AIMC Topic: Neoplastic Stem Cells

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Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks.

Biomolecules
Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer...

Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma.

CNS oncology
Glioblastoma is a heterogeneous lethal disease, regulated by a stem-cell hierarchy and the neurotransmitter microenvironment. The identification of chemotherapies targeting individual cancer stem cells is a clinical need. A robotic workstation was ...

Early Prediction of Single-Cell Derived Sphere Formation Rate Using Convolutional Neural Network Image Analysis.

Analytical chemistry
Functional identification of cancer stem-like cells (CSCs) is an established method to identify and study this cancer subpopulation critical for cancer progression and metastasis. The method is based on the unique capability of single CSCs to survive...

A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS).

Nature communications
Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be supe...

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.

Cancer gene therapy
Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patient...

An ontology for representing hematologic malignancies: the cancer cell ontology.

BMC bioinformatics
BACKGROUND: Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell...

Explaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems.

Seminars in cancer biology
Facing metastasis is the most pressing challenge of cancer research. In this review, we discuss recent advances in understanding phenotypic plasticity of cancer cells, highlighting the kinetics of cancer stem cell and the role of the epithelial mesen...

Salinomycin-loaded lipid-polymer nanoparticles with anti-CD20 aptamers selectively suppress human CD20+ melanoma stem cells.

Acta pharmacologica Sinica
Melanoma is the deadliest type of skin cancer. CD20+ melanoma stem cells (CSCs) are pivotal for metastasis and initiation of melanoma. Therefore, selective elimination of CD20+ melanoma CSCs represents an effective treatment to eradicate melanoma. Sa...

Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.

Medicine
Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cancer (CRC). However, the mechanisms that sustain CSCs in CRC remain unclear. This study aimed to identi...

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation.

Cancer immunology, immunotherapy : CII
BACKGROUND: Chaperonin containing TCP1 subunit 5 (CCT5), a vital component of the molecular chaperonin complex, has been implicated in tumorigenesis, cancer stemness maintenance, and therapeutic resistance. Nevertheless, its comprehensive roles in pa...