ArticleACS biomaterials science & engineering2025
A Deep Learning Approach for Tracking Colorectal Cancer-Derived Extracellular Vesicles in Colon and Lung Models.
Article in ACS biomaterials science & engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Decoding Glioblastoma Complexity Through Extracellular Vesicles, Organ-on-Chip Models, and Deep Learning.Cells · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the International Agency for Research on Cancer and the World Health Organization, colorectal cancer (CRC) is the third most common cancer in the world and the main cause of gastrointestinal cancer-related deaths. Despite advances in therapeutic regimens, the incidence of metastatic CRC is increasing due to the development of resistance to conventional treatments. Metastases, particularly in the liver and lungs, represent the leading cause of death and poor prognosis in CRC patients. Recent evidence demonstrates that extracellular vesicles (EVs) are involved in communication between cancer cells and the surrounding environment. Understanding the potential mechanisms underlying EV-driven metastasis and tumor progression could facilitate the development of innovative strategies for early diagnosis and effective treatment of CRC metastasis. In this work, we developed a deep learning-based approach to track CRC-derived EVs in colon and lung models, enabling precise quantification of their uptake and trafficking
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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 Socratic graph.