios where the admission of patients was decided according to the prognostic index. When the prognostic index was used to predict mortality in the validation cohort, the AUROCC was 0.947 (95% CI, 0.925-0.97) and the Brier score was 0.0188. Conclusions: The RCOS prognostic index could help improve the decision making in the current COVID-19 pandemic, especially in resource-limited settings with poor healthcare infrastructure such as India. However, implementation in other settings is needed to cross-validate and verify our findings

Profile driven Collaborative Filtering

The problem of Recommender systems is to apply knowledge discovery techniques and to provide personalized recommendations. A recommender system tracks past actions of a group of users to make recommendations to individual members of the group. In today’s e-commerce world these systems are achieving widespread success. Content based and collaborative filtering based approaches are the two major approaches under which these systems are classified. The two are combined to form a hybrid approach. The major challenges before today’s recommender systems are to provide high quality recommendations and to provide these in a large-scale environment. In this paper we present a hybrid solution that remains scalable and efficient. Our system in a novel way makes use of content based and collaborative filtering approaches. We determine the profile of the user from the items he is interested in, and then apply collaborative filtering to determine his nearest neighbors with users of same profile. This way we exploit both content and collaborative filtering approaches. Frequent item-set construction algorithm was employed in determining the profile of the user and cosine similarity measure in determining the neighbors. We present the evaluation of the approach by recommending movies on Indian movie dataset

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018