L-methioninase degrades methionine, which is essential in methionine-dependent cancer cells, resulting in specific cell death. Normal cells can synthesize their own methionine amino acids even in the absence of exogenous methionine. This selective targeting of cancer cells makes L-methioninase a promising therapeutic candidate for cancer. In this study, L-methioninase was partially purified from Brevibacterium linens BL2. The specific activity of the enzyme was found as 3.055 units/mg. IC₅₀ values (24 h) of the enzyme were 5.792 units/mL for U87MG cell line and 5.215 units/mL for T98G cell line. When L-methioninase and etoposide were used in combination, synergistic cytotoxic and cell migration inhibition effects on U87MG and T98G cells alongside decreased cytotoxic activity on the Mouse Embryonic Fibroblast and HaCaT cells compared to etoposide alone were observed. Additionally, colony numbers of U87MG cells were significantly reduced by L-methioninase and etoposide administration after 21 days of incubation. Furthermore, L-methioninase suppressed the expression levels of survivin and c-Myc while increasing the expression level of Caspase-3 in both glioblastoma cell lines. These effects were enhanced when etoposide was used in combination with etoposide. This investigation reveals that the L-methioninase enzyme not only exhibited cytotoxic effects on U87MG and T98G cells but also enhanced the anti-proliferative effects of etoposide when used in combination while also demonstrating fewer adverse effects on normal cells.

Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network (ANN) and adaptive-network-based fuzzy inference system (ANFIS) for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 (HE4), carbohydrate antigen 125 (CA-125), and carcinoembryonic antigen (CEA), are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9% in predicting ovarian cancer. With an accuracy rate of at least 89%, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

Nutritional information on menus can assist customers in making healthier eating choices. One technique being utilized to tackle the rise of overweight and obesity is the use of nutritional information on menus. Menu engineering strategies can be used to improve sales of generally healthier and higher margin items. For today's food and beverage companies, menu engineering has become essential. Companies must continually evaluate their menus in order to keep up with changing customer demands and the conditions of the competitive market. Menu engineering's core involves comparing the effectiveness of each menu. At this point, correct decision-making under numerous factors is thought to be a very challenging procedure. To evaluate alternatives according to many features, several Multi-Criteria Decision-Making (MCDM) approaches have been created. The main novelty of this paper is that four MCDM methods, including Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), Fuzzy TOPSIS, VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Fuzzy VIKOR, are employed to evaluate menu options. Comparative analysis of MCDM methods is another contribution of this study. The process of evaluating and selecting healthier menu alternatives can become challenging and time-consuming. This study pointed out how crucial it is to conduct comparative analysis using various MCDA methods and to carefully determine the right ones when addressing the issue of selecting the best menu, taking into account the values of the criterion in fuzzy numbers.

This study aimed to comparatively optimize and evaluate the quality characteristics of Washington Navel orange slices using vacuum microwave drying (VMD) and conventional hot air drying (HAD) systems. Response Surface Methodology based on the Box–Behnken design was applied to both systems. For the models developed in the VMD system, the coefficient of determination (R²) was found to be in the range of 0.96–0.97, and the optimum conditions were determined as 4 kW power, 60 °C temperature, and 2 mm slice thickness. For HAD, the optimum conditions were determined as 78 °C temperature, 1.57 m/s air velocity, of 2.3 mm slice thickness. VMD showed superior performance compared to hot air drying in terms of total phenolic preservation, retention of bioactive compounds, and rehydration capacity. Hydroxymethylfurfural (HMF) formation was higher during hot-air drying. The effective moisture diffusivity (D_eff) was significantly higher in VMD (8.38 × 10⁻¹⁰ m²/s) than in HAD (1.49 × 10⁻¹⁰ m²/s), indicating enhanced internal moisture transport under vacuum microwave conditions. The results revealed that VMD is an efficient technology for producing high-quality dried citrus products with improved bioactive retention and reduced processing time.