Uludag University Academic Repository
Not a member yet
50710 research outputs found
Sort by
The relationship between serum bisphenol a levels and diabetic peripheral polyneuropathy in patients with type 2 diabetes mellitus
No full textObjective: The present investigation sought to explore the potential link between serum bisphenol A (BPA)-a well-recognized endocrine-disrupting agent-and diabetic peripheral polyneuropathy (PNP) among patients diagnosed with type 2 diabetes mellitus (DM). Methods: Eighty patients underwent clinical and electrophysiologic assessment of PNP and single-time-point serum BPA quantification by enzyme-linked immunosorbent assay. Results:The average age and diabetes duration did not differ significantly between patients with PNP and those without. However, fasting glucose, glycosylated hemoglobin (HbA1c), and triglyceride levels were notably elevated in the PNP group relative to non-PNP patients. But serum BPA levels were not statistically different between the groups. Serum BPA levels were negatively correlated with the glomerular filtration rate and HbA1c and positively correlated with the body mass index and high-density lipoprotein cholesterol levels. Conclusion: No significant association was observed between serum BPA concentrations and the presence of PNP in patients with type 2 DM. These results confirm that known vascular risk factors such as glycemic control are closely related to the presence of PNP
Breaking the cross-sensitivity degeneracy in fbg sensors: A physics-informed co-design framework for robust discrimination
No full textThe simultaneous measurement of strain and temperature using Fiber Bragg Grating (FBG) sensors presents a significant challenge due to the intrinsic cross-sensitivity of the Bragg wavelength. While recent studies have increasingly employed "black-box" machine learning algorithms to address this ambiguity, such approaches often overlook the physical limitations of the sensor's spectral response. This paper challenges the assumption that advanced algorithms alone can compensate for data that is physically ambiguous. We propose a "Sensor-Algorithm Co-Design" methodology, demonstrating that robust discrimination is achievable only when the sensor architecture exhibits a unique, orthogonal physical signature. Using a rigorous Transfer Matrix Method (TMM) and 4 x 4 polarization analysis, we evaluate three distinct architectures. Quantitative analysis reveals that a standard Quadratically Chirped FBG (QC-FBG) functions as an "ill-conditioned baseline" failing to distinguish measurands due to feature space collapse (K-cond>4600). Conversely, we validate two robust co-designs: (1) An Amplitude-Modulated Superstructure FBG (S-FBG) paired with an Artificial Neural Network (ANN), utilizing thermally induced duty-cycle variations to achieve high accuracy (similar to 3.4 degrees C error) under noise; and (2) A Polarization-Diverse Inverse-Gaussian FBG (IG-FBG) paired with a 4 x 4 K-matrix, exploiting strain-induced birefringence (K-cond approximate to 64). Furthermore, we address the data scarcity issue in AI-driven sensing by introducing a Physics-Informed Neural Network (PINN) strategy. By embedding TMM physics directly into the loss function, the PINN improves data efficiency by 2.2x compared to standard models, effectively bridging the gap between physical modeling and data-driven inference, addressing the critical data scarcity bottleneck identified in recent optical sensing roadmaps
Genetic parameter estimation for live weight during different life periods in anatolian buffalo raised in Istanbul
No full textIn T & uuml;rkiye, as in many parts of the world, buffalo play a significant role in livestock production alongside cattle. Although buffaloes generally exhibit lower productivity than cattle, they are valued for their resilience against challenging environmental conditions and for the unique quality of their milk and meat products. This study aimed to estimate the genetic parameters of live-weight gain, a key trait for improving profitability in buffalo breeding. Heritability estimates were obtained for birth weight (BW), live weight at 6 months (LW6), and live weight at 12 months (LW12) using data from 910 animals across 42 farms in the province of Istanbul. Genetic evaluations were performed using the BUGA 1.0 software, applying the AI-REML algorithm.The mean weights were 39.02 +/- 0.169 kg (BW), 140.86 +/- 0.4 kg (LW6), and 255.97 +/- 0.692 kg (LW12). The corresponding heritability estimates were 0.5006 +/- 0.000029 for BW, 0.5001 +/- 0.000035 for LW6, and 0.5000 +/- 0.0000012 for LW12. Additive genetic effects exhibited moderate to high accuracy, ranging from 0.63 to 0.68. The proportion of animals with positive additive genetic effects was relatively high for LW6 and LW12 at 49.34 % and 48.13 %, respectively. Genetic trend analysis was also conducted over time for all three traits, highlighting the potential for selection-based improvement in Anatolian buffalo.Ministry of Agriculture and Forestry, Directorate General for Agricultural Research and Policies (TAGEM
The mediating role of subjective career success on the relationship between different types of capital, well-being and unethical work behaviour
No full textPurposeThis study investigates the mediating role of subjective career success (SCS) in the relationship between different types of capital, general well-being (GWB) and unethical work behaviour (UWB), drawing on "conservation of resources theory (CRT)", "social resources theory (SRT)", and "human capital theory (HCT)".Design/methodology/approachUsing convenience sampling, data were collected via a questionnaire from 607 nurses working in public hospitals in Turkey and analysed using Structural Equation Modelling (SEM).FindingsThe results indicate that psychological capital (PC), human capital (HC), and cultural capital (CC) positively influence SCS, which in turn positively affects GWB and negatively impacts UWB. Additionally, SCS significantly mediates the relationships between PC, HC, and CC, and both GWB and UWB. However, social capital (SC) was not found to have a significant direct or indirect effect on SCS, GWB, or UWB.Research limitations/implicationsAs the data were collected from only eight hospitals in the Marmara region means the findings should be interpreted with caution.Originality/valueBy integrating three different theories, this study examines the mediating role of SCS between different types of capital and the GWB and UWB of employees. Additionally, the study introduces CC as an underutilised antecedent of SCS. Besides employee GWB, the study also investigates another possible consequence of SCS, namely UWB, which is a relatively under-researched outcome of SCS. Finally, the study is conducted in the context of hospitals in a developing country
Assessment of the oil release and insect repellent activity of spray-dried gum arabic/citronella oil microcapsules
No full textEssential oils are natural insect repellents, which can be microencapsulated and protected by wall materials to provide prolonged protection against insects. The protection and release of these repellents depend on various parameters, including morphology and production conditions. Herein, twenty-seven gum arabic/citronella essential oil (GA/CEO) spray-dried microcapsules were produced by using three wall-to-core ratios (3:1, 4:1, 6:1), three inlet temperatures (120, 150, 180 degrees C), and three feed rates (1, 2.5, 5 mL/min). The morphology, particle size, encapsulation efficiency, and release rates were evaluated. The insect repellent activity of microcapsules (0.25, 0.5, and 1 g) against Drosophila melanogaster flies was tested. A systematic process optimization was carried out by evaluating the effects of both emulsion concentration and process parameters on the release rates. Microcapsules with smooth surfaces and homogeneous particle sizes were produced. Encapsulation efficiency reached 90% by increasing the inlet temperature and feed rate. Slower release rates (approximately 40%) were achieved with higher concentrations of the wall material and temperatures, generally. Optimal process conditions were determined as a wall-to-core ratio of 4:1, temperatures exceeding 150 degrees C, and feed rates above 2.5 mL/min. The highest repellent activity achieved was 95%, indicating effectiveness of GA/CEO microcapsules as insect repellent materials
Breast masses in childhood: A single-center experience
No full textObjective Pediatric breast masses are rare conditions. Although most of them are benign, they can cause concern in families. The present study aims to determine breast masses' clinical and pathological outcomes in childhood.Material and method The records of patients who underwent further evaluations for breast masses between 2010 and 2023 at a single center were retrospectively reviewed.Results A total of 32 patients with breast tumors were included in the study. The median age of the patients was 16 years (1-18 years); 90.6% (n = 29) were female, and 9.4% (n = 3) were male. Most patients, 90.6% (n = 29), had a painless, palpable mass. A family history of breast cancer was present in four patients. One patient had received chemotherapy for neuroblastoma and one for teratoma. The most common location was the upper outer quadrant in 35.5% of patients (n = 11). Bilateral mass involvement was present in five patients (15.6%). The mean tumor size was 32.64 +/- 17.4 mm (range 9-80 mm). The mean tumor diameter was 24.6 +/- 14.2 mm in patients who were followed without surgery and 39.2 +/- 17.4 mm in those who underwent surgery (P = 0.017). A biopsy was performed in 53.1% (n = 17) of the patients, and surgery in 56.2% (n = 18). The most frequent pathology was fibroepithelial lesion and fibroadenomas 57.1% (n = 20). Malignant tumors (leiomyosarcoma and T cell lymphoma) were observed in 6.3% of the patients (n = 2) and borderline phyllodes tumors in 18.7% (n = 6). Recurrence was observed in 18.7% of the patients (n = 6) during the follow-up.Conclusion In childhood, the most commonly encountered breast tumors are benign. However, careful monitoring is crucial due to the potential occurrence of malignant tumors. Further evaluations should be undertaken in patients with a history of malignancy or radiotherapy, masses larger than 5 cm, or masses with progressive growth
Detection of hyperglycemia and hypoglycemia using deep learning from facial images obtained with an ai image generator
No full textThe identification of hyperglycemia and hypoglycemia is paramount in diabetes care, facilitating prompt interventions to mitigate potential health complications. A novel method is introduced for identifying glycemic states using deep learning from facial images generated by artificial intelligence (AI). Specifically, the EfficientB0 model-a pre-trained convolutional neural network (CNN)-is employed, utilizing the transfer learning technique to leverage its learned features for glycemic state classification. The proposed method offers a non-invasive and remote monitoring solution, allowing for convenient glycemic status assessment without the need for invasive procedures or continuous glucose monitoring devices. The experimental results confirm the effectiveness of the proposed method. The achieved accuracy rates, recall rates, and F1-scores validate the model's ability to accurately identify individuals at risk of glycemic abnormalities. The integration of deep learning techniques with facial image analysis holds promise for personalized healthcare solutions tailored to individuals with diabetes, facilitating early detection and intervention for improved glycemic control. By leveraging AI-driven facial image analysis, individuals with diabetes can benefit from early detection and prediction of hyperglycemic and hypoglycemic events, enabling timely interventions and adjustments in treatment regimens. This approach holds promise for improving glycemic control, reducing the risk of acute complications, and enhancing overall quality of life for individuals with diabetes. The non-invasive approach for detecting glycemic states presented in this paper has the potential to revolutionize healthcare management for individuals with diabetes
Hyperspectral imaging-based non-destructive detection of freshness changes in MAP stew-braised duck neck during refrigerated storage
No full textStew-braised duck (SBD) products packaged with modified atmosphere packaging (MAP) are prone to quality deterioration during refrigerated storage. Traditional detection methods are time-consuming and invasive. This study aimed to investigate the quality changes of MAP-packaged SBD and to achieve real-time, non-destructive detection using hyperspectral imaging (HSI) without opening the packages. Freshness indicators were evaluated using traditional methods, including pH, total viable count (TVC), low-field nuclear magnetic resonance (LF-NMR), and total volatile basic nitrogen (TVB-N) at 4 °C and 10 °C. A unique image segmentation approach was applied to extract spectral data in the 900–1700 nm range, which were analyzed to evaluate quality changes during 19 days, with a focus on moisture distribution and TVB-N levels. A three-stage fusion strategy involving machine learning models (PLS, RF, PLS-RF), preprocessing techniques (MSC, SG, SNV) and feature extraction methods (CARS, GA, IVSO) was developed. Ultimately, the full-wavelength model at 4 °C using PLS-RF (Rc2 = 0.967, RMSEC = 0.710, Rp2 = 0.749, RMSEP = 1.951, RPD = 2.026) and the model at 10 °C with SNV-CARS preprocessing using PLS-RF (Rc2 = 0.961, RMSEC = 0.944, Rp2 = 0.747, RMSEP = 2.431, RPD = 2.003) were identified as optimal for visualizing pixel-level predictions of TVB-N content. This research confirms the feasibility and potential of HSI for non-destructive and rapid detection in MAP-packaged products
Ganglioneuroma has a potential for lymph node metastasis, not impacting recurrence
No full textGanglioneuromas are rare, benign tumors arising from neuroblastic cells in the autonomic sympathetic nervous system. While generally considered indolent, limited case reports suggest their potential for regional metastasis. This retrospective study analyzed the clinical, demographic, and pathological features of 25 adult patients diagnosed with ganglioneuroma at Bursa Uludag University, Faculty of Medicine, between April 2007 and November 2023. The cohort comprised 18 females (72%) and seven males (28%), with a median age of 42 years (range: 19-79). Tumors were most commonly located in the abdomen (64%), followed by the thoracic (24%) and head and neck regions (12%), with the adrenal gland being the primary site in 32% of cases. Symptoms were present in 56% of patients, including pain, vision loss, hypertension, and palpable masses, while the remaining were asymptomatic. Surgical resection was performed in 92% of cases, with a median tumor size of 7.5 cm (1.5-18 cm). Median follow-up time was 88.3(16.2 - 217.8) months. Regional lymph node metastases were identified in 8% of patients, but no distant metastases or recurrences were observed during follow-up. These findings, including the novel observation of regional metastases, contribute valuable insights to the limited literature on ganglioneuromas. Despite its benign nature, this study highlights the potential for lymph node metastasis. However, the relationship between lymph node metastasis and recurrence has not been documented. In this context, further research is essential to better understand the risk factors, tumorigenesis, and the optimal management strategies for this rare tumor
Carbohydrate counting in traditional Turkish fast foods for individuals with type 1 diabetes: Can artificial intelligence models replace dietitians?
No full textObjectives Carbohydrate counting is a recommended approach for achieving glycemic control in individuals with type 1 diabetes (T1D). This study aimed to compare the accuracy of carbohydrate content estimations for traditional Turkish fast foods made by artificial intelligence (AI) models and dietitian. Methods Children and adolescents with T1D were pretested to identify the 12 most preferred Turkish fast-food items. Standardized recipes were developed for these meals, and the meals were photographed under standardized angular and lighting conditions. The photos were then uploaded to AI applications (ChatGPT-4.0, DeepSeek, Gemini, and CarbManager) and each model was prompted to estimate the carbohydrate content of the respective food items. Dietitians were asked to estimate the carbohydrate content based on these photographs. Results Of the dietitians in the study ( n = 40), 50% had postgraduate education, and 17.5% of those providing carbohydrate counting education ( n = 20, 50.0%) had been doing so for more than 7 y. No significant difference was found between the carbohydrate estimates of dietitians who provided and those who did not provide carbohydrate counting training ( P > 0.05). The intraclass correlation coefficient (ICC) between the AI models was 0.3554 (95% confidence interval [CI]: 0.0974–0.6801), indicating low reliability. The highest agreement with the estimates of dietitians who provided carbohydrate counting training (ICC = 0.417, 95% CI: 0.247–0.685) and those who did not (ICC = 0.307, 95% CI: 0.163–0.578) was observed with ChatGPT. Conclusions AI models can assist individuals with diabetes and healthcare professionals in estimating the carbohydrate content of foods, and consequently, can make a significant contribution to diabetes self-management