In this study we aim to assess the daily ingestion rates of synthetic particles by the fin whales (Balaenoptera physalus) that feed off the western coast of Iceland. To do so, we collected and analysed samples from the stomach content of 25 fin whales, consisting solely of northern krill (Meganyctiphanes norvegica). The particles found consisted of fibres and fragments, mainly blue, black and red, with an average size of 1.2 ± 1.3 mm. To confirm the synthetic nature of these particles, we used Micro-Fourier Transform Infrared Spectroscopy and comparison with a polymer library. The mean concentration of synthetic particles in the krill samples found in the stomachs of whales was 0.057 particles per gram, a value much lower than that previously reported for particle uptake by krill. From this concentration in krill, we estimated that the daily intake of synthetic particles for the North Atlantic fin whale would be ranging from 38,646 ± 43,392 to 77,292 ± 86,784 particles per day. Although at this level it is not possible to assess the impact of synthetic particles and their associated chemicals on the North Atlantic fin whale population, concentrations of these contaminants are likely to increase in the future, potentially causing adverse effects on whales and other marine mammals.
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http://dx.doi.org/10.1016/j.chemosphere.2021.130564 | DOI Listing |
ACS Nano
January 2025
School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Dr., Atlanta, Georgia 30332, United States.
Structural variants of the synthetic opioid fentanyl are a major threat to public health. Following an investigation showing that many derivatives are poorly detected by commercial lateral flow and related assays, we created hapten conjugate vaccines using an immunogenic virus-like particle carrier and eight synthetic fentanyl derivatives designed to mimic the structural features of several of the more dangerous analogues. Immunization of mice elicited strong antihapten humoral responses, allowing the screening of hundreds of hapten-specific hybridomas for binding strength and specificity.
View Article and Find Full Text PDFInorg Chem
January 2025
Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania 17601, United States.
One method of achieving spatially specific, multi-component nanoheterostructures is to combine multiple forms of post-synthetic modification. Applying cation or anion exchange to CuS nanorods creates complex nanoheterostructures. Combining such anion and cation exchanges generates a system which uncovers the interplay between these two processes and understands the cooperativity between postsynthetic modifications more broadly.
View Article and Find Full Text PDFNanomaterials (Basel)
January 2025
Enikolopov Institute of Synthetic Polymer Materials Russian Academy of Sciences (ISPM RAS), Profsoyuznaya St. 70, 117393 Moscow, Russia.
The results of a comprehensive investigation into the structure and properties of nanodiamond soot (NDS), obtained from the detonation of various explosive precursors (trinitrotoluene, a trinitrotoluene/hexogen mixture, and tetryl), are presented. The colloidal behavior of the NDS particles in different liquid media was studied. The results of the scanning electron microscopy, dynamic light scattering, zeta potential measurements, and laser diffraction analysis suggested a similarity in the morphology of the NDS particle aggregates and agglomerates.
View Article and Find Full Text PDFBMC Cancer
January 2025
Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran.
Background And Aim: Zinc oxide and copper oxide nanoparticles are known for their promising biological activities. This study aims to synthesize zinc oxide nanoparticles and copper-doped zinc oxide nanoparticles to harness the combined cytotoxic and anticancer effects of them in vitro and in vivo studies.
Methods: Zinc oxide nanoparticles, both doped and undoped, were synthesized using a chemical co-precipitation method.
Nature
January 2025
Machine Learning Lab, University of Freiburg, Freiburg, Germany.
Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories, gradient-boosted decision trees have dominated tabular data for the past 20 years.
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