Among ionic mercury species methyl mercury (MMHg) is the most toxic form present in the environment, which is known to be bio-accumulative neurotoxin in the aquatic food chain and could provide the major route of exposure for humans to mercury through consumption of marine food products. The availability of reliable analytical methods for evaluating spatial and temporal contamination trends of MMHg in the ocean is an important prerequisite for marine monitoring. Sound strategies for marine monitoring call also for measurement systems capable of producing comparable analytical results with demonstrated quality. A sensitive analytical procedure for environmental monitoring of MMHg content in seawater, based on specific extraction and Gas Chromatography Atomic Fluorescence Spectrometry validated according to the requirements of international guidelines and standards, ISO 17025 and Eurachem guidelines, is presented in this study. The entire measurement process was described by mathematical equations and all factors influencing the results were systematically investigated. Selectivity, working range, linearity, recovery (94 ± 4%), repeatability (3.3%-4.5%), intermediate precision (2.9%), limits of detection (0.0004 ng kgas Hg) were systematically assessed. The relative expanded uncertainties obtained were in the range from 16% to 25%, (k = 2). Modelling of the entire measurement process related obtained values for MMHg in seawater to the International System of units (Kg). The potential of this analytical procedure was tested and additionally validated via inter laboratory comparison exercise organised under the Geotraces programme. Obtained results were in excellent agreement with the assigned values. The proposed analytical procedure from the sample preparation to the measurement step combined with the high efficiency of the new generation of the automated MMHg analyzers is fit for purpose for routine monitoring studies on the dissolved MMHg in the costal and open ocean seawaters.
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http://dx.doi.org/10.1016/j.talanta.2021.122492 | DOI Listing |
J Neurosurg
January 2025
1Department of Neurosurgery, Baylor College of Medicine, Houston, Texas.
Objective: Deep brain stimulation (DBS) is an effective neurosurgical option for patients with treatment-resistant obsessive-compulsive disorder (OCD). Despite being more costly than neuroablative procedures of comparable efficacy, DBS has gained popularity over the years for its reversibility and adjustability. Although the cost-effectiveness of DBS has been investigated extensively in movement disorders, few economic analyses of DBS for psychiatric disorders exist.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Nursing, Hanseo University, Seosan-Si, Republic of Korea.
Purpose: Despite the advent of heated tobacco products (HTPs), their relationship to mental health remains unclear. This study aimed to determine associations between the use of combustible cigarettes (CCs) and HTPs with depressive symptoms.
Methods: This descriptive-analytical cross-sectional study was conducted in March 2023.
Certain medicinal plants utilized in the traditional ayurvedic system are poisonous when used raw, but are used following a detoxification process. The Ayurvedic Formulary of India (AFI) provides details about these detoxification (known as "sodhana") processes as per traditional procedures. This research endeavor aimed to uncover the fundamental principles underlying the detoxification approach applied to , commonly referred to as "swet chitrak", in which plumbagin is the primary toxic constituent.
View Article and Find Full Text PDFRheumatology (Oxford)
January 2025
Department of Rheumatology, Acute Rheumatology Centre Rhineland-Palatinate, Bad Kreuznach, Germany.
Objective: To examine the longitudinal associations of optical spectral transmission (OST) with clinical inflammatory arthritis activity markers in order to investigate its potential in monitoring disease activity.
Methods: OST measurements were performed in 1,312 wrist and finger joints of 60 patients with clinical suspicion of inflammatory activity, within the context of known rheumatic inflammatory diseases at two separate time intervals. In each time point, patients underwent additional clinical and laboratory examinations.
J Phys Chem A
January 2025
Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Shenyang 110819, China.
Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet.
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