The increase in price of the available refined petroleum products for local consumption in Nigeria had led to the emergence of indigenous technology for petroleum refining in some parts of the Niger Delta region. This study, therefore characterized and quantified artisanal refineries' gaseous emissions for possible air pollutants based on various unit operations involved and evaluated their impacts. It measured the emissions directly from source using E8500 Portable Combustion Analyzer. It also categorized oven sizes/processing capacity of the refineries into various ranges in order to estimate emissions according to processing capacity. The result revealed that; pollutants emission varied significantly between the unit operations and increased with increase in processing capacity. When the emissions were compared with daily limits set by the Environmental Guidelines and Standard for Petroleum Industry in Nigeria (EGASPIN) 2002, the emissions (CO, NO, and SO) breached the available set limits. While with the Federal Environmental Protection Agency (FEPA), 1991 set limits for emissions from stationary source; HC and CO breached their limits. SO and HS breached their lower limits but were below the upper limit, while NOx emissions were found within its set limit. The study concluded that, Nigeria Artisanal Petroleum Refineries are sources of air pollution, as they impact the host environment.
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http://dx.doi.org/10.1016/j.heliyon.2020.e05608 | DOI Listing |
JAMA Netw Open
January 2025
Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles.
Importance: The phase 3 randomized EMBARK trial evaluated enzalutamide with or without leuprolide in high-risk nonmetastatic hormone-sensitive prostate cancer. Eligibility relied on conventional imaging, which underdetects metastatic disease compared with prostate-specific membrane antigen-positron emission tomography (PSMA-PET).
Objective: To describe the staging information obtained by PSMA-PET/computed tomography (PSMA-PET/CT) in a patient cohort eligible for the EMBARK trial.
J Med Chem
January 2025
Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
Thorium-227 (Th) is an α-emitting radionuclide currently under investigation for targeted alpha therapy. Available chelators used for this isotope suffer from challenging multistep syntheses. Here, we present the synthesis and preclinical evaluation of a novel bifunctional chelator, SCN-Bn-DOTHOPO, which contains an isothiocyanate group that is suitable for conjugation to biological molecules.
View Article and Find Full Text PDFJ Interv Card Electrophysiol
January 2025
Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shimmachi, Suita, Osaka, 564-8565, Japan.
Background: Non-response to cardiac resynchronization therapy (CRT) is an important issue in the treatment of heart failure with reduced ejection fraction (HFrEF) and non-left bundle branch block (LBBB). Electrocardiogram-gated myocardial perfusion single-photon emission computed tomography imaging (G-MPI SPECT) is typically used to assess left ventricular (LV) dyssynchrony. This study aimed to determine whether G-MPI parameters are associated with non-responsiveness to CRT.
View Article and Find Full Text PDFTech Coloproctol
January 2025
Department of Surgical Sciences, University of Turin, Turin, Italy.
Introduction: Anorectal melanoma (ARM) is rare and highly lethal neoplasm. It has a poorer prognosis compared with cutaneous ones. Sentinel lymph node biopsy (SLNB) has become the preferred method of nodal staging method for cutaneous melanoma.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Korea University, Sejong, Sejong, Korea, Republic of (South).
Background: Amyloid-β accumulation is a pivotal factor in Alzheimer's disease (AD) progression. As treatment for AD has not been successful yet, the most effective approach lies in early diagnosis and the subsequent delay of disease progression. Hence, this study introduces a deep learning model to predict amyloid-β accumulation in the brain.
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