White blood cells
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White Blood Cells: Roles, Genetic Insights, and Advanced Detection Methods
Introduction to White Blood Cells and Their Functions
White blood cells (WBCs) are crucial components of the immune system, responsible for defending the body against infections and foreign invaders. Produced in the bone marrow, WBCs circulate in the blood and lymphatic system, playing key roles in both innate and adaptive immunity3. There are five primary types of WBCs: neutrophils, lymphocytes, monocytes, eosinophils, and basophils, each with specific functions in immune response9.
Genetic Insights into White Blood Cell Traits
Genetic Associations and Variability
Recent large-scale genetic studies have provided significant insights into the genetic factors influencing WBC traits. An exome-wide association analysis identified 16 common genetic variants associated with WBC counts and subtypes, many of which are also linked to autoimmune diseases1. These variants include genes involved in cell differentiation, transcription factors, and molecules related to cell adhesion and apoptosis1. Another study replicated ten associations with total WBC count and five WBC subtypes, identifying seven novel loci and highlighting the heritability and genetic variability of these traits5.
Trans-Ethnic Meta-Analysis
A trans-ethnic meta-analysis further explored the genetic basis of WBC counts across diverse populations, identifying six new loci and emphasizing the importance of multi-ethnic studies in understanding complex traits8. This approach helps to account for differences in genetic linkage and allelic effects among different ancestry groups, providing a more comprehensive understanding of WBC genetics8.
Advanced Methods for White Blood Cell Detection and Classification
Image Processing and Deep Learning Techniques
Traditional methods for WBC counting, such as manual microscopy and hematology counters, can be labor-intensive and costly. Recent advancements in image processing and deep learning have significantly improved the accuracy and efficiency of WBC detection and classification. For instance, convolutional neural networks (CNNs) combined with feature selection methods have achieved high classification success rates, with models like AlexNet, GoogLeNet, and ResNet-50 showing an overall success rate of 97.95%3.
Computer-Aided Systems and Segmentation Methods
Computer-aided systems (CAS) have been developed to analyze blood smear images, utilizing various color segmentation methods to accurately count WBCs. Techniques such as color space correction, morphological filtering, and Circle Hough Transform have been employed to enhance segmentation accuracy, achieving up to 96.92% accuracy in WBC counting4.
Label-Free Identification Using Machine Learning
Innovative label-free approaches using imaging flow cytometry and machine learning algorithms have also been developed. These methods classify live, unstained WBCs with high accuracy, minimizing cell disturbance and allowing for the identification of WBC subtypes without the need for fluorescent markers7. This approach achieved an average F1-score of 97%, demonstrating its potential for robust and non-invasive WBC analysis7.
Conclusion
White blood cells are essential for immune function, and understanding their genetic basis and improving detection methods are critical for diagnosing and treating various diseases. Advances in genetic research have identified key loci associated with WBC traits, while innovative image processing and machine learning techniques have enhanced the accuracy and efficiency of WBC detection and classification. These developments hold promise for better clinical diagnostics and personalized medicine.
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Most relevant research papers on this topic
Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases.
16 common genetic variants are associated with white blood cell traits and autoimmune diseases, suggesting a shared genetic architecture with inflammatory and autoimmune diseases.
Meta-Analysis
Rigorous JournalSeparation of White Blood Cells
EDTA-blood can be separated into white blood cells by allowing it to settle in siliconized glasses, or by adding agents that aggregate erythrocytes to the blood.
Highly CitedClassification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods
Using Convolutional Neural Network models with feature selection methods, and a combination of AlexNet, GoogLeNet, and ResNet-50, achieved a 97.95% success rate in white blood cell classification.
Highly CitedWhite blood cell (WBC) counting analysis in blood smear images using various color segmentation methods
Using S color component of HSV and nucleus-based detection for white blood cell counting in blood smear images provides the highest segmentation accuracy and 96.56% counting accuracy.
Highly CitedMultiple Loci Are Associated with White Blood Cell Phenotypes
Multiple genomic loci are associated with white blood cell count and subtypes, revealing functional relationships across cell types and diverse ancestral backgrounds.
Meta-AnalysisHighly CitedSEPARATION OF WHITE BLOOD CELLS.
EDTA-blood can be separated into white blood cells by allowing it to settle in siliconized glasses and pipetting off the leucocyte-rich supernatant after 1.5 hours or more.
Highly CitedLabel‐Free Identification of White Blood Cells Using Machine Learning
This label-free approach using an imaging flow cytometer and machine learning algorithms accurately identifies live, unstained white blood cells, minimizing cell disturbance and allowing for liquid biopsy applications.
Highly CitedTrans-ethnic meta-analysis of white blood cell phenotypes.
Trans-ethnic analysis reveals new genetic loci associated with white blood cell count, highlighting the importance of multi-ethnic cohorts for exploring complex phenotypes.
Meta-AnalysisWhite blood cells detection and classification based on regional convolutional neural networks.
The study developed a computer-aided automated system that accurately detects and classifies white blood cell types in blood images, with ResNet50 showing the best performance with transfer learning.
Highly Cited