Lymphoma

Comparative analysis and investigation of ultrasonication on juice yield and bioactive compounds of kinnow fruit using RSM and ANN models.

Ultrasonication (US) is a promising non-thermal technique widely applied in the food sector for improving the extraction process and preserving nutrients. Kinnow fruits have yields of 40-60% juice; the rest of the parts are discarded as waste. The study explored ultrasonication (US) as a pre-treatment to improve juice expression and preserve heat-sensitive nutrients such as vitamin C. The optimiza...

Risk assessment of potentially toxic elements and mapping of groundwater pollution indices using soft computer models in an agricultural area, Northeast Algeria.

Groundwater (GW) quality and contamination by potentially toxic elements (PTEs) are major concerns for environmental sustainability, particularly in arid regions. The aim of this study was to assess the human health risks associated with GW contamination by PTEs in the Terminal Complex (TC) aquifer of the Tolga oasis, located in northeastern Algeria. Seventeen GW samples were analyzed using standa...

Multitask learning model for predicting non-coding RNA-disease associations: Incorporating local and global context.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial non-coding RNAs involved in various diseases. Understanding these interactions is vi...

Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.

Detection of Alzheimer's Disease (AD) is critical for successful diagnosis and treatment, involving the common practice of screening for Mild Cognitiv...

Global Cross-Entropy Loss for Deep Face Recognition.

Contemporary deep face recognition techniques predominantly utilize the Softmax loss function, designed based on the similarities between sample featu...

Validity of recurrent neural networks to predict pedal forces and lower limb kinetics in cycling.

Dynamic variables contribute to understand the mechanics of pedalling and can assist with injury prevention. Measuring pedal forces and joint moments ...

Impact of [F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role.

Lung cancer remains one of the most prevalent cancers globally and the leading cause of cancer-related deaths, accounting for nearly one-fifth of all ...

Developing a real-time water quality simulation toolbox using machine learning and application programming interface.

Rivers are vital for sustaining human life as they foster social development, provide drinking water, maintain aquatic ecosystems, and offer recreatio...

[MP-MRI in the evaluation of non-operative treatment response, for residual and recurrent tumor detection in head and neck cancer].

As non-surgical therapies gain acceptance in head and neck tumors, the importance of imaging has increased. New therapeutic methods (in radiation ther...

TTGA U-Net: Two-stage two-stream graph attention U-Net for hepatic vessel connectivity enhancement.

Accurate segmentation of hepatic vessels is pivotal for guiding preoperative planning in ablation surgery utilizing CT images. While non-contrast CT i...

Plasma Cytokine and Chemokine Profiles Predict Efficacy and Toxicity of Anti-CD19 CAR-T Cell Therapy in Large B-Cell Lymphoma.

BACKGROUND: Anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising treatment for large B-cell lymphoma (LBCL); however,...

Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.

AIM: To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image quality of non-ToF PET images for different tracers, ...

Assessment of AI-based computational H&E staining versus chemical H&E staining for primary diagnosis in lymphomas: a brief interim report.

Microscopic review of tissue sections is of foundational importance in pathology, yet the traditional chemistry-based histology laboratory methods are...

A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors.

Interest in HDAC3 inhibitors (HDAC3i) for pharmacological applications outside of cancer is growing. However, concerns regarding the possible mutageni...

Artificial Intelligence in Lymphoma Histopathology: Systematic Review.

BACKGROUND: Artificial intelligence (AI) shows considerable promise in the areas of lymphoma diagnosis, prognosis, and gene prediction. However, a com...

Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.

Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a cha...

Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning.

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management, yet existing methods are often context-specific...

Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.

This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC...

Importance of dataset design in developing robust U-Net models for label-free cell morphology evaluation.

Advances in regenerative medicine highlighted the need for label-free cell image analysis to replace conventional microscopic observation for non-inva...

Towards autonomous robotic THz-based in vivo skin sensing: the PicoBot.

Terahertz (THz) light has the unique properties of being very sensitive to water, non-ionizing, and having sub-millimeter depth resolution, making it ...