Unlocking the Potential of Nutraceuticals in Cancer Chemotherapy: A Comprehensive Review.

Cancer remains a leading global cause of morbidity and mortality. While conventional therapies such as chemotherapy and radiation are essential, growing evidence supports the adjunctive use of nutraceuticals-bioactive compounds from natural food sources-with anticancer potential. To synthesize current evidence on the mechanisms, therapeutic roles, and clinical challenges of nutraceuticals in cancer chemotherapy.

Comparative effectiveness of sodium-glucose cotransporter 2 inhibitors along with colchicine versus colchicine alone among coronary artery disease and type 2 diabetes patients.

Background: Colchicine, an anti-inflammatory agent, has been demonstrated to reduce adverse cardiovascular events in coronary artery disease (CAD) patients. Sodium-Glucose Cotransporter 2 Inhibitors (SGLT2i) provide cardiovascular prevention beyond glycemic control including anti-inflammatory effect, and reduced heart failure risk. Given their complementary mechanisms, combining colchicine and SGLT2i may offer synergistic benefits in CAD patients with T2DM, yet the incremental effects of this combination remain underexplored.

Efficacy and safety of cetuximab-based versus platinum-based chemoradiation in HNSCC: evidence from a meta-analysis of 10 randomized controlled trials.

Background: Platinum-based chemoradiotherapy (CTRT) is the standard treatment for head and neck squamous cell carcinoma (HNSCC). While cetuximab-based radiotherapy (CxRT) has been proposed as an alternative, its efficacy remains controversial. Multiple meta-analyses have compared CxRT with CTRT for HNSCC though they combined randomized controlled trials (RCTs) with lower-evidence studies, compromising result validity.

Inductive and Transfer Learning-Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases.

Purpose: This study presents NeuroDL, a novel deep learning-based diagnostic framework designed for the automated detection of brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI). The objective is to enhance diagnostic precision and efficiency in neurology through advanced computer-aided decision support.

Methods: NeuroDL utilizes convolutional neural networks (CNNs) trained on two publicly available, annotated MRI datasets.

Enhancing leaf disease classification using GAT-GCN hybrid model.

Agriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. Progress in agricultural techniques has helped boost crop yield, along with a growing need for precise disease monitoring solutions. This requires accurate, efficient, and timely disease detection methods.