Gençay Sevim

The growing clinical demand for positron-emitting radionuclides has increased the need for accurate nuclear data and optimized production routes for medical applications. Iodine-124 (¹²⁴I), owing to its relatively long half-life and suitability for immunoPET and theranostic applications, represents a particularly important radionuclide. In this study, proton-induced production of ¹²⁴I via the ¹²⁴Te(p,n)¹²⁴I and ¹²⁶Te(p,3n)¹²⁴I reactions was investigated using conventional nuclear reaction codes (TALYS and EMPIRE) and artificial neural network (ANN). Reaction cross sections were calculated and benchmarked against available experimental data, and production yield and activity analyses were performed under medically relevant irradiation conditions. The results indicate that while TALYS and EMPIRE reproduce the general trends of the excitation functions, their predictive accuracy is limited in certain energy regions due to uncertainties in nuclear input parameters. In contrast, the ANN model achieved significantly improved agreement with experimental data, yielding lower mean squared error and root mean squared error values for both reaction channels. The calculated activity and yield values confirm the feasibility of both production routes, with higher yields obtained via the (p,3n) reaction at increased proton energies. These findings demonstrate that ANN-based modeling provides a reliable complementary tool for nuclear reaction analysis and optimization of ¹²⁴I production in medical cyclotron facilities.

Geleceğin Tıbbına Multidisipliner Bir Bakış: Nanoteknolojiden Yapay Zekaya Tıp ve biyolojik bilimler, tarihinin en hızlı dönüşüm süreçlerinden birini yaşıyor. Artık hastalıkları sadece makro düzeyde değil, moleküler ve hücresel düzeyde müdahalelerle yönetiyoruz. Peki, bu karmaşık yöntemlerin hangisi nerede devreye giriyor? Bu kitap, biyomedikal bilimlerde kullanılan güncel deneysel, hesaplamalı ve klinik yöntemleri tek bir çatı altında toplayan kapsamlı bir başvuru eseridir. Spinel ferrit nanoyapıların tıbbi uygulamalarından, hücresel elektrofizyolojinin altın standardı olan patch-clamp tekniklerine; PCR temelli moleküler analizlerden optogenetik ve fiber fotometri gibi ileri nörobilim yöntemlerine kadar geniş bir metodolojik panorama sunar. Ayrıca biyosensör teknolojilerinin tanı ve izlemdeki rolü ve perioperatif klinik uygulamalarda yapay zekâ entegrasyonunun etik boyutları eleştirel bir yaklaşımla tartışılmaktadır. Bilimi yalnızca teknik bir araç olarak değil, insan sağlığına yön veren çok boyutlu bir süreç olarak ele alan bu eser, okuru günümüz tıbbının geleceğine hazırlıyor.

This study covers the period 1994–2024 of bibliographic research on Diffuse Optical Tomography (DOT), using data retrieved from the Web of Science (WOS) database. The primary objective is to conduct a comprehensive bibliometric analysis of academic publications on DOT by constructing networks of authorship, sources, citations, keywords, institutions, and countries. The analysis was performed using the "Bibliometrix" package in R, supplemented by descriptive statistical methods. Over the past 30 years, the study identified key publication trends, evolving research priorities, and emerging thematic areas within the field. A total of 759 scientific documents published across 293 different sources were analyzed. The author collaboration network revealed an average of 5.39 co-authors per publication, and approximately 26% of the studies involved international collaboration. The most productive authors contributed up to 50 or more publications, while over 65% of the contributors authored only a single article. These findings highlight both concentrated research efforts by leading scholars and a wide distribution of interest among broader scientific communities.

The technology of MEMS/NEMS is based on the integration and relationship of mechanical, electronic, and even optical components at the micro- and nanoscale. This technology has provided more sensitive and more effective devices/systems for medical diagnosis, treatment, and monitoring. For example, it has enabled molecular imaging and early diagnosis of diseases with its integration with medical imaging and biosensor technology. Also, its use in drug targeting and controlled drug release systems has paved the way for promising, effective, and personalized treatments. In short, the technology of MEMS/NEMS currently plays a significant role in medicine and will have even greater potential in the future. Due to increasing studies, researchers who work or will work in this field have started to need a general perspective. For this reason, this review study aims to provide an overview of MEMS/NEMS-based medical applications. This review addresses the applications of MEMS/NEMS in medicine, including biosensors, medical imaging, surgical devices, drug studies, Lab-on-a-Chip, and Organ-on-a-Chip systems. Also, it briefly expresses the fundamentals of MEMS/NEMS and highlights the challenges, and future uses for MEMS/NEMS in medicine.

Introduction: The effectiveness of pharmaceutical treatment methods is vital in cancer treatment. In this context, various targeted drug delivery systems are being developed to minimize or eliminate existing deficiencies and harms. This study aimed to model the interaction of MEN-based drug-targeting systems with cancer cells and determine the properties of interacting MENs. Methods: Magnetoelectric Nanostructures (MENs) have both targeting and nano-electroporation effects due to their ferroic properties. Among these structures, the most used nanoparticles as targeting mechanisms are CoFe2O4-BaTiO3 structures. For this purpose, the electrical field produced by MENs was modeled using MATLAB R2023b, and a theoretical data pool of appropriate physical properties was created. Testing and applying other magnetoelectric materials defined in the literature may be costly and time-consuming. Results: The problems with MENs can be eliminated by performing theoretical simulations of each material before proceeding with laboratory tests. Conclusion: By simulating the interaction of CoFe2O4-BaTiO3 MENs with cancer cells, physical properties affecting drug targeting were theoretically identified and a data pool of MENs with these properties was created.

In this study, we examined the literature for cross section calculations from 2019 to 2023 using the ISI Web of Science (WOS) database to understand the dynamics of scientific communication. Our primary objective was to perform a bibliometric analysis and model networks among authors, texts, sources, citations, keywords, organizations, and countries. Using the R tool "Bibliometrix" and descriptive statistical techniques, we identified significant publication trends in co-authorship, citation patterns, institutional collaborations, and the geographic distribution of authorship. Our findings highlight the importance of international collaboration and interdisciplinary research. Our analysis for cross section calculations has uncovered significant publication trends, particularly regarding co-authorships, citation patterns, institutional collaborations, and the geographic origins of authors.

In this study, the literature for cross section calculations examined from 2019 to 2023 using the ISI Web of Science (WOS) database to understand the dynamics of scientific communication. The main objective was to perform a bibliometric analysis and model networks among authors, texts, sources, citations, keywords, organizations, and countries. Using the R tool "Bibliometrix" and descriptive statistical techniques, the significant publication trends in co-authorship, citation patterns, institutional collaborations, and the geographic distribution of authorship have been identifed. The present findings highlight the importance of international collaboration and interdisciplinary research. The presented bibliometric analysis for cross section calculations has uncovered significant publication trends, particularly regarding co-authorships, citation patterns, institutional collaborations, and the geographic origins of authors.

The Monte Carlo (MC) method is a computer simulation that is widely used in different disciplines including physics, biology, biophysics, medical imaging, biomedical engineering, etc. In addition, MC method is often used to simulate the interaction of radiation with cells, tissues, and the environment. In the present study, mass attenuation coefficient, stopping power, and penetrating distance calculations were performed for cell membranes having an approximately 60-100Å thickness. These calculations have been done for lipid bilayer structure of cell membrane via MC techniques employing two of the most known computer-aided calculation and simulation software which are MC methods such as SRIM-2013 (The Stopping and Range of Ions in Matter) and MCNPv6 (Monte Carlo N-Particle) with XCOM software. Stopping power and penetrating distance calculations were obtained using SRIM-2013. Also, both XCOM software and MCNPv6 simulation code were used to obtain photon interaction parameters within the energy range of 0.01 – 10000keV. Obtained all results from different codes have been visualized by graphing for evaluation.

In Diffuse Optical Tomography (DOT), data processing and reconstruction stages are crucial to obtain high-quality images. Thus, choosing suitable algorithms for the system is a critical choice. This study aims to determine an appropriate reconstruction algorithm for DOT imaging. There are several reconstruction algorithms used in DOT systems. Some algorithms have been improved for solving specific cases, and some still need to be improved. In this study, we used three algorithms for the reconstruction process: Singular Value Decomposition (SVD), Bi-Conjugated Gradient (Bi-CG), and Transpose Free Quasi Minimal Residual (TFQMR). In testing the algorithms, data of the simulation experiments have been used. The simulation experiments model the tumoral tissue within the breast. All three algorithms were produced correct images while the tumor close to the surface. In the case of the tumor that is not close to the breast surface, the tumor location on the images created by Bi-CG and SVD algorithms was not its actual location. However, the tumor location in the image created by the TFQMR algorithm was close to its actual location. Outcomes of the reconstruction algorithms were evaluated based on correctly defining the location of the tumors by using Mean Percentage Error (MPE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) metrics. We have demonstrated the TFQMR algorithm is a more appropriate reconstruction technique for DOT systems. Thus, we have concluded that TFQMR can have the potential to be used in medical imaging systems.