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Patient-specific gingival recession system based on periodontal disease prediction Patientenbezogene Simulation des Zahnfleischrückgangs basierend auf einer parodontalen Risikovorhersage
No full textAim: To develop a periodontal disease prediction (PDP) software program and a patient-based gingival recession simulator for clinical practice with the aim of improving the oral hygiene motivation of patients with periodontal problems. Materials and methods: The developed PDP software has three components: a) A data loading window (DLW), b) A three-dimensional mouth model (3DM), and c) a periodontal attachment loss indicator (PLI). The demographic and clinical examination details of 1057 volunteers were recorded to the DLW. An unsupervised machine learning K means clustering analysis was used to categorize the data obtained from the study population and to identify the periodontal risk groups. An intraoral scanner was utilized to capture the direct optical intraoral data of the patients, which was transferred to the 3DM. The intraoral model underwent two algorithm steps to obtain a recessed model: First, the gingival curves separating the gingiva and tooth were extracted using a Dijkstra’s algorithm. Then, the limit curves determining the boundaries of the recessed regions in the intraoral model were obtained using the gingival curves. Results: Study participants were divided into three different periodontal risk categories: low- (n = 462), medium- (n = 336), and high-risk (n = 259) groups. The gingival curves separating the gingiva and tooth were extracted, and recessed models were obtained and given inputs for the expected amount of recession via the here-proposed method/algorithm. Furthermore, the user can also demonstrate the gingival recession gradually via the slider method incorporated into the developed program. Conclusions: A user-friendly computer-based periodontal risk estimation tool that is also a patient-specific gingival recession simulator was developed and presented for clinical use by dentists
An analytical method for the determination of pentachloroaniline and pentachlorobenzene in ginseng tea samples by gas chromatography–mass spectrometry after liquid phase microextraction
No full textThis study describes a sensitive and accurate analytical method for the determination of pentachloroaniline (PCA) and pentachlorobenzene (PCB) at trace levels in ginseng tea samples. For this purpose, spraying based fine droplet formation liquid phase microextraction (SFDF-LPME) method was implemented to extract/preconcentrate the target analytes before their separation and detection by gas chromatography–mass spectrometry (GC–MS). Herein, a lab-made spraying system was used to distribute the extraction solvent throughout the aqueous sample solution. Limit of detection/quantitation (LOD/LOQ) values for PCA and PCB were found as 0.24/0.80 and 0.26/0.85 μg/kg, respectively. When LOD values of GC–MS and SFDF-LPME-GC–MS methods were compared to each other, enhancement in detection power values for PCA and PCB were 285 and 226.9 folds, respectively. Percent recovery results for PCA and PCB were calculated as 92–124 % and 86–129 %, respectively. Green evaluation and practicability of developed SFDF-LPME-GC–MS method was done by Eco-scale (89), AGREEprep (0.39) and BAGI (62.5) tools
Investigating the interaction of preosteoblast cells with poly-L lysine surface-modified chitosan/hydroxyapatite scaffolds and their potential applications in bone tissue engineering
No full textBone is a multifunctional organ that undergoes constant structural and biological changes. In cases of damage due to trauma, cancer, infection or hormonal imbalances, medical intervention is required for bone regeneration. This study aims to develop a tissue scaffold that promotes bone tissue regeneration by enhancing cell adhesion, proliferation, and mineralization. For this purpose, tissue scaffolds with varying contents were produced using chitosan (Ch), hydroxyapatite (HA), and poly-L-lysine (PLL) as scaffold materials by freeze-drying method and characterized. In studies conducted to evaluate the biological activities of the scaffolds on MC3T3-E1 preosteoblast, the PLL-Ch/HA 1:2 scaffold exhibited approximately 20 % higher cell viability than the control on days 3 and 7 of extract analysis. In cells cultured on the scaffold, PLL-coated Ch/HA scaffolds showed a greater proliferative effect than uncoated Ch/HA scaffolds on day 7 of culture, resulting in a significant increase in cell viability. Furthermore, the observed increase in calcification and mineralization when cells were cultured on PLL-modified scaffolds could be attributed to PLL promoting cell adhesion and proliferation, resulting in increased calcium deposition. The surface modification of Ch/HA composite scaffolds with PLL has revealed optimal performance in bone tissue engineering due to their favorable performance
Multi-Omic Integrated Analysis of Endometriosis and Autoimmune Diseases: Identification of Novel Biomarkers
No full textOn function-on-function linear quantile regression
No full textWe present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated using a basis expansion approach. Consequently, we approximate the infinite-dimensional function-on-function linear quantile regression model using a multivariate quantile regression model constructed from these partial quantile regression components. To evaluate the efficacy of our proposed techniques, we conduct a series of Monte Carlo experiments and analyze an empirical dataset, demonstrating superior performance compared to existing methods in finite-sample scenarios. Our techniques have been implemented in the ffpqr package in R.</span
ECG Biometrics on Mobile Devices: High-Accuracy Authentication Using i-Vectors and Cepstral Coefficients
No full textIn recent years, the growing importance of personal data security has led to a rapid increase in demand for biometric authentication systems. This study proposes a biometric authentication method based on electrocardiogram (ECG) data that holds potential for use in mobile devices. The proposed approach integrates Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) features with the i-vector technique to effectively capture the biometric information present in the ECG signal within the 1–35 Hz range. In particular, the complementary effect of GFCC at low frequencies enhances the robustness of the MFCC-based features, thereby providing a more reliable biometric representation. The method was evaluated using the ECG-ID, Heartprint, and a dataset collected specifically for this study. While all datasets were obtained at different time intervals, the Heartprint dataset also includes recordings from multiple sessions. Performance metrics obtained from the Heartprint dataset—namely, 94.39% accuracy, 93.17% true acceptance rate (TAR), and 5.61% false acceptance rate (FAR)—demonstrate that the i-vector-based approach yields results on large datasets that are comparable to those achieved by deep learning and conventional machine learning methods. The use of a brief 5-second ECG signal minimizes memory and processing power requirements, enabling rapid data processing and real-time authentication. The data collected specifically for this study were acquired using the cardiochip BMD101; this sensor offers easy integration with wearable devices such as smartwatches or phone cases, thereby supporting the feasibility of applying the proposed method on mobile platforms. In conclusion, the integration of MFCC and GFCC features with the i-vector technique provides an effective and mobile-compatible solution for ECG-based biometric authentication, while also establishing a solid foundation for future developments in biometric systems
Conceptual Evolution of City Branding: A Systematic Review and the Progress of Terms “Social Media” and “Smart City” in Literature
No full textThe impact of N-acetylcysteine on early periods of tendon healing: histopathologic, immunohistochemical, and biomechanical analysis in a rat model
No full textPurpose: This study aimed to evaluate the early effects of N-acetylcysteine, which has antioxidant, inflame-modulatory, and cytoprotective properties, on tendon healing. Materials and methods: Thirty-five male Wistar Hannover rats were divided into five groups: first-week treatment (Group 1T), first-week control (Group 1C), third-week treatment (Group 3T), third-week control (Group 3C), and native tendons (Group N). Bilateral Achilles tenotomy was performed on all rats except Group N. After tenotomy, 150 mg/kg N-acetylcysteine was administered daily intraperitoneally to treatment groups, while isotonic saline was given to the control groups. Tendons were evaluated histopathologically, immunohistochemically, and biomechanically after sacrifice in the first and third weeks. Results: No significant differences were observed in the first week (p > 0.05). Movin and Bonar scores (lower scores reflect improved histologic healing) were significantly lower in Group 3T than in Group 3C (p = 0.002). Collagen type-I/type-III ratios were higher in Group 3T compared to Group 3C (p = 0.001). Fmax (N) values were similar across Group 3T, Group 3C, and Group N (p = 0.772). However, cross-sectional areas (mm2) were significantly smaller in Group 3T than in Group 3C (p = 0.001), with the smallest areas observed in native tendons. Thus, tensile strength (MPa, load per unit area) and toughness (J/103 mm3, energy absorbed per unit volume) were significantly higher in Group 3T than in Group 3C (p = 0.001). Conclusion: N-acetylcysteine supplied some improved results on early markers of tendon healing. Although our findings support the potential of NAC as a therapeutic adjunct in tendon injuries, further studies are needed to evaluate the long-term effects and underlying mechanisms
The green attraction: mediating role of environmental CSR and green HRM in the relationship between green culture and organizational attractiveness
No full textPurpose: This study aims to examine the mediating role of green human resources management (GHRM) and environmental corporate social responsibility (ECSR) in the relationship between organizational green culture (OGC) and organizational attractiveness (OA). Design/methodology/approach: The study used an explanatory sequential mixed-methods design. Survey data of 544 employees from 281 firms was analyzed using SPSS, Amos and PROCESS Macro. Qualitative data was collected through interviews with 13 human resources professionals. Findings: Results indicated that OGC impacts OA through GHRM and ECSR. Interview findings supported the quantitative results. Also, qualitative results extended the quantitative results by revealing that organizational pride, commitment, satisfaction, motivation and work meaningfulness have the potential to be underlying mechanisms in the impact of ECSR on OA. In addition, interview findings emphasized that for Generation Z, ECSR and GHRM are important factors for increasing OA. Originality/value: This study strengthens social identity theory by using mixed-method design to reveal how GHRM and ECSR can be significant factors in the relationship between OGC and OA. Using a qualitative study extends the understanding of the quantitative results and proposes fruitful topics for future research
Organizational reflections of the relationship between artificial ıntelligence and emotional ıntelligence in the context of phenomenology and Cartesian dualism
No full textPurpose: The purpose of this article is to deepen understanding of how emotional intelligence (EI) and artificial intelligence (AI) affect organizational behavior from a phenomenological perspective. Through philosophical lenses – particularly Descartes, Husserl and Merleau-Ponty – it highlights the contrasts and similarities between these forms of intelligence. The study aims to explore how AI and EI shape human experience and meaning-making in organizations, providing insights into how AI integration can foster more human-centered organizational practices. Design/methodology/approach: This study employs a phenomenological approach to explore the philosophical underpinnings of EI and AI. By examining Descartes’ Cartesian dualism and Husserl’s phenomenology, the study analyzes the alignment and divergence between AI and these philosophical perspectives. The methodology integrates literature review and conceptual analysis to link philosophical insights with their organizational behavior implications, offering a framework that critically examines AI’s impact on human experience and organizational dynamics. Findings: The findings highlight that emotional intelligence, rooted in the body-mind interaction, offers a human-centered view of experience, distinct from artificial intelligence. However, combining AI with EI can enhance organizational behavior by promoting more empathetic approaches. While AI can mimic cognitive functions, it lacks the embodied emotional experiences essential for human interaction. This insight emphasizes the need for AI systems to support, rather than disrupt, organizational meaning-making processes. Originality/value: This article offers an original interdisciplinary perspective, merging phenomenological philosophy with organizational behavior. By examining emotional and artificial intelligence through Descartes, Husserl and Merleau-Ponty, the study presents fresh insights into AI design that prioritizes human-centered development. It contributes to AI ethics and organizational behavior literature by emphasizing the role of emotional intelligence in guiding AI integration within organizational contexts