{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T08:07:04Z","timestamp":1788941224944,"version":"build-2803163510"},"reference-count":24,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82472369"],"award-info":[{"award-number":["82472369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82272415"],"award-info":[{"award-number":["82272415"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010120","name":"Huashan Hospital","doi-asserted-by":"publisher","award":["30302164004"],"award-info":[{"award-number":["30302164004"]}],"id":[{"id":"10.13039\/501100010120","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017950","name":"Shanghai Municipal Health Commission","doi-asserted-by":"publisher","award":["2022YQ045"],"award-info":[{"award-number":["2022YQ045"]}],"id":[{"id":"10.13039\/100017950","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Lab Hematology"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Current\n                      <jats:italic>CALR<\/jats:italic>\n                      mutation screening in myeloproliferative neoplasms (MPNs) faces a trade\u2010off between coverage and cost: allele\u2010specific qPCR and fragment analysis are inexpensive but cover only part of the variant landscape, while sequencing costs $300\u2013500 per sample with 3\u20135\u2009day turnaround. High\u2010resolution melting (HRM) offers both but suffers from subjective interpretation. We developed HRM\u2013CALR\u2013AI, an automated\n                      <jats:italic>CALR<\/jats:italic>\n                      screening platform.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      HRM\u2013CALR\u2013AI was developed on 2667 samples and combines YOLOv8s\u2010cls deep learning with CatBoost meta\u2010classifier for open\u2010set recognition of untrained mutations. Validation included three independent laboratories, 188 consecutive\n                      <jats:italic>JAK2<\/jats:italic>\n                      \u2010negative MPN patients, and a head\u2010to\u2010head comparison with capillary electrophoresis fragment analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      HRM\u2013CALR\u2013AI processed 96 samples in 90\u2010min at &lt;\u2009$10 per test, with limits of detection of 6.25% (\n                      <jats:italic>CALR\u20101<\/jats:italic>\n                      ) and 12.5% (\n                      <jats:italic>CALR\u20102<\/jats:italic>\n                      ) variant allele frequency. On a 480\u2010sample held\u2010out set, it achieved 93.9% accuracy (95% CI: 91.7\u201395.8), AUC of 0.996 (95% CI: 0.993\u20130.998), and 89.1% (57\/64) recognition of untrained rare variants. Clinical screening identified 160 wild\u2010type (85.1%), 15\n                      <jats:italic>CALR\u20101<\/jats:italic>\n                      (8.0%), nine\n                      <jats:italic>CALR\u20102<\/jats:italic>\n                      (4.8%), and four rare variants (2.1%), including a previously unreported variant (c.1145_1158delinsTCCT) classified as\n                      <jats:italic>CALR\u20102<\/jats:italic>\n                      \u2010like. Multicenter accuracy was 98.1% (ICC 0.868). Against fragment analysis, HRM\u2013CALR\u2013AI showed equivalent detection of length\u2010altering variants (kappa\u2009=\u20090.933) and additionally covered nonlength\u2010altering variants that are invisible to size\u2010based separation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>HRM\u2013CALR\u2013AI combines open\u2010set recognition with low\u2010cost HRM to detect rare and unreported variants at ~50\u2010fold lower cost and same\u2010day turnaround. Multicenter reproducibility and benchmarking against fragment analysis support implementation as a frontline screening tool where NGS access is limited.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ijlh.70150","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T02:59:59Z","timestamp":1779159599000},"page":"1113-1122","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Based High Resolution Melting Analysis for Comprehensive Calreticulin Mutation Screening and Novel Variant Detection in Myeloproliferative Neoplasms"],"prefix":"10.1111","volume":"48","author":[{"given":"Qingyun","family":"Zhang","sequence":"first","affiliation":[{"name":"Central Laboratory Huashan Hospital, Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotong","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Huashan Hospital Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanli","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Huashan Hospital Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusi","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Huashan Hospital Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Huashan Hospital Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0957-6420","authenticated-orcid":false,"given":"Xuemei","family":"Tang","sequence":"additional","affiliation":[{"name":"Central Laboratory Huashan Hospital, Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Xu","sequence":"additional","affiliation":[{"name":"CellSee (Wuxi) Intelligent Technology Co. Ltd  Wuxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8796-2653","authenticated-orcid":false,"given":"Ming","family":"Guan","sequence":"additional","affiliation":[{"name":"Central Laboratory Huashan Hospital, Fudan University  Shanghai China"},{"name":"Department of Laboratory Medicine, Huashan Hospital Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3554-2412","authenticated-orcid":false,"given":"Zhiyuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Huashan Hospital Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1312542"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2022017578"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.14762"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.15252\/embr.202152904"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/09537104.2020.1869712"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41408\u2010024\u201001028\u20104"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22073371"},{"key":"e_1_2_12_9_1","unstructured":"Calreticulin (CALR) Mutation Analysis \u201cTest Detail. 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