Browsing by Author "Avramović, Nataša (23134505800)"

Cancer is one of the leading causes of death in children and adolescents worldwide; among the types of liver cancer, hepatoblastoma (HBL) is the most common in childhood. Although it affects only two to three individuals in a million, it is mostly asymptomatic at diagnosis, so by the time it is detected it has already advanced. There are specific recommendations regarding HBL treatment, and ongoing studies to stratify the risks of HBL, understand the pathology, and predict prognostics and survival rates. Although magnetic resonance imaging spectroscopy is frequently used in diagnostics of HBL, high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy of HBL tissues is scarce. Using this technique, we studied the alterations among tissue metabolites of ex vivo samples from (a) HBL and non-cancer liver tissues (NCL), (b) HBL and adjacent non-tumor samples, and (c) two regions of the same HBL samples, one more centralized and the other at the edge of the tumor. It was possible to identify metabolites in HBL, then metabolites from the HBL center and the border samples, and link them to altered metabolisms in tumor tissues, highlighting their potential as biochemical markers. Metabolites closely related to liver metabolisms such as some phospholipids, triacylglycerides, fatty acids, glucose, and amino acids showed differences between the tissues. © 2022 by the authors.

Cancer is one of the leading causes of death in children and adolescents worldwide; among the types of liver cancer, hepatoblastoma (HBL) is the most common in childhood. Although it affects only two to three individuals in a million, it is mostly asymptomatic at diagnosis, so by the time it is detected it has already advanced. There are specific recommendations regarding HBL treatment, and ongoing studies to stratify the risks of HBL, understand the pathology, and predict prognostics and survival rates. Although magnetic resonance imaging spectroscopy is frequently used in diagnostics of HBL, high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy of HBL tissues is scarce. Using this technique, we studied the alterations among tissue metabolites of ex vivo samples from (a) HBL and non-cancer liver tissues (NCL), (b) HBL and adjacent non-tumor samples, and (c) two regions of the same HBL samples, one more centralized and the other at the edge of the tumor. It was possible to identify metabolites in HBL, then metabolites from the HBL center and the border samples, and link them to altered metabolisms in tumor tissues, highlighting their potential as biochemical markers. Metabolites closely related to liver metabolisms such as some phospholipids, triacylglycerides, fatty acids, glucose, and amino acids showed differences between the tissues. © 2022 by the authors.

Bipolar disorder (BD) is a brain disorder that causes changes in a person’s mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A 1H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD. © 2023 by the authors.

Bipolar disorder (BD) is a brain disorder that causes changes in a person’s mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A 1H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD. © 2023 by the authors.

Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia. © 2022 by the authors.

Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia. © 2022 by the authors.

Cancer is one of the most common fatal diseases in humans nowadays. About 20 million new cancer cases are expected in the next two decades worldwide. The development of new chemotherapeutic agents with improved properties is presently the main challenge in the medicinal chemistry. Cisplatin was introduced to oncology in 1978 as first chemotherapeutic agent regarding its specific interaction with DNA, leading to its damage and causing the cell death. Since the first application of cisplatin in cancer therapy, there has been a growing interest in new metal-based compounds, in particular platinum and ruthenium complexes, with better anticancer activity and less side-effects compared to cisplatin. Carboplatin and oxaliplatin have shown promising action against some types of cancer, which are resistant to cisplatin. With the aim to overcome cross-resistance to these Pt(II) drugs, bioavailable platinum complexes (satraplatin and picoplatin) firstly found application as orally administered drugs, as well as some combined therapies of Pt(II) drugs (cisplatin, picoplatin) with specific resistant modulators. In recent years, novel polymer and liposomal formulations of platinum drugs (prolindac, lipoplatin, lipoxal, aroplatin) have been designed with strategy to improve drug delivery to target cancer cells and reduce toxicity. Complexes based on ruthenium have great potential to become leading candidates for the medical use in anticancer therapy. Some of these compounds have shown good anticancer activity, both in vitro and in vivo and two of them (KP1019 and NAMI-A) have passed clinical trials and given promising results. © 2019, Serbia Medical Society. All rights reserved.

Papillary thyroid carcinoma (PTC) is generally considered an indolent cancer. However, patients with cervical lymph node metastasis (LNM) have a higher risk of local recurrence. This study evaluated and compared four machine learning (ML)-based classifiers to predict the presence of cervical LNM in clinically node-negative (cN0) T1 and T2 PTC patients. The algorithm was developed using clinicopathological data from 288 patients who underwent total thyroidectomy and prophylactic central neck dissection, with sentinel lymph node biopsy performed to identify lateral LNM. The final ML classifier was selected based on the highest specificity and the lowest degree of overfitting while maintaining a sensitivity of 95%. Among the models evaluated, the k-Nearest Neighbor (k-NN) classifier was found to be the best fit, with an area under the receiver operating characteristic curve of 0.72, and sensitivity, specificity, positive and negative predictive values, F1 and F2 scores of 98%, 27%, 56%, 93%, 72%, and 85%, respectively. A web application based on a sensitivity-optimized kNN classifier was also created to predict the potential of cervical LNM, allowing users to explore and potentially build upon the model. These findings suggest that ML can improve the prediction of LNM in cN0 T1 and T2 PTC patients, thereby aiding in individual treatment planning. © 2023 by the authors.