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  1. AU="Sreeja Attur"
  2. AU=Song Kyung Chul
  3. AU=Klimovich Pavel V.
  4. AU="Jingbo Chen"
  5. AU="Viazlo, Oleksander"
  6. AU="Toshiki Iwabuchi"
  7. AU="Dissanayake, Lakmali"
  8. AU="Michael Denkinger"
  9. AU="Abilio J. F. N. Sobral"
  10. AU="Geller, Alan"
  11. AU=Petrat Sren
  12. AU="Sterling, Shanique"
  13. AU="Qi, Zeqiang"
  14. AU="Thongstisubskul, A"
  15. AU="Daniel C. Schneider, PhD"
  16. AU="Völker, Christoph"
  17. AU="El Aoud, S"
  18. AU="Yi, Tongpei"
  19. AU="Anil K. Mantha"
  20. AU="Artzner, Christoph"
  21. AU=Diana Giovanni
  22. AU="Kinloch, Sabine"
  23. AU="Nuertey, David"
  24. AU="Ojubolamo, Olakunle"

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  1. Artikel ; Online: Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait

    Moh A. Alkhamis / Mohammad Al Jarallah / Sreeja Attur / Mohammad Zubaid

    Scientific Reports, Vol 14, Iss 1, Pp 1-

    2024  Band 13

    Abstract: Abstract The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to ... ...

    Abstract Abstract The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 610
    Sprache Englisch
    Erscheinungsdatum 2024-01-01T00:00:00Z
    Verlag Nature Portfolio
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.

    Faisal Alsayegh / Moh A Alkhamis / Fatima Ali / Sreeja Attur / Nicholas M Fountain-Jones / Mohammad Zubaid

    PLoS ONE, Vol 17, Iss 1, p e

    2022  Band 0262997

    Abstract: Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship between ... ...

    Abstract Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship between ACS and patient risk factors is typically non-linear and highly variable across patient lifespan. Here, we aim to uncover deeper insights into the factors that shape ACS outcomes in hospitals across four Arabian Gulf countries. Further, because anemia is one of the most observed comorbidities, we explored its role in the prognosis of most prevalent ACS in-hospital outcomes (mortality, heart failure, and bleeding) in the region. We used a robust multi-algorithm interpretable machine learning (ML) pipeline, and 20 relevant risk factors to fit predictive models to 4,044 patients presenting with ACS between 2012 and 2013. We found that in-hospital heart failure followed by anemia was the most important predictor of mortality. However, anemia was the first most important predictor for both in-hospital heart failure, and bleeding. For all in-hospital outcome, anemia had remarkably non-linear relationships with both ACS outcomes and patients' baseline characteristics. With minimal statistical assumptions, our ML models had reasonable predictive performance (AUCs > 0.75) and substantially outperformed commonly used statistical and risk stratification methods. Moreover, our pipeline was able to elucidate ACS risk of individual patients based on their unique risk factors. Fully interpretable ML approaches are rarely used in clinical settings, particularly in the Middle East, but have the potential to improve clinicians' prognostic efforts and guide policymakers in reducing the health and economic burdens of ACS worldwide.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 610
    Sprache Englisch
    Erscheinungsdatum 2022-01-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients

    Faisal Alsayegh / Moh A. Alkhamis / Fatima Ali / Sreeja Attur / Nicholas M. Fountain-Jones / Mohammad Zubaid

    PLoS ONE, Vol 17, Iss

    2022  Band 1

    Abstract: Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship between ... ...

    Abstract Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship between ACS and patient risk factors is typically non-linear and highly variable across patient lifespan. Here, we aim to uncover deeper insights into the factors that shape ACS outcomes in hospitals across four Arabian Gulf countries. Further, because anemia is one of the most observed comorbidities, we explored its role in the prognosis of most prevalent ACS in-hospital outcomes (mortality, heart failure, and bleeding) in the region. We used a robust multi-algorithm interpretable machine learning (ML) pipeline, and 20 relevant risk factors to fit predictive models to 4,044 patients presenting with ACS between 2012 and 2013. We found that in-hospital heart failure followed by anemia was the most important predictor of mortality. However, anemia was the first most important predictor for both in-hospital heart failure, and bleeding. For all in-hospital outcome, anemia had remarkably non-linear relationships with both ACS outcomes and patients’ baseline characteristics. With minimal statistical assumptions, our ML models had reasonable predictive performance (AUCs > 0.75) and substantially outperformed commonly used statistical and risk stratification methods. Moreover, our pipeline was able to elucidate ACS risk of individual patients based on their unique risk factors. Fully interpretable ML approaches are rarely used in clinical settings, particularly in the Middle East, but have the potential to improve clinicians’ prognostic efforts and guide policymakers in reducing the health and economic burdens of ACS worldwide.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 610
    Sprache Englisch
    Erscheinungsdatum 2022-01-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  4. Artikel ; Online: Transactivation of ErbB Family of Receptor Tyrosine Kinases Is Inhibited by Angiotensin-(1-7) via Its Mas Receptor.

    Saghir Akhtar / Bindu Chandrasekhar / Sreeja Attur / Gursev S Dhaunsi / Mariam H M Yousif / Ibrahim F Benter

    PLoS ONE, Vol 10, Iss 11, p e

    2015  Band 0141657

    Abstract: Transactivation of the epidermal growth factor receptor (EGFR or ErbB) family members, namely EGFR and ErbB2, appears important in the development of diabetes-induced vascular dysfunction. Angiotensin-(1-7) [Ang-(1-7)] can prevent the development of ... ...

    Abstract Transactivation of the epidermal growth factor receptor (EGFR or ErbB) family members, namely EGFR and ErbB2, appears important in the development of diabetes-induced vascular dysfunction. Angiotensin-(1-7) [Ang-(1-7)] can prevent the development of hyperglycemia-induced vascular complications partly through inhibiting EGFR transactivation. Here, we investigated whether Ang-(1-7) can inhibit transactivation of ErbB2 as well as other ErbB receptors in vivo and in vitro. Streptozotocin-induced diabetic rats were chronically treated with Ang-(1-7) or AG825, a selective ErbB2 inhibitor, for 4 weeks and mechanistic studies performed in the isolated mesenteric vasculature bed as well as in cultured vascular smooth muscle cells (VSMCs). Ang-(1-7) or AG825 treatment inhibited diabetes-induced phosphorylation of ErbB2 receptor at tyrosine residues Y1221/22, Y1248, Y877, as well as downstream signaling via ERK1/2, p38 MAPK, ROCK, eNOS and IkB-α in the mesenteric vascular bed. In VSMCs cultured in high glucose (25 mM), Ang-(1-7) inhibited src-dependent ErbB2 transactivation that was opposed by the selective Mas receptor antagonist, D-Pro7-Ang-(1-7). Ang-(1-7) via Mas receptor also inhibited both Angiotensin II- and noradrenaline/norephinephrine-induced transactivation of ErbB2 and/or EGFR receptors. Further, hyperglycemia-induced transactivation of ErbB3 and ErbB4 receptors could be attenuated by Ang-(1-7) that could be prevented by D-Pro7-Ang-(1-7) in VSMC. These data suggest that Ang-(1-7) via its Mas receptor acts as a pan-ErbB inhibitor and might represent a novel general mechanism by which Ang-(1-7) exerts its beneficial effects in many disease states including diabetes-induced vascular complications.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 616
    Sprache Englisch
    Erscheinungsdatum 2015-01-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Artikel ; Online: Cationic Polyamidoamine Dendrimers as Modulators of EGFR Signaling In Vitro and In Vivo.

    Saghir Akhtar / Bashayer Al-Zaid / Ahmed Z El-Hashim / Bindu Chandrasekhar / Sreeja Attur / Mariam H M Yousif / Ibrahim F Benter

    PLoS ONE, Vol 10, Iss 7, p e

    2015  Band 0132215

    Abstract: Cationic polyamidoamine (PAMAM) dendrimers are branch-like spherical polymers being investigated for a variety of applications in nanomedicine including nucleic acid drug delivery. Emerging evidence suggests they exhibit intrinsic biological and ... ...

    Abstract Cationic polyamidoamine (PAMAM) dendrimers are branch-like spherical polymers being investigated for a variety of applications in nanomedicine including nucleic acid drug delivery. Emerging evidence suggests they exhibit intrinsic biological and toxicological effects but little is known of their interactions with signal transduction pathways. We previously showed that the activated (fragmented) generation (G) 6 PAMAM dendrimer, Superfect (SF), stimulated epidermal growth factor receptor (EGFR) tyrosine kinase signaling-an important signaling cascade that regulates cell growth, survival and apoptosis- in cultured human embryonic kidney (HEK 293) cells. Here, we firstly studied the in vitro effects of Polyfect (PF), a non-activated (intact) G6 PAMAM dendrimer, on EGFR tyrosine kinase signaling via extracellular-regulated kinase 1/2 (ERK1/2) and p38 mitogen-activated protein kinase (MAPK) in cultured HEK 293 cells and then compared the in vivo effects of a single administration (10mg/kg i.p) of PF or SF on EGFR signaling in the kidneys of normal and diabetic male Wistar rats. Polyfect exhibited a dose- and time-dependent inhibition of EGFR, ERK1/2 and p38 MAPK phosphorylation in HEK-293 cells similar to AG1478, a selective EGFR inhibitor. Administration of dendrimers to non-diabetic or diabetic animals for 24h showed that PF inhibited whereas SF stimulated EGFR phosphorylation in the kidneys of both sets of animals. PF-mediated inhibition of EGFR phosphorylation as well as SF or PF-mediated apoptosis in HEK 293 cells could be significantly reversed by co-treatment with antioxidants such as tempol implying that both these effects involved an oxidative stress-dependent mechanism. These results show for the first time that SF and PF PAMAM dendrimers can differentially modulate the important EGFR signal transduction pathway in vivo and may represent a novel class of EGFR modulators. These findings could have important clinical implications for the use of PAMAM dendrimers in nanomedicine.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 500
    Sprache Englisch
    Erscheinungsdatum 2015-01-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Artikel ; Online: Activation of ErbB2 and Downstream Signalling via Rho Kinases and ERK1/2 Contributes to Diabetes-Induced Vascular Dysfunction.

    Saghir Akhtar / Mariam H M Yousif / Gursev S Dhaunsi / Fatma Sarkhouh / Bindu Chandrasekhar / Sreeja Attur / Ibrahim F Benter

    PLoS ONE, Vol 8, Iss 6, p e

    2013  Band 67813

    Abstract: Diabetes mellitus leads to vascular complications but the underlying signalling mechanisms are not fully understood. Here, we examined the role of ErbB2 (HER2/Neu), a transmembrane receptor tyrosine kinase of the ErbB/EGFR (epidermal growth factor ... ...

    Abstract Diabetes mellitus leads to vascular complications but the underlying signalling mechanisms are not fully understood. Here, we examined the role of ErbB2 (HER2/Neu), a transmembrane receptor tyrosine kinase of the ErbB/EGFR (epidermal growth factor receptor) family, in mediating diabetes-induced vascular dysfunction in an experimental model of type 1 diabetes. Chronic treatment of streptozotocin-induced diabetic rats (1 mg/kg/alt diem) or acute, ex-vivo (10(-6), 10(-5) M) administration of AG825, a specific inhibitor of ErbB2, significantly corrected the diabetes-induced hyper-reactivity of the perfused mesenteric vascular bed (MVB) to the vasoconstrictor, norephinephrine (NE) and the attenuated responsiveness to the vasodilator, carbachol. Diabetes led to enhanced phosphorylation of ErbB2 at multiple tyrosine (Y) residues (Y1221/1222, Y1248 and Y877) in the MVB that could be attenuated by chronic AG825 treatment. Diabetes- or high glucose-mediated upregulation of ErbB2 phosphorylation was coupled with activation of Rho kinases (ROCKs) and ERK1/2 in MVB and in cultured vascular smooth muscle cells (VSMC) that were attenuated upon treatment with either chronic or acute AG825 or with anti-ErbB2 siRNA. ErbB2 likley heterodimerizes with EGFR, as evidenced by increased co-association in diabetic MVB, and further supported by our finding that ERK1/2 and ROCKs are common downstream effectors since their activation could also be blocked by AG1478. Our results show for the first time that ErbB2 is an upstream effector of ROCKs and ERK1/2 in mediating diabetes-induced vascular dysfunction. Thus, potential strategies aimed at modifying actions of signal transduction pathways involving ErbB2 pathway may prove to be beneficial in treatment of diabetes-induced vascular complications.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 571
    Sprache Englisch
    Erscheinungsdatum 2013-01-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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