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Papers
61,005 resultsShowing papers similar to Identification of functional immune and neuronal tumour cells in glioma
ClearMapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging
Researchers used advanced single-cell techniques to map the different cell types and their spatial arrangement within human bone marrow. The study identified nine distinct non-blood-cell subtypes and revealed how they are organized in specific neighborhoods, providing new insights into how the bone marrow microenvironment supports blood cell production.
Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data
This paper presents a new computational method for analyzing single-cell genomic data by clustering cells based on both their gene expression and chromatin accessibility patterns. The technique uses a skip aggregation network to better integrate information from multiple data types. While not related to microplastics, this type of advanced analytical tool could potentially be applied to study how microplastic exposure affects gene expression at the single-cell level in human tissues.
Identification and validation of novel signature associated with hepatocellular carcinoma prognosis using Single-cell and WGCNA analysis
This study identified a novel gene signature associated with hepatocellular carcinoma using TCGA datasets and validated key molecular targets with potential prognostic and therapeutic significance. The findings advance understanding of the molecular mechanisms driving liver cancer progression.
Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare
Algorithmic analysis of patient-derived cell clusters from liquid biopsy samples was combined with tumor models to develop an early disease prediction tool applicable across cancer types. The approach offers a label-free, non-invasive method for early cancer detection that could supplement or reduce reliance on conventional tissue biopsy.
Multidimensional analysis methods for flow cytometry : Pushing the boundaries
This thesis developed new methods for analyzing multidimensional flow cytometry data to better identify cell populations. While a bioinformatics and immunology paper, flow cytometry is also used in cutting-edge research to detect and quantify micro- and nanoplastics in biological fluids.
Novel application of microparticles demonstrate myeloid uptake and induce phenotypic change within the brain tumor microenvironment 2254
Researchers demonstrated that microparticles can be taken up by myeloid cells within glioblastoma tumors and can induce phenotypic changes in tumor-associated macrophages and neutrophils, suggesting that particle-based approaches may be able to modify the immunosuppressive tumor microenvironment.
The nervous system is the major target for Gold nanoparticles: Evidence from RNA sequencing data of C. elegans
This review examines research suggesting that the nervous system is a primary target for gold nanoparticle toxicity, with evidence from animal models showing that these particles cross the blood-brain barrier and accumulate in neural tissue. While focused on gold nanoparticles, the findings are relevant to understanding how nanoplastics may also affect the brain.
AI-Enhanced Patient-Derived Cancer Organoids: Integrating Machine Learning for Precision Oncology
This review explores how combining patient-derived cancer organoids with artificial intelligence enables more precise drug sensitivity predictions and biomarker discovery in oncology research. While not directly related to microplastic research, the study demonstrates how AI and advanced biological models can be integrated to analyze complex datasets. The approaches described may inform future methods for studying how environmental contaminants interact with human tissues.
Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms
Researchers developed machine learning and deep learning frameworks applied directly to raw digital hologram images to classify neuroblastoma cell phenotypes without time-consuming phase retrieval, achieving accurate label-free single-cell analysis.
Morphological profiling by high-throughput single-cell biophysical fractometry
Researchers developed a high-speed imaging technique called single-cell biophysical fractometry that measures the complex, irregular geometry of individual cells at a rate of about 10,000 cells per second. This tool can detect subtle structural differences between cancer cell subtypes and track how cells respond to drugs, offering a more detailed picture of cell health than standard methods.
A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning
Researchers built a microfluidic device — a chip with tiny channels — that measures how easily cells deform as they squeeze through narrow passages, using deep learning to classify cancer cells by how aggressively they spread. The system achieved 92.4% accuracy in distinguishing cancer invasiveness and could tell cancer cells apart from normal immune cells with 89.5% accuracy, pointing toward faster clinical tools for diagnosing cancer stage.
Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry
Researchers developed a label-free method using holographic flow cytometry and artificial intelligence to identify and classify neuroblastoma cancer cells without the need for traditional staining. The approach analyzes cell shape and structure to distinguish between different cancer cell subtypes. While not directly related to microplastics, the technique advances rapid screening capabilities for bioparticle analysis in fluid samples.
Single-cell transcriptomic analysis of mouse liver reveals nonparenchymal cells’ intricate responses to PCB126 exposure
Using single-cell RNA sequencing, researchers found that PCB126 exposure triggers cell-type-specific responses in mouse liver, activating the AhR signaling pathway mainly in endothelial cells while altering immune cell transcriptional profiles, revealing previously hidden heterogeneity in PCB toxicity.
DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species
This paper describes a new database for analyzing single-cell gene expression data across different animal species including fruit flies. This genomics database tool is unrelated to microplastic research.
Learning Single‐Cell Distances from Cytometry Data
This computational paper developed machine learning methods to measure distances between individual cells in flow cytometry data. While a bioinformatics paper, the methodology is potentially applicable to automated classification of microplastic particles in environmental samples.
Tumour-associated macrophages: versatile players in the tumour microenvironment
This review explores tumour-associated macrophages, immune cells that play complex and sometimes contradictory roles in cancer, both helping tumours grow and fighting them. Researchers describe newly discovered behaviors of these cells, including their ability to transform into other cell types. The study highlights the potential of targeting these macrophages as a strategy in cancer immunotherapy.
ASGR2 and CLEC12A as Prognostically Relevant C-Type Lectin Hubs in Glioblastoma
Scientists found two proteins called ASGR2 and CLEC12A that help aggressive brain tumors called glioblastoma shut down the body's immune system, making them harder to treat. These proteins work with immune cells that actually protect the tumor instead of fighting it, and patients with higher levels of these proteins tend to have worse outcomes. This discovery could lead to new treatments that target these proteins to help the immune system better fight brain cancer.
4 Single cell RNA-seq samples exposed to nano plastic particles
Researchers used microfluidic chip-based single-cell RNA sequencing to profile the transcriptional responses of human peripheral blood immune cells exposed to carboxylated polystyrene nanoparticles of three sizes (40 nm, 200 nm, or a mixture), providing a cell-type-resolved view of nanoplastic effects on the immune system.
Identification of extracellular vesicles from their Raman spectra via self-supervised learning
Researchers developed a deep learning method to identify and classify tiny biological particles called extracellular vesicles — which cells release and which may signal disease — using Raman spectroscopy without any chemical labels. The model achieved over 92% accuracy in distinguishing vesicles from different biological sources, including cancer patients versus healthy controls.
Correlation Between Tumor Differentiation and Biomarkers in Hepatocellular Carcinoma: Implications for Early Diagnosis and Treatment
Researchers examined the correlation between tumor differentiation levels and biomarker expression in patients with hepatocellular carcinoma. The study found significant associations between tumor grade and certain biomarker levels, suggesting these markers may have potential value for early diagnosis and treatment planning in liver cancer.
Quantifying the influence of micro and nanoplastics characteristics on cytotoxicity in caco-2 cells through machine learning modelling.
This systematic review uses machine learning to identify which characteristics of micro and nanoplastics are most toxic to intestinal cells. The researchers found that particle size, shape, and concentration all play important roles in how much damage these plastics cause to gut lining cells, helping us understand how ingested microplastics might affect digestive health.
Food nutrition and toxicology targeting on specific organs in the era ofsingle-cell sequencing
This review examines how single-cell sequencing technologies can reveal organ-specific effects of food nutrients and toxicants, including contaminants like microplastics, by uncovering cellular heterogeneity and tissue-biased responses that traditional methods miss.
Quantifying the influence of micro and nanoplastics characteristics on cytotoxicity in caco-2 cells through machine learning modelling.
This systematic review uses machine learning to determine which properties of micro and nanoplastics drive toxicity in human intestinal cell models. The findings reveal that smaller particles and higher concentrations cause more cell damage, which is important for understanding how the microplastics we swallow in food and water might harm our gut lining.
Glioblastoma-derived migrasomes promote migration and invasion by releasing PAK4 and LAMA4
Researchers discovered that glioblastoma brain tumor cells produce structures called migrasomes that release proteins into surrounding tissue, helping the cancer spread more aggressively. Blocking formation of these migrasomes significantly reduced tumor cell migration and invasion, identifying new potential targets for treating this deadly brain cancer.