Search Results (7)

Background: Accurate and timely diagnosis of endometrial cancer is crucial for guiding effective treatment and improving patient survival. Endometrial cancer diagnosis, staging, metastasis detection, and treatment planning utilize endometrial biopsy, magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET/CT) scanning as crucial diagnostic modalities. Aggressive subtypes such as serous, mixed cell, clear cell, and grade 3 endometrioid carcinomas present considerable diagnostic and therapeutic obstacles given their unfavorable prognosis, underscoring the importance of accurate preoperative evaluation. Methods: A retrospective analysis was conducted using data from seventy patients diagnosed with serous, mixed cell, clear cell, or grade 3 endometrioid endometrial cancer, who received surgical treatment from 2020 to 2023. To assess the diagnostic capabilities of each modality in determining final pathology and disease staging, a comparison was performed using results from preoperative endometrial biopsy, MRI, PET/CT, and postoperative histopathology. Cohen’s kappa coefficient was employed to determine the level of agreement observed between pre- and postoperative results. Results: Endometrial biopsy demonstrated moderate yet statistically significant concordance with definitive histopathological diagnoses (κ = 0.537, p < 0.001); however, diagnostic errors were observed, especially in instances of mixed and clear cell carcinomas. MRI demonstrated efficacy in identifying local tumor invasion, yet its capacity to detect distant metastases was demonstrably limited. PET/CT was most effective in identifying distant metastases and omental involvement in advanced-stage disease. Conclusions: Definitive pathological diagnosis and staging of endometrial carcinoma are effectively established using endometrial biopsy and MRI. The utility of PET/CT is particularly pronounced in identifying distant metastases in patients with serous carcinoma and advanced-stage disease. Integrating biopsy, MRI, and PET/CT into a multimodal diagnostic strategy enhances diagnostic accuracy and enables personalized treatment planning, particularly for aggressive tumor subtypes. Full article

In this study, carrot (orange and black) powder substitution (0–15%) and different dough applications (guar gum (GG) addition, pregelatinization (PG) and a PG + GG combination) were researched in gluten-free pasta preparation to improve the bioactive components and technological properties. Some quality attributes and bioactive components of the pasta were determined. Black carrot powder substitution into the pasta revealed rich functional properties with higher total dietary fiber (TDF), Ca, K, Mg, P and total phenolic content (TPC) than orange carrot powder. An increased carrot powder addition ratio in the gluten-free pasta formulation resulted in enrichment in ash, mineral, β-carotene, total anthocyanin, TDF, antioxidant activity and TPC. The amounts of β-carotene and anthocyanin in the pasta samples rose to 4.42 mg/100 g and 26.08 mg CGE/100 g with the addition of 15% orange and black carrot powders, respectively. Increasing cooking loss due to high utilization ratios of carrot powder was eliminated by PG and PG + GG applications, and technologic quality was improved, especially with the PG + GG combination. Full article

Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model’s predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples. Full article

The developing fetus is dependent on the maternal nutritional environment. This study was conducted to determine the effects of a maternal high-fat diet (HFD) applied during pregnancy and/or lactation on the expression levels of some lipid-related genes in rat models. Half of the pregnant rats (n: 6) were fed an HFD (energy from fat: 45%), while the other half (n: 6) were fed a control diet (CD) (energy from fat, 7.7%) during the pregnancy period. During lactation, dams in both groups were divided into two subgroups, with half fed the CD and the other half fed the HFD. Thus, four groups were obtained: CD-CD, CD-HFD, HFD-CD, and HFD-HFD. At the end of lactation, all mothers and half of the offspring were sacrificed. The remaining offspring were fed a CD for five weeks. The average birth weight of the CD group offspring was found to be lower than that of the HFD group (p < 0.05). The amount of adipose tissue was highest in CD-HFD (p < 0.05), while gene expression levels were similar between groups (p > 0.05), and the most degenerative histological changes were observed in the eight-week HFD-HFD (p < 0.05). This study suggests that maternal HFD during pregnancy and lactation may increase adiposity in offspring rats, especially during the weaning period. Full article

Heat-related traits in chickpea (Cicer arietinum L.) play a crucial role in reducing the harmful effect of heat stress, as the increase in heat stress is predicted to occur in the coming years due to global warming as a result of climate change. The advantage of multiple pods per peduncle and compound (imparipinnate) leaf traits in kabuli chickpea has not been properly revealed under heat stress conditions. We, therefore, want (i) to provide insight into the advantage of multiple pods and compound leaf traits over single pod per node and simple (unifoliolate) leaf traits, and (ii) to determine the highest direct and indirect effects of agro-morphological traits on seed yield in chickpeas under rainfed conditions with prevailing heat stress. With a delayed sowing time, the plants were subjected to heat stress of more than 43 °C in flowering and pod setting stages under field conditions. According to the number of pods per node and leaf shape, plants were evaluated for yield and yield components, and were divided into six groups, namely (i) single-pod and compound leaf, (ii) single-pod and simple leaf, (iii) double-pods and compound leaf, (iv) double-pods and simple leaf, (v) multi-pods and compound leaf, and (vi) multi-pods and simple leaf. Plants with multi-pods and compound leaf traits had the highest seed yield, followed by plants with double-pods and compound leaf, while plants with single-pod and simple leaf had the lowest yield. The number of seeds and pods per plant, 100-seed weight, and leaf shape were the highest determinants of seed yield under heat stress conditions. It was concluded that multi-pods per peduncle and compound leaf traits had an obviously incontrovertible advantage in kabuli chickpeas under heat stress conditions. The plant shapes that nature has evolved for millions of years, which are mostly found in wild plants, have been proven by the current study to have a better fitness ability than plants shaped by human hands. Full article

Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results. Full article

Accurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have a significant role in predicting of COVID-19. In this study, we performed binary classification (COVID-19 vs. other types of coronavirus) by extracting features from genome sequences. Support vector machines, naive Bayes, K-nearest neighbor, and random forest methods were used for classification. We used viral gene sequences from the 2019 Novel Coronavirus Resource Database. Experimental results presented show that a decision tree method achieved 93% accuracy. Full article