WeChat Mini Program
Old Version Features

Methods Established for the Detection of Mineral Levels in Whole Blood by the Dried Blood Spot Technique

Cheng,Zehao Wang,Huilian Duan, Qi Wu,Xukun Chen,Liyang Zhang,Di Wang, Mengtong Yang, Zhenghua Huang, Zhaolun Su,Zhongxia Li,Ruikun He,Wen Li,Zhenshu Li,Guowei Huang

TALANTA(2025)

Tianjin Med Univ | BYHEALTH Inst Nutr & Hlth

Cited 0|Views6
Abstract
Mineral are intimately related to human health and disease, and detection of mineral content in the body is of great significance for the diagnosis and prevention of diseases. In this study, we validated the method developed to detect magnesium (Mg), copper (Cu), iron (Fe), zinc (Zn), and selenium (Se) levels in dried blood spots (DBS). In accordance with the requirements of the guidelines for the Bioanalytical Method Validation Guidance for Industry, we evaluate the linearity, sensitivity, precision, accuracy and selectivity of the developed methods. In addition, Mg, Cu, Fe, Zn and Se were quantified in 195 older adults using DBS technique, and its accuracy was assessed by comparing the results to those detected by inductively coupled plasma-mass spectrometry (ICP-MS). The method has excellent sensitivity and linear range to cover the concentration range of mineral elements in the general population with the required precision, accuracy and selectivity. The correlation coefficients of Mg, Cu, Fe, Zn and Se levels in blood detected by the DBS technique and ICP-MS were 0.638, 0.823, 0.463, 0.728 and 0.751, respectively (all P < 0.05), which indicated that there was a strong correlation between the detection results of the two methods. More than 95% of the sample results in the Bland-Altman consistency test were within the acceptable limits of agreement (LOA) range, indicating that they had good consistency. DBS technique has good accuracy and reliability in detecting blood mineral levels in the elderly, suggesting potential in the quantification of mineral level in blood.
More
Translated text
Key words
Dried blood spot technique,Methods established,Mineral levels,Whole blood
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined