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Assessment of nutrient canals in relation to jaw cysts, trabecular bone structure and dental regions: A CBCT study
No full textObjective: To investigate the relationship of nutrient canals with jaw cysts, trabecular bone density, and dental regions using cone-beam computed tomography (CBCT). Design: This retrospective study analyzed 120 CBCT images from 60 patients with unilateral jaw cysts. Nutrient canals were identified and localized on both the cyst-affected side and contralateral, unaffected side of the jaw for each patient. Alveolar trabecular structure was assessed by comparing Hounsfield Unit (HU) values between affected and unaffected sides. Statistical analyses included paired t-tests, Fisher's exact test, and intra-observer reliability assessment. Results: Nutrient canals were significantly more prevalent in cyst-affected jaw regions. Their presence was associated with areas exhibiting pericystic trabecular sclerosis, as reflected by elevated HU values. While nutrient canals were commonly observed in the anterior regions of both healthy and affected jaws, their presence in premolar and molar regions was restricted to cyst-affected areas. Conclusions: Nutrient canals, typically found in the anterior jaws, appear to develop in posterior regions in association with jaw cysts, particularly in the presence of trabecular sclerosis. Their identification on CBCT scans is important for treatment planning and for minimizing potential surgical complications
A new carbazole-chalcone based chemosensor: Fluorescence “turn-on-off-on” applications for OH− anion DFT calculation, docking studies, in living cells and real food/environmental samples
No full textCarbazole-chalcone derivative fluorescent chemosensor ( CRB1 ) was successfully synthesized and characterized to recognize the effect on OH− ion. CRB1 showed an “ on - off-on ” specific response towards OH− among different competing cations and anions. The detection process of orange color of sensor CRB1 selectively quenching at 563 nm in the presence of OH− anion was monitored. Sensor CRB1 was observed to realize the lowest detection limit of 0.19 μM and the binding constant of 4.91 × 105 M−1 for the detection of OH−. The binding ability of sensor CRB1 with OH− was demonstrated using fluorometric, UV–Vis, and 1H NMR titrations, reversibility with EDTA, Job's plot, docking study, and density functional theory studies (DFT). Furthermore, selectivity experiments of OH− anion were performed, test strips, real food sample, and environmental analysis to determine the practical applicability of the sensor. Additionally, the activities of CRB1 in three different cell lines (MCF-7, MDA-MB-231, and WI-38) were examined
Evaluation of the hydrogen volumetric storage performance of zeolitic imidazolate scaffold doped with anatase TiO2
No full textZIF-8, a member of the zeolitic imidazolate framework family, has attracted great interest for hydrogen storage due to its well-defined microporous structure, chemical tunability and excellent thermal and mechanical stability. In this study, ZIF-8 was synthesized using methanol as a reaction medium and subsequently modified by incorporating different amounts (1–4 %) of anatase titanium dioxide (TiO₂) to improve its hydrogen storage properties. A range of advanced characterization methods – including SEM, STEM, XRD, Raman spectroscopy, XPS, BET surface analysis and FTIR spectroscopy - were employed to thoroughly investigate the structural and morphological properties of the resulting materials. The analyses confirmed the successful integration of TiO₂ into the ZIF-8 framework without compromising its crystalline integrity. Among the composites, the sample with 3.87 % TiO₂ showed the largest surface area and better hydrogen uptake performance. These results highlight the potential of TiO₂-functionalised ZIF-8 as an efficient material for hydrogen storage and provide valuable insights for the development of next-generation energy storage systems
Advancing equity for people with intellectual disabilities: Closing the neglected cancer policy gap
No full textThe Potential Of Machine Learning Models in Predicting Recurrence Risk in Nasopharyngeal Carcinoma
No full textAim The aim of this study is to predict the risk of post-treatment recurrence in patients with nasopharyngeal carcinoma treated at our center using machine learning models. Material-methods This retrospective study included 40 nasopharyngeal carcinoma patients diagnosed, treated, and followed at Sivas Cumhuriyet University Faculty of Medicine between 2014 and 2024. Demographic, clinical, molecular, laboratory, and survival data were obtained from hospital records. Machine learning analyses were performed in Python (v2.3) using the PyCaret library with Z-score normalization and SMOTE for preprocessing. The dataset was randomly divided into training and testing sets, and dimensionality reduction was applied. Random Forest, AdaBoost, and correlation-based models were tested with a 0.90 feature selection threshold, and performance was evaluated by AUC, accuracy, sensitivity, specificity, and F1 score. Results The median age of the 40 patients was 46 years (18–75); 80% were male. Comorbidities were present in 25%, smoking in 50%, alcohol use in 5%, and family history in 25%. Stage distribution was 27.5% stage II, 52.5% stage III, and 20% stage IV, with distant metastasis in 12.5%. Chemoradiotherapy was administered to 95%. EBV status was positive in 30%, negative in 25%, and unknown in 45%. During a median follow-up of 56.7 months, recurrence occurred in 50%. Median values were neutrophils 4.7, lymphocytes 1.1, monocytes 0.5, platelets 238, and hemoglobin 13.1. The Gradient Boosting model showed moderate predictive ability (accuracy 66.7%, ROC-AUC 0.74, F1-score 0.71). Hemoglobin was the strongest predictor of recurrence, followed by neutrophil count, age, lymphocyte count, metastasis, and stage, while other clinical factors had limited impact. Conclusion Machine learning models showed moderate effectiveness in predicting recurrence in nasopharyngeal carcinoma. Hemoglobin and inflammatory markers were identified as principal predictors, underscoring their potential utility for risk stratification and prognostic assessment. Keywords: Nasopharyngeal carcinoma, machine learning, recurrence, prognosis </p
Approximations of the set of trajectories, attainable sets and integral funnel of the control system with mixed constraints on the control functions
No full textIn this paper, the set of trajectories, attainable sets and integral funnel of a control system described by an ordinary differential equation are studied. The system is nonlinear with respect to the phase state vector and affine with respect to the control vector. It is assumed that the admissible control functions satisfy mixed constraints, including both integral and geometric constraints. Step by step, the set of admissible control functions is replaced by a set consisting of a finite number of piecewise-constant control functions that generate a finite number of trajectories. First, an error evaluation between the set of trajectories and the set consisting of a finite number of trajectories is presented. Then, the trajectories generated by the piecewise-constant control functions are changed with Euler's broken lines, and an error estimation between the set of trajectories of the system and the set consisting of a finite number of Euler's broken lines is obtained. Similar estimations for attainable sets of the system are also provided. By applying these results, we derive an approximation with error evaluation for the integral funnel of the system. It is shown that by appropriately defining discretization parameters, the Hausdorff distance between the set of trajectories, the attainable sets, the integral funnel and their approximations can be made sufficiently small. The impact of upper bounds of the geometric and integral constraints on the presented approximations is discussed
Three dimensional shear wave velocity (Vs) structure and dynamic soil properties of Adıyaman-Gölbaşı basin using HVSR and SPAC methods
No full textOn February 6, 2023, two devastating earthquakes (Mw 7.8 and Mw 7.6) struck southeastern Türkiye, two of the most destructive seismic events in the country's history. This study investigates the structural damage and seismic vulnerability in the Gölbaşı Basin, located in Adıyaman Province—one of the regions most severely affected by these events. Geophysical techniques, the HVSR (Nakamura) and spatial autocorrelation (SPAC) methods, were employed to develop shear wave velocity (Vs) profiles and evaluate the dynamic soil properties of the basin. Shear wave velocities within the Gölbaşı Basin, down to a depth of 300 m, range from 211 to 923 m/s, with the lowest values observed near the lake, indicating weak and loose soil conditions. Natural site periods vary between 0.1 s and 2.86 s, with the longest periods (T > 2.5 s) also concentrated in the vicinity of the lake. In areas where the engineering bedrock (Vs > 760 m/s) lies deeper than 250 m, natural periods frequently exceed 1.5 s. These findings suggest that zones with thick alluvial deposits and low Vs values are particularly susceptible to seismic hazards. Structural damage was most severe in areas where Vs is below 350 m/s, site periods exceed 1 s, and the engineering bedrock lies deeper than 50 m. Notably, low-rise industrial buildings and low-rise structures with basement floors remained intact despite poor soil conditions. In contrast, in areas with more competent ground conditions, structural collapses were more likely caused by deficiencies in engineering design or construction quality