This study presents a novel method for the analysis of low molecular weight (LMW) fatty acids in cave drip water and speleothems. The method development included optimization of sample preparation procedures, e.g., blank reduction, solid phase extraction, concentration of extracts as well as liquid chromatography coupled to electrospray ion-trap mass spectrometry (HPLC-ESI-IT/MS) measurement parameters. Retention times for five analytes (lauric acid, myristic acid, palmitic acid, stearic acid and arachidic acid) were between 5 and 13.5 min. Spiking experiments were performed to accomplish external calibrations which ranged from 12.5 to 75 ng per spiked water sample. The correlation coefficient ranged from 0.9558 to 0.9989. Inter-batch precision, expressed as the relative standard deviation of three replicates, was <7 %. Limits of detection ranged from 0.77 to 55.97 ng for the diverse analytes; obtained recoveries varied from 30 to 103 %. For a first application, cave drip water and stalagmite samples from Herbstlabyrinth-Adventhöhle cave system were analyzed. Concentrations ranged from 38.37 to 9,982.54 ng L(-1) for water samples and 2.52 to 1,344.96 ng g(-1) for the stalagmite. Thereby, the different fatty acids showed a distinctive variation. Whereas shorter-chained fatty acids exhibited similarities, arachidic acid showed opposite trends. Diverse correlations were found, which could improve the understanding of different organic sources of the lipids transported by drip water and preserved in speleothems. This new method provides a more selective extraction process, particularly adjusted to LMW fatty acids and therefore reduces the required sample size. Furthermore, it is applicable to stalagmite as well as cave drip water samples.
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http://dx.doi.org/10.1007/s00216-014-7743-6 | DOI Listing |
Adv Sci (Weinh)
January 2025
SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, South Korea.
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Swift and efficient enrichment and isolation of extracellular vesicles (EVs) are crucial for enhancing precise disease diagnostics and therapeutic strategies, as well as elucidating the complex biological roles of EVs. Conventional methods of isolating EVs are often marred by lengthy and laborious processes. In this study, we introduce an innovative approach to enrich and isolate EVs by leveraging the capabilities of DNA nanotechnology.
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Background/purpose: Oral squamous cell carcinoma (OSCC) is notorious for its low survival rates, due to the advanced stage at which it is commonly diagnosed. To enhance early detection and improve prognostic assessments, our study harnesses the power of machine learning (ML) to dissect and interpret complex patterns within mRNA-sequencing (RNA-seq) data and clinical-histopathological features.
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Biochem Biophys Rep
March 2025
Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Introduction: Gastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including stemness mechanisms facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients.
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Water Res X
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School of Environmental Science and Engineering, Shandong University, Qingdao, Shandong 266237, China.
Anaerobic ammonia oxidation (anammox) which converts nitrite and ammonium to dinitrogen gas is an energy-efficient nitrogen removal process. One of the bottlenecks for anammox application in wastewater treatment is the stable supply of nitrite for anammox bacteria. Dissimilatory nitrate reduction to ammonium (DNRA) is a process that converts nitrate to nitrite and then to ammonium.
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