Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
3 results
Search Results
Article Advances in Optical Biosensors: Technologies and Trends in Point of Care Applications(Academic Press Inc., 2025) Sözmen, A.B.; Bayraktar, A.E.; Ülker, Ö.; Arslan-Yildiz, A.A sensor detects changes in its environment and converts them into readable data using three key components: a receptor to sense changes, a transducer to generate a signal, and a detection system to output the signal. Optical sensors are devices that use a receptor and optical transducer to produce signals corresponding to an analyte, and optical biosensors combine a biological sensing element with an optical transducer to detect and quantify specific analytes. They offer easy-to-read, real-time signals, such as color changes or light emission, sometimes even detectable by the naked eye, reducing the need for external devices and providing versatile Point-of-Care (PoC) applicability. Their portability and rapid response time enable remote testing and monitoring, further improving accessibility. They allow sensitive and selective detection of various analytes, making them utile in areas like glucose monitoring, drug testing, and pathogen detection. Many of these sensors provide label-free and non-invasive detection, further enhancing patient comfort and safety. This chapter provides an overview of optical biosensors; it starts with categorizing them by biorecognition elements, transducers, and detection modes. It investigates biosensors that utilize nanomaterials, polymers, and engineered biorecognition elements are discussed, with examples from literature. Technologies such as miniaturization, multiplexing, and wearable designs, which enhance PoC feasibility, are also examined. Lastly, challenges in development and operation are addressed, and future research directions for advancing optical biosensors in PoC diagnostics are discussed. © 2025Book Part Toward Accurate in Silico Prediction of Antigen Binding Affinities for Antibody Engineering(Academic Press Inc., 2025) Uluçay, T.; Arslan, M.; Döşeme, H.; Kalyoncu, S.; Kale, S.In clinical applications and life sciences research, antibodies represent an important diagnostic and therapeutic potential thanks to their high target affinity, specificity, and broad developability. While the antigen affinity, one of the primary success assessors of an antibody, can be measured at reasonably high precision and reliability, the scalability of the measurements can be cumbersome and limited. This is troubling because the affinity must be monitored throughout all steps of the developability approaches such as affinity maturation and humanization of an antibody. In this context, in silico approaches present a lucrative opportunity at a fraction of the cost and time typically invested in a comparable wet lab undertaking. In addition to their high-throughput potential, in silico approaches can provide an invaluable side product, i.e., identifying the molecular driving forces behind affinity. Here, we investigated the performance of six different high-throughput servers in two settings common in antibody engineering applications: (i) de novo prediction of the experimental antibody-antigen binding constants, and (ii) the free energy change in binding due to single point mutations. We find that the accuracy of these tools can be significantly low in the two regimes relevant to antibody development: (i) prediction of high-affinity binding, and (ii) prediction of favorable mutations. These issues are intricately related to the training sets used in the underlying models of these tools where high-affinity complexes and favorable point mutations are typically underrepresented. We showed that biophysical characteristics of single point mutations, especially changes in bulkiness and hydrophobicity, increase the prediction error. We argue that while the prediction of mutational impact can be predicted within one kcal per mol using re-parameterized versions of the present in silico tools, the de novo prediction of the affinity likely requires revisiting the underlying physical models behind these tools. © 2025Book Part Citation - WoS: 4Citation - Scopus: 8Advances in Model-Based Testing of Graphical User Interfaces(Academic Press Inc., 2017) Belli, Fevzi; Beyazıt, Mutlu; Budnik, Christof J.; Tuğlular, TuğkanGraphical user interfaces (GUIs) enable comfortable interactions of the computer-based systems with their environment. Large systems usually require complex GUIs, which are commonly fault prone and thus are to be carefully designed, implemented, and tested. As a thorough testing is not feasible, techniques are favored to test relevant features of the system under test that will be specifically modeled. This chapter summarizes, reviews, and exemplifies conventional and novel techniques for model-based GUI testing.