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    Development of Dual-Functional Hydrogel-Based Conductive Electrodes for Accelerated Wound Healing and Motion Sensing
    (Wiley-V C H Verlag Gmbh, 2025) Boztepe, Cihangir; Bulucu, Firat Orhan; Zengin, Reyhan
    Flexible conductive hydrogel-based electrodes are promising for biomedical use, combining enhanced wound healing, bioelectrical signaling, and real-time motion sensing, with broad potential in personalized medicine, wearable electronics, and smart prosthetics. In this study, electrically conductive hydrogels with dual functionality were developed for accelerated wound healing and motion sensing applications. The hydrogel electrodes were synthesized via a freeze-thaw cross-linking method using polyvinyl alcohol (PVA), crystalline nanocellulose (CNC), Laponite (LAP), and polyaniline (PANI). The influence of CNC and LAP content on the electrical conductivity, mechanical strength, swelling capacity, and degradation behavior of the hydrogels was systematically investigated. The PVA-CNC-LAP/PANI hydrogel optimized for electrical conductivity (1.5 wt.% CNC and 1.25 wt.% LAP) demonstrated outstanding multifunctional performance, combining robust mechanical strength (490 kPa tensile strength, 2.57 mm/mm elongation, 162 kPa elastic modulus, and 656 kJ/m(3) toughness) with excellent electrical properties, including high conductivity (33.65 S/m), reliable sensitivity (gauge factor = 1.74), and remarkable durability (>500 cycles at 20% strain). Biocompatibility and cell migration potential of this hydrogel electrode were assessed through scratch assays using human dermal fibroblasts (HDF). Additionally, the hydrogel's performance was evaluated in flexible sensor, smart finger actuator, and electrocardiogram (ECG) electrode applications. The biocompatible PVA/CNC/PANI-LAP hydrogel electrodes exhibited satisfactory electrical conductivity, excellent mechanical integrity, and electroresponsive behavior, thereby effectively supporting HDF proliferation, directed migration, and motion detection capabilities.
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    Early diagnosis of pancreatic cancer by machine learning methods using urine biomarker combinations
    (Tubitak Scientific & Technological Research Council Turkey, 2023) Acer, Irem; Bulucu, Firat Orhan; Icer, Semra; Latifoglu, Fatma
    The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancers. The five-year survival rate for PDAC due to late diagnosis is 9%. Early diagnosed PDAC patients survive longer than patients diagnosed at a more advanced stage. Biomarkers can play an essential role in the early detection of PDAC to assist the health professional. Machine learning and deep learning methods are used with biomarkers obtained in recent studies for diagnostic purposes. In order to increase the survival rates of PDAC patients, early diagnosis of the disease with a noninvasive test is a critical need. Our study offers a promising approach for the early detection of PDAC with noninvasive urinary biomarkers and carbohydrate antigen 19-9 (CA19-9). The Kaggle Urinary Biomarkers for Pancreatic Cancer (2020) open-access dataset consisting of 590 participants was used in this study. Seven machine learning classifiers (support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (kNN), random forest (RF), light gradient boosting machine (LightGBM), AdaBoost, and gradient boosting classifier (GBC)) to detect PDAC disease classifier were used. Binary and multiple classification processes were carried out. Data was validated in our study using 5-10-fold crossvalidation. This study aimed to determine the best machine learning model by analyzing the performance of machine learning models in determining the classes of healthy controls, pancreatic disorders, and patients with PDAC. It is a remarkable finding that ensemble learning models were more successful in all our groups. The most successful classification method in classifying healthy controls and patients with PDAC was CV-10, while the GBC (92.99%) model was (AUC = 0.9761). The most successful classification method in classifying patients with pancreatic disorders and PDAC was CV-10, while the LightGBM (86.37%) model was (AUC = 0.9348). In the classification of healthy controls, pancreatic disorders, and patients with PDAC, the most successful classification method was CV-5, while the GBC (72.91%) model was (AUC = 0.8733).