Magnetic Nano-Сomposites and their Industrial Applications

A state-of-the-art analysis of pharmacological delivery and artificial intelligence techniques for inner ear disease treatment

Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012-2022) using machine learning.

Application of nanotechnology in breast cancer screening under obstetrics and gynecology through the use of CNN and ANFIS

Breast cancer is the leading reason of death among women aged 35 to 54. Breast cancer diagnosis still presents significant challenges, and preventing the disease's most severe symptoms requires early detection. The role of nanotechnology in the tumor-treatment has recently attracted a lot of interest. In cancer therapies, nanotechnology plays a major role in the medication distribution process. Nanoparticles have the ability to target tumors. Nanoparticles are favorable and maybe preferable for usage in tumor detection and imaging due to their incredibly small size. Quantum dots, semiconductor crystals with increased labeling and imaging capabilities for cancer cells, are one of the particles that have received the most research attention. The design of the research is cross-sectional and descriptive. From April through September of 2020, data were gathered at the State Hospital. All pregnant women who came to the hospital throughout the first and second trimesters of the research's data collection were included in the study population. 100 pregnant women between the ages of 20 and 40 who had not yet had a mammogram comprised the research sample. 1100 digitized mammography images are included in the dataset, which was obtained from a hospital. Convolutional neural networks (CNN) were used to scan all images, and breast masses and mass comparisons were made using the malignant-benign categorization. The adaptive neuro-fuzzy inference system (ANFIS) then examined all of the data obtained by CNN in order to identify breast cancer early using inputs based on the nine different inputs. The precision of the mechanism used in this technique to determine the ideal radius value is significantly impacted by the radius value. Nine variables that define breast cancer indicators were utilized as inputs to the ANFIS classifier, which was then used to identify breast cancer. The parameters were given the necessary fuzzy functions, and the combined dataset was applied to train the method. Testing was initially performed by 30% of dataset that was later done with the real data obtained from the hospital. The accuracy of the results for 30% data was 84% (specificity =72.7%, sensitivity =86.7%) and the results for the real data was 89.8% (sensitivity =82.3%, specificity =75.9%), respectively.

Green synthesis of Fe3O4 nanoparticles using Alliaceae waste (Allium sativum) for a sustainable landscape enhancement using support vector regression

The synthesis of metal nanoparticles using green chemistry methods has gained significant attention in the field of landscape enhancement. Researchers have paid close attention to the development of very effective green chemistry approaches for the production of metal nanoparticles (NPs). The primary goal is to create an environmentally sustainable technique for generating NPs. At the nanoscale, ferro- and ferrimagnetic minerals such as magnetite exhibit superparamagnetic properties (Fe3O4). Magnetic nanoparticles (NPs) have received increased interest in nanoscience and nanotechnology due to their physiochemical properties, small particle size (1-100 nm), and low toxicity. Biological resources such as bacteria, algae, fungus, and plants have been used to manufacture affordable, energy-efficient, non-toxic, and ecologically acceptable metallic NPs. Despite the growing demand for Fe3O4 nanoparticles in a variety of applications, typical chemical production processes can produce hazardous byproducts and trash, resulting in significant environmental implications. The purpose of this study is to look at the ability of Allium sativum, a member of the Alliaceae family recognized for its culinary and medicinal benefits, to synthesize Fe3O4 NPs. Extracts of Allium sativum seeds and cloves include reducing sugars like glucose, which may be used as decreasing factors in the production of Fe3O4 NPs to reduce the requirement for hazardous chemicals and increase sustainability. The analytic procedures were carried out utilizing machine learning as support vector regression (SVR). Furthermore, because Allium sativum is widely accessible and biocompatible, it is a safe and cost-effective material for the manufacture of Fe3O4 NPs. Using the regression indices metrics of root mean square error (RMSE) and coefficient of determination (R2), the X-ray diffraction (XRD) study revealed the lighter, smoother spherical forms of NPs in the presence of aqueous garlic extract and 70.223 nm in its absence. The antifungal activity of Fe3O4 NPs against Candida albicans was investigated using a disc diffusion technique but exhibited no impact at doses of 200, 400, and 600 ppm. This characterization of the nanoparticles helps in understanding their physical properties and provides insights into their potential applications in landscape enhancement.

Recent Trends in Magnetic Polymer Nanocomposites for Aerospace Applications: A Review

Polymers have had an enormous impact on science and technology, and their interest relating to the development of new macromolecular materials has exponentially increased. Polymer nanocomposites, materials based on a polymeric matrix covalently coupled to reinforcement, display properties of both components. In the aerospace industry, polymer nanocomposites are attractive due to their promising characteristics, among which lightness, mechanical and thermal resistance, radiation and corrosion resistance, and conductive and magnetic properties stand out. The use of them, instead of metal-based materials, has allowed the optimization of design processes and applications in order to provide safer, faster, and eventually cheaper transportation in the future. This comparative review collects the most relevant and prominent advances in the development of polymer nanocomposites with aerospace applications starting from basic aspects such as the definition of polymer nanocomposite to more specialized details such as synthesis, characterization, and applications, in addition to proposing new research branches related to this topic.