AB

Bibliografia publikacji pracowników
Państwowej Szkoły Wyższej w Białej Podlaskiej

Baza tworzona przez Bibliotekę Akademii Bialskiej im. Jana Pawła II.


Zapytanie: HORBAŃCZUK JAROSŁAW OLAV
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Nr opisu: ga Piotr^bPiotr^c^d^e^f^g^h^i ^m_^n_^oReliga Piotr^pReliga Piotr^rReliga^sPiotr^u^t^qReliga P^w^x0000026524^zReliga Piotr^aThornetr^rReliga^sPiotr^u^t^qReliga P^w^x0000026524^zReliga Piotr^aThorne
Autorzy: , , .
Tytuł czasopisma:
Charakterystyka merytoryczna:
Język publikacji:
Wskaźnik Impact Factor ISI: artykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://link.springer.com/article/10.1007/s12026-024-09514-4100^a0257-277X^bQ^e1559-0755^iX^jXY^kQ008122^a003^b003^c2024-08-08, 14:37^d2025-01-09, 14:44^e3022968802^f2929958795^aThe impact of BDNF and CD4 + T cell crosstalk on depression^aImmunologic Research^a2024^bVol. 72^cissue 5^dp.883--894^a0257-277X^b1559-0755^a2024/2025^a10.1007/s12026-024-09514-4^aPaszkiewicz, Justyna^cy^abrain-derived neurotrophic factor's (BDNF)^aKopia dostępna w Sekcji Bibliometrii.^aDepression represents a prevalent health concern since it affects approximately 350 million people of all ages worldwide [1, 2]. Both genders suffer from depression, albeit women are disproportionally affected by it more than men [3]. Notably, depression has the potential to impact individuals across various phases of their lifespan. Around 15% of adolescents aged 14 to 18 will encounter at least a single significant depressive episode [4]. Depression continues to be a concern within the elderly community. Namely, approximately 7% of individuals aged 60 and above experience symptoms of depression [5, 6]. These observations highlight that depression is pervasive in our society. Brain-derived neurotrophic factor's (BDNF) role in depression is highly affected by various factors [7,8,9]. BDNF is a neurotrophin known for its roles in synaptic plasticity, dendritic spine morphology, cognitive function, and mood regulation [10,11,12,13,14]. BDNF polymorphisms, such as Val66met, can significantly influence an individual's predisposition to depression [15,16,17,18,19,20]. Epigenetic alterations of BDNF, such as increase/decrease in DNA methylation at specific CpG sites, have recently emerged as relevant mechanisms promoting susceptibility to the development of depressive-like symptoms [21, 22]. These genetic and epigenetic factors are deeply intertwined with environmental influences, such as early-life experiences, social support, and exposure to stressors. Together, they contribute to the role of BDNF in the onset and progression of depression [23,24,25]. Recently, CD4 + T cells, a fundamental constituent of the adaptive immune system, have come under the attention of various research groups for their potential involvement in mediating neuroinflammatory processes that influence mood and cognition [26,27,28]. CD4 + T cells are a heterogeneous group of cells that include both pro- (e.g., Th1 and Th17) and anti-inflammatory cell types (e.g., Treg). CD4 + T cells can influence depression onset and development in various ways, including direct effects on neurons, astrocytes, and microglia through the production of cytokines and expressing receptors for molecules produced by brain cells [29,30,31,32,33]. BDNF seems to have a positive effect on CD4 + T cell proliferation and differentiation. However, our understanding of BDNF's effects on each cell type remains limited. Surprisingly, IL-17, which is produced by pro-inflammatory Th17 cells, and IL-4, which is produced by anti-inflammatory Tregs, both seem to increase BDNF production by astrocytes. The reason behind this counterintuitive phenomenon remains unknown. This review aims to investigate the mechanisms of interaction between CD4 + T cells and BDNF in the context of depression to acquire a deeper understanding of depression and the development of more effective therapies. Therefore, we first cover the role of BDNF in depression, followed by discussing what is currently known about the role of C^aCD4 + T cells^adepression
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kuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://link.springer.com/article/10.1007/s12026-024-09514-4100^a0257-277X^bQ^e1559-0755^iX^jXY^kQ008122^a003^b003^c2024-08-08, 14:37^d2025-01-09, 14:44^e3022968802^f2929958795^aThe impact of BDNF and CD4 + T cell crosstalk on depression^aImmunologic Research^a2024^bVol. 72^cissue 5^dp.883--894^a0257-277X^b1559-0755^a2024/2025^a10.1007/s12026-024-09514-4^aPaszkiewicz, Justyna^cy^abrain-derived neurotrophic factor's (BDNF)^aKopia dostępna w Sekcji Bibliometrii.^aDepression represents a prevalent health concern since it affects approximately 350 million people of all ages worldwide [1, 2]. Both genders suffer from depression, albeit women are disproportionally affected by it more than men [3]. Notably, depression has the potential to impact individuals across various phases of their lifespan. Around 15% of adolescents aged 14 to 18 will encounter at least a single significant depressive episode [4]. Depression continues to be a concern within the elderly community. Namely, approximately 7% of individuals aged 60 and above experience symptoms of depression [5, 6]. These observations highlight that depression is pervasive in our society. Brain-derived neurotrophic factor's (BDNF) role in depression is highly affected by various factors [7,8,9]. BDNF is a neurotrophin known for its roles in synaptic plasticity, dendritic spine morphology, cognitive function, and mood regulation [10,11,12,13,14]. BDNF polymorphisms, such as Val66met, can significantly influence an individual's predisposition to depression [15,16,17,18,19,20]. Epigenetic alterations of BDNF, such as increase/decrease in DNA methylation at specific CpG sites, have recently emerged as relevant mechanisms promoting susceptibility to the development of depressive-like symptoms [21, 22]. These genetic and epigenetic factors are deeply intertwined with environmental influences, such as early-life experiences, social support, and exposure to stressors. Together, they contribute to the role of BDNF in the onset and progression of depression [23,24,25]. Recently, CD4 + T cells, a fundamental constituent of the adaptive immune system, have come under the attention of various research groups for their potential involvement in mediating neuroinflammatory processes that influence mood and cognition [26,27,28]. CD4 + T cells are a heterogeneous group of cells that include both pro- (e.g., Th1 and Th17) and anti-inflammatory cell types (e.g., Treg). CD4 + T cells can influence depression onset and development in various ways, including direct effects on neurons, astrocytes, and microglia through the production of cytokines and expressing receptors for molecules produced by brain cells [29,30,31,32,33]. BDNF seems to have a positive effect on CD4 + T cell proliferation and differentiation. However, our understanding of BDNF's effects on each cell type remains limited. Surprisingly, IL-17, which is produced by pro-inflammatory Th17 cells, and IL-4, which is produced by anti-inflammatory Tregs, both seem to increase BDNF production by astrocytes. The reason behind this counterintuitive phenomenon remains unknown. This review aims to investigate the mechanisms of interaction between CD4 + T cells and BDNF in the context of depression to acquire a deeper understanding of depression and the development of more effective therapies. Therefore, we first cover the role of BDNF in depression, followed by discussing what is currently known about the role of C^aCD4 + T cells^adepression

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Nr opisu: cek^sTomas^u^t^qVanecek T^w^x0000036941^zVanecek Tomas^aPaszkiewicz
Autorzy: , , , Justyna Mariusz Piotr Oryginalny artykuł naukowy publikacja bezkosztowa 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR70.000PUNKTACJA UWM 009929.000 Q 003 Vol. 46 CC-BY PaszkiewiczSacharczukReligaoriginal-articleF000.00996100009996.2001467-3037003Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in PrecisionCurrent Issues in Molecular Biology20241467-30452023/2024https://doi.org/10.3390/cimb46080490Paszkiewicz, JustynaaiFINAL_PUBLISHEDThe accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.cancerclinical testcopy number variantgenomic markersmetastasisneural network, Justyna Mariusz Piotr Oryginalny artykuł naukowy publikacja bezkosztowa 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR70.000PUNKTACJA UWM 009929.000 Q 003 Vol. 46 CC-BY PaszkiewiczSacharczukReligaoriginal-articleF000.00996100009996.2001467-3037003Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in PrecisionCurrent Issues in Molecular Biology20241467-30452023/2024https://doi.org/10.3390/cimb46080490Paszkiewicz, JustynaaiFINAL_PUBLISHEDThe accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.cancerclinical testcopy number variantgenomic markersmetastasisneural network.
Tytuł równoległy: SacharczukReligaoriginal-articleF000.00996100009996.2001467-3037003Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in PrecisionCurrent Issues in Molecular Biology20241467-30452023/2024https://doi.org/10.3390/cimb46080490Paszkiewicz, JustynaaiFINAL_PUBLISHEDThe accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.cancerclinical testcopy number variantgenomic markersmetastasisneural network : Justyna : Mariusz : Piotr : Oryginalny artykuł naukowy : publikacja bezkosztowa : 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR70.000PUNKTACJA UWM : 009929.000 : Q : 003 : Vol. 46 : CC-BY
Tytuł czasopisma:
Charakterystyka merytoryczna:
Język publikacji:
Wskaźnik Impact Factor ISI:
Punktacja ministerstwa:
Praca recenzowana
Słowa kluczowe ang.: TACJA UWM^a009996.200^b009929.000^c009999.000^d009929.000202420242024Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves00000481150000000208AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1467-3045/46/8/490100^a1467-3037^bQ^e1467-3045^iX^jXY^kQ004712^a003^b003^c2024-10-02, 10:59^d2024-11-22, 14:12^e3021029180^f3019828827^aUsing Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision^aCurrent Issues in Molecular Biology^a2024^bVol. 46^cissue 8^dp. 8301--8319^a1467-3045^a2023/2024^ahttps://doi.org/10.3390/cimb46080490^aPaszkiewicz, Justyna^cy^aai^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aThe accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.^acancer^aclinical test^acopy number variant^agenomic markers^ametastasis^aneural network
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Autorzy: , , .
Tytuł pracy:
Tytuł pracy w innym języku: F018328.00993999009994.1001422-0067003Investigation of Mutated in Colorectal Cancer (MCC) Gene Family Evolution History Indicates a Putative Role in Th17/Treg DifferentiationInternational Journal of Molecular Sciences20231422-00672022/202310.3390/ijms241511940Paszkiewicz, Justynacolorectal cancerWpływ endogennego rozszczelnienia bariery krew-mózg w patogenezie zróżnicowanej podatności na inokulowanego czerniaka u linii myszy selekcjonowanych w kierunku wysokiej i niskiej analgezji postresowejFINAL_PUBLISHEDThe MCC family of genes plays a role in colorectal cancer development through various immunological pathways, including the Th17/Treg axis. We have previously shown that MCC1 but not MCC2 plays a role in Treg differentiation. Our understanding of the genetic divergence patterns and evolutionary history of the MCC family in relation to its function, in general, and the Th17/Treg axis, in particular, remains incomplete. In this investigation, we explored 12 species' genomes to study the phylogenetic origin, structure, and functional specificity of this family. In vertebrates, both MCC1 and MCC2 homologs have been discovered, while invertebrates have a single MCC homolog. We found MCC homologs as early as Cnidarians and Trichoplax, suggesting that the MCC family first appeared 741 million years ago (Ma), whereas MCC divergence into the MCC1 and MCC2 families occurred at 540 Ma. In general, we did not detect significant positive selection regulating MCC evolution. Our investigation, based on MCC1 structural similarity, suggests that they may play a role in the evolutionary changes in Tregs' emergence towards complexity, including the ability to utilize calcium for differentiation through the use of the EFH calcium-binding domain. We also found that the motif NPSTGE was highly conserved in MCC1, but not in MCC2. The NPSTGE motif binds KEAP1 with high affinity, suggesting an Nrf2-mediated function for MCC1. In the case of MCC2, we found that the "modifier of rudimentary" motif is highly conserved. This motif contributes to the regulation of alternative splicing. Overall, our study sheds light on how the evolution of the MCC family is connected to its function in regulating the Th17/Treg axis.differentiationevolutionTh17Treg : Oryginalny artykuł naukowy : utrzymanie i rozwój potencjału badawczego - art. 365 pkt 2 ustawy : 998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM : 009859.000 : Q : 003 : Vol. 24 : Akademia Bialska im. Jana Pawła II w ramach Regulaminu wsparcia rozwoju zawodowego pracowników uczelni : CC-BY
Strony: F018328.00993999009994.1001422-0067003Investigation of Mutated in Colorectal Cancer (MCC) Gene Family Evolution History Indicates a Putative Role in Th17/Treg DifferentiationInternational Journal of Molecular Sciences20231422-00672022/202310.3390/ijms241511940Paszkiewicz, Justynacolorectal cancerWpływ endogennego rozszczelnienia bariery krew-mózg w patogenezie zróżnicowanej podatności na inokulowanego czerniaka u linii myszy selekcjonowanych w kierunku wysokiej i niskiej analgezji postresowejFINAL_PUBLISHEDThe MCC family of genes plays a role in colorectal cancer development through various immunological pathways, including the Th17/Treg axis. We have previously shown that MCC1 but not MCC2 plays a role in Treg differentiation. Our understanding of the genetic divergence patterns and evolutionary history of the MCC family in relation to its function, in general, and the Th17/Treg axis, in particular, remains incomplete. In this investigation, we explored 12 species' genomes to study the phylogenetic origin, structure, and functional specificity of this family. In vertebrates, both MCC1 and MCC2 homologs have bee, Oryginalny artykuł naukowy, utrzymanie i rozwój potencjału badawczego - art. 365 pkt 2 ustawy, 998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM, 009859.000, Q, 003, Vol. 24, Akademia Bialska im. Jana Pawła II w ramach Regulaminu wsparcia rozwoju zawodowego pracowników uczelni, CC-BY, 009999.000, 2023-09-29, 13:32, issue 15, y, PB/5/2022, AT_PUBLICATION, 009859.000202320232023Investigation of Mutated in Colorectal Cancer (MCC) Gene Family Evolution History Indicates a Put00000464160000000342AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1422-0067/24/15/11940100, 2024-06-25, 14:13, article number 11940
Seria: 8328.00993999009994.1001422-0067003, 998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM, 009859.000, Q, 003, 009999.000, 2023-09-29, 13: ; 009859.000202320232023Investigation of Mutated in Colorectal Cancer (MCC) Gene Family Evolution History Indicates a Put00000464160000000342AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1422-0067/24/15/11940100
Charakterystyka formalna: 22-0067^a2022/2023^a10.3390/ijms241511940^aPaszkiewicz, Justyna^cy^acolorectal cancer^aWpływ endogennego rozszczelnienia bariery krew-mózg w patogenezie zróżnicowanej podatności na inokulowanego czerniaka u linii myszy selekcjonowanych w kierunku wysokiej i niskiej analgezji postresowej^bAkademia Bialska im. Jana Pawła II w ramach Regulaminu wsparcia rozwoju zawodowego pracowników uczelni^cPB/5/2022^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aThe MCC family of genes plays a role in colorectal cancer development through various immunological pathways, including the Th17/Treg axis. We have previously shown that MCC1 but not MCC2 plays a role in Treg differentiation. Our understanding of the genetic divergence patterns and evolutionary history of the MCC family in relation to its function, in general, and the Th17/Treg axis, in particular, remains incomplete. In this investigation, we explored 12 species' genomes to study the phylogenetic origin, structure, and functional specificity of this family. In vertebrates, both MCC1 and MCC2 homologs have been discovered, while invertebrates have a single MCC homolog. We found MCC homologs as early as Cnidarians and Trichoplax, suggesting that the MCC family first appeared 741 million years ago (Ma), whereas MCC divergence into the MCC1 and MCC2 families occurred at 540 Ma. In general, we did not detect significant positive selection regulating MCC evolution. Our investigation, based on MCC1 structural similarity, suggests that they may play a role in the evolutionary changes in Tregs' emergence towards complexity, including the ability to utilize calcium for differentiation through the use of the EFH calcium-binding domain. We also found that the motif NPSTGE was highly conserved in MCC1, but not in MCC2. The NPSTGE motif binds KEAP1 with high affinity, suggesting an Nrf2-mediated function for MCC1. In the case of MCC2, we found that the "modifier of rudimentary" motif is highly conserved. This motif contributes to the regulation of alternative splicing. Overall, our study sheds light on how the evolution of the MCC family is connected to its function in regulating the Th17/Treg axis.^adifferentiation^aevolution^aTh17^aTreg
Język publikacji: a2022/2023^a10.3390/ijms241511940^aPaszkiewicz, Justyna^cy^acolorectal cancer^aWpływ endogennego rozszczelnienia bariery krew-mózg w patogenezie zróżnicowanej podatności na inokulowanego czerniaka u linii myszy selekcjonowanych w kierunku wysokiej i niskiej analgezji postresowej^bAkademia Bialska im. Jana Pawła II w ramach Regulaminu wsparcia rozwoju zawodowego pracowników uczelni^cPB/5/2022^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aThe MCC family of genes plays a role in colorectal cancer development through various immunological pathways, including the Th17/Treg axis. We have previously shown that MCC1 but not MCC2 plays a role in Treg differentiation. Our understanding of the genetic divergence patterns and evolutionary history of the MCC family in relation to its function, in general, and the Th17/Treg axis, in particular, remains incomplete. In this investigation, we explored 12 species' genomes to study the phylogenetic origin, structure, and functional specificity of this family. In vertebrates, both MCC1 and MCC2 homologs have been discovered, while invertebrates have a single MCC homolog. We found MCC homologs as early as Cnidarians and Trichoplax, suggesting that the MCC family first appeared 741 million years ago (Ma), whereas MCC divergence into the MCC1 and MCC2 families occurred at 540 Ma. In general, we did not detect significant positive selection regulating MCC evolution. Our investigation, based on MCC1 structural similarity, suggests that they may play a role in the evolutionary changes in Tregs' emergence towards complexity, including the ability to utilize calcium for differentiation through the use of the EFH calcium-binding domain. We also found that the motif NPSTGE was highly conserved in MCC1, but not in MCC2. The NPSTGE motif binds KEAP1 with high affinity, suggesting an Nrf2-mediated function for MCC1. In the case of MCC2, we found that the "modifier of rudimentary" motif is highly conserved. This motif contributes to the regulation of alternative splicing. Overall, our study sheds light on how the evolution of the MCC family is connected to its function in regulating the Th17/Treg axis.^adifferentiation^aevolution^aTh17^aTreg
Słowa kluczowe ang.:

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Nr opisu: na^pPaszkiewicz Justyna^rPASZKIEWICZ^sJUSTYNA^u^tZakład Pielęgniarstwa^qPaszkiewicz J^w930593^x0000013936^zPaszkiewicz Justyna^aTeodorowicz
Autorzy: , , , , Patrycja Tomasz Jarosław Olav Gina Mariusz Michel-Edwar Oryginalny artykuł naukowyACZartykuł w czasopiśmie zagranicznym2.800IF 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR70.000PUNKTACJA UWM 009929.000 Q 003 Vol. 45 CC-BY TeodorowiczKockiHorbańczukMandaSacharczukMickaeloriginal-article996100009996.2001467-3037003Investigation of the Molecular Evolution of Treg Suppresion Mechanisms Indicates a Covergent OriginCurrent Issues in Molecular Biology20231467-30452022/202310.3390/cimb45010042aiFINAL_PUBLISHEDRegulatory T cell (Treg) suppression of conventional T cells is a central mechanism that ensures immune system homeostasis. The exact time point of Treg emergence is still disputed. Furthermore, the time of Treg-mediated suppression mechanisms' emergence has not been identified. It is not yet known whether Treg suppression mechanisms diverged from a single pathway or converged from several sources. We investigated the evolutionary history of Treg suppression pathways using various phylogenetic analysis tools. To ensure the conservation of function for investigated proteins, we augmented our study using nonhomology-based methods to predict protein functions among various investigated species and mined the literature for experimental evidence of functional convergence. Our results indicate that a minority of Treg suppressor mechanisms could be homologs of ancient conserved pathways. For example, CD73, an enzymatic pathway known to play an essential role in invertebrates, is highly conserved between invertebrates and vertebrates, with no evidence of positive selection (w = 0.48, p-value < 0.00001). Our findings indicate that Tregs utilize homologs of proteins that diverged in early vertebrates. However, our findings do not exclude the possibility of a more evolutionary pattern following the duplication degeneration-complementation (DDC) model. Ancestral sequence reconstruction showed that Treg suppression mechanism proteins do not belong to one family; rather, their emergence seems to follow a convergent evolutionary pattern.evolutionmolecular evolutiontregs, Patrycja Tomasz Jarosław Olav Gina Mariusz Michel-Edwar Oryginalny artykuł naukowyACZartykuł w czasopiśmie zagranicznym2.800IF 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR70.000PUNKTACJA UWM 009929.000 Q 003 Vol. 45 CC-BY TeodorowiczKockiHorbańczukMandaSacharczukMickaeloriginal-article996100009996.2001467-3037003Investigation of the Molecular Evolution of Treg Suppresion Mechanisms Indicates a Covergent OriginCurrent Issues in Molecular Biology20231467-30452022/202310.3390/cimb45010042aiFINAL_PUBLISHEDRegulatory T cell (Treg) suppression of conventional T cells is a central mechanism that ensures immune system homeostasis. The exact time point of Treg emergence is still disputed. Furthermore, the time of Treg-mediated suppression mechanisms' emergence has not been identified. It is not yet known whether Treg suppression mechanisms diverged from a single pathway or converged from several sources. We investigated the evolutionary history of Treg suppression pathways using various phylogenetic analysis tools. To ensure the conservation of function for investigated proteins, we augmented our study using nonhomology-based methods to predict protein functions among various investigated species and mined the literature for experimental evidence of functional convergence. Our results indicate that a minority of Treg suppressor mechanisms could be homologs of ancient conserved pathways. For example, CD73, an enzymatic pathway known to play an essential role in invertebrates, is highly conserved between invertebrates and vertebrates, with no evidence of positive selection (w = 0.48, p-value < 0.00001). Our findings indicate that Tregs utilize homologs of proteins that diverged in early vertebrates. However, our findings do not exclude the possibility of a more evolutionary pattern following the duplication degeneration-complementation (DDC) model. Ancestral sequence reconstruction showed that Treg suppression mechanism proteins do not belong to one family; rather, their emergence seems to follow a convergent evolutionary pattern.evolutionmolecular evolutiontregs.
Tytuł czasopisma:
Charakterystyka formalna: Mariusz^u^t^qSacharczuk M^w^x0000026532^zSacharczuk Mariusz^aMickael
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Strony: RadziszewskiFraczekWolińskaPaszkiewiczReligaSacharczukF014101.00993999009994.1001422-0067003The Journey of Cancer Cells to the BrainInternational Journal of Molecular Sciences20231422-00672022/202310.3390/ijms24043854Paszkiewicz, JustynabraineFINAL_PUBLISHEDCancer metastases into the brain constitute one of the most severe, but not uncommon, manifestations of cancer progression. Several factors control how cancer cells interact with the brain to establish metastasis. These factors include mediators of signaling pathways participating in migration, infiltration of the blood-brain barrier, interaction with host cells (e.g., neurons, astrocytes), and the immune system. Development of novel therapies offers a glimpse of hope for increasing the diminutive life expectancy currently forecasted for patients suffering from brain metastasis. However, applying these treatment strategies has not been sufficiently effective. Therefore, there is a need for a better , Jakub, Karolina, Renata, Justyna, Piotr, Mariusz, utrzymanie i rozwój potencjału badawczego - art. 365 pkt 2 ustawy, 998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM, 009859.000, Q, 003, Challenges and Opportunities, Vol. 24, CC-BY, , , , 017, , , 009999.000, 2023-03-15, 10:29, issue 4, y, AT_PUBLICATION, , , , WNZS0101, , , 009859.000202320232023Journey of Cancer Cells to the Brain Challenges and Opportunities00000450840000000466AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1422-0067/24/4/3854100, 2024-06-25, 14:23, article number 3854
ISBN: Radziszewski
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Język publikacji: arczuk Mariusz^bMariusz^c^d^e^f^g^h^i ^m_^n_^oSacharczuk Mariusz^pSacharczuk Mariusz^rSacharczuk^sMariusz^u^t^qSacharczuk M^w^x0000026532^zSacharczuk MariuszŁazarczyk Marzena Mickael Michel Edward Skiba Dominik Kurzejamska Ewa Ławiński Michał Horbańczuk Jarosław Olav^aF01^butrzymanie i rozwój potencjału badawczego - art. 365 pkt 2 ustawy^a4101.00^11ACZartykuł w czasopiśmie zagranicznym4.900IF^a993999^b998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM^a009994.100^b009859.000^c009999.000^d009859.000202320232023Journey of Cancer Cells to the Brain Challenges and Opportunities00000450840000000466AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1422-0067/24/4/3854100^a1422-0067^bQ^iX^jXY^kQ009159^a003^b003^c2023-03-15, 10:29^d2024-06-25, 14:23^e3127899210^f3024798816^aThe Journey of Cancer Cells to the Brain^bChallenges and Opportunities^aInternational Journal of Molecular Sciences^a2023^bVol. 24^cissue 4^darticle number 3854^a1422-0067^a2022/2023^a10.3390/ijms24043854^aPaszkiewicz, Justyna^cy^abraine^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aCancer metastases into the brain constitute one of the most severe, but not uncommon, manifestations of cancer progression. Several factors control how cancer cells interact with the brain to establish metastasis. These factors include mediators of signaling pathways participating in migration, infiltration of the blood-brain barrier, interaction with host cells (e.g., neurons, astrocytes), and the immune system. Development of novel therapies offers a glimpse of hope for increasing the diminutive life expectancy currently forecasted for patients suffering from brain metastasis. However, applying these treatment strategies has not been sufficiently effective. Therefore, there is a need for a better understanding of the metastasis process to uncover novel therapeutic targets. In this review, we follow the journey of various cancer cells from their primary location through the diverse processes that they undergo to colonize the brain. These processes include EMT, intravasation, extravasation, and infiltration of the blood-brain barrier, ending up with colonization and angiogenesis. In each phase, we focus on the pathways engaging molecules that potentially could be drug target candidates.^acancer^aimmune cells^ametastasis
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Nr opisu: z^bMariusz^c^d^e^f^g^h^i ^m_^n_^oSacharczuk Mariusz^pSacharczuk Mariusz^rSacharczuk^sMariusz^u^t^qSacharczuk M^w^x0000026532^zSacharczuk MariuszLazarczyk Marzena Duda Kamil Mickael Michel Edward AK Onurhan Paszkiewicz Justyna Kowalczyk Agnieszka^aoriginal-article^bOryginalny artykuł naukowy^aF01^butrzymanie i rozwój potencjału badawczego - art. 365 pkt 2 ustawy^a1247.77^11ACZartykuł w czasopiśmie zagranicznym4.600IF^a994300^b998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM^a009994.400^b009859.000^c009999.000^d009859.000202220222022Adera 2.0. A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Network00000442170000000549AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1420-3049/27/19/6453100^a1420-3049^bQ^iX^jXY^kQ014587^a003^b003^c2022-10-10, 13:28^d2024-04-17, 10:03^e3220948911^f3026879236^aAdera 2.0. A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks^aMolecules^a2022^bVol. 27^cissue 19^darticle number 6453^a1420-3049^a2021/2022^a10.3390/molecules27196453^aPaszkiewicz, Justyna^cy^adeep neural network^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aDrug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medications. Neuroimmunological diseases, such as Alzheimer's, Parkinson's, multiple sclerosis, and depression, include various pathologies that result from the interaction between the central nervous system and the immune system. However, the repurposing of NI medications is hindered by the vast amount of information that needs mining. We previously presented Adera1.0, which was capable of text mining PubMed for answering query-based questions. However, Adera1.0 was not able to automatically identify chemical compounds within relevant sentences. To challenge the need for repurposing known medications for neuroimmunological diseases, we built a deep neural network named Adera2.0 to perform drug repurposing. The workflow uses three deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network uses a mean squared error (MSE) loss function to predict answers in the form of embedded matrices. The third network, which constitutes the main novelty in our updated workflow, also uses a MSE loss function. Its main usage is to extract compound names from relevant sentences resulting from the previous network. To optimize the network function, we compared eight different designs. We found that a deep neural network consisting of an RNN neural network and a leaky ReLU could achieve 0.0001 loss and 67% sensitivity. Additionally, we validated Adera2.0's ability to predict NI drug usage against the DRUG Repurposing Hub database. These results establish the ability of Adera2.0 to repurpose drug candidates that can shorten the development of the drug cycle. The workflow could be download online.^adrug repu
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Tytuł pracy w innym języku: original-articleF011247.77994300009994.4001420-3049003Adera 2.0. A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural NetworksMolecules20221420-30492021/202210.3390/molecules27196453Paszkiewicz, Justynadeep neural networkFINAL_PUBLISHEDDrug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medications. Neuroimmunological diseases, such as Alzheimer's, Parkinson's, multiple sclerosis, and depression, include various pathologies that result from the interaction between the central nervous system and the immune system. However, the repurposing of NI medications is hindered by the vast amount of information that needs mining. We previously presented Adera1.0, which was capable of text mining PubMed for answering query-based questions. However, Adera1.0 was not able to automatically identify chemical compounds within relevant sentences. To challenge the need for repurposing known medications for neuroimmunological diseases, we built a deep neural network named Adera2.0 to perform drug repurposing. The workflow uses three deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network uses a mean squared error (MSE) loss function to predict answers in the form of embedded matrices. The third network, which constitutes the main novelty in our updated workflow, also uses a MSE loss function. Its main usage is to extract compound names from relevant sentences resulting from the previous network. To optimize the network function, we compared eight different designs. We found that a deep neural network consisting of an RNN neural network and a leaky ReLU could achieve 0.0001 loss and 67% sensitivity. Additionally, we validated Adera2.0's ability to predict NI drug usage against the DRUG Repurposing Hub data : Oryginalny artykuł naukowy : utrzymanie i rozwój potencjału badawczego - art. 365 pkt 2 ustawy : 998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM : 009859.000 : Q : 003 : Vol. 27 : CC-BY
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Słowa kluczowe: 4-04-17, 10:03^e3220948911^f3026879236^aAdera 2.0. A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks^aMolecules^a2022^bVol. 27^cissue 19^darticle number 6453^a1420-3049^a2021/2022^a10.3390/molecules27196453^aPaszkiewicz, Justyna^cy^adeep neural network^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aDrug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medications. Neuroimmunological diseases, such as Alzheimer's, Parkinson's, multiple sclerosis, and depression, include various pathologies that result from the interaction between the central nervous system and the immune system. However, the repurposing of NI medications is hindered by the vast amount of information that needs mining. We previously presented Adera1.0, which was capable of text mining PubMed for answering query-based questions. However, Adera1.0 was not able to automatically identify chemical compounds within relevant sentences. To challenge the need for repurposing known medications for neuroimmunological diseases, we built a deep neural network named Adera2.0 to perform drug repurposing. The workflow uses three deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network uses a mean squared error (MSE) loss function to predict answers in the form of embedded matrices. The third network, which constitutes the main novelty in our updated workflow, also uses a MSE loss function. Its main usage is to extract compound names from relevant sentences resulting from the previous network. To optimize the network function, we compared eight different designs. We found that a deep neural network consisting of an RNN neural network and a leaky ReLU could achieve 0.0001 loss and 67% sensitivity. Additionally, we validated Adera2.0's ability to predict NI drug usage against the DRUG Repurposing Hub database. These results establish the ability of Adera2.0 to repurpose drug candidates that can shorten the development of the drug cycle. The workflow could be download online.^adrug repurposing^aneuro-immunology
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