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There have been growing apprehensions and concerns regarding the increasing presence of plastic pollutants in the holiest river of India, the Ganga, and its major tributary, Yamuna. In response to this issue, the current study aimed to conduct a comprehensive investigation of the seasonal and spatial distribution of macro to microplastics (MPs) in the surface water, water column, and sediments from the River Ganga and Yamuna. MP samples were collected from various points of these Rivers, including upstream, downstream, and drainage points around the vicinity of Haridwar, Agra, Prayagraj, and Patna cities. With a significant seasonal variation, the estimated MPs and plastic flux were higher during the wet season than during the dry season. MPs sized 300 µm-1 mm and fibre-shaped blue and black colored MPs were pre-dominant in both rivers. Polyacrylamide, polyamide, and polyvinyl chloride were the most ascertained polymers. MPs including hazardous polymers (hazard score >1000) may pose a risk to the population of Indo-Gangetic Plain via direct and indirect exposure to MPs. The information provided in this study could serve as a starting point for the action plan required by municipal corporations to mitigate plastic pollution and target the possible sources at each location., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: Biomedical natural language processing tools are increasingly being applied for broad-coverage information extraction-extracting medical information of all types in a scientific document or a clinical note. In such broad-coverage settings, linking mentions of medical concepts to standardized vocabularies requires choosing the best candidate concepts from large inventories covering dozens of types. This study presents a novel semantic type prediction module for biomedical NLP pipelines and two automatically-constructed, large-scale datasets with broad coverage of semantic types., Methods: We experiment with five off-the-shelf biomedical NLP toolkits on four benchmark datasets for medical information extraction from scientific literature and clinical notes. All toolkits adopt a staged approach of mention detection followed by two stages of medical entity linking: (1) generating a list of candidate concepts, and (2) picking the best concept among them. We introduce a semantic type prediction module to alleviate the problem of overgeneration of candidate concepts by filtering out irrelevant candidate concepts based on the predicted semantic type of a mention. We present MedType, a fully modular semantic type prediction model which we integrate into the existing NLP toolkits. To address the dearth of broad-coverage training data for medical information extraction, we further present WikiMed and PubMedDS, two large-scale datasets for medical entity linking., Results: Semantic type filtering improves medical entity linking performance across all toolkits and datasets, often by several percentage points of F-1. Further, pretraining MedType on our novel datasets achieves state-of-the-art performance for semantic type prediction in biomedical text., Conclusions: Semantic type prediction is a key part of building accurate NLP pipelines for broad-coverage information extraction from biomedical text. We make our source code and novel datasets publicly available to foster reproducible research., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Intravenous magnesium therapy in acute myocardial infarction (MI) has been shown to be beneficial in many studies. However, these effects are not consistent from one trial to another, and their clinical significance is often questionable. A total of 78 consecutive patients were included in a prospective placebo controlled, randomized study of the effects of intravenous magnesium sulfate infusion in acute MI. Of these, 52 (66%) received intravenous streptokinase and the rest belonged to a non-thrombolysed group. These patients were randomized to receive magnesium sulfate infusion (8 mmol over 5 min followed by 65 mmol over 24 h) or placebo. The end points were serious arrhythmias, left ventricular ejection fraction (LVEF) and death during hospital stay and at the end of 28 days. LVEF measured by radionuclide ventriculography at discharge (9 +/- 5 days) was similar in two groups (41 +/- 11 vs. 39 +/- 10; magnesium vs. placebo respectively, P = 0.40). However, the LVEF of the placebo-treated thrombolysed group was higher than the non-thrombolysed group (42 +/- 10 vs. 37 +/- 0, P = 0.02). Incidence of nonsustained ventricular tachycardia (NSVT) was higher with placebo than with magnesium (50% vs. 23% respectively, P < 0.02, odds ratio = 0.29; 95% confidence intervals (CI) 0.1-0.85). Mortality during hospital stay and at the end of 28 days was also similar in both the groups. Thus, the administration of magnesium infusion as an adjunct to thrombolytic therapy remains to be of questionable benefit.