Oncology/Hematology

Nanozymes: The Emerging Frontier in Catalytic Nanomaterials and Their Therapeutic Applications.

Artificial nanomaterials known as nanozymes, which possess inherent enzyme-mimetic characteristics, have transformed environmental research, biomedicine, and catalysis. Compared with natural enzymes, nanozymes are advantageous due to their high stability, low cost, and adaptability. They exhibit oxidation, reduction, peroxidation, and superoxide dismutation activities, which are essential for envi...

Computational Innovations in Cancer Research and How Computing is Transforming Drug Discovery and Development: A Review.

INTRODUCTION: Cancer is a major global health concern, causing millions of deaths each year due to the uncontrolled growth and spread of abnormal cells. Despite advances in diagnosis and treatment, its complexity and therapy resistance keep it a leading cause of mortality. This review highlights the impact of CADD and related computational approaches in advancing cancer research and therapy. METHO...

Pan-cancer evolution signatures link clonal expansion to dynamic changes in the tumor immune microenvironment.

Cancer is an evolutionary process characterized by profound intratumor heterogeneity (ITH), which can be quantified using in silico estimates of cance...

PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports.

Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence (AI) models and conducting ...

Metabolic networks in the tumor microenvironment: roles of amino acid and lipid metabolism pathways in cancer progression and therapy.

Metabolic rewiring, a defining hallmark of cancer, sustains cell proliferation and biosynthesis while coordinating adaptive interactions within the tu...

Machine learning-based proteogenomic data modeling identifies circulating plasma biomarkers for early detection of lung cancer.

BACKGROUND: Genetic aberrations are among the critical driving factors of lung cancer. Importantly, the impact of genetic variations on proteomic dysr...

An explainable radiomics model based on multiparametric magnetic resonance for differentiating benign and malignant orbital tumors.

OBJECTIVE: To develop and internally test a multiparametric radiomics combined model for differentiating benign and malignant orbital tumors. PATIENTS...

Practical AI-based cell extraction and spatial statistics for large 3D bone marrow tissue images.

Although the molecular regulation of hematopoiesis is well characterized, the spatial organization of hematopoietic cells within bone marrow (BM) rema...

A Multicenter Study on Deep Learning Model-Assisted Detection of Brain Metastases in MR Images.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a deep learning-based brain metastasis detection model (BMDM) in magnetic resonance...

Artificial intelligence-based analysis of thrombotic and infectious risk factors in peripherally inserted central catheters (PICCs).

INTRODUCTION: Peripherally inserted central catheters (PICCs) are increasingly used in France for prolonged intravenous therapies such as chemotherapy...

Design of novel PI3Kα and PI3Kγ inhibitors for cancer treatment using pharmacophore, protein-ligand contacts, and machine learning methods.

Cancer is a complex disease characterised by the unregulated growth of abnormal cells. The intracellular signaling pathway, specifically phosphatidyli...

Optimizing material composition determination in dual-energy computed tomography: a comparative study of a linear model and a fully connected neural network.

Accurate elemental decomposition in dual-energy computed tomography (DECT) is crucial for precision in radiation therapy planning. We present a compar...

Improving visibility of small anatomical details on low and ultra-low dose computed tomography with artificial intelligence-based image reconstructions.

To assess how computed tomography (CT) image reconstruction techniques affect perceived diagnostic image quality at varying radiation dose levels in c...

Overview of combined adjuvant strategies for tumor biomarker detection.

Cancer is one of the leading causes of death worldwide, and early tumor detection can significantly reduce mortality rates. Liquid biopsy is a minimal...

Loop of N2-polarized neutrophils and exhausted CD8 + T cells induces immunotherapy resistance in NSCLC.

While neutrophils represent a prominent myeloid component in non-small cell lung cancer (NSCLC), the specific immunosuppressive functions of N2-polari...

Integrating Generative AI into Nomograms for Breast Cancer Nodal Risk Predictions.

BACKGROUND: Nomograms predicting the likelihood of sentinel lymph node (SLN) metastasis in early-stage breast cancer can aid surgical decision-making ...

Microrobotic copper-rich electrochemical interfacing for targeted cancer theranostics in the gut.

The exquisite spatiotemporal regulation of drug biodistribution is paramount for optimal targeted cancer theranostics. Robotic ingestible devices prom...

Functional-based multi-omics early prediction of radiation pneumonitis in NSCLC using AI-generated perfusion and ventilation from planning CT.

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and...

Correction: Development and validation of a multimodal deep learning model for early esophageal squamous neoplasia detection and invasion depth prediction.

Colonoscopy surveillance in Lynch syndrome: what it prevents and what it does not.

Lynch syndrome (LS), synonymous with hereditary non-polyposis colorectal cancer (HNPCC), is caused by germline pathogenic variants in MLH1, MSH2, MSH6...