AIMC Topic: Neoplasms

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Decellularized tumor matrices as biomimetic cancer niche: a new perspective on cancer research and therapy.

Biomedical materials (Bristol, England)
Cancer is among the major causes of mortality, responsible for approximately 15% of all deaths worldwide. Despite remarkable progress in modern medicine, it remains a significant global health challenge. Nevertheless, conventional therapies such as c...

Sentiment analysis of cancer screening in Chinese social media: Qualitative studies based on machine learning.

PloS one
PURPOSE: Explore public perceptions and sentiments about cancer screening on social media. The dissemination of misinformation and negative attitudes continue to impede the access of many individuals with perceived risk to cancer screening services d...

Multi-modal single-cell platform for nanoparticle-enhanced time-series metabolic profiles of CD8 T cell exhaustion in tumor immunosurveillance.

Journal of nanobiotechnology
Cytotoxic T cells (CD8) play a pivotal role in immunosurveillance by identifying and eliminating tumor cells. However, the onset of CD8 T cell exhaustion, characterized by overexpression of immune checkpoint receptors, impairs their function, allowin...

Deep learning-guided rational engineering of synergistic PD-1 and LAG-3 blockade for enhanced tumor immunomodulation.

Journal of computer-aided molecular design
Evolution has optimized proteins over time by the incorporation of precise and context-specific amino acid substitutions adapted to structural and functional demands. We have reconceptualized this principle using deep learning to engineer monoclonal ...

A bibliometric study on mathematical oncology: interdisciplinarity, internationality, collaboration and trending topics.

Bulletin of mathematical biology
Mathematical oncology is an interdisciplinary research field where the mathematical sciences meet cancer research. Being situated at the intersection of these two fields makes mathematical oncology highly dynamic, as practicing researchers are incent...

Functional biomaterials and machine learning approaches for phenotyping heterogeneous tumor cells and extracellular vesicles.

Biomaterials science
Heterogeneity in cancer is known to be a contributor to the formation of metastatic lesions, poor prognosis, and ultimately undermines therapeutic efficacy. This same tumor heterogeneity is reflected in circulating tumor cells (CTCs) and tumor derive...

Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.

BMC cancer
PURPOSE: Objectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) s...

ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling.

Nature communications
Accurate detection of somatic variants in tumors is of critical importance and remains challenging. Current methods typically require matched normal samples for reliable detection, which are often unavailable in real-world research and clinical scena...

Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.

RNA biology
Cancer diagnosis at an early stage is crucial for improving overall health outcomes. However, existing cancer diagnostic techniques are mostly invasive and tend to identify the disease only in its advanced stages. MicroRNAs (miRNAs), which are small ...

Artificial intelligence in cancer: applications, challenges, and future perspectives.

Molecular cancer
Artificial intelligence (AI) is rapidly revolutionizing the landscape of oncological research and the advancement of personalized clinical interventions. Progress in three interconnected areas, including the development of methods and algorithms for ...