ArticleAmerican journal of translational research2018
Mouse dendritic cell migration in abdominal lymph nodes by intraperitoneal administration.
Article in American journal of translational research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Magnetic Particle Imaging Is a Sensitive In Vivo Imaging Modality for the Detection of Dendritic Cell Migration.Molecular imaging and biology · 2022Article
- A Perspective on Cell Tracking with Magnetic Particle Imaging.Tomography (Ann Arbor, Mich.) · 2020Article
- Fluorine-19 Cellular MRI Detection of In Vivo Dendritic Cell Migration and Subsequent Induction of Tumor Antigen-Specific Immunotherapeutic Response.Molecular imaging and biology · 2020Article
- Effect of route of administration on the efficacy of dendritic cell vaccine in PDAC mice.American journal of cancer research · 2020Article
- Article
- Dendritic cell immunotherapy induces anti-tumor effect in a transgenic mouse model of pancreatic ductal adenocarcinoma.American journal of cancer research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 2 institutions in 2 countries.
Funding
Abstract
Dendritic cell (DC) based immunotherapy is a promising approach for cancer treatment and has been approved in clinical settings for decades. Clinical trials have demonstrated relatively poor therapeutic efficacy. The efficacy of DC immunotherapy is strongly influenced by their ability to migrate to the draining lymph nodes (LNs). Therefore, it is critical to deliver DCs and monitor the in vivo biodistributions of DCs after administration. The purpose of this study is to determine whether a novel injection route of DCs improves DC migration to LNs, tissues, organs and lymphatics. In the present study, a modified method was investigated to acquire DCs from mouse bone marrow. Cultured antibody labeled DCs were analyzed by flow cytometry. India ink was used to visualize mouse abdominal LNs and PKH26 was utilized to label DCs for intraperitoneal (IP) injection, results were evaluated by histology. Our results showed that large amounts of DCs with a relatively high purity were acquired. IP injection of india ink marked the abdominal LNs and PKH26 labeled DCs showed IP was an effective administration route to increase the absorption of viable DCs, and different time points after IP inject showed no significant difference of the migrated DCs. The findings indicated that large amounts of high purity DCs can be acquired through our method and IP injection accelerates DCs migration to abdominal LNs, which can be directly translated to clinical settings, especially for abdominal cancers. This study makes a foundation for future researches of DC-based immunotherapy as a treatment modality against cancer.
Indexed as
Identifiers
30323872PMC6176238W2916111879What OpenQuestion holds
Registered trials
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.