ResearcherUniversity of PennsylvaniaActive in the last 2 years
David Aaron Issadore
Matched on ORCID iD 0000-0002-5461-8653; OpenAlex author ID A5062571062; NIH RePORTER profile ID 10223439. Sources: OpenAlex.
Their work
- Papers
- 3 (first author 0, last author 2)
- Trials
- 0
- Grants
- 4
- Labs led
- 1
- Collaborators
- 10
2017: 1 paper · 2018: 1 paper · 2019: 1 grant · 2020: 1 paper · 2021: none · 2022: 1 grant · 2023: none · 2024: none · 2025: 1 grant · 2026: 1 grant
Papers 3
- A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosis and Staging of Pancreatic Ductal Adeno… Co-author DOI 10.1158/1078-0432.ccr-19-3313↗
- miRNA Profiling of Magnetic Nanopore-Isolated Extracellular Vesicles for the Diagnosis of Pancreatic Cancer. Last author DOI 10.1158/0008-5472.can-17-3703↗
- Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes. Last author DOI 10.1021/acsnano.7b05503↗
Grants 4
- High Throughput Digital Droplet Assays for Ultrasensitive Multimodal (DNA, RNA, and Protein) Diagnostics R33CA287135↗
- Combining Machine Learning and Nanofluidic Technology for The Multiplexed Diagnosis of Pancreatic Adenocarcinoma R33CA278551↗
- High Throughput Digital Droplet ELISA for Ultrasensitive Multiplexed Diagnostics R21CA236653↗
- Rapid unbiased isolation and in situ RNA analysis of circulating tumor cells using a magnetic micropore-based diagnostic chip R33CA206907↗
Closest collaborators
The 10 strongest of 10 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Erica L. Carpenter · 3 shared papers, 1 shared grant, 2017–2025 · strength 1.30
- Grant Combining Machine Learning and Nanofluidic Technology for The Multiplexed Diagnosis of Pancreatic Adenocarcinoma R33CA278551↗
- Paper A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosi… DOI 10.1158/1078-0432.ccr-19-3313↗
- Paper miRNA Profiling of Magnetic Nanopore-Isolated Extracellular Vesicles for the Diagnosis of Pancreatic Cancer. DOI 10.1158/0008-5472.can-17-3703↗
- Neha Bhagwat · 3 shared papers, 2017–2020 · strength 0.23
- Paper A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosi… DOI 10.1158/1078-0432.ccr-19-3313↗
- Paper miRNA Profiling of Magnetic Nanopore-Isolated Extracellular Vesicles for the Diagnosis of Pancreatic Cancer. DOI 10.1158/0008-5472.can-17-3703↗
- Paper Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes. DOI 10.1021/acsnano.7b05503↗
- Jina Ko · 3 shared papers, 2017–2020 · strength 0.23
- Paper A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosi… DOI 10.1158/1078-0432.ccr-19-3313↗
- Paper miRNA Profiling of Magnetic Nanopore-Isolated Extracellular Vesicles for the Diagnosis of Pancreatic Cancer. DOI 10.1158/0008-5472.can-17-3703↗
- Paper Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes. DOI 10.1021/acsnano.7b05503↗
- Ben Z. Stanger · 3 shared papers, 2017–2020 · strength 0.23
- Paper A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosi… DOI 10.1158/1078-0432.ccr-19-3313↗
- Paper miRNA Profiling of Magnetic Nanopore-Isolated Extracellular Vesicles for the Diagnosis of Pancreatic Cancer. DOI 10.1158/0008-5472.can-17-3703↗
- Paper Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes. DOI 10.1021/acsnano.7b05503↗
- Mark H. O’Hara · 2 shared papers, 2017–2020 · strength 0.15
- Charles M. Vollmer · 1 shared paper, 2020 · strength 0.09
- Zijian Yang · 1 shared paper, 2020 · strength 0.09
- Jacob Edward Till · 1 shared paper, 2020 · strength 0.09
- Bryson W. Katona · 1 shared paper, 2020 · strength 0.09
- Nicole M. Aiello · 1 shared paper, 2017 · strength 0.06
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.