Leukemia

Robotic high-throughput biomanufacturing and functional differentiation of human pluripotent stem cells.

Efficient translation of human induced pluripotent stem cells (hiPSCs) requires scalable cell manufacturing strategies for optimal self-renewal and functional differentiation. Traditional manual cell culture is variable and labor intensive, posing challenges for high-throughput applications. Here, we established a robotic platform and automated all essential steps of hiPSC culture and differentiat...

IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.

The parasite species of genus causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinic...

IoT Application of Transfer Learning in Hybrid Artificial Intelligence Systems for Acute Lymphoblastic Leukemia Classification.

Acute lymphoblastic leukemia is the most common cancer in children, and its diagnosis mainly includes microscopic blood tests of the bone marrow. Ther...

Optimizing Lipofectamine LTX Complex and G-418 Concentration for Improvement of Transfection Efficiency in Human Mesenchymal Stem Cells.

Conventional cancer therapies, including surgery, radiotherapy, and chemotherapy, are not tumor site-specific and have cytotoxic and harmful side effe...

Design and Characterization of Liposomal Methotrexate and Its Effect on BT-474 Breast Cancer Cell Line.

Breast cancer is the most common type of cancer among women worldwide. Traditional treatments, including chemotherapy, surgery, mastectomy, and radio...

Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma.

BACKGROUND: Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the pre...

Genetic diversity in nutritional composition of oat ( L.) germplasm reported from Pakistan.

In the present study, 30 potential germplasm of oat ( L.) were subjected to proximate, elemental, and HPLC analysis to provide a scientific basis to g...

Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke.

Background Conventional prognostic scores usually require predefined clinical variables to predict outcome. The advancement of natural language proces...

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.

To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute t...

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an ima...

Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy.

Cardiotoxicity is a severe side effect for colorectal cancer (CRC) patients undergoing fluoropyrimidine-based chemotherapy. To develop and compare mac...

Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning.

We define cell morphodynamics as the cell's time dependent morphology. It could be called the cell's shape shifting ability. To measure it we use a bi...

WBC-based segmentation and classification on microscopic images: a minor improvement.

Introduction White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBC...

How to predict relapse in leukemia using time series data: A comparative in silico study.

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements ...

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods.

BACKGROUND: Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtyp...

A deep learning method for counting white blood cells in bone marrow images.

BACKGROUND: Differentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and...

Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach.

Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, in...

Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers.

OBJECTIVES: To develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in...

Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.

Artificial intelligence-based tools designed to assist in the diagnosis of lymphoid neoplasms remain limited. The development of such tools can add va...

Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study.

OBJECTIVES: Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatmen...