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- MakaleAçık Erişim2024Scopus SJRQ3Unveiling the significance of Gremlin-1 as a novel biomarker for enhanced severity prediction in COVID-19 patients
Celik, Muhammet; Tekin, Erdal; Koçak, Mehmet Nuri; Uzunçakmak, Sevgi Karabulut; Bayraktar, Mustafa
Gremlin-1 is associated with lung disease and plays a role in the initiation and progression of pulmonary fibrosis. This suggests that Gremlin-1 may be associated with lung involvement in COVID-19 and poor clinical outcomes and warrants further investigation. This prospective, cross-sectional, single-blind study was the first to investigate Gremlin-1 levels in COVID-19 patients and whether Gremlin-1 levels could be used to predict and guide clinical follow-up in outpatients and inpatients. In this context, serum Gremlin-1 levels were measured and compared in 2 groups of patients who were diagnosed as positive for COVID-19 by RT-PCR and were followed up as outpatients (n = 28) or required hospitalization (n = 30). The median Gremlin-1 values were statistically significantly different between outpatients and intensive care unit patients (0.367 ng/mL [IQR = 0.377], 1.858 ng/mL [IQR = 2.245], respectively) (P < .0001). The area under the ROC curve value to determine the discriminative power of Gremlin-1 was found to be 0.772 (95% CI: 0.672-0.871; P < .0001), and the cutoff value of Gremlin-1 to discriminate between outpatients and hospitalized patients was found to be 1.242 ng/mL with 66.7% sensitivity and 67.2% specificity. Serum Gremlin-1 level is an important biomarker that can be used as a clinical decision-making tool for COVID-19 positive patients.
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İncele - MakaleAçık Erişim2022Scopus SJRQ2Determination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approach
Teke, Çağatay
Ductile irons (DIs) have properties such as high strength, ductility, and toughness, as well as a low degree of melting, good fluidity, and good machining. Having these characteristics make them the most preferred among cast irons. The combination of excellent properties, especially in DI castings with a thin section, make it an alternative for steel casting and forging. But in the manufacture of thin-section parts, fluidity characteristics need to be improved and the liquid metal must fill the mold completely. The fluidity of liquid metal is influenced by many factors depending on the casting processes such as material and mold properties, casting temperature, inoculation, globalization, and grain refinement. In this study, an artificial neural network (ANN) model has been developed that allows for determining the flow distance of the liquid metal in the sand mold casting method under changing casting conditions of DI. Thus, the flow distance was estimated depending on the cross-sectional thickness during the sand casting under changing casting conditions. The experimental parameters were determined as casting temperature, liquid metal metallurgy quality, cross-sectional thickness, and filling time. Filling modeling was performed with FlowCast software. When the results were examined, it was seen that the developed ANN model has high success in predicting the flow distances of the liquid metal under different casting conditions. The calculated coefficient of determination (R2) value of 0.986 confirms the high prediction performance of the model.
AtıfScopus4WoS4İncele - MakaleAçık Erişim2021Scopus SJRQ1Prediction of water-adulteration within honey by air-line de-embedding waveguide measurements
Hasar, Hafize; Hasar, Ugur Cem; Kaya, Yunus; Ozturk, Hamdullah; Izginli, Mucahit; Ozbek, Ibrahim Yucel; Oztas, Taskin; Canbolat, Mustafa Yildirim; Ertugrul, Mehmet
An efficient de-embedding air-line microwave method has been proposed for accurate relative complex permittivity, εr=εr′−iεr′′, measurement of water-adulteration level within honey. It could be effectively applied to eliminate the errors arising from usage of imperfect calibration standards because it bypasses the requirement of these standards. Its accuracy is improved by utilizing the unitary and similarity properties of a passive two-port network, and then is compared with the accuracy of a calibration-dependent method present in the literature by using normalized root-mean-square-error (N-RMSE) values of εr′ and εr′′ of distilled water, in reference to the Debye value. From this comparison, it is observed that N-RMSE values calculated for εr′ (and εr′′) by using this calibration-dependent method and the (improved) proposed method are, respectively, around 0.1955 (0.1002) and 0.1962 (1.1067), indicating a good agreement between them. After validation the proposed de-embedding method using distilled water measurements, tested pure honey was adulterated with distilled water by different percentage values δ ranging from 1% to 10% in 1% increments. It is observed that the maximum distance between extracted εr′ (or εr′′) values of adulterated honey by the applied calibration-dependent method and the proposed method is less than 2%. Afterward, an empirical formula was devised to fit εr′ and εr′′ values from measured εr of water-adulterated honey with various δ levels. It is noted that extracted εr′ is much more better fitted than extracted εr′′, especially for δ≤4. Next, an optimization process is followed to evaluate the frequency for optimum prediction of adulteration levels using the empirical formula based on εr′ or εr′′. It is noticed that optimized δ values using the empirical formula based on εr′ (with an average prediction error of around 0.071 at 4.5 GHz) are superior to optimized δ values using the empirical formula based on εr′′ (with an average prediction error of around 0.085 at 4.2 GHz) for prediction of previously known δ values. Sensitivity and uncertainty analyses were performed to assess and improve the accuracy of the proposed method.
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