Advanced receptor modeling of near–real–time, ambient PM2.5 and its associated components collected at an urban–industrial site in Toronto, Ontario

AbstractPM2.5 and other atmospheric pollutants were continuously monitored at high time resolution for 1 year at an urban–industrial location in Toronto, ON, Canada's largest city. The data collected for these pollutants were examined to determine seasonal trends and potential sources. Advanced receptor models including residence time weighted concentration (RTWC) and simplified quantitative transport bias analysis (sQTBA) trajectory ensemble models (TEM) and conditional probability function (CPF) were applied to these data to identify potential local and regional sources of pollution impacting this receptor site. Seasonal trends showed that concentrations of PM2.5 were more frequently high in winter than in any other season. Median concentrations of lead and arsenic were highest in fall while median levels of chromium were not significantly different over the four seasons. The black carbon–derived measurement commonly known as Delta C (i.e., BC370nm–BC880nm) had its greatest abundance in winter and lowest levels in summer. The seasonality of Delta C is indicative of the impact of residential wood combustion near the receptor site. CPF indicated that lead and iron had the most unidirectional radial plots with sectors located west–southwest of the receptor being the most likely local source regions. Winter CPF for Delta C is almost of equal strengths in all directions suggestive of near–uniform isotropic local impacts. The sQTBA model provided the most satisfactory spatial representation of impacting sources. The strongest sources of PM2.5 identified by the sQTBA model were both local and transboundary in origin. More potential source regions were found in winter and summer than in spring and fall

Establishing a core outcome set for autosomal dominant polycystic kidney disease: Report of the Standardized Outcomes in Nephrology–Polycystic Kidney Disease (SONG-PKD) consensus workshop

The omission of outcomes that are of relevance to patients, clinicians, and regulators across trials in autosomal dominant polycystic kidney disease (ADPKD) limits shared decision making. The Standardized Outcomes in Nephrology–Polycystic Kidney Disease (SONG-PKD) Initiative convened an international consensus workshop on October 25, 2018, to discuss the identification and implementation of a potential core outcome set for all ADPKD trials. This article summarizes the discussion from the workshops and the SONG-PKD core outcome set. Key stakeholders including 11 patients/caregivers and 47 health professionals (nephrologists, policy makers, industry, and researchers) attended the workshop. Four themes emerged: “Relevance of trajectory and impact of kidney function” included concerns about a patient's prognosis and uncertainty of when they may need to commence kidney replacement therapy and the lack of an early prognostic marker to inform long-term decisions; “Discerning and defining pain specific to ADPKD” highlighted the challenges in determining the origin of pain, adapting to the chronicity and repeated episodes of pain, the need to place emphasis on pain management, and to have a validated measure for pain; “Highlighting ADPKD consequences” encompassed cyst-related complications and reflected patient's knowledge because of family history and the hereditary nature of ADPKD; and “Risk for life-threatening but rare consequences” such as cerebral aneurysm meant considering both frequency and severity of the outcome. Kidney function, mortality, cardiovascular disease, and pain were established as the core outcomes for ADPKD

Development of Predisposition,Injury,Response,Organ failure model for predicting acute kidney injury in acute on chronic liver failure.

Background and Aim There is limited data on predictors of acute kidney injury(AKI) in ACLF. We developed a PIRO model (Predisposition, Injury, Response, Organ failure) for predicting AKI in a multi-centric cohort of ACLF patients. Patients and Methods Data of 2360 patients from APASL-ACLF Research Consortium (AARC) was analysed. Multivariate logistic regression model (PIRO score) was developed from a derivation cohort (n=1363) which was validated in another prospective multicentric cohort of ACLF patients (n=997) Results Factors significant for P component were serum creatinine[(≥2mg/dl)OR 4.52, 95% CI (3.67-5.30)], bilirubin [(/dL,OR 1) versus (12-30 mg/dL,OR 1.45, 95% 1.1-2.63) versus (≥30 mg/dL,OR 2.6, 95% CI 1.3-5.2)], serum potassium [(/LOR-1)versus (3-4.9 mmol/L,OR 2.7, 95% CI 1.05-1.97) versus (≥5 mmol/L,OR 4.34, 95% CI 1.67-11.3)] and blood urea (OR 3.73, 95% CI 2.5-5.5); for I component nephrotoxic medications (OR-9.86, 95% CI 3.2-30.8); for R component,Systemic Inflammatory Response Syndrome,(OR-2.14, 95% CI 1.4-3.3); for O component, Circulatory failure (OR-3.5, 95% CI 2.2-5.5). The PIRO score predicted AKI with C-index of 0.95 and 0.96 in the derivation and validation cohort.The increasing PIRO score was also associated with mortality (p \u3c 0.001) in both the derivation and validation cohorts. Conclusions The PIRO model identifies and stratifies ACLF patients at risk of developing AKI. It reliably predicts mortality in these patients, underscoring the prognostic significance of AKI in patients with ACLF

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