TY  - JOUR
AU  - Filipović, Dragana
AU  - Novak, Božidar
AU  - Xiao, Jinqiu
AU  - Tadić, Predrag
AU  - Turck, Christoph W.
PY  - 2024
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/13084
AB  - Chronic social isolation (CSIS) generates two stress-related phenotypes: resilience and susceptibility. However, the molecular mechanisms underlying CSIS resilience remain unclear. We identified altered proteome components and biochemical pathways and processes in the prefrontal cortex cytosolic fraction in CSIS-resilient rats compared to CSIS-susceptible and control rats using liquid chromatography coupled with tandem mass spectrometry followed by label-free quantification and STRING bioinformatics. A sucrose preference test was performed to distinguish rat phenotypes. Potential predictive proteins discriminating between the CSIS-resilient and CSIS-susceptible groups were identified using machine learning (ML) algorithms: support vector machine-based sequential feature selection and random forest-based feature importance scores. Predominantly, decreased levels of some glycolytic enzymes, G protein-coupled receptor proteins, the Ras subfamily of GTPases proteins, and antioxidant proteins were found in the CSIS-resilient vs. CSIS-susceptible groups. Altered levels of Gapdh, microtubular, cytoskeletal, and calcium-binding proteins were identified between the two phenotypes. Increased levels of proteins involved in GABA synthesis, the proteasome system, nitrogen metabolism, and chaperone-mediated protein folding were identified. Predictive proteins make CSIS-resilient vs. CSIS-susceptible groups linearly separable, whereby a 100% validation accuracy was achieved by ML models. The overall ratio of significantly up- and downregulated cytosolic proteins suggests adaptive cellular alterations as part of the stress-coping process specific for the CSIS-resilient phenotype.
T2  - International Journal of Molecular Sciences
T1  - Prefrontal Cortex Cytosolic Proteome and Machine Learning-Based Predictors of Resilience toward Chronic Social Isolation in Rats
VL  - 25
IS  - 5
SP  - 3026
DO  - 10.3390/ijms25053026
ER  - 

@article{
author = "Filipović, Dragana and Novak, Božidar and Xiao, Jinqiu and Tadić, Predrag and Turck, Christoph W.",
year = "2024",
abstract = "Chronic social isolation (CSIS) generates two stress-related phenotypes: resilience and susceptibility. However, the molecular mechanisms underlying CSIS resilience remain unclear. We identified altered proteome components and biochemical pathways and processes in the prefrontal cortex cytosolic fraction in CSIS-resilient rats compared to CSIS-susceptible and control rats using liquid chromatography coupled with tandem mass spectrometry followed by label-free quantification and STRING bioinformatics. A sucrose preference test was performed to distinguish rat phenotypes. Potential predictive proteins discriminating between the CSIS-resilient and CSIS-susceptible groups were identified using machine learning (ML) algorithms: support vector machine-based sequential feature selection and random forest-based feature importance scores. Predominantly, decreased levels of some glycolytic enzymes, G protein-coupled receptor proteins, the Ras subfamily of GTPases proteins, and antioxidant proteins were found in the CSIS-resilient vs. CSIS-susceptible groups. Altered levels of Gapdh, microtubular, cytoskeletal, and calcium-binding proteins were identified between the two phenotypes. Increased levels of proteins involved in GABA synthesis, the proteasome system, nitrogen metabolism, and chaperone-mediated protein folding were identified. Predictive proteins make CSIS-resilient vs. CSIS-susceptible groups linearly separable, whereby a 100% validation accuracy was achieved by ML models. The overall ratio of significantly up- and downregulated cytosolic proteins suggests adaptive cellular alterations as part of the stress-coping process specific for the CSIS-resilient phenotype.",
journal = "International Journal of Molecular Sciences",
title = "Prefrontal Cortex Cytosolic Proteome and Machine Learning-Based Predictors of Resilience toward Chronic Social Isolation in Rats",
volume = "25",
number = "5",
pages = "3026",
doi = "10.3390/ijms25053026"
}

Filipović, D., Novak, B., Xiao, J., Tadić, P.,& Turck, C. W.. (2024). Prefrontal Cortex Cytosolic Proteome and Machine Learning-Based Predictors of Resilience toward Chronic Social Isolation in Rats. in International Journal of Molecular Sciences, 25(5), 3026.
https://doi.org/10.3390/ijms25053026

Filipović D, Novak B, Xiao J, Tadić P, Turck CW. Prefrontal Cortex Cytosolic Proteome and Machine Learning-Based Predictors of Resilience toward Chronic Social Isolation in Rats. in International Journal of Molecular Sciences. 2024;25(5):3026.
doi:10.3390/ijms25053026 .

Filipović, Dragana, Novak, Božidar, Xiao, Jinqiu, Tadić, Predrag, Turck, Christoph W., "Prefrontal Cortex Cytosolic Proteome and Machine Learning-Based Predictors of Resilience toward Chronic Social Isolation in Rats" in International Journal of Molecular Sciences, 25, no. 5 (2024):3026,
https://doi.org/10.3390/ijms25053026 . .

TY  - CONF
AU  - Tadić, Predrag
AU  - Petrović, Jovana
AU  - Đorđević, Natalija
AU  - Ivanović, Marija
AU  - Lazović, Aleksandar
AU  - Vukčević, Vladan
AU  - Ristić, Arsen
AU  - Hadžievski, Ljupčo
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/13044
AB  - We present our progress on the “Multi-SENSor SysteM and ARTificial intelligence in service of heart failure diagnosis (SensSmart)” project, which was introduced at the last year’s edition of the Workshop [1]. The goal of the SensSmart project is to enable early diagnosis of heart failure, through the development of: 1) a multi-sensor polycardiograph apparatus (PCG) that produces simultaneous acquisition of the subject’s electrocardiogram (ECG), photoplethysmogram (PPG), heart sounds, and heart movements, and 2) AI-assisted analysis of the acquired signals. This presentation is going to focus on the acquisition and processing of PPG signals. PPG is obtained by using a pulse oximeter which illuminates the skin and measures the changes in light absorption, thereby enabling the detection of blood volume changes in the vessels. Our PCG apparatus measures the blood flow through the brachial, radial, and carotid arteries. During each heartbeat, the generated waveform typically exhibits several characteristic points [2]. The magnitudes and time distances between these points are useful indicators of many cardiac conditions, including heart failure [3]. However, the inter-patient variability of the PPG waveform makes it challenging to derive simple rule-based diagnostic procedures. This has led many researchers to turn to statistical or machine learning methods for processing of PPG signals [4].  In this presentation, we give an overview of AI-based signal processing methods for PPG, and present some preliminary results and challenges in extracting features from real-world signals obtained using our PCG.
PB  - Belgrade : Institute of Physics
C3  - 16th Photonics Workshop : Book of abstracts
T1  - Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure
SP  - 24
EP  - 24
UR  - https://hdl.handle.net/21.15107/rcub_vinar_13044
ER  - 

@conference{
author = "Tadić, Predrag and Petrović, Jovana and Đorđević, Natalija and Ivanović, Marija and Lazović, Aleksandar and Vukčević, Vladan and Ristić, Arsen and Hadžievski, Ljupčo",
year = "2023",
abstract = "We present our progress on the “Multi-SENSor SysteM and ARTificial intelligence in service of heart failure diagnosis (SensSmart)” project, which was introduced at the last year’s edition of the Workshop [1]. The goal of the SensSmart project is to enable early diagnosis of heart failure, through the development of: 1) a multi-sensor polycardiograph apparatus (PCG) that produces simultaneous acquisition of the subject’s electrocardiogram (ECG), photoplethysmogram (PPG), heart sounds, and heart movements, and 2) AI-assisted analysis of the acquired signals. This presentation is going to focus on the acquisition and processing of PPG signals. PPG is obtained by using a pulse oximeter which illuminates the skin and measures the changes in light absorption, thereby enabling the detection of blood volume changes in the vessels. Our PCG apparatus measures the blood flow through the brachial, radial, and carotid arteries. During each heartbeat, the generated waveform typically exhibits several characteristic points [2]. The magnitudes and time distances between these points are useful indicators of many cardiac conditions, including heart failure [3]. However, the inter-patient variability of the PPG waveform makes it challenging to derive simple rule-based diagnostic procedures. This has led many researchers to turn to statistical or machine learning methods for processing of PPG signals [4].  In this presentation, we give an overview of AI-based signal processing methods for PPG, and present some preliminary results and challenges in extracting features from real-world signals obtained using our PCG.",
publisher = "Belgrade : Institute of Physics",
journal = "16th Photonics Workshop : Book of abstracts",
title = "Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure",
pages = "24-24",
url = "https://hdl.handle.net/21.15107/rcub_vinar_13044"
}

Tadić, P., Petrović, J., Đorđević, N., Ivanović, M., Lazović, A., Vukčević, V., Ristić, A.,& Hadžievski, L.. (2023). Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure. in 16th Photonics Workshop : Book of abstracts
Belgrade : Institute of Physics., 24-24.
https://hdl.handle.net/21.15107/rcub_vinar_13044

Tadić P, Petrović J, Đorđević N, Ivanović M, Lazović A, Vukčević V, Ristić A, Hadžievski L. Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure. in 16th Photonics Workshop : Book of abstracts. 2023;:24-24.
https://hdl.handle.net/21.15107/rcub_vinar_13044 .

Tadić, Predrag, Petrović, Jovana, Đorđević, Natalija, Ivanović, Marija, Lazović, Aleksandar, Vukčević, Vladan, Ristić, Arsen, Hadžievski, Ljupčo, "Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure" in 16th Photonics Workshop : Book of abstracts (2023):24-24,
https://hdl.handle.net/21.15107/rcub_vinar_13044 .

TY  - JOUR
AU  - Filipović, Dragana
AU  - Inderhees, Julica
AU  - Korda, Alexandra
AU  - Tadić, Predrag
AU  - Schwaninger, Markus
AU  - Inta, Dragoš
AU  - Borgwardt, Stefan
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/11355
AB  - The increasing prevalence of depression requires more effective therapy and the understanding of antidepressants’ mode of action. We carried out untargeted metabolomics of the prefrontal cortex of rats exposed to chronic social isolation (CSIS), a rat model of depression, and/or fluoxetine treatment using liquid chromatography–high resolution mass spectrometry. The behavioral phenotype was assessed by the forced swim test. To analyze the metabolomics data, we employed univariate and multivariate analysis and biomarker capacity assessment using the receiver operating characteristic (ROC) curve. We also identified the most predictive biomarkers using a support vector machine with linear kernel (SVM-LK). Upregulated myo-inositol following CSIS may represent a potential marker of depressive phenotype. Effective fluoxetine treatment reversed depressive-like behavior and increased sedoheptulose 7-phosphate, hypotaurine, and acetyl-L-carnitine contents, which were identified as marker candidates for fluoxetine efficacy. ROC analysis revealed 4 significant marker candidates for CSIS group discrimination, and 10 for fluoxetine efficacy. SVM-LK with accuracies of 61.50% or 93.30% identified a panel of 7 or 25 predictive metabolites for depressive-like behavior or fluoxetine effectiveness, respectively. Overall, metabolic fingerprints combined with the ROC curve and SVM-LK may represent a new approach to identifying marker candidates or predictive metabolites for ongoing disease or disease risk and treatment outcome.
T2  - International Journal of Molecular Sciences
T1  - Metabolic Fingerprints of Effective Fluoxetine Treatment in the Prefrontal Cortex of Chronically Socially Isolated Rats: Marker Candidates and Predictive Metabolites
VL  - 24
IS  - 13
SP  - 10957
DO  - 10.3390/ijms241310957
ER  - 

@article{
author = "Filipović, Dragana and Inderhees, Julica and Korda, Alexandra and Tadić, Predrag and Schwaninger, Markus and Inta, Dragoš and Borgwardt, Stefan",
year = "2023",
abstract = "The increasing prevalence of depression requires more effective therapy and the understanding of antidepressants’ mode of action. We carried out untargeted metabolomics of the prefrontal cortex of rats exposed to chronic social isolation (CSIS), a rat model of depression, and/or fluoxetine treatment using liquid chromatography–high resolution mass spectrometry. The behavioral phenotype was assessed by the forced swim test. To analyze the metabolomics data, we employed univariate and multivariate analysis and biomarker capacity assessment using the receiver operating characteristic (ROC) curve. We also identified the most predictive biomarkers using a support vector machine with linear kernel (SVM-LK). Upregulated myo-inositol following CSIS may represent a potential marker of depressive phenotype. Effective fluoxetine treatment reversed depressive-like behavior and increased sedoheptulose 7-phosphate, hypotaurine, and acetyl-L-carnitine contents, which were identified as marker candidates for fluoxetine efficacy. ROC analysis revealed 4 significant marker candidates for CSIS group discrimination, and 10 for fluoxetine efficacy. SVM-LK with accuracies of 61.50% or 93.30% identified a panel of 7 or 25 predictive metabolites for depressive-like behavior or fluoxetine effectiveness, respectively. Overall, metabolic fingerprints combined with the ROC curve and SVM-LK may represent a new approach to identifying marker candidates or predictive metabolites for ongoing disease or disease risk and treatment outcome.",
journal = "International Journal of Molecular Sciences",
title = "Metabolic Fingerprints of Effective Fluoxetine Treatment in the Prefrontal Cortex of Chronically Socially Isolated Rats: Marker Candidates and Predictive Metabolites",
volume = "24",
number = "13",
pages = "10957",
doi = "10.3390/ijms241310957"
}

Filipović, D., Inderhees, J., Korda, A., Tadić, P., Schwaninger, M., Inta, D.,& Borgwardt, S.. (2023). Metabolic Fingerprints of Effective Fluoxetine Treatment in the Prefrontal Cortex of Chronically Socially Isolated Rats: Marker Candidates and Predictive Metabolites. in International Journal of Molecular Sciences, 24(13), 10957.
https://doi.org/10.3390/ijms241310957

Filipović D, Inderhees J, Korda A, Tadić P, Schwaninger M, Inta D, Borgwardt S. Metabolic Fingerprints of Effective Fluoxetine Treatment in the Prefrontal Cortex of Chronically Socially Isolated Rats: Marker Candidates and Predictive Metabolites. in International Journal of Molecular Sciences. 2023;24(13):10957.
doi:10.3390/ijms241310957 .

Filipović, Dragana, Inderhees, Julica, Korda, Alexandra, Tadić, Predrag, Schwaninger, Markus, Inta, Dragoš, Borgwardt, Stefan, "Metabolic Fingerprints of Effective Fluoxetine Treatment in the Prefrontal Cortex of Chronically Socially Isolated Rats: Marker Candidates and Predictive Metabolites" in International Journal of Molecular Sciences, 24, no. 13 (2023):10957,
https://doi.org/10.3390/ijms241310957 . .