Evidence map›Paper›PMID 41674977›Full record

ArticleTranslational cancer research2026

Multiplex droplet digital polymerase chain reaction for rapid diagnosing suspected bloodstream infections in patients with hematologic malignancies.

Fangyi Dong, Shishuang Wu, Xiaoqiang Fan, Ge Jiang, Ran Li, Liping Zhu, Jing Wang, Jiang Xia, Yu Zheng, Junmin Li and 2 more

Abstract read
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Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Fangyi Dong *Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID https://orcid.org/0000-0003-4647-5100
Shishuang Wu *Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID https://orcid.org/0000-0002-4419-2564
Xiaoqiang Fan *Department of Hematology, The Myeloma and Lymphoma Center, Changzheng Hospital, Naval Medical University, Shanghai, China.
Ge JiangShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ran LiShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Liping ZhuShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jing WangShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiang XiaPilot Gene Technology (Hangzhou) Co., Ltd., Hangzhou, China.
Yu ZhengShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Junmin LiShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Kai QingShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Kai XueShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients with acute leukemia are at increased risk of microbial infections due to factors such as the disease itself, intensive chemotherapy, and transplantation. Untimely or inadequate treatment can prolong therapy, raise costs, and even threaten patient survival, impacting overall cancer treatment outcomes. Traditional microbial identification relies on blood cultures (BCs), but their low positivity rate and lengthy processing time often hinder prompt diagnosis and the identification of the infecting pathogens. This study aimed to use droplet digital polymerase chain reaction (ddPCR), known for its sensitivity in single-molecule amplification, to detect pathogen DNA and drug-resistant genes in blood. Methods: We included a total of 47 patients with hematologic malignancies who were over 18 years old and had neutropenia accompanied by fever [suspected bloodstream infection (BSI)] from August 2022 to November 2022. Patients who failed resuscitation after severe shock, with severe liver or kidney dysfunction, and in the terminal stage were excluded. We conducted ddPCR testing for bacteria/fungi/viruses with the patient's blood on the first day, third day, and fifth day of the occurrences of neutropenic fever with suspected BSI. In case of positive results indicating the presence of bacteria, we used the remaining nucleic acid samples to detect drug resistance genes. Results: BC and ddPCR yielded positive results indicating the presence of bacteria in five patients (10.64%) and 14 patients (29.79%), respectively, with ddPCR demonstrating acceptable positive rate (81.44%). Regarding the breadth of detection, ddPCR identified 10 different pathogens, while only two pathogens went undetected. In contrast, BC detected only five different pathogens. In terms of the diversity of pathogens detected in single samples, among the 14 polymerase chain reaction (PCR)-positive patients, three had the presence of two different pathogens synchronously. Furthermore, ddPCR also revealed the presence of drug resistance genes. Among the 14 PCR-positive patients, four were found to have drug resistance genes, including one case of Conclusions: ddPCR is a versatile and adaptable platform that can serve as a complement to traditional BCs.

Indexed as

bloodstream infection (BSI)Clinical validationdroplet digital polymerase chain reaction (ddPCR)hematologic malignancies

Identifiers

PMID41674977
PMCPMC12885790

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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.