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Article Recognition and Misclassification Patterns of Basic Emotional Facial Expressions: An Eye-Tracking Study in Young Healthy Adults(MDPI, 2025) Alkan, NeşeAccurate recognition of basic facial emotions is well documented, yet the mechanisms of misclassification and their relation to gaze allocation remain under-reported. The present study utilized a within-subjects eye-tracking design to examine both accurate and inaccurate recognition of five basic emotions (anger, disgust, fear, happiness, and sadness) in healthy young adults. Fifty participants (twenty-four women) completed a forced-choice categorization task with 10 stimuli (female/male poser x emotion). A remote eye tracker (60 Hz) recorded fixations mapped to eyes, nose, and mouth areas of interest (AOIs). The analyses combined accuracy and decision-time statistics with heatmap comparisons of misclassified versus accurate trials within the same image. Overall accuracy was 87.8% (439/500). Misclassification patterns depended on the target emotion, but not on participant gender. Fear male was most often misclassified (typically as disgust), and sadness female was frequently labeled as fear or disgust; disgust was the most incorrectly attributed response. For accurate trials, decision time showed main effects of emotion (p < 0.001) and participant gender (p = 0.033): happiness was categorized fastest and anger slowest, and women responded faster overall, with particularly fast response times for sadness. The AOI results revealed strong main effects and an AOI x emotion interaction (p < 0.001): eyes received the most fixations, but fear drew relatively more mouth sampling and sadness more nose sampling. Crucially, heatmaps showed an upper-face bias (eye AOI) in inaccurate trials, whereas accurate trials retained eye sampling and added nose and mouth AOI coverage, which aligned with diagnostic cues. These findings indicate that the scanpath strategy, in addition to information availability, underpins success and failure in basic-emotion recognition, with implications for theory, targeted training, and affective technologies.Article Effects of Pomegranate Seed Oil on Lower Extremity Ischemia-Reperfusion Damage: Insights into Oxidative Stress, Inflammation, and Cell Death(MDPI, 2025) Bozok, Ummu Gulsen; Ergorun, Aydan Iremnur; Kucuk, Aysegul; Yigman, Zeynep; Dursun, Ali Dogan; Arslan, MustafaAim: This study sought to clarify the therapeutic benefits and mechanisms of action of pomegranate seed oil (PSO) in instances of ischemia–reperfusion (IR) damage in the lower extremities. Materials and Methods: The sample size was determined, then 32 rats were randomly allocated to four groups: Control (C), ischemia–reperfusion (IR), low-dose PSO (IR + LD, 0.15 mL/kg), and high-dose PSO (IR + HD, 0.30 mL/kg). The ischemia model in the IR group was established by occluding the infrarenal aorta for 120 min. Prior to reperfusion, PSO was delivered to the IR + LD and IR + HD groups at doses of 0.15 mL/kg and 0.30 mL/kg, respectively, followed by a 120 min reperfusion period. Subsequently, blood and tissue specimens were obtained. Statistical investigation was executed utilizing Statistical Package for the Social Sciences version 20.0 (SPSS, IBM Corp., Armonk, NY, USA). Results: Biochemical tests revealed significant variations in total antioxidant level (TAS), total oxidant level (TOS), and the oxidative stress index (OSI) across the groups (p < 0.0001). The IR group had elevated TOS and OSI levels, whereas PSO therapy resulted in a reduction in these values (p < 0.05). As opposed to the IR group, TASs were higher in the PSO-treated groups. Histopathological analysis demonstrated muscle fiber degeneration, interstitial edema, and the infiltration of cells associated with inflammation in the IR group, with analogous results noted in the PSO treatment groups. Immunohistochemical analysis revealed that the expressions of Tumor Necrosis Factor-alpha (TNF-α), Nuclear Factor kappa B (NF-κB), cytochrome C (CYT C), and caspase 3 (CASP3) were elevated in the IR group, while PSO treatment diminished these markers and attenuated inflammation and apoptosis (p < 0.05). The findings demonstrate that PSO has a dose-dependent impact on IR injury. Discussion: This research indicates that PSO has significant protective benefits against IR injury in the lower extremities. PSO mitigated tissue damage and maintained mitochondrial integrity by addressing oxidative stress, inflammation, and apoptotic pathways. Particularly, high-dose PSO yielded more substantial enhancements in these processes and exhibited outcomes most comparable to the control group in biochemical, histological, and immunohistochemical investigations. These findings underscore the potential of PSO as an efficacious natural treatment agent for IR injury. Nevertheless, additional research is required to articulate this definitively.Article Citation - WoS: 8Citation - Scopus: 12Protective Effects of BPC 157 on Liver, Kidney, and Lung Distant Organ Damagein Rats with Experimental Lower-Extremity Ischemia–Reperfusion Injury(MDPI, 2025) Demirtas, Hueseyin; Ozer, Abdullah; Yildirim, Alperen Kutay; Dursun, Ali Dogan; Sezen, Saban Cem; Arslan, MustafaBackground and Objectives: Ischemia–reperfusion (I/R) injury can affect multiple distant organs following I/R in the lower extremities. BPC-157’s anti-inflammatory and free radical-neutralizing properties suggest its potential in mitigating ischemia–reperfusion damage. This study evaluates the protective effects of BPC-157 on remote organ damage, including the kidneys, liver, and lungs, in a rat model of skeletal muscle I/R injury. Materials and Methods: A total of 24 male Wistar albino rats were randomly divided into four groups: sham (S), BPC-157(B), lower extremity I/R(IR) and lower extremity I/R+BPC-157(I/RB). Some 45 min of ischemia of lower extremity was followed by 2 h of reperfusion of limbs. BPC-157 was applied to groups B and I/RB at the beginning of the procedure. After 2 h of reperfusion, liver, kidney and lung tissues were harvested for biochemical and histopathological analyses. Results: In the histopathological examination, vascular and glomerular vacuolization, tubular dilation, hyaline casts, and tubular cell shedding in renal tissue were significantly lower in the I/RB group compared to other groups. Lung tissue showed reduced interstitial edema, alveolar congestion, and total damage scores in the I/RB group. Similarly, in liver tissue, sinusoidal dilation, necrotic cells, and mononuclear cell infiltration were significantly lower in the I/RB group. Additionally, the evaluation of TAS, TOS, OSI, and PON-1 revealed a statistically significant increase in antioxidant activity in the liver, lung, and kidney tissues of the I/RB group. Conclusions: The findings of this study demonstrate that BPC-157 exerts a significant protective effect against distant organ damage in the liver, kidneys, and lungs following lower extremity ischemia–reperfusion injury in rats.Article Citation - WoS: 1Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review(MDPI, 2025) Ayhan, Cagri; Mekhaeil, Marina; Channawi, Rita; Ozcan, Alp Eren; Akargul, Elif; Deger, Atakan; Soliman, OsamaAcute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today's data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models-particularly ensemble algorithms and biomarker-integrated approaches-have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI's growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics-enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia-showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling-potentially through the development of digital twins-will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA.
