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Article Transcatheter Aortic Valve Implantation in Nonagenarians: A Comparative Analysis of Baseline Characteristics and 1-Year Outcomes(MDPI, 2025) Guney, Murat Can; Bozkurt, EnginBackground: Transcatheter aortic valve implantation (TAVI) is increasingly used in elderly patients with severe aortic stenosis, yet data on nonagenarians remain limited. This study aimed to compare clinical characteristics and outcomes of patients aged >= 90 years with those aged <90 years undergoing TAVI. Methods: We retrospectively analyzed 620 patients who underwent transfemoral TAVI. Patients were divided into two groups: <90 years (n = 545) and >= 90 years (n = 75). Baseline clinical, procedural, and outcome data were compared. Results: Nonagenarians had lower body mass index (BMI) and a lower prevalence of comorbidities such as diabetes, hyperlipidemia, and prior coronary artery bypass grafting CABG (all p < 0.05). All-cause mortality was higher in nonagenarians at 1 month (8.0% vs. 5.5%, p = 0.425), 6 months (9.3% vs. 7.9%, p = 0.838), and 1 year (21.3% vs. 16.7%, p = 0.405), though these differences were not statistically significant. In-hospital stroke occurred more frequently in patients >= 90 years (6.7% vs. 2.2%, p = 0.044). Conclusions: Despite a higher rate of in-hospital stroke, nonagenarians undergoing TAVI had comparable mortality outcomes to younger patients. These findings support the feasibility of TAVI in selected very elderly patients, while highlighting the need for tailored stroke prevention strategies. Trial Registration: The trial is retrospectively registered, and a clinical trial number is not applicable.Article Citation - WoS: 3Citation - Scopus: 4Financial Constraints and the ESG-Firm Performance Nexus in the Automotive Industry: Evidence From a Global Panel Study(MDPI, 2025) Dincergok, Burcu; Pirgaip, BurakThis study examines the complex relationship between environmental, social, and governance (ESG) and financial performance in the automotive industry, with a particular focus on how financial constraints shape this relationship. Using a global data set for the period 2008 to 2023 and employing a range of panel data techniques, including those addressing endogeneity concerns, we find that higher ESG scores positively affect financial performance. Specifically, a one-point rise in ESG score corresponds to an estimated 1-1.7% increase in the market-to-book ratio, with the effect reaching approximately 1.6% for firms facing financial constraints. These findings highlight the economic significance of ESG engagement, particularly for resource-constrained companies. The novelty of this study is that it focuses on the automotive sector, an industry with limited ESG-specific research, and that it makes a theoretical contribution by linking ESG performance outcomes to financial constraints, an angle largely overlooked in prior research. The findings offer critical policy insights, emphasizing the strategic importance of ESG initiatives for value creation under varying financial conditions.Article Citation - Scopus: 1Benefits of Best Practice Guidelines in Spine Fusion: Comparable Correction in Ais With Higher Density and Fewer Complications(MDPI, 2023) Fernandes,P.; Flores,I.; Soares do Brito,J.Background: There is significant variability in surgeons’ instrumentation patterns for adolescent idiopathic scoliosis surgery. Implant density and costs are difficult to correlate with deformity correction, safety, and quality of life measures. Materials and Methods: Two groups of postoperative adolescents were compared based on exposure to a best practice guidelines program (BPGP) introduced to decrease complications. Hybrid and stainless steel constructs were dropped, and posterior-based osteotomies, screws, and implant density were increased to 66.8 ± 12.03 vs. 57.5 ± 16.7% (p < 0.001). The evaluated outcomes were: initial and final correction, rate of correction loss, complications, OR returns, and SRS-22 scores (minimum two-year follow-up). Results: 34 patients were operated on before BPGP and 48 after. The samples were comparable, with the exceptions of a higher density and longer operative times after BPGP. Initial and final corrections before BPGP were 67.9° ± 22.9 and 64.6° ± 23.7; after BPGP, the corrections were 70.6° ± 17.4 and 66.5° ± 14.9 (sd). A regression analysis did not show a relation between the number of implants and postoperative correction (beta = −0.116, p = 0.307), final correction (beta = −0.065, p = 0.578), or loss of correction (beta= −0.137, p = 0.246). Considering screw constructs only (n = 63), a regression model controlled for flexibility continued to show a slight negative effect of density on initial correction (b = −0.274; p = 0.019). Only with major curve concavity was density relevant in initial correction (b = 0.293; p = 0.038), with significance at 95% not being achieved for final correction despite a similar beta (b = 0.263; p = 0.069). Complications and OR returns dropped from 25.6% to 4.2%. Despite this, no difference was found in SRS-22 (4.30 ± 0.432 vs. 4.42 ± 0.39; sd) or subdomain scores pre- and post-program. Findings: Although it appears counterintuitive that higher density, osteotomies, and operative time may lead to fewer complications, the study shows the value of best practice guidelines in spinal fusions. It also shows that a 66% implant density leads to better safety and efficacy, avoiding higher costs. © 2023 by the authors.Article Citation - Scopus: 1From Street Canyons To Corridors: Adapting Urban Propagation Models for an Indoor IQRF Network(MDPI, 2025) Doyan, Talip Eren; Yalcinkaya, Bengisu; Dogan, Deren; Dalveren, Yaser; Derawi, MohammadAmong wireless communication technologies underlying Internet of Things (IoT)-based smart buildings, IQRF (Intelligent Connectivity Using Radio Frequency) technology is a promising candidate due to its low power consumption, cost-effectiveness, and wide coverage. However, effectively modeling the propagation characteristics of IQRF in complex indoor environments for simple and accurate network deployment remains challenging, as architectural elements like walls and corners cause substantial signal attenuation and unpredictable propagation behavior. This study investigates the applicability of a site-specific modeling approach, originally developed for urban street canyons, to characterize peer-to-peer (P2P) IQRF links operating at 868 MHz in typical indoor scenarios, including line-of-sight (LoS), one-turn, and two-turn non-line-of-sight (NLoS) configurations. The received signal powers are compared with well-known empirical models, including international telecommunication union radio communication sector (ITU-R) P.1238-9 and WINNER II, and ray-tracing simulations. The results show that while ITU-R P.1238-9 achieves lower prediction error under LoS conditions with a root mean square error (RMSE) of 5.694 dB, the site-specific approach achieves substantially higher accuracy in NLoS scenarios, maintaining RMSE values below 3.9 dB for one- and two-turn links. Furthermore, ray-tracing simulations exhibited notably larger deviations, with RMSE values ranging from 7.522 dB to 16.267 dB and lower correlation with measurements. These results demonstrate the potential of site-specific modeling to provide practical, computationally efficient, and accurate insights for IQRF network deployment planning in smart building environments.Article Citation - WoS: 9Citation - Scopus: 8Parameter Identification and Speed Control of a Small-Scale BLDC Motor: Experimental Validation and Real-Time PI Control with Low-Pass Filtering(MDPI, 2025) Abouseda, Ayman Ibrahim; Doruk, Resat Ozgur; Amini, AliThis paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional-integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors.Article Citation - WoS: 2Citation - Scopus: 3Latent Psychological Pathways in Thermal Comfort Perception: The Mediating Role of Cognitive Uncertainty on Depression and Vigour(MDPI, 2025) Ozbey, Mehmet Furkan; Turhan, Cihan; Alkan, Nese; Akkurt, Gulden GokcenThermal comfort is the condition of mind that expresses satisfaction with the thermal environment, and it is assessed through subjective evaluation, according to the American Society of Heating, Refrigerating, and Air-Conditioning Engineers. While research has traditionally emphasised physical factors, growing evidence highlights the role of the state of mind in shaping thermal perception. In a prior Monte Carlo sensitivity analysis, six mood subscales-Anger, Confusion, Vigour, Tension, Depression, and Fatigue-were examined for how they affect the absolute difference between actual and predicted thermal sensation. Depression and vigour were found to be the most influential, while confusion appeared least impactful. However, to accurately assess the role of confusion, it is necessary to consider its potential interactions with other mood subscales. To this end, a mediation analysis was conducted using Hayes' PROCESS tool. The mediation analyses revealed that confusion partially mediated depression's effect in males and vigour's effect in females. These results suggest that, despite a weak direct impact, confusion critically influences thermal perception by altering the effects of key mood states. Accounting for the indirect effects of mood states may lead to more accurate predictions of human sensory experiences and improve the design of occupant-centred environments.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: 2Citation - Scopus: 2Physics-Informed Neural Network for Nonlinear Bending Analysis of Nano-Beams: A Systematic Hyperparameter Optimization(MDPI, 2025) Esfahani, Saba Sadat Mirsadeghi; Fallah, Ali; Aghdam, Mohammad MohammadiThis paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton's principle, incorporating nonlocal strain gradient theory, and based on Euler-Bernoulli beam theory. In the PINN method, the solution is approximated by a deep neural network, with network parameters determined by minimizing a loss function that consists of the governing equation and boundary conditions. Despite numerous reports demonstrating the applicability of the PINN method for solving various engineering problems, tuning the network hyperparameters remains challenging. In this study, a systematic approach is employed to fine-tune the hyperparameters using hyperparameter optimization (HPO) via Gaussian process-based Bayesian optimization. Comparison of the PINN results with available reference solutions shows that the PINN, with the optimized parameters, produces results with high accuracy. Finally, the impacts of boundary conditions, different loads, and the influence of nonlocal strain gradient parameters on the bending behavior of nano-beams are investigated.Article Citation - WoS: 11Citation - Scopus: 14Protective 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: 1Citation - Scopus: 2Applications 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.
