Physicochemical Characterization of Moroccan Honey Varieties from the Fez-Meknes Region and Their Antioxidant and Antibacterial Properties.

Honey, with its varied and extensive characteristics, is a complex and diverse biological substance that has been used since ancient times. The aim of this study is to thoroughly characterize the physicochemical, phytochemical, and biological properties of four floral honey varieties from the Fez-Meknes region in Morocco, with the goal of promoting the valorization of Moroccan honey in skincare and cosmetic products. The analyses of their physicochemical characteristics encompass various parameters such as pH, acidity, density, water content, Brix index, conductivity, ash content, hydroxymethylfurfural (HMF) content, and color.

A Study of the Synergistic Effects of Essential Oils from and with Commercial Antibiotics against Highly Prioritized Multidrug-Resistant Bacteria for the World Health Organization.

The irrational use of antibiotics has favored the emergence of resistant bacteria, posing a serious threat to global health. To counteract antibiotic resistance, this research seeks to identify novel antimicrobials derived from essential oils that operate through several mechanisms. It aims to evaluate the quality and composition of essential oils from and ; test their antimicrobial activity against various strains; explore their synergies with commercial antibiotics; predict the efficacy, toxicity, and stability of compounds; and understand their molecular interactions through docking and dynamic simulations.

Machine Learning for Diagnosis and Screening of Chronic Lymphocytic Leukemia Using Routine Complete Blood Count (CBC) Results.

The comprehensive epidemiology and global disease burdens reported recently suggest that chronic lymphocytic leukemia (CLL) constitutes 25-30% of leukemias thus being the most common leukemia subtype. However, there is an insufficient presence of artificial intelligence (AI)-based techniques for CLL diagnosis. The novelty of this study is in the investigation of data-driven techniques to leverage the intricate CLL-related immune dysfunctions reflected in routine complete blood count (CBC) alone.

An Artificial Intelligence-Based Diagnostic System for Acute Lymphoblastic Leukemia Detection.

This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-specific parameters using a feature selection approach. Next, Grid Search-based hyperparameter tuning with a five-fold cross-validation scheme was adopted to build classifiers using Random Forest, XGBoost, and Decision Tree algorithms.