From the Lab: Integrating Chemokines and Machine Learning In Primary Immune Thrombocytopenia - EMJ

From the Lab: Integrating Chemokines and Machine Learning in Primary Immune Thrombocytopenia

1 Mins
Hematology

A new study has highlighted chemokines as potential biomarkers that could enhance the diagnosis and bleeding evaluation in patients with primary immune thrombocytopenia (ITP), an autoimmune disorder that affects blood clotting. 

Researchers from Tianjin Institutes of Health Sciences, China, conducted a prospective analysis involving 60 ITP patients and 17 patients with other forms of thrombocytopenia to identify chemokines that could distinguish between ITP and non-ITP cases. Using a Luminex-based assay, the team quantified plasma chemokine levels, followed by machine learning algorithms to identify patterns. 

Notably, the following chemokines; CCL20, IL-2, CCL26, CCL25, and CXCL1, were found to be highly accurate in diagnosing ITP. Additionally, 12 ITP patients who experienced significant bleeding (defined by an ITP-2016 bleeding grade ≥2) were compared with 33 non-bleeding ITP patients, leading to the identification of CCL21, CXCL8, CXCL10, CCL8, CCL3, and CCL15 as indicators of bleeding risk. 

The findings were validated using enzyme-linked immunosorbent assays (ELISA) on another cohort of 43 ITP patients and 19 non-ITP patients, confirming the potential of these chemokines as biomarkers for ITP. 

Overall, the study presents a promising approach for improving ITP diagnostics and evaluating bleeding complications, potentially allowing for more targeted treatment strategies. 

Helena Bradbury, EMJ 

 

Reference 

Wen Q et al. Integrating chemokines and machine learning algorithms for diagnosis and bleeding assessment in primary immune thrombocytopenia: A prospective cohort study. BJ Haem. 2024.  

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