A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities.

The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection.

Bone and muscle mass characteristics in patients with gastroenteropancreatic neuroendocrine neoplasms.

Background: Neuroendocrine neoplasms (NEN) are rare tumors arising from neuroendocrine cells most commonly in the gastrointestinal-tract. In recent years, advancements in therapeutics have increased survival rates in patients with NEN leading to a greater clinical burden compared to the general population.

Methods: The aim of this single-center case-control study was to investigate the incidence of low bone mass and changes in body composition in adult patients diagnosed with gastroenteropancreatic neuroendocrine tumors (GEPNET).

Evaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities.

Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection.

Preliminary Study on Lutetium-177 and Gold Nanoparticles: Apoptosis and Radiation Enhancement in Hepatic Cancer Cell Line.

This study investigates a novel approach toward enhancing the efficacy of Lutetium-177 (Lu-177) radiopharmaceutical therapy by combining it with gold nanoparticles (AuNPs) in the HepG2 hepatic cancer cell line. Lu-177, known for its effective β radiation, also emits gamma rays at energies (113 keV and 208 keV) near the photoelectric absorption range, suggesting potential for targeted and localized radiation enhancement when used in conjunction with AuNPs. Thus, HepG2 cells were treated at two different activity levels (74 MBq and 148 MBq), with Lu-177 alone, with a combination of Lu-177 and AuNPs in two sizes (10 nm and 50 nm), while some received no treatment.

Chemotherapeutic Drug Delivery Nanoplatform Development: From Physicochemical to Preclinical Evaluation.

Through this study, the synergistic behavior of small-molecular-weight, amphiphilic surfactant molecules and the triblock copolymer Pluronic 188 was extensively evaluated based on their ability to formulate nanocarriers with novel properties for the delivery of class II and IV (biopharmaceutical classification system) chemotherapeutic compounds. The combination of four different surfactants at multiple weight ratios and twelve initially formulated nanosystems resulted in four hybrid delivery platforms, which were further studied in terms of multiple physicochemical characteristics, as well as their stability in protein-rich media (fetal bovine serum/phosphate-buffer saline). Finally, we obtained a single final nanoformulation that exhibited a high loading capacity (%EE ≥ 75%) and a sustained drug release profile under physiological conditions (model drug methotrexate), without altering the original physicochemical characteristics of the carrier.