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The standard addition method (SAM) is widely used to measure the isotopic compositions of natural samples, particularly those with a complex matrix. However, traditional SAM has limitations for isotope systems with significant variations in isotope composition due to its reliance on approximation in calculation and the requirement for estimates of analyte isotopic compositions and accurate concentrations. To overcome the issues, our work proposes an improved SAM that explicitly calculates isotope ratio (i.e., E/E, S/S for example) instead of approximating * (mass number of isotope X divided by total mass number of all isotopes of an element) with in SAM. Additionally, the sample fraction within standard-sample mixture in improved SAM is determined using the isotope compositions of standards, sample-standard mixtures, and the mixtures of both standards, rather than relying on sample concentrations and volumes. Both improvements not only overcome the shortcomings of traditional SAM but also empowered the approach's ability to accurately determine sample concentrations. To validate its effectiveness, we applied the improved SAM to natural samples with substantial sulfur (S) isotope variation (1.94 to 27.19‰) and low S concentration (0.81 to 3.47 μg g). The calculated δS values and concentrations of these samples are consistent with direct measurements within the error ranges while reducing sample sizes to 20% of those required for direct measurement. Moreover, our method achieves higher accuracy in δS values compared with traditional SAM. Both comparisons affirm the reliability and superiority of improved SAM.
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http://dx.doi.org/10.1021/acs.analchem.4c02960 | DOI Listing |
J Med Imaging (Bellingham)
September 2025
Vanderbilt University, Data Science Institute, Nashville, Tennessee, United States.
Purpose: Recent developments in computational pathology have been driven by advances in vision foundation models (VFMs), particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the use of cell-specific SAM models for direct segmentation. These approaches enable effective segmentation across a range of nuclei and cells.
View Article and Find Full Text PDFDigit Health
September 2025
Department of Respiratory and Critical Care Medicine, The Sixth Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Objective: Accurate segmentation of breast lesions, especially small ones, remains challenging in digital mammography due to complex anatomical structures and low-contrast boundaries. This study proposes DVF-YOLO-Seg, a two-stage segmentation framework designed to improve feature extraction and enhance small-lesion detection performance in mammographic images.
Methods: The proposed method integrates an enhanced YOLOv10-based detection module with a segmentation stage based on the Visual Reference Prompt Segment Anything Model (VRP-SAM).
Curr Med Chem
August 2025
Department of Pharmaceutical Sciences, Shalom Institute of Health and Allied Sciences, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad, 211007, Uttar Pradesh, India.
Globally, millions of individuals suffer from infectious diseases, which are major public health concerns caused by bacteria, fungi, viruses, or parasites. These diseases can be transmitted directly or indirectly from person to person, potentially leading to a pandemic or epidemic. Several advancements have been made in molecular genetics for infectious disease management, which include pharmaceutical chemistry, medicine, and infection tracking; however, these advancements still lack control over human infections.
View Article and Find Full Text PDFdiscusses what nurse leaders can learn from examining the practices of other industries - those that aim to learn from mistakes rather than blame people for them - thus helping to improve patient safety.
View Article and Find Full Text PDFBrief Bioinform
September 2025
Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seoul 04763, Republic of Korea.
Motivation: Mobile genetic elements (MGEs) play an important role in facilitating the acquisition of antibiotic resistance genes (ARGs) within microbial communities, significantly impacting the evolution of antibiotic resistance. Understanding the mechanism and trajectory of ARG acquisition requires a comprehensive analysis of the ARG-carrying mobilome-a collective set of MGEs carrying ARGs. However, identifying the mobilome within complex microbiomes poses considerable challenges.
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