Search Results (15)

Background/Objectives: Tumor budding (TB)—clusters of one to five tumor cells at the invasive front—has emerged as a prognostic marker in various cancers. Its prognostic value in head and neck squamous cell carcinoma (HNSCC) is unclear. Methods: We retrospectively analyzed 98 HNSCC patients. The tumor buds were counted on hematoxylin–eosin-stained sections as per the 2016 International Tumor Budding Consensus Conference (ITBCC) guidelines. An optimal cutoff was determined by ROC analysis using excisional lymph nodes and five-year overall survival (OS) as the endpoint, stratifying patients into low- (≤4 buds) and high-risk (>4 buds) groups. The associations with clinicopathological features, OS, and disease-free survival (DFS) were assessed using Kaplan–Meier curves and Cox regression. Results: Among the 98 patients (median follow-up 58 months, range 18–108), 32 (32.7%) died. The optimal TB cutoff was 4.5 (AUC 0.85, 95% CI 0.76–0.93). High TB was associated with poorer five-year OS (26.4% vs. 85.3%). Multivariate Cox regression identified TB and extranodal extension as independent predictors of OS (TB HR: 3.4, 95% CI 1.3–9.2, p = 0.013). In the laryngeal cancer subgroup, TB was associated with worse survival in the univariate analysis (HR 7.5, 95% CI 1.6–35.6, p = 0.011), though this was not significant in the multivariate modeling. High TB independently predicted neck lymph node metastasis (multivariate OR 4.9, 95% CI 1.2–20.5, p = 0.029), which was present in 65.8% of the high-TB vs. 31.7% of the low-TB patients. High TB correlated with advanced AJCC stage and lymphovascular invasion. No clinicopathological factors, including TB, independently predicted DFS, in either the full cohort or the laryngeal subgroup. Conclusions: High tumor budding denotes an aggressive HNSCC phenotype and may guide decisions on elective neck dissection. Its assessment is simple, cost-effective, and potentially valuable for routine pathology, pending external validation. Full article

Background/Objectives: Catheter ablation has become the standard of care for patients with symptomatic and drug-refractory atrial fibrillation (AF). Both Class IC and Class III antiarrhythmic drugs (AADs) are effective in preventing early recurrences of AF, but not late recurrences, compared with the usual care. We aimed to compare the effects of two months of Class IC versus Class III AADs following AF catheter ablation on clinical outcomes, including arrhythmia recurrence and safety endpoints. Methods: All patients undergoing AF catheter ablation between January 2015 and November 2024 were screened, and cases meeting the inclusion criteria were included. Primary outcome was defined as atrial tachycardia recurrence-free survival. Results: A total of 98 patients (mean age 54.2 ± 14.0 years; 55.1% male) were enrolled, with 66.3% presenting with paroxysmal atrial fibrillation (AF). The mean left atrial diameter was 38.7 ± 5.1 mm, and 78.6% underwent cryoballoon ablation. Class IC AADs were administered to 62 cases, while the remaining 36 patients received amiodarone following catheter ablation. The rate of atrial tachycardia (ATa) recurrence was comparable between the patients treated with Class IC and Class III AADs (9.7% vs. 19.4%; p = 0.169). Predictors of ATa recurrence were identified as history of direct current cardioversion—DCCV (HR: 5.86; 95%CI: 1.44–23.82)—and LA diameter (HR: 1.17; 95%CI: 1.04–1.31). The most frequent AAD-related adverse event was symptomatic bradycardia (6.1%), which resolved in all cases following dose reduction. Conclusions: Class IC and Class III antiarrhythmics show comparable efficacy in terms of preventing ATa recurrence following AF catheter ablation. AAD-related adverse event rates are negligible for short-term use. Full article

This study aimed to compare the efficiency of different green extraction methods for obtaining bioactive compounds from the roots of Rheum cordatum Losinsk and to evaluate their antioxidant and antimicrobial properties. The presence of some important phytochemicals in the extracts obtained using ultrasound-assisted extraction (UAE), subcritical ethanol extraction (Sbc-EtOH), and supercritical CO₂ (ScCO₂) extraction was determined by LC-MS/MS, and their antioxidant and antimicrobial properties were examined against Staphylococcus aureus, Enterococcus faecalis, Pseudomonas aeruginosa, and Escherichia coli. The goal was to determine the optimal extraction conditions that maximize the yield of bioactive compounds while preserving their biological properties. Different pressures (100 bar and 400 bar) were tested in UAE extraction, different solvents and times were tested in Sbc-EtOH extraction, and different pressures were tested in ScCO₂ extraction. Most of the 53 important phenolic compounds have been extracted using the ScCO₂ extraction method, either exclusively or in the highest amounts. It has been observed that more and higher amounts of phenolic compounds were extracted at lower pressure. The highest antioxidant activity was exhibited by the ScCO₂ extracts. Additionally, the ScCO₂-100 extract obtained at 100 bar showed strong antimicrobial activity, with a minimum inhibitory concentration (MIC) ranging from 31.25 to 250 μg/mL. Gallic acid, epicatechin gallate, epigallocatechin gallate, and catechin were found in extracts. Additionally, molecular docking studies against the 1QWZ, 2ANQ, 3H77, and 6QXS proteins revealed that epicatechin exhibited docking scores of −6.127, −9.479, −5.836, and −7.067 kcal/mol, respectively. Full article

This study investigates the bioactive compound profiles of 12 wild edible plant species from the Amaranthaceae, Apiaceae, Asteraceae, Brassicaceae, Caryophyllaceae, Lamiaceae, Malvaceae, Polygonaceae, and Urticaceae families, consumed as aromatic and medicinal vegetables by local communities and forming part of the natural vegetation of Mount Ergan. The plants were collected and analyzed for their anthocyanin, organic acid, and sugar contents, using advanced liquid chromatography techniques. Statistically significant differences were observed between species for each compound analyzed, highlighting their diverse phytochemical profiles. Malva neglecta, Brassica nigra, and Taraxacum phaleratum exhibited the highest anthocyanin contents, suggesting their strong potential as natural antioxidant sources. Organic acid levels were notably elevated in Polygonum cognatum, T. phaleratum, Urtica dioica, and M. neglecta, which positions these species as promising candidates for use as natural acid regulators in food and pharmaceutical formulations. In terms of sugar content, Chenopodium album, Mentha longifolia, and T. phaleratum had the lowest levels, while M. neglecta, Cirsium arvense, P. cognatum, and Tragopogon buphthalmoides showed significantly higher concentrations, indicating potential applications in the development of natural sweeteners. This study’s findings provide valuable insights into the phytochemical diversity of these wild plant species, emphasizing their potential utility in health nutrition, pharmaceuticals, and cosmetics. This study emphasizes the significance of investigating underexploited plant species for their bioactive chemicals and illustrates their potential contribution to the development of sustainable, natural product-based solutions for diverse industrial uses. Full article

Background/Objectives: For transcatheter aortic valve implantation (TAVI) therapy, a catheter-guided crimped valve is deployed into the aortic root. Valve types such as Edwards balloon-expandable valves and Medtronic self-expandable valves come in different sizes and are chosen based on patient-specific aortic anatomy, including aortic root diameter measurement. Complications may arise due to variations in anatomical characteristics and the implantation procedure, making pre-implantation assessment important for predicting complications. Methods: Computational modeling, particularly finite element analysis (FEA), has become popular for assessing wall stresses and deformations in TAVI. In this study, a finite element model including the aorta, native leaflets, and TAVI device was used to simulate procedures and assess patient-specific wall stresses and deformations. Results: Using the Medtronic Evolut R valve, we simulated TAVI for 14 patients to analyze the effects of geometrical variations on structural stresses. Virtual TAVIs with different valve sizes were also simulated to study the influence of TAV size on stresses. Our results show that variations in aortic wall geometries and TAV sizes significantly influence wall stresses and deformations. Conclusions: Our study is one of the first comprehensive FEA investigations of aortic geometrical variations and valve sizes on post-TAVI stresses, demonstrating the non-linear relationship between aortic dimensions, TAV sizes, and wall stresses. Full article

Recent advances in tunnel infrastructure have emphasized safety, operational efficiency and low operating costs. Modern tunnels are equipped with systems to improve both safety and operational performance. This study investigates the effect of tunnel lighting and vehicle breakdown scenarios on driver lane changing behaviour (LCB) using a driving simulator modelled on the third longest twin-tube tunnel. Data were collected from 125 drivers considering various driver characteristics with different lighting conditions and the presence of a stopped vehicle in a lane. The results show that drivers tend to slow down and change lanes more safely in response to red and flashing lights. In contrast, blue sky lights, which are designed to reduce stress and compare with other dangerous scenarios, had no significant effect on LCB. In addition, demographic factors such as gender and previous simulator experience played a role in influencing LCB tendencies. Female drivers and those familiar with simulators showed more cautious behaviour. The findings showed valuable insights into how tunnel lighting systems can improve safety. Results highlighted the potential for dynamic lighting and targeted driver training programs to improve tunnel safety. All these findings may contribute to ongoing efforts to improve traffic management and reduce accidents in tunnel environments. Full article

Background/Objectives: Duchenne muscular dystrophy (DMD) is the most prevalent progressive muscular dystrophy, and the guidelines recommend the regular assessment of respiratory muscle function. This study aimed to assess the relationship between maximum inspiratory pressure (MIP), maximum expiratory pressure (MEP) and sniff nasal inspiratory pressure (SNIP) measurements and upright-supine spirometry parameters in children with DMD, the predictability of upright–supine spirometry in terms of diaphragm involvement, and the impact of nutrition on muscle strength. Methods: This prospective cross-sectional study examined patients with DMD by comparing upright and supine FVC, MIP, MEP, and SNIP measurements. The effects of the ambulatory status, kyphoscoliosis, chest deformity, and low BMI on respiratory parameters were investigated. Results: Forty-four patients were included in the study. The mean patient age was 10.8 ± 2.9 years. Twenty-five patients were ambulatory. A significant decrease in FVC, FEV1, and FEF25–75 values was detected in the supine position in both ambulatory and non-ambulatory patients (p < 0.05). All patients had low MIP, MEP, and SNIP measurements (less than 60 cm H₂O). MIP, MEP, and SNIP values were significantly lower in patients with a low BMI than in those without (p < 0.05). Conclusions: To accurately assess respiratory muscle strength, supine FVC should be combined with upright FVC, MIP, MEP, and SNIP measurements. It is crucial to regularly screen patients for nutrition, as this can significantly affect respiratory muscle function during pulmonology follow-up. Full article

Climate change has intensified abiotic stresses, notably salinity, detrimentally affecting crop yield. To counter these effects, nanomaterials have emerged as a promising tool to mitigate the adverse impacts on plant growth and development. Specifically, zinc oxide nanoparticles (ZnO-NPs) have demonstrated efficacy in facilitating a gradual release of zinc, thus enhancing its bioavailability to plants. With the goal of ensuring sustainable plant production, our aim was to examine how green-synthesized ZnO-NPs influence the seedling growth of quinoa (Chenopodium quinoa L. Cv Atlas) under conditions of salinity stress. To induce salt stress, solutions with three different NaCl concentrations (0, 100, and 200 mM) were prepared. Additionally, Zn and ZnO-NPs were administered at four different concentrations (0, 50, 100, and 200 ppm). In this study, plant height (cm), plant weight (g), plant diameter (mm), chlorophyll content (SPAD), K/Na value, Ca/Na value, antioxidant enzyme activities (SOD: EU g⁻¹ leaf; CAT: EU g⁻¹ leaf; POD: EU g⁻¹ leaf), H₂O₂ (mmol kg⁻¹), MDA (nmol g⁻¹ DW), proline (µg g⁻¹ FW), and sucrose (g L⁻¹), content parameters were measured. XRD analysis confirmed the crystalline structure of ZnO nanoparticles with identified planes. Salinity stress significantly reduced plant metrics and altered ion ratios, while increasing oxidative stress indicators and osmolytes. Conversely, Zn and ZnO-NPs mitigated these effects, reducing oxidative damage and enhancing enzyme activities. This supports Zn’s role in limiting salinity uptake and improving physiological responses in quinoa seedlings, suggesting a promising strategy for enhancing crop resilience. Overall, this study underscores nanomaterials’ potential in sustainable agriculture and stress management. Full article

Induction heating (IH) technology is widely recognized and utilized in residential applications due to its high efficiency and safe operating characteristics. Resonant inverter circuits are widely used in IH systems because of their high efficiency and ability to perform soft switching. Among the various resonant inverters used in IH systems, the single-switch quasi-resonant (SSQR) inverter topology is typically preferred for low-cost and low-output-power applications. Despite its cost advantage, the SSQR topology has a relatively narrow soft-switching range, which can be unstable depending on the electrical parameters of the load and the resonant converter circuit. Accurately determining the capacitance value of the resonant capacitor and the inductance value of the induction coil, which are the key circuit elements of the SSQR induction cooker, is crucial for designing a reliable, efficient, and durable cooking system. In other words, there exists a critical relationship between the resonant converter circuit parameters, load characteristics, and safe operating conditions. Additionally, when considering closed-loop control methods used for power control and safety, selecting appropriate resonant circuit elements becomes vital in ensuring both reliable and efficient operation. This paper focuses on a novel and simplified design method for the SSQR inverter utilized in household appliances. The proposed method and its advantages in terms of the safe operating area of the switch are theoretically investigated and verified through simulations and prototype circuits. Full article

This paper presents a comparative evaluation of wide-bandgap power semiconductor devices for domestic induction heating application, which is currently a serious alternative to traditional heating techniques. In the induction heating system, the power transferred to the output depends on the equivalent resistance of the load, and the resistance depends on the operating frequency. Due to the switching characteristics of wide-bandgap power semiconductor devices, an induction heating system can be operated at higher operating frequencies. In this study, SiC and Si semiconductor devices are used in the comparison. These devices are compared according to different evaluation issues such as the turn-off energy losses, turn-off times, current fall time, the power losses of the internal diodes, and the conduction voltage drops issues. To perform the proposed evaluation, the series-resonant half-bridge inverter, which is frequently used in state-of-the-art induction heating systems, has been selected. The device suitability in an induction heating system is analyzed with the help of a test circuit. A comparison is made in terms of criteria determined by using the selected switches in the experimental circuit, which is operated in the 200 W to 1800 W power range and 45 kHz to 125 kHz switching frequency range. System efficiency is measured as 97.3% when Si IGBT is used. In the case of using SiC cascode JFET, the efficiency of the system is increased up to 99%. Full article

Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature. Full article

Directed energy deposition (DED) is an additive manufacturing process used in manufacturing free form geometries, repair applications, coating and surface modification, and fabrication of functionally graded materials. It is a process in which focused thermal energy is used to fuse materials by melting. Thermal effects can cause distortions and defects on the parts during the DED process, therefore they should be evaluated and taken into account during the manufacturing of products. Melting pool control and DED bead geometries should be defined properly as well. In this work, an Artificial Neural Network model has been applied considering the DED process parameters in order to predict the geometrical patterns and create a local reinforced product as a hybrid manufacturing technology. Although lots of studies are available on topology optimization for manufacturing methods such as casting, extrusion, and powder bed fusion, topology optimization for the DED process is not widely taken into consideration to predict the design geometrical patterns. DOE RSM and ANN approaches were applied in this study to predict convenient dimensions, topology based geometrical patterns of local stiffeners and heat source power optimizing the energy, total mass, and peak force results of the hybrid part. A single bead track deposition is simulated in terms of validation of the numerical heat source model, and cross-sections of the beads are analysed. A cross-member structure is manufactured using the DED device and the structure is correlated under the three point bending physical conditions on test bench. It has been investigated that locally reinforced cross beam has much more energy absorption and peak force values than plain model. The results showed that the proposed NN-GA is a promising approach to generate the topology based geometrical patterns and process parameters which can be used to create a local reinforced product as hybrid manufacturing technologies. Full article

Wild edible plants have been used since antiquity as folk medicine and as preservatives in foods. This study aimed to determine the antioxidant activities, phenolic compounds, and hormone contents of 12 species of edible wild plants belonging to 9 families, which are consumed as vegetables by the local people at Ergan Mountain in Erzincan in Turkey. Polygonum cognatum and Malva neglecta were determined to have more antioxidant enzyme activity, more phenolic compounds, and higher hormone content than the other species. The highest catalase (CAT), peroxidase (POD), glutathione reductase (GR), glutathione-s-transferase (GST) values for P. cognatum were determined as 45.12, 94.83, 36.76, and 1218.35 EU g⁻¹, respectively. The highest superoxide dismutase (SOD) and ascorbate peroxidase (AxPOD) content for M. neglecta were determined as 97.53 EU g⁻¹ and 81.93 EU g⁻¹, respectively. P. cognatum is the species in which the highest levels of the hormones indolacetic acid (IAA), gibberellic acid (GA), salicylic acid (SA), cytokinin, zeatin and jasmonic acid were detected. The highest levels of caftaric acid (CA), catechin (CAE), ferulic acid (FA), malvidin-3-o-glucoside (MG), myricetin (MYR), rutin (RT), trans-coumaric acid (TPCA), tyrosol (TY), and vanilic acid (VA) compounds were found in M. neglecta. It was determined that Falcaria vulgaris species had the highest levels of ferulic acid (FA) and quercetin (QUE) phenolics. The results show that edible wild vegetables consumed and studied by the people of the region are an important source of natural antioxidants. The possibilities of using these wild plants as functional foods should be investigated. Full article

A communication system based on unmanned aerial vehicles (UAVs) is a viable alternative for meeting the coverage and capacity needs of future wireless networks. However, because of the limitations of UAV-enabled communications in terms of coverage, energy consumption, and flying laws, the number of studies focused on the sustainability element of UAV-assisted networking in the literature was limited thus far. We present a solution to this problem in this study; specifically, we design a Q-learning-based UAV placement strategy for long-term wireless connectivity while taking into account major constraints such as altitude regulations, nonflight zones, and transmit power. The goal is to determine the best location for the UAV base station (BS) while reducing energy consumption and increasing the number of users covered. Furthermore, a weighting method is devised, allowing energy usage and the number of users served to be prioritized based on network/battery circumstances. The suggested Q-learning-based solution is contrasted to the standard k-means clustering method, in which the UAV BS is positioned at the centroid location with the shortest cumulative distance between it and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption. Full article

A paramount challenge of prohibiting increased CO₂ emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO₂ emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO₂ reduction gains of up to 31.83%. Full article