ArticleInternational journal of laboratory hematology2026
Artificial Intelligence Based High Resolution Melting Analysis for Comprehensive Calreticulin Mutation Screening and Novel Variant Detection in Myeloproliferative Neoplasms.
Article in International journal of laboratory hematology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
introductionCurrent CALR mutation screening in myeloproliferative neoplasms (MPNs) faces a trade-off between coverage and cost: allele-specific qPCR and fragment analysis are inexpensive but cover only part of the variant landscape, while sequencing costs $300-500 per sample with 3-5 day turnaround. High-resolution melting (HRM) offers both but suffers from subjective interpretation. We developed HRM-CALR-AI, an automated CALR screening platform.
methodsHRM-CALR-AI was developed on 2667 samples and combines YOLOv8s-cls deep learning with CatBoost meta-classifier for open-set recognition of untrained mutations. Validation included three independent laboratories, 188 consecutive JAK2-negative MPN patients, and a head-to-head comparison with capillary electrophoresis fragment analysis.
resultsHRM-CALR-AI processed 96 samples in 90-min at < $10 per test, with limits of detection of 6.25% (CALR-1) and 12.5% (CALR-2) variant allele frequency. On a 480-sample held-out set, it achieved 93.9% accuracy (95% CI: 91.7-95.8), AUC of 0.996 (95% CI: 0.993-0.998), and 89.1% (57/64) recognition of untrained rare variants. Clinical screening identified 160 wild-type (85.1%), 15 CALR-1 (8.0%), nine CALR-2 (4.8%), and four rare variants (2.1%), including a previously unreported variant (c.1145_1158delinsTCCT) classified as CALR-2-like. Multicenter accuracy was 98.1% (ICC 0.868). Against fragment analysis, HRM-CALR-AI showed equivalent detection of length-altering variants (kappa = 0.933) and additionally covered nonlength-altering variants that are invisible to size-based separation.
conclusionsHRM-CALR-AI combines open-set recognition with low-cost HRM to detect rare and unreported variants at ~50-fold lower cost and same-day turnaround. Multicenter reproducibility and benchmarking against fragment analysis support implementation as a frontline screening tool where NGS access is limited.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.