A sensitive, accurate and simple analytical method was developed to determine cadmium by slotted quartz tube-flame atomic absorption spectrometry after preconcentration/extraction with polystyrene coated magnetic nanoparticles based dispersive solid phase extraction. The surface of FeO based magnetic nanoparticles was modified with polystyrene to yield higher selectivity and sensitivity in adsorption efficiency of cadmium. The nanoparticles were dispersed into the aqueous solution to extract/separate cadmium. Significant parameters of the method including magnetic nanoparticle amount, mixing effect, effect of ionic strength, eluent concentration and sonication period were optimized to achieve optimal conditions for the analyte. The limit of detection and quantification values of the developed method were found to be 0.62 and 2.1 ng/mL, respectively. Under the optimum conditions, enhancement of the detection power for cadmium were calculated as 102 folds for the developed method. The developed method was then applied to cigarette ash to test its accuracy and applicability. Total cadmium was found to be between 402 and 450 ng/g in the ashes of different cigarette brands commercially available in Turkey. The accuracy of quantifying cadmium in the complex ash samples was enhanced by using the matrix matching calibration strategy. The developed method provides sensitive and selective determination of cadmium at ng/mL levels even at complex cigarette ash samples. High percent recovery results (90-102%) were obtained from spiked real samples.
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http://dx.doi.org/10.1007/s44211-022-00104-8 | DOI Listing |
JCI Insight
January 2025
Medical Oncology Department, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, Netherlands.
Background: Previously, we demonstrated that changes in circulating tumor DNA (ctDNA) are promising biomarkers for early response prediction (ERP) to immune checkpoint inhibitors (ICI) in metastatic urothelial cancer (mUC). In this study, we investigated the value of whole blood immunotranscriptomics for ERP-ICI and integrated both biomarkers into a multimodal model to boost accuracy.
Methods: Blood samples of 93 patients were collected at baseline and after 2-6 weeks of ICI for ctDNA (N=88) and immunotranscriptome (N=79) analyses.
Brief Bioinform
November 2024
School of Artificial Intelligence, Jilin University, Qianjin Street 2699, 130010 Changchun, China.
Imaging-based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution. The subcellular localization of RNA molecules plays a crucial role in the compartmentalization-dependent regulation of genes within individual cells. Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, 999077, China.
The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis.
View Article and Find Full Text PDFJ Am Assoc Nurse Pract
January 2025
Division of Cardiology, Department of Medicine, Duke Health Integrated Practice, Duke University Health System, Durham, North Carolina.
Background: Increasing patient demand and clinician burnout in rheumatology practices have highlighted the need for more efficient models of care (MOC). Interprofessional collaboration is essential for improving patient outcomes and clinician satisfaction.
Local Problem: Our current MOC lacks standardization and formal integration of Nurse Practitioners (NPs) and Physician Assistants (PAs), resulting in reduced clinician satisfaction and limited patient access.
J Med Internet Res
January 2025
Department of Industrial and Systems Engineering, The University of Florida, GAINESVILLE, FL, United States.
Background: The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored.
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