Sandi Baressi Baressi Šegota, S. Lysdahlgaard, S. Hess, R. Antulov
The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.
{"title":"USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION","authors":"Sandi Baressi Baressi Šegota, S. Lysdahlgaard, S. Hess, R. Antulov","doi":"10.46793/iccbi21.263bs","DOIUrl":"https://doi.org/10.46793/iccbi21.263bs","url":null,"abstract":"The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89167866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jelisaveta Ignjatović, T. Šušteršič, S. Cvijić, Aleksandar Bodić, Jelena Đuriš, N. Filipovic
Computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM) appeared as an alternative approach to the commonly used in vitro methods for the assessment of dry powders for inhalation (DPI) aerodynamic properties. The aim of this study was to compare the parameters that describe DPI aerodynamic performance, obtained computationally by CFD-DPM and in vitro by next generation impactor (NGI). The analyzed parameters included: emitted fraction (EF), fine particle fraction (FPF), mass median aerodynamic diameter (MMAD) and geometric standard deviation (GSD). The results showed that CFD-DPM simulated EF values were generally comparable to the NGI obtained values, but there were some differences between the results obtained by these two methods. On the other hand, CFD-DPM predicted MMAD values were almost twice bigger than the NGI determined values, while the predicted GSD values were lower than NGI obtained values. In addition, CFD-DPM predicted values indicated larger differences between MMAD for different formulations in comparison to the NGI results. The largest difference between CFD-DPM and NGI results was observed for FPF values. Namely, CFD-DPM predicted FPF values were markedly lower than the NGI determined values for four of five tested formulations. Overall, although the designed CFD-DPM model and NGI measurements provided comparable data on the DPI EF values, the other relevant parameters obtained by these two approaches largely diverged indicating the need for further refinement of computational models to fully capture DPI aerodynamic performance.
{"title":"COMPUTATIONAL VS. IN VITRO APPROACH TO PREDICT AERODYNAMIC PERFORMANCE OF DRY POWDERS FOR INHALATION","authors":"Jelisaveta Ignjatović, T. Šušteršič, S. Cvijić, Aleksandar Bodić, Jelena Đuriš, N. Filipovic","doi":"10.46793/iccbi21.096i","DOIUrl":"https://doi.org/10.46793/iccbi21.096i","url":null,"abstract":"Computational fluid dynamics (CFD) coupled with discrete phase modeling (DPM) appeared as an alternative approach to the commonly used in vitro methods for the assessment of dry powders for inhalation (DPI) aerodynamic properties. The aim of this study was to compare the parameters that describe DPI aerodynamic performance, obtained computationally by CFD-DPM and in vitro by next generation impactor (NGI). The analyzed parameters included: emitted fraction (EF), fine particle fraction (FPF), mass median aerodynamic diameter (MMAD) and geometric standard deviation (GSD). The results showed that CFD-DPM simulated EF values were generally comparable to the NGI obtained values, but there were some differences between the results obtained by these two methods. On the other hand, CFD-DPM predicted MMAD values were almost twice bigger than the NGI determined values, while the predicted GSD values were lower than NGI obtained values. In addition, CFD-DPM predicted values indicated larger differences between MMAD for different formulations in comparison to the NGI results. The largest difference between CFD-DPM and NGI results was observed for FPF values. Namely, CFD-DPM predicted FPF values were markedly lower than the NGI determined values for four of five tested formulations. Overall, although the designed CFD-DPM model and NGI measurements provided comparable data on the DPI EF values, the other relevant parameters obtained by these two approaches largely diverged indicating the need for further refinement of computational models to fully capture DPI aerodynamic performance.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76033728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Car, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Sandi Baressi Baressi Šegota
The collection of image data is an extremely common procedure in clinical practice today. Many of the diagnostic approaches generate such data – computed tomography (CT), X-ray radiography, magnetic resonance imaging (MRI), and others. This data collection process allows for the use of computer vision approaches to be applied with the goal of analysis and diagnostics. Artificial Intelligence (AI) based algorithms have repeatedly been shown to be the best performing computer vision algorithms, in many fields including medicine. AI-based – or more precisely machine learning (ML) based, algorithms have capabilities which allow them to learn the patterns contained in the data from the data itself. Among the best performing algorithms are artificial neural networks (ANNs), or more precisely convolutional neural networks (CNNs). Their pitfall is the need for the large amounts of data – but as it has been previously mentioned, the amount of data collected in today’s clinical practice is large and ever increasing. This allows for the development of Smart Diagnostic systems which are meant to serve as support systems to the health professionals. In this paper first, the standard practices and review of the field is given – with the focus on challenges and best practices. Then, multiple examples of the research applying AI-based algorithm analysis are given – including diagnostics of various cancer types (bladder and oral) as well as COVID-19 severity diagnostics and image quality determination.
{"title":"APPLICATION OF ARTIFICIAL INTELLIGENCE-BASED IMAGE ANALYSIS IN BIOINFORMATICS","authors":"Z. Car, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Sandi Baressi Baressi Šegota","doi":"10.46793/iccbi21.047c","DOIUrl":"https://doi.org/10.46793/iccbi21.047c","url":null,"abstract":"The collection of image data is an extremely common procedure in clinical practice today. Many of the diagnostic approaches generate such data – computed tomography (CT), X-ray radiography, magnetic resonance imaging (MRI), and others. This data collection process allows for the use of computer vision approaches to be applied with the goal of analysis and diagnostics. Artificial Intelligence (AI) based algorithms have repeatedly been shown to be the best performing computer vision algorithms, in many fields including medicine. AI-based – or more precisely machine learning (ML) based, algorithms have capabilities which allow them to learn the patterns contained in the data from the data itself. Among the best performing algorithms are artificial neural networks (ANNs), or more precisely convolutional neural networks (CNNs). Their pitfall is the need for the large amounts of data – but as it has been previously mentioned, the amount of data collected in today’s clinical practice is large and ever increasing. This allows for the development of Smart Diagnostic systems which are meant to serve as support systems to the health professionals. In this paper first, the standard practices and review of the field is given – with the focus on challenges and best practices. Then, multiple examples of the research applying AI-based algorithm analysis are given – including diagnostics of various cancer types (bladder and oral) as well as COVID-19 severity diagnostics and image quality determination.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76681356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Sekulić, M. Stojanović, Tanja B. Trakić, S. Radosavljević, Filip Popović
This paper presents the currently known records on the diversity of earthworm fauna on Šar Mountain. The Šar Mt. is located in the south part of Serbia and is a part of the Šar-Pindus Mountain system. The complete list of present taxa of the family Lumbricidae in the researches area was formed by reviewing data from old collections, relevant literary sources and by its own field research in the period from 2019 to 2020. The list comprises 24 taxa, belonging to 10 genera of the family Lumbricidae. The genera with the largest number of the registered taxa are Dendrobaena (7) and Aporrectodea (5), while the genera Bimastos, Eiseniella, Helodrilus, Octodrilus and Octolasion are represented by one taxon. With respect to the zoogeographical analysis, the majority of the recorded taxa belong to the group of peregrine species (12). The endemic species are represented by two taxa and belong to the genera Dendrobaena and Helodrilus. The Balkan endemic subspecies Helodrilus balcanicus plavensis (Karaman, 1972) and illyric Dendrobaena illyrica (Cognetti, 1906), were recorded on new sites from the Šar Mt., that represent the southernmost limits of the geographical range of these species at the moment.
{"title":"DIVERSITY OF EARTHWORMS (CLITELLATA: OLIGOCHAETA) FROM SERBIAN SIDE OF ŠAR MOUNTAIN","authors":"J. Sekulić, M. Stojanović, Tanja B. Trakić, S. Radosavljević, Filip Popović","doi":"10.46793/iccbi21.198s","DOIUrl":"https://doi.org/10.46793/iccbi21.198s","url":null,"abstract":"This paper presents the currently known records on the diversity of earthworm fauna on Šar Mountain. The Šar Mt. is located in the south part of Serbia and is a part of the Šar-Pindus Mountain system. The complete list of present taxa of the family Lumbricidae in the researches area was formed by reviewing data from old collections, relevant literary sources and by its own field research in the period from 2019 to 2020. The list comprises 24 taxa, belonging to 10 genera of the family Lumbricidae. The genera with the largest number of the registered taxa are Dendrobaena (7) and Aporrectodea (5), while the genera Bimastos, Eiseniella, Helodrilus, Octodrilus and Octolasion are represented by one taxon. With respect to the zoogeographical analysis, the majority of the recorded taxa belong to the group of peregrine species (12). The endemic species are represented by two taxa and belong to the genera Dendrobaena and Helodrilus. The Balkan endemic subspecies Helodrilus balcanicus plavensis (Karaman, 1972) and illyric Dendrobaena illyrica (Cognetti, 1906), were recorded on new sites from the Šar Mt., that represent the southernmost limits of the geographical range of these species at the moment.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73630154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ž. Čupić, Ana I vanović Šašić, S. Maćešić, S. Anic, Ljiljana Kolar- Anić
The first discovered homogeneous oscillatory reaction was the Bray-Liebhafsky (BL) one, described in a paper published exactly 100 years ago. However, the applicability of oscillatory reactions in chemical computing was recently discovered. Here we intend to expose the native computing concept applied to intermittent states of the BL reaction, because we believe that this particular state may have some advantages. For this purpose, numerical simulations will be used based on the known model. Sequences of perturbations will be introduced by adding iodate (IO3-) and hydrogen peroxide (H2O2), separately, as well as in various combinations with one another. It will be shown that dynamic states obtained after perturbations with same species depend very much on the sequence in which these species were used in perturbations. Additionally, it will be shown that obtained dynamic states shift the system from chaotic intermittent dynamic state to different complex periodic states. Hence, the applicability of the BL reaction system in chemical computing was demonstrated.
{"title":"APPLICABILITY OF BRAY-LIEBHAFSKY REACTION FOR CHEMICAL COMPUTING","authors":"Ž. Čupić, Ana I vanović Šašić, S. Maćešić, S. Anic, Ljiljana Kolar- Anić","doi":"10.46793/iccbi21.431c","DOIUrl":"https://doi.org/10.46793/iccbi21.431c","url":null,"abstract":"The first discovered homogeneous oscillatory reaction was the Bray-Liebhafsky (BL) one, described in a paper published exactly 100 years ago. However, the applicability of oscillatory reactions in chemical computing was recently discovered. Here we intend to expose the native computing concept applied to intermittent states of the BL reaction, because we believe that this particular state may have some advantages. For this purpose, numerical simulations will be used based on the known model. Sequences of perturbations will be introduced by adding iodate (IO3-) and hydrogen peroxide (H2O2), separately, as well as in various combinations with one another. It will be shown that dynamic states obtained after perturbations with same species depend very much on the sequence in which these species were used in perturbations. Additionally, it will be shown that obtained dynamic states shift the system from chaotic intermittent dynamic state to different complex periodic states. Hence, the applicability of the BL reaction system in chemical computing was demonstrated.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76240513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Milena Živković, D. Nikezic, T. Miladinović, J. Stajic, D. Krstić
The goal of this research is to determine the levels of natural and artificial radioactivity in 13 different samples of commonly consumed foods from Serbian markets. A gamma spectrometry was used to measure the activity concentrations of 226Ra, 232Th, 40K, and 137Cs. The annual whole-body doses from 137Cs and natural radionuclides, due to the consumption of tea for an adult, are in the range of 2.3– 8.5 nSv for 137Cs, 14.1 – 21.7 nSv for 232Ra, 18.4 – 73.6 nSv for 232Th and for 40K 10.4 – 22.9 nSv. These doses are not harmful to the general public’s health.
{"title":"RADIOACTIVITY ASSESSMENT OF NATURAL RADIONUCLIDES AND 137CS IN COMMONLY CONSUMED FOODS","authors":"Milena Živković, D. Nikezic, T. Miladinović, J. Stajic, D. Krstić","doi":"10.46793/iccbi21.145z","DOIUrl":"https://doi.org/10.46793/iccbi21.145z","url":null,"abstract":"The goal of this research is to determine the levels of natural and artificial radioactivity in 13 different samples of commonly consumed foods from Serbian markets. A gamma spectrometry was used to measure the activity concentrations of 226Ra, 232Th, 40K, and 137Cs. The annual whole-body doses from 137Cs and natural radionuclides, due to the consumption of tea for an adult, are in the range of 2.3– 8.5 nSv for 137Cs, 14.1 – 21.7 nSv for 232Ra, 18.4 – 73.6 nSv for 232Th and for 40K 10.4 – 22.9 nSv. These doses are not harmful to the general public’s health.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79949214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Stojković, V. Jevtić, Đ. Petrović, Sandra S. Jovičić Milić, N. Vukovic, Milena D. Vukić, B. Stojanovic
This paper examines the synthesis of two new complexes of platinum(II/IV) ion, general formula [PtL2]Cl2 and [PtL2]Cl4, where L is 2-amino-5-methyl-4-phenylthiazole. The structures of the above mentioned compounds were determined by elemental microanalysis, infrared, 1H and 13C NMR spectroscopy.
{"title":"SYNTHESIS AND CHARACTERIZATION OF PLATINUM(II/IV) COMPLEXES WITH 2-AMINO-5-METHYL-4-PHENYLTHIAZOLE","authors":"D. Stojković, V. Jevtić, Đ. Petrović, Sandra S. Jovičić Milić, N. Vukovic, Milena D. Vukić, B. Stojanovic","doi":"10.46793/iccbi21.339s","DOIUrl":"https://doi.org/10.46793/iccbi21.339s","url":null,"abstract":"This paper examines the synthesis of two new complexes of platinum(II/IV) ion, general formula [PtL2]Cl2 and [PtL2]Cl4, where L is 2-amino-5-methyl-4-phenylthiazole. The structures of the above mentioned compounds were determined by elemental microanalysis, infrared, 1H and 13C NMR spectroscopy.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74425144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bladder cancer is one of the most common malignancies of the urinary tract. It is characterized by high metastatic potential and a high recurrence rate, which significantly complicates diagnosis and treatment. In order to increase the accuracy of the diagnostic procedure, algorithms based on artificial intelligence are introduced. This paper presents the principle of selection of convolutional neural network (CNN) models based on a multi-objective approach that maximizes classification and generalization performance. Model selection is performed on two standard CNN architectures, AlexNet and VGG-16. Classification performances are measured by using ROC analysis and the resulting AUC value. On the other hand, generalization performances are evaluated by using a 5-fold cross-validation procedure. By using these two metrics, a multi-objective fitness function, used in meta-heuristic algorithms, is designed. The multi-objective search was performed using a Genetic algorithm (GA) and a Discrete Particle Swarm (D-PS) algorithm. From obtained results, it can be noticed that such an approach has resulted in CNN models that are defined with high classification and generalization performances. When a GA-based approach is used, fitness values up to 0.97 are achieved. On the other hand, by using the D-PS approach, fitness values up to 0.99 are achieved pointing towards the conclusion that such an approach has provided models with higher classification and generalization performances.
{"title":"A META-HEURISTIC MULTI-OBJECTIVE APPROACH TO THE MODEL SELECTION OF CONVOLUTION NEURAL NETWORKS FOR URINARY BLADDER CANCER DIAGNOSIS","authors":"I. Lorencin, Klara Smolić, D. Markić, J. Španjol","doi":"10.46793/iccbi21.243l","DOIUrl":"https://doi.org/10.46793/iccbi21.243l","url":null,"abstract":"Bladder cancer is one of the most common malignancies of the urinary tract. It is characterized by high metastatic potential and a high recurrence rate, which significantly complicates diagnosis and treatment. In order to increase the accuracy of the diagnostic procedure, algorithms based on artificial intelligence are introduced. This paper presents the principle of selection of convolutional neural network (CNN) models based on a multi-objective approach that maximizes classification and generalization performance. Model selection is performed on two standard CNN architectures, AlexNet and VGG-16. Classification performances are measured by using ROC analysis and the resulting AUC value. On the other hand, generalization performances are evaluated by using a 5-fold cross-validation procedure. By using these two metrics, a multi-objective fitness function, used in meta-heuristic algorithms, is designed. The multi-objective search was performed using a Genetic algorithm (GA) and a Discrete Particle Swarm (D-PS) algorithm. From obtained results, it can be noticed that such an approach has resulted in CNN models that are defined with high classification and generalization performances. When a GA-based approach is used, fitness values up to 0.97 are achieved. On the other hand, by using the D-PS approach, fitness values up to 0.99 are achieved pointing towards the conclusion that such an approach has provided models with higher classification and generalization performances.","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72817629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emilija Milović, Nenad Ž. Janković, Jelena Petronijevic, Nenad Joksimović
Tetrahydropyrimidines (THPMs) attracted attention as a very important class of aza heterocycles with broad pharmacological activities during the past years. In many studies have been proven that THPMs have anticancer, anti-inflammatory, antimicrobial, antioxidant, antifungal, anti-HIV activity. Bearing in mind our interest in medicinal and Biginelli chemistry, we investigated interaction with important biomacromolecules (DNA, BSA) and our earlier synthetized THPMs derivatives with proven very good cytotoxic activity.[1] Investigation of affinity of compounds A and B (Figure 1) to bind to bovine serum albumin (BSA) is based on the fact that the efficiency of drugs depends on their ability to bind for carrier protein. Binding properties were investigated by using the fluorescence emission titration of BSA with A and B. The obtained values of Ka, which are in optimum range which is considered to be 106-107M-1 indicate that both compounds have great ability to bind to BSA. In addition, Ka values for A-BSA and B-BSAshow that both compounds are suitable for drug-cell
{"title":"CHEMICO-BIOLOGICAL INTERACTION OF SELECTED TETRAHYDROPYRIMIDINES","authors":"Emilija Milović, Nenad Ž. Janković, Jelena Petronijevic, Nenad Joksimović","doi":"10.46793/iccbi21.347m","DOIUrl":"https://doi.org/10.46793/iccbi21.347m","url":null,"abstract":"Tetrahydropyrimidines (THPMs) attracted attention as a very important class of aza heterocycles with broad pharmacological activities during the past years. In many studies have been proven that THPMs have anticancer, anti-inflammatory, antimicrobial, antioxidant, antifungal, anti-HIV activity. Bearing in mind our interest in medicinal and Biginelli chemistry, we investigated interaction with important biomacromolecules (DNA, BSA) and our earlier synthetized THPMs derivatives with proven very good cytotoxic activity.[1] Investigation of affinity of compounds A and B (Figure 1) to bind to bovine serum albumin (BSA) is based on the fact that the efficiency of drugs depends on their ability to bind for carrier protein. Binding properties were investigated by using the fluorescence emission titration of BSA with A and B. The obtained values of Ka, which are in optimum range which is considered to be 106-107M-1 indicate that both compounds have great ability to bind to BSA. In addition, Ka values for A-BSA and B-BSAshow that both compounds are suitable for drug-cell","PeriodicalId":9171,"journal":{"name":"Book of Proceedings: 1st International Conference on Chemo and BioInformatics,","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73510831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Grujović, K. Mladenović, Z. Simić, Simona R. Đuretanović
This research aimed to investigate the quality of drinking water from the rural area of village Pajsijević (Šumadija, central Serbia). The water is consumed as raw since it is not purified or chlorinated before consumption. The water was collected at three sampling points – in the spring of Kotlenik Mountain stream (W1 sample), in the local reservoir (W2 sample), and from the tap (W3 sample). Also, the sediment samples (soil and sand) were analyzed, too. The health risks related to the presence and concentration of some major and trace elements (Ca, Cr, Cu, Fe, Mg, Mn, Ni, Zn) and N, NO, NN3, NH4, P, P2O5, and PO4 were evaluated. Additionally, the presence and the number of total coliform bacteria and Escherichia coli (as an indicator of fecal contamination) were evaluated. The concentrations of analyzed major and trace elements in all water samples were below those at which toxic effects may occur. The exception was the concentrations of Fe (2.02 – 2012 mg/L), which were higher than is allowed. The origin of Fe in water is from sediment (soil and sand), which also showed high content of Fe (3006.0 mg/g and 2229.9 mg/g, respectively). The results of the Colorimetric test indicated the presence of coliform bacteria as well as the presence of E. coli in all water samples. Further research needs to include characterization of isolated coliform bacteria and serological investigation of E. coli strains in order to evaluate the risks of consumption related to waterborne illness.
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