In Vitro and In Vivo Evaluation of Alectinib-Loaded Dendrimer Nanoparticles as a Drug Delivery System for Non-Small Cell Lung Carcinoma.

: Alectinib, a second-generation tyrosine kinase inhibitor indicated for the treatment of non-small-cell lung cancer (NSCLC), exhibits suboptimal oral bioavailability, primarily attributable to its inherently low aqueous solubility and limited dissolution kinetics. This study aimed to enhance Alectinib's solubility and therapeutic efficacy by formulating a G4-NH2-PAMAM dendrimer complex. : The complex was prepared using the organic solvent evaporation method and characterized by DSC, FTIR, dynamic light scattering (DLS), and zeta potential measurements.

Emerging Approaches in Data-Driven Drug Discovery for Rare Diseases.

Rare diseases present unique challenges in drug discovery and development, primarily due to small patient populations, limited clinical data, and significant variability in disease mechanisms. The primary objective of this review is to examine the integration of pharmacokinetics (PK) and drug metabolism data into data-driven drug discovery approaches, particularly in the context of rare diseases. By incorporating advanced computational techniques such as Machine Learning (ML) and Artificial Intelligence (AI), researchers can better predict PK parameters, optimize drug candidates, and identify personalized therapeutic strategies.

Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.

Cardiovascular diseases (CVDs) continue to drive global mortality rates, underscoring an urgent need for advancements in healthcare solutions. The development of point-of-care (POC) devices that provide rapid diagnostic services near patients has garnered substantial attention, especially as traditional healthcare systems face challenges such as delayed diagnoses, inadequate care, and rising medical costs. The advancement of machine learning techniques has sparked considerable interest in medical research and engineering, offering ways to enhance diagnostic accuracy and relevance.

Deep learning-driven insights into the transmission dynamics of hepatitis B virus with treatment.

Viral infections have spread globally, profoundly affecting social and economic aspects of life and causing widespread suffering. Infection caused by the hepatitis B virus (HBV) is one of the significant global health challenges but can be effectively controlled with appropriate treatment and vaccination. In this study, we present a fractional modeling approach and novel computational technique to analyze the impact of treatment on HBV transmission dynamics using the Caputo derivative.

Biomarkers in diabetic nephropathy: A comprehensive review of their role in early detection and disease progression monitoring.

Diabetic nephropathy (DN) is a primary contributor to end-stage renal disease (ESRD), arising from intricate pathways that include glomerular hypertension, inflammation, and oxidative stress. Conventional indicators like albuminuria and serum creatinine frequently identify renal impairment only at advanced stages, constraining early intervention. This thorough review assesses both established and novel biomarkers, including those signalling tubular injury (e.

Human factors frameworks in analysis of contributory factors to medication error: A systematic review.

Background: Medication safety remains a significant concern within healthcare systems globally. Identifying the factors that contribute to medication errors is essential for enhancing patient safety. Human factor (HF) frameworks address this by providing a comprehensive and systematic methodology for analysing these factors.

A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets.

This study explores the performance of deep learning models, specifically Convolutional Neural Networks (CNN) and XGBoost, in predicting alpha and beta thalassemia using both public and private datasets. Thalassemia is a genetic disorder that impairs hemoglobin production, leading to anemia and other health complications. Early diagnosis is essential for effective management and prevention of severe health issues.

Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.

Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electroencephalogram (EEG)-based detection systems. Due to the high-dimensional nature of EEG signals and the subtle temporal patterns of drowsiness, there is increasing recognition of the need for deep neural networks (DNNs) to capture the dynamics of drowsy driving better.

Employing artificial bee and ant colony optimization in machine learning techniques as a cognitive neuroscience tool.

Higher education is essential because it exposes students to a variety of areas. The academic performance of IT students is crucial and might fail if it isn't documented to identify the features influencing them, as well as their strengths and shortcomings. The student academic prediction system needs to be enhanced so that teachers can forecast their students' performance.

Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm.

Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction. The accuracy of these systems is of utmost concern and depends on effective feature selection, which directly impacts their ability to accurately detect facial expressions across various poses. This research proposes a new hybrid approach called QIFABC (Hybrid Quantum-Inspired Firefly and Artificial Bee Colony Algorithm), which combines the Quantum-Inspired Firefly Algorithm (QIFA) with the Artificial Bee Colony (ABC) method to enhance feature selection for a multi-pose facial expression recognition system.

A deep learning based model for diabetic retinopathy grading.

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy.

Thermophysical analysis of time-dependent magnetized Casson hybrid nanofluid flow (Cu + GO/Kerosene Oil) using Darcy-Forchheimer and thermal radiative models for industrial cooling applications.

This paper presents an in-depth analytical investigation into the time-dependent flow of a Casson hybrid nanofluid over a radially stretching sheet. The study introduces the effects of magnetic fields and thermal radiation, along with velocity and thermal slip, to model real-world systems for enhancing heat transfer in critical industrial applications. The hybrid nanofluid consists of three nanoparticles-Copper and Graphene Oxide-suspended in Kerosene Oil, selected for their stable and superior thermal properties.

A longitudinal analysis of physical exercise in shaping language learners' emotional well-being: a comparative analysis between L1 and L2 students.

Background: Students' psychological wellness is one of the key elements that improve their well-being and shape their academic progress in the realm of language learning. Among various strategies, physical exercise emerges as an effective approach, allowing learners to manage their emotions considerably.

Methods: Employing a quasi-experimental research design, this study examines the impact of a three-month physical running exercise intervention on emotional regulation behaviors among L1 (Arabic language) and L2 (English as a foreign language learning) students.

Effect of Drilling Parameters and Tool Diameter on Delamination and Thrust Force in the Drilling of High-Performance Glass/Epoxy Composites for Aerospace Structures with a New Design Drill.

Real service requirements of the assembly performance and joining properties of design components are critical for composite usage in the aerospace industry. This experimental study offers a novel and comprehensive analysis of dry drilling optimization for glass-reinforced, high-performance epoxy matrix composites used in aerospace structures, focusing on thrust force and delamination. The study presents a first-time investigation into the combined effects of spindle speed (1000, 2250, 4000 and 5750 rpm), feed rate (0.

Sustainable synthesis of carbon dots via bio-waste recycling for biomedical imaging.

Bio-waste is a side product of biomedical research containing carbon, which can be utilized for developing carbon dots (CDs). CDs are known to be useful for a variety of applications because of their unique photoluminescence, low toxicity, and straightforward synthesis. In this paper, we employed a one-step hydrothermal method to prepare CDs from bio-waste as the only reactant.

Association Between Oral Anticoagulant Adherence and Serious Clinical Outcomes in Patients With Atrial Fibrillation: A Long-Term Retrospective Cohort Study.

Background: Patients with atrial fibrillation are frequently nonadherent to oral anticoagulants (OACs) prescribed for stroke and systemic embolism (SSE) prevention. We quantified the relationship between OAC adherence and atrial fibrillation clinical outcomes using methods not previously applied to this problem.

Methods And Results: Retrospective observational cohort study of incident cases of atrial fibrillation from population-based administrative data over 23 years.

Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications.

Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM).

Deodorant use and related adverse effects: A cross-sectional survey among Palestinian students.

Background: Deodorants are widely used to mask unpleasant body odors. They are reported to cause some adverse effects depending on the form and ingredients. The purpose of this study was to assess the prevalence of deodorant use and related adverse effects among Palestinian students.

Evaluation of Solubility, Dissolution Rate, and Oral Bioavailability of β-Cyclodextrin and Hydroxypropyl β-Cyclodextrin as Inclusion Complexes of the Tyrosine Kinase Inhibitor, Alectinib.

This study aims to improve the solubility and dissolution rate of alectinib (ALB), a tyrosine kinase inhibitor commonly used for treating non-small-cell carcinoma (NSCLC). Given ALB's low solubility and bioavailability, complexation with β-cyclodextrin (βCD) and hydroxy propyl β-cyclodextrin (HPβCD) was evaluated. Some of the different preparation methods used with varying ALB-to-CD ratios led to the formation of complexes that were characterized using Fourier-Transform Infrared (FTIR) techniques and Differential Scanning Calorimetry (DSC) to prove complex formation.

Effect of Sex and Lateral Ankle Sprain History on Dorsiflexion Range Of Motion Asymmetry During the Weight Bearing Lunge Test.

Background: Reduced dorsiflexion range of motion (DFROM) which is commonly seen following lateral ankle sprain (LAS) has the potential to influence lower extremity biomechanics which have been linked to increased injury risk in the female athlete. Current research on the effect of sex and LAS history on DFROM is limited.

Hypothesis/purpose: This study had three aims 1) to determine the effect of sex, leg dominance and LAS history on DFROM, 2) to determine the effect of sex and LAS history on magnitude of DFROM symmetry and 3) to examine the association of sex on direction (whether dominant or non-dominant limb had the higher DFROM) of symmetry.

Allicin and Cancer Hallmarks.

Natural products, particularly medicinal plants, are crucial in combating cancer and aiding in the discovery and development of new therapeutic agents owing to their biologically active compounds. They offer a promising avenue for developing effective anticancer medications because of their low toxicity, diverse chemical structures, and ability to target various cancers. Allicin is one of the main ingredients in garlic ( L.

Incorporating the Antioxidant Fullerenol into Calcium Phosphate Bone Cements Increases Cellular Osteogenesis without Compromising Physical Cement Characteristics.

Herein, fullerenol (Ful), a highly water-soluble derivative of C fullerene with demonstrated antioxidant activity, is incorporated into calcium phosphate cements (CPCs) to enhance their osteogenic ability. CPCs with added carboxymethyl cellulose/gelatin (CMC/Gel) are doped with biocompatible Ful particles at concentrations of 0.02, 0.

Effect of Drilling Parameters and Tool Geometry on the Thrust Force and Surface Roughness of Aerospace Grade Laminate Composites.

Carbon fiber-reinforced plastics (CFRPs) have been specially developed to enhance the performance of commercial and military aircraft because of their strength, high stiffness-to-density ratios, and superior physical properties. On the other hand, fasteners and joints of CFRP materials may be weak due to occurring surface roughness and delamination problems during drilling operations. This study's aim is to investigate the drilling characterization of CFRPs with different drilling parameters and cutting tools.