Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
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.
2 · The registry
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.
Changhyun Kim *Department of Surgery, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Republic of Korea.
Seok ChungSchool of Mechanical Engineering, Korea University, Seoul, Republic of Korea.
Soo Yeun ParkColorectal Cancer Center, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
Ilwoo ParkDepartment of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Republic of Korea.
Jun Seok ParkColorectal Cancer Center, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. parkjs0802@knu.ac.kr.ORCID http://orcid.org/0000-0001-5443-6748
Hakho LeeCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA. hlee@mgh.harvard.edu.ORCID http://orcid.org/0000-0002-0087-0909
Funding
Expanding early cancer detection with high throughput OCEANA - Ovarian Cancer Exosome Analysis with Nanoplasmonic ArrayU01CA284982 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI Cesar M Castro, Hakho Lee · 2023 to 2026
$3.7M
Clinical platform for high-throughput analyses of extracellular vesiclesR01CA229777 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI LEE, HAKHO, SKOG, JOHAN · 2018 to 2022
$3.2M
Imaging and Liquid Biopsy for Glioma Diagnosis and Treatment MonitoringR01CA239078 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI BALAJ, LEONORA, LEE, HAKHO · 2020 to 2024
$3.1M
High-throughput Phenotyping of iPSC-derived Airway Epithelium by Multiscale Machine Learning MicroscopyR01HL163513 · NHLBI · BOSTON CHILDREN'S HOSPITAL · PI Hakho Lee, Kwonmoo Lee · 2023 to 2026
$3.1M
Standardized Molecular Analyses of Glioma EVsR01CA237500 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI CARTER, BOB S, LEE, HAKHO · 2020 to 2024
$3.0M
High throughput nanoplasmonic exosome testing (NEXT) of immunotherapies in bladder cancerR01CA264363 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI CASTRO, CESAR M, LEE, HAKHO · 2021 to 2024
$2.4M
Composing CODAs to cervical cancer screening through an integrated CRISPR and fluorescent nucleic acid approachU01CA279858 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI Cesar M Castro, Hakho Lee · 2023 to 2026
$2.1M
3D Fourier Imaging System for High Throughput Analyses of Cancer OrganoidsR21CA267222 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI LEE, HAKHO · 2022 to 2024
$613k
Streamlining sample preparation with high throughput SpinEx (Separation processing integration for Extracellular vesicles)R61CA297878 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI Hakho Lee · 2025 to 2026
$463k
High-throughput Integrated Magneto-electrochemical Exosome (HiMEX) platform to identify neurodevelopmental markers associated with pre and postnatal oxycodone exposureR21DA049577 · NIDA · MASSACHUSETTS GENERAL HOSPITAL · PI LEE, HAKHO, PENDYALA, GURUDUTT · 2019 to 2020
$458k
NCI NIH HHS R01 CA229777NCI NIH HHS R01 CA237500NCI NIH HHS R01 CA239078NCI NIH HHS R01 CA264363NCI NIH HHS R21 CA267222NCI NIH HHS R61 CA297878NCI NIH HHS U01 CA279858NCI NIH HHS U01 CA284982NHLBI NIH HHS R01 HL163513NIDA NIH HHS R21 DA049577U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) R01CA229777U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) U01CA284982
6 · The paper itself
Abstract
Circulating extracellular vesicles can be used for tumour diagnostics. However, current isolation methods are time consuming, require manual handling and are prone to contamination. Here we report on SpinEx (separation-processing integration for extracellular vesicles), a compact disc device for automatic isolation and multiplex immunolabelling of whole-blood samples. SpinEx integrates on-disc chromatography, centripetal liquid transfer and bead-based vesicle capture with antibody labelling. The system processes 150 µl of whole blood, enriching and labelling vesicles for 16 protein targets in under 75 minutes. Detection is performed by measuring dual fluorescence signals from labelled extracellular vesicles captured on microbeads. In a pilot clinical study, SpinEx was used to process 221 plasma samples for multiplex profiling of 30 vesicle-associated proteins. Using fluorescence flow cytometry to analyse cancer-specific biomarker expression, we found that vesicles processed by SpinEx distinguished cancer from non-cancer samples with 90% accuracy and 97% specificity, and classified 5 tumour types with 96% accuracy. SpinEx enables automated and multiplex processing of extracellular vesicles from blood, which may support the development of clinically viable assays for cancer detection and classification.
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 OpenQuestion graph.