Evidence map›Paper›PMID 38854127›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Ultra-Rapid Droplet Digital PCR Enables Intraoperative Tumor Quantification.

Zachary R Murphy, Emilia C Bianchini, Andrew Smith, Lisa I Körner, Teresa Russell, David Reinecke, Yuxiu Wang, Matija Snuderl, Daniel A Orringer, Gilad D Evrony

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Zachary R MurphyCenter for Human Genetics and Genomics, New York University Grossman School of Medicine, USA.
Emilia C BianchiniCenter for Human Genetics and Genomics, New York University Grossman School of Medicine, USA.
Andrew SmithDepartment of Neurosurgery, New York University Grossman School of Medicine, USA.
Lisa I KörnerDepartment of Neurosurgery, New York University Grossman School of Medicine, USA.
Teresa RussellDepartment of Neurosurgery, New York University Grossman School of Medicine, USA.
David ReineckeDepartment of Neurosurgery, New York University Grossman School of Medicine, USA.
Yuxiu WangDepartment of Pathology, New York University Grossman School of Medicine, USA.
Matija SnuderlDepartment of Pathology, New York University Grossman School of Medicine, USA.
Daniel A OrringerDepartment of Neurosurgery, New York University Grossman School of Medicine, USA.
Gilad D EvronyCenter for Human Genetics and Genomics, New York University Grossman School of Medicine, USA.

Funding

Clinical Translation of Stimulated Raman HistologyR01CA226527 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ORRINGER, DANIEL · 2018 to 2025
$4.4M
NCI NIH HHS R01 CA226527
6 · The paper itself

Abstract

The diagnosis and treatment of tumors often depends on molecular-genetic data. However, rapid and iterative access to molecular data is not currently feasible during surgery, complicating intraoperative diagnosis and precluding measurement of tumor cell burdens at surgical margins to guide resections. To address this gap, we developed Ultra-Rapid droplet digital PCR (UR-ddPCR), which can be completed in 15 minutes from tissue to result with an accuracy comparable to standard ddPCR. We demonstrate UR-ddPCR assays for the IDH1 R132H and BRAF V600E clonal mutations that are present in many low-grade gliomas and melanomas, respectively. We illustrate the clinical feasibility of UR-ddPCR by performing it intraoperatively for 13 glioma cases. We further combine UR-ddPCR measurements with UR-stimulated Raman histology intraoperatively to estimate tumor cell densities in addition to tumor cell percentages. We anticipate that UR-ddPCR, along with future refinements in assay instrumentation, will enable novel point-of-care diagnostics and the development of molecularly-guided surgeries that improve clinical outcomes.

Identifiers

PMID38854127
PMCPMC11160868

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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.