Leukemia

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors.

Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are searching for new allosteric inhibitors as orthosteric inhibitors are non-selective and exert off-target effects. In the current study, we proposed an int...

Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.

Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react...

Prediction of interactions between cell surface proteins by machine learning.

Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins for...

Diagnostic accuracy of radiomics-based machine learning for neoadjuvant chemotherapy response and survival prediction in gastric cancer patients: A systematic review and meta-analysis.

BACKGROUND: In recent years, researchers have explored the use of radiomics to predict neoadjuvant chemotherapy outcomes in gastric cancer (GC). Yet, ...

Listen to the patients! Identifying CML patients' needs analyzing patient-generated content with AI-driven methodologies.

BACKGROUND: Various patient support programs exist to provide successful therapy options for patients. Pharmaceutical companies are increasingly recog...

Phosphorylated p38 MAP kinase expression by leucocytes is increased in allergic humans and associated with IgE responses.

Mitogen-activated protein kinases (MAPK) activate cascades that regulate cell proliferation, differentiation and death. Phosphorylated (phos-)p38 MAPK...

Scaffold hopping based designing of selective ALDH1A1 inhibitors to overcome cyclophosphamide resistance: synthesis and biological evaluation.

Aldehyde dehydrogenase 1A1 (ALDH1A1) is an isoenzyme that catalyzes the conversion of aldehydes to acids. However, the overexpression of ALDH1A1 in a ...

Artificial intelligence in breast imaging: potentials and challenges.

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, i...

Broadening the horizon: potential applications of CAR-T cells beyond current indications.

Engineering immune cells to treat hematological malignancies has been a major focus of research since the first resounding successes of CAR-T-cell the...

Identification of new potential candidates to inhibit EGF via machine learning algorithm.

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computati...

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where gr...

Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations.

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for...

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology.

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, s...

A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors.

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding...

Cytotoxic activity of ethyl acetate extract of on MCF7 and T47D breast cancer cells.

One of the most cancers in women that can be fatal is breast cancer. Radiation therapy, chemotherapy, or a combination of the two are often used to tr...

Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers...

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.

BACKGROUND: Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients res...

Accurate classification of major brain cell types using in vivo imaging and neural network processing.

Comprehensive analysis of tissue cell type composition using microscopic techniques has primarily been confined to ex vivo approaches. Here, we introd...

High-Content Image-Based Screening and Deep Learning for the Detection of Anti-Inflammatory Drug Leads.

We developed a high-content image-based screen that utilizes the pro-inflammatory stimulus lipopolysaccharide (LPS) and murine macrophages (RAW264.7) ...

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) im...