The Academy of Diagnostics and Laboratory Medicine are pleased to announce the winners of the 2026 Distinguished Abstracts Awards. A group of Fellows selected these 18 abstracts for their scientific excellence from a pool of more than 849 abstracts accepted for ADLM 2026.
Winning abstracts will display the Academy blue ribbon during the ADLM 2026 poster sessions in Anaheim.
A-007 – Robert Christenson, Baltimore, MD
Comparisons of High-Sensitivity Cardiac Troponin I Assays: Quantitative Differences in Non-Selectivity in Patient Samples.
A-014 – Asmita Hazra, Saguna, India
Evaluation of 82 LDL-Cholesterol Calculation Methods — 41 Formulae, 7 Lookup-Tables, 26 Machine Learning, and 8 Deep Learning Architectures — Against Direct LDL-C in over 26000 Eastern Indian Lipid Profiles.
A-097 – Sheng-Wei Pan, Taipei, Taiwan
Quantitative assessment of circulating blood ESAT-6 for non-sputum identification of pulmonary tuberculosis.
A-150 – Elikem Kumahor, Accra, Ghana
The Hidden Burden of Diabetes Mellitus: Impact of Sickle Cell Trait on HbA1c Measurement by Immunoassay vs. HPLC in a West African Population (Ghana).
A-157 – Yichen Ma, Beijing, China
Clinical Performance of LC-MS/MS and Immunoassays for Serum Thyroglobulin in Assessing Structural Disease in Differentiated Thyroid Cancer.
A-266 – Fabiano Mattesco, São Paulo, Brazil
Improving Laboratory Efficiency and Patient Safety Through Data-Driven Autoverification in Hematology and Coagulation.
A-349 – Wen Gu, Dallas, TX
A Dual-Marker Panel Predicts the Likelihood of Biliary Atresia in Infants with Superior Specificity than Current Practice.
B-017 – Audrianna Kern, Ankeny, IA
A Purified Recombinant Human Albumin Produced in Thermothelomyces heterothallica (C1) Better Mimics Native Human Serum Albumin than Recombinant Albumin from Other Expression Systems.
B-091 – Se-eun Koo, Seoul, Republic of Korea
Machine Learning–Enhanced Diagnostic Performance of Plasma Metanephrines in PPGL: A Large-Scale Real-World Clinical Cohort Study.
B-095 – Laisheng Li, Guangzhou, China
Precision Diagnosis of Early-Stage Hepatocellular Carcinoma: Development of a PIVKA-II Based Diagnostic Model.
B-123 – Ming Wang, Shenzehn, China
Explainable Machine Learning with CHI3L1 for Detecting Hepatocellular Carcinoma in a Multicenter Prospective Cohort study.
B-132 – Shujun Zhang, Jinan, China
DNA Methylation Profiling of Peripheral Blood Mononuclear Cells Unveils Systemic Immune Dysregulation and Enables Multi-Cancer Early Detection.
B-134 – Annalara Fischer, Louisville, KY
Integrating ctDNA Monitoring with Peripheral Immunophenotyping to Define Acquired Immunotherapy Resistance in Metastatic Melanoma.
B-194 – Qishui Ou, Fuzhou, China
Bile Acid Metabolomics Reveals Distinct Immunometabolic Niches and Enables Accurate Diagnosis of AQP4-IgG–Seronegative NMOSD.
B-321 – Yulin Ren, Xianyang, Shaanxi, China
Toward Deployable ddPCR: A Polycarbonate Integrated Platform with Stable Droplets, Broad Dynamic Range, and Low Consumable Cost.
B-350– Ryan Pearce, Rochester, MN
Clinical Validation of High-Resolution Mass Spectrometry for Blood-Based Minimal Residual Disease Detection to Reduce the Need for Bone Marrow Aspiration.
B-389 – Christian Grenier, Woburn, WA
A High-Throughput, Fully Automated Magnetic Bead-Based Extraction for Multi-Class Drug-of-Abuse Analysis in Oral Fluid.
B-402– Hannah Lusk, San Francisco, CA
Development of a Rapid Multiplex LC-MS/MS Assay for Therapeutic Drug Monitoring of Antituberculosis Drugs and Metabolites.
Fellows of the Academy use the designation of FADLM. This designation is equivalent to FACB and FAACC, the previous designations used by fellows of the National Academy of Clinical Biochemistry and AACC Academy. Those groups were rebranded as Academy of Diagnostics & Laboratory Medicine in 2023.
