{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:13:08Z","timestamp":1755223988845,"version":"3.43.0"},"reference-count":9,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Br J Haematol"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:p>Immune thrombocytopenia (ITP) is a highly heterogeneous autoimmune bleeding disorder characterized by low platelet counts due to an immune\u2010mediated platelet destruction and impaired platelet production. The pathophysiology is multifactorial and remains to be fully unravelled. Consequently, disease trajectories and responses to therapeutics, despite the availability of multiple agents, can be unpredictable and differing between patients. There is an urgent need for the identification of diagnostic and therapeutic biomarkers, but this has proven to be challenging to achieve. To shed light on this, two studies in this issue of the <jats:italic>British Journal of Haematology<\/jats:italic> have recognized the opportunity of using high\u2010throughput Omics technologies in ITP. Sun et\u00a0al. performed proteomics, and Li et\u00a0al. metabolomics, on bone marrow biopsy samples of patients with ITP. This was conducted using mass spectrometry and, due to the generation of large datasets, in combination with machine learning. These studies set the stage for further investigations exploring the high potential of multi\u2010omics technologies in order to shed light on the heterogeneity in ITP, accelerating the path towards a much needed personalized medicine approach.<\/jats:p><jats:p>Commentary on: Li et\u00a0al. Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: A prospective cohort study. Br J Haematol 2024;204:2405\u20102417.<\/jats:p><jats:p>Commentary on: Sun et\u00a0al. Proteomics landscape and machine learning prediction of long\u2010term response to splenectomy in primary immune thrombocytopenia. Br J Haematol 2024;204:2418\u20102428.<\/jats:p>","DOI":"10.1111\/bjh.19514","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T20:33:30Z","timestamp":1715200410000},"page":"2159-2161","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Broadening the horizon of immune thrombocytopenia through Omics approaches"],"prefix":"10.1111","volume":"204","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1608-876X","authenticated-orcid":false,"given":"Rick","family":"Kapur","sequence":"first","affiliation":[{"name":"Sanquin Research, Department of Experimental Immunohematology, Amsterdam and Landsteiner Laboratory, Amsterdam UMC University of Amsterdam  Amsterdam The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2008-07-162503"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm6020016"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2019000812"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2019000966"},{"issue":"6","key":"e_1_2_2_6_1","first-page":"E574","article-title":"Immune thrombocytopenia: recent advances in pathogenesis and treatments","volume":"5","author":"Audia S","year":"2021","journal-title":"Hema"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/MOH.0000000000000612"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10153403"},{"key":"e_1_2_2_9_1","doi-asserted-by":"crossref","first-page":"2418","DOI":"10.1111\/bjh.19420","article-title":"Proteomics landscape and machine learning prediction of long\u2010term response to splenectomy in primary immune thrombocytopenia","volume":"204","author":"Sun T","year":"2024","journal-title":"Br J Haematol"},{"key":"e_1_2_2_10_1","doi-asserted-by":"crossref","first-page":"2405","DOI":"10.1111\/bjh.19391","article-title":"Metabolomics profile and machine learning prediction of treatment responses in immune thrombocytopenia: a prospective cohort study","volume":"204","author":"Li Y","year":"2024","journal-title":"Br J Haematol"}],"container-title":["British Journal of Haematology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/bjh.19514","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T10:08:48Z","timestamp":1754993328000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/bjh.19514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,8]]},"references-count":9,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["10.1111\/bjh.19514"],"URL":"https:\/\/doi.org\/10.1111\/bjh.19514","archive":["Portico"],"relation":{},"ISSN":["0007-1048","1365-2141"],"issn-type":[{"type":"print","value":"0007-1048"},{"type":"electronic","value":"1365-2141"}],"subject":[],"published":{"date-parts":[[2024,5,8]]},"assertion":[{"value":"2024-04-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}