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1,637 results on '"Kuznetsova, Tatiana"'

1. Stoichiometric Control and Optical Properties of BaTiO3 Thin Films Grown by Hybrid MBE

Author

Yalcin, Benazir Fazlioglu, Suceava, Albert, Kuznetsova, Tatiana, Wang, Ke, Gopalan, Venkatraman, and Engel-Herbert, Roman

Subjects
Condensed Matter - Materials Science
Abstract

BaTiO3 is a technologically relevant material in the perovskite oxide class with above room temperature ferroelectricity and a very large electro optical coefficient, making it highly suitable for emerging electronic and photonic devices. An easy, robust, straightforward, and scalable growth method is required to synthesize epitaxial BaTiO3 thin films with sufficient control over the film stoichiometry to achieve reproducible thin film properties. Here we report the growth of BaTiO3 thin films by hybrid molecular beam epitaxy. A self regulated growth window is identified using complementary information obtained from reflection high energy electron diffraction, the intrinsic film lattice parameter, film surface morphology, and scanning transmission electron microscopy. Subsequent optical characterization of the BaTiO3 films by spectroscopic ellipsometry revealed refractive index and extinction coefficient values closely resembling those of stoichiometric bulk BaTiO3 crystals for films grown inside the growth window. Even in the absence of a lattice parameter change of BaTiO3 thin films, degradation of optical properties was observed, accompanied by the appearance of a wide optical absorption peak in the infrared spectrum, attributed to optical transitions involving defect states present. Therefore, the optical properties of BaTiO3 can be utilized as a much finer and more straightforward probe to determine the stoichiometry level present in BaTiO3 films.

Published
2022
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13. Clinical and Echocardiographic Diversity Associated With Physical Fitness in the Project Baseline Health Study: Implications for Heart Failure Staging

Author

CAUWENBERGHS, NICHOLAS, HADDAD, FRANCOIS, DAUBERT, MELISSA A., CHATTERJEE, RANEE, SALERNO, MICHAEL, MEGA, JESSICA L., HEIDENREICH, PAUL, HERNANDEZ, ADRIAN, AMSALLEM, MYRIAM, KOBAYASHI, YUKARI, MAHAFFEY, KENNETH W., SHAH, SVATI H., BLOOMFIELD, GERALD S., KUZNETSOVA, TATIANA, and DOUGLAS, PAMELA S.

Published
2023
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15. Analysis of the level regime of the Iga river of the Nizhnevartovsk district of the Khanty-Mansi autonomous district Siberia

Author

Tusupbekov Zhanbolat, Popova Natalia, Ryapolova Natalia, Nadtochiy Victoria, and Kuznetsova Tatiana

Subjects
Environmental sciences, GE1-350
Abstract

Obtaining hydrological information in general, as well as determining the estimated water levels on water bodies, are mostly associated with the construction of hydraulic structures that allow the use of water for the needs of the national economy. The oil production industry is widely developed on the territory of the Khanty-Mansi Autonomous Okrug. Often oil and gas fields are located in sparsely populated areas where there is no information about the hydrological regime of watercourses. In order to ensure the safe operation of construction projects, there is a need to obtain hydrological information for crossed or nearby watercourses. Thus, this type of work is performed almost everywhere to solve similar problems. In this article, studies of the Iga River, located in the right bank of the Ob River, were carried out. To identify the degree of influence of the river level in different periods of the year, hydrological calculations of spring floods and rain floods were performed. Based on the calculations, a graph of the relationship between costs and levels was constructed. To determine the possible backup of the level of the Ob River to the level regime of the Iga River during periods of high waters, the levels of the Ob River were determined. Based on the analysis of joint levels, a conclusion was made about the influence of the Ob on the hydrological regime of the Iga River

Published
2024
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16. Science, Technology, and Innovation Policy in Response to the COVID-19 Crisis: The Case of Russia

Author

Gershman, Mikhail, Gokhberg, Leonid, Kuznetsova, Tatiana, Gokhberg, Leonid, Series Editor, Meissner, Dirk, Series Editor, Carayannis, Elias G., Editorial Board Member, Gault, Fred, Editorial Board Member, Lee, Jeong-Dong, Editorial Board Member, Linton, Jonathan, Editorial Board Member, Miles, Ian, Editorial Board Member, Phillips, Fred Young, Editorial Board Member, Saritas, Ozcan, Editorial Board Member, Shapira, Philip, Editorial Board Member, Sokolov, Alexander, Editorial Board Member, Vonortas, Nicholas, Editorial Board Member, Polat, Mustafa, editor, and Burmaoglu, Serhat, editor

Published
2022
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20. Association of left ventricular diastolic function with coronary artery calcium score: A Project Baseline Health Study

Author

Haddad, Francois, Cauwenberghs, Nicholas, Daubert, Melissa A., Kobayashi, Yukari, Bloomfield, Gerald S., Fleischman, Dominik, Koweek, Lynne, Maron, David J., Rodriguez, Fatima, Liao, Yaping Joyce, Moneghetti, Kegan, Amsallem, Myriam, Mega, Jessica, Hernandez, Adrian, Califf, Robert, Mahaffey, Kenneth W., Shah, Svati H., Kuznetsova, Tatiana, and Douglas, Pamela S.

Published
2022
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21. Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA

Author

Kakadiaris, Ioannis A, Vrigkas, Michalis, Yen, Albert A, Kuznetsova, Tatiana, Budoff, Matthew, and Naghavi, Morteza

Subjects
Aging, Atherosclerosis, Patient Safety, Heart Disease, Prevention, Cardiovascular, Good Health and Well Being, Aged, Cardiovascular Diseases, Coronary Artery Disease, Female, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Machine Learning, Male, Middle Aged, Risk Assessment, Risk Factors, Sensitivity and Specificity, Support Vector Machine, Artificial intelligence, Machine learning, clinical decision support, cardiovascular risk, cardiovascular disease risk factors, cardiovascular disease prevention, atherosclerosis, prediction statistics, statin, Cardiorespiratory Medicine and Haematology
Abstract

Background Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machine Learning ( ML ) to tackle this problem. Methods and Results We developed a ML Risk Calculator based on Support Vector Machines ( SVM s) using a 13-year follow up data set from MESA (the Multi-Ethnic Study of Atherosclerosis) of 6459 participants who were atherosclerotic CVD-free at baseline. We provided identical input to both risk calculators and compared their performance. We then used the FLEMENGHO study (the Flemish Study of Environment, Genes and Health Outcomes) to validate the model in an external cohort. ACC / AHA Risk Calculator, based on 7.5% 10-year risk threshold, recommended statin to 46.0%. Despite this high proportion, 23.8% of the 480 "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.76, specificity 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended only 11.4% to take statin, and only 14.4% of "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.86, specificity 0.95, and AUC 0.92. Similar results were found for prediction of "All CVD " events. Conclusions The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in short-term CVD risk prediction.

Published
2018

22. Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

Author

Kakadiaris, Ioannis A, Vrigkas, Michalis, Yen, Albert A, Kuznetsova, Tatiana, Budoff, Matthew, and Naghavi, Morteza

Subjects
Humans, Cardiovascular Diseases, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Risk Assessment, Risk Factors, Sensitivity and Specificity, Aged, Middle Aged, Female, Male, Coronary Artery Disease, Machine Learning, Support Vector Machine, Artificial intelligence, Machine learning, atherosclerosis, cardiovascular disease prevention, cardiovascular disease risk factors, cardiovascular risk, clinical decision support, prediction statistics, statin, Cardiorespiratory Medicine and Haematology
Abstract

Background Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machine Learning ( ML ) to tackle this problem. Methods and Results We developed a ML Risk Calculator based on Support Vector Machines ( SVM s) using a 13-year follow up data set from MESA (the Multi-Ethnic Study of Atherosclerosis) of 6459 participants who were atherosclerotic CVD-free at baseline. We provided identical input to both risk calculators and compared their performance. We then used the FLEMENGHO study (the Flemish Study of Environment, Genes and Health Outcomes) to validate the model in an external cohort. ACC / AHA Risk Calculator, based on 7.5% 10-year risk threshold, recommended statin to 46.0%. Despite this high proportion, 23.8% of the 480 "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.76, specificity 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended only 11.4% to take statin, and only 14.4% of "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.86, specificity 0.95, and AUC 0.92. Similar results were found for prediction of "All CVD " events. Conclusions The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in short-term CVD risk prediction.

Published
2018

30. Empowering In-Memory Relational Database Engines with Native Graph Processing

Author

Hassan, Mohamed S., Kuznetsova, Tatiana, Jeong, Hyun Chai, Aref, Walid G., and Sadoghi, Mohammad

Subjects
Computer Science - Databases
Abstract

The plethora of graphs and relational data give rise to many interesting graph-relational queries in various domains, e.g., finding related proteins satisfying relational predicates in a biological network. The maturity of RDBMSs motivated academia and industry to invest efforts in leveraging RDBMSs for graph processing, where efficiency is proven for vital graph queries. However, none of these efforts process graphs natively inside the RDBMS, which is particularly challenging due to the impedance mismatch between the relational and the graph models. In this paper, we propose to treat graphs as first-class citizens inside the relational engine so that operations on graphs are executed natively inside the RDBMS. We realize our approach inside VoltDB, an open-source in-memory relational database, and name this realization GRFusion. The SQL and the query engine of GRFusion are empowered to declaratively define graphs and execute cross-data-model query plans formed by graph and relational operators, resulting in up to four orders-of-magnitude in query-time speedup w.r.t. state-of-the-art approaches.

Published
2017

32. The origins and spread of domestic horses from the Western Eurasian steppes

Author

Librado, Pablo, Khan, Naveed, Fages, Antoine, Kusliy, Mariya A., Suchan, Tomasz, Tonasso-Calvière, Laure, Schiavinato, Stéphanie, Alioglu, Duha, Fromentier, Aurore, Perdereau, Aude, Aury, Jean-Marc, Gaunitz, Charleen, Chauvey, Lorelei, Seguin-Orlando, Andaine, Der Sarkissian, Clio, Southon, John, Shapiro, Beth, Tishkin, Alexey A., Kovalev, Alexey A., Alquraishi, Saleh, Alfarhan, Ahmed H., Al-Rasheid, Khaled A. S., Seregély, Timo, Klassen, Lutz, Iversen, Rune, Bignon-Lau, Olivier, Bodu, Pierre, Olive, Monique, Castel, Jean-Christophe, Boudadi-Maligne, Myriam, Alvarez, Nadir, Germonpré, Mietje, Moskal-del Hoyo, Magdalena, Wilczyński, Jarosław, Pospuła, Sylwia, Lasota-Kuś, Anna, Tunia, Krzysztof, Nowak, Marek, Rannamäe, Eve, Saarma, Urmas, Boeskorov, Gennady, Lōugas, Lembi, Kyselý, René, Peške, Lubomír, Bălășescu, Adrian, Dumitrașcu, Valentin, Dobrescu, Roxana, Gerber, Daniel, Kiss, Viktória, Szécsényi-Nagy, Anna, Mende, Balázs G., Gallina, Zsolt, Somogyi, Krisztina, Kulcsár, Gabriella, Gál, Erika, Bendrey, Robin, Allentoft, Morten E., Sirbu, Ghenadie, Dergachev, Valentin, Shephard, Henry, Tomadini, Noémie, Grouard, Sandrine, Kasparov, Aleksei, Basilyan, Alexander E., Anisimov, Mikhail A., Nikolskiy, Pavel A., Pavlova, Elena Y., Pitulko, Vladimir, Brem, Gottfried, Wallner, Barbara, Schwall, Christoph, Keller, Marcel, Kitagawa, Keiko, Bessudnov, Alexander N., Bessudnov, Alexander, Taylor, William, Magail, Jérome, Gantulga, Jamiyan-Ombo, Bayarsaikhan, Jamsranjav, Erdenebaatar, Diimaajav, Tabaldiev, Kubatbeek, Mijiddorj, Enkhbayar, Boldgiv, Bazartseren, Tsagaan, Turbat, Pruvost, Mélanie, Olsen, Sandra, Makarewicz, Cheryl A., Valenzuela Lamas, Silvia, Albizuri Canadell, Silvia, Nieto Espinet, Ariadna, Iborra, Ma Pilar, Lira Garrido, Jaime, Rodríguez González, Esther, Celestino, Sebastián, Olària, Carmen, Arsuaga, Juan Luis, Kotova, Nadiia, Pryor, Alexander, Crabtree, Pam, Zhumatayev, Rinat, Toleubaev, Abdesh, Morgunova, Nina L., Kuznetsova, Tatiana, Lordkipanize, David, Marzullo, Matilde, Prato, Ornella, Bagnasco Gianni, Giovanna, Tecchiati, Umberto, Clavel, Benoit, Lepetz, Sébastien, Davoudi, Hossein, Mashkour, Marjan, Berezina, Natalia Ya., Stockhammer, Philipp W., Krause, Johannes, Haak, Wolfgang, Morales-Muñiz, Arturo, Benecke, Norbert, Hofreiter, Michael, Ludwig, Arne, Graphodatsky, Alexander S., Peters, Joris, Kiryushin, Kirill Yu., Iderkhangai, Tumur-Ochir, Bokovenko, Nikolay A., Vasiliev, Sergey K., Seregin, Nikolai N., Chugunov, Konstantin V., Plasteeva, Natalya A., Baryshnikov, Gennady F., Petrova, Ekaterina, Sablin, Mikhail, Ananyevskaya, Elina, Logvin, Andrey, Shevnina, Irina, Logvin, Victor, Kalieva, Saule, Loman, Valeriy, Kukushkin, Igor, Merz, Ilya, Merz, Victor, Sakenov, Sergazy, Varfolomeyev, Victor, Usmanova, Emma, Zaibert, Viktor, Arbuckle, Benjamin, Belinskiy, Andrey B., Kalmykov, Alexej, Reinhold, Sabine, Hansen, Svend, Yudin, Aleksandr I., Vybornov, Alekandr A., Epimakhov, Andrey, Berezina, Natalia S., Roslyakova, Natalia, Kosintsev, Pavel A., Kuznetsov, Pavel F., Anthony, David, Kroonen, Guus J., Kristiansen, Kristian, Wincker, Patrick, Outram, Alan, and Orlando, Ludovic

Published
2021
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33. Author Correction: Circulating Biomarkers to Identify Responders in Cardiac Cell therapy.

Author

Jokerst, Jesse V, Cauwenberghs, Nicholas, Kuznetsova, Tatiana, Haddad, Francois, Sweeney, Timothy, Hou, Jiayi, Rosenberg-Hasson, Yael, Zhao, Eric, Schutt, Robert, Bolli, Roberto, Traverse, Jay H, Pepine, Carl J, Henry, Timothy D, Schulman, Ivonne H, Moyé, Lem, Taylor, Doris A, and Yang, Phillip C

Abstract

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Published
2018

36. Temporal changes in soluble angiotensin-converting enzyme 2 associated with metabolic health, body composition, and proteome dynamics during a weight loss diet intervention: a randomized trial with implications for the COVID-19 pandemic

Author

Cauwenberghs, Nicholas, Prunicki, Mary, Sabovčik, František, Perelman, Dalia, Contrepois, Kévin, Li, Xiao, Snyder, Michael P, Nadeau, Kari C, Kuznetsova, Tatiana, Haddad, Francois, and Gardner, Christopher D

Published
2021
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38. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging

Author

Sayed, Nazish, Huang, Yingxiang, Nguyen, Khiem, Krejciova-Rajaniemi, Zuzana, Grawe, Anissa P., Gao, Tianxiang, Tibshirani, Robert, Hastie, Trevor, Alpert, Ayelet, Cui, Lu, Kuznetsova, Tatiana, Rosenberg-Hasson, Yael, Ostan, Rita, Monti, Daniela, Lehallier, Benoit, Shen-Orr, Shai S., Maecker, Holden T., Dekker, Cornelia L., Wyss-Coray, Tony, Franceschi, Claudio, Jojic, Vladimir, Haddad, François, Montoya, José G., Wu, Joseph C., Davis, Mark M., and Furman, David

Published
2021
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39. Abstract 14174: Abnormal Exercise Electrocardiography With Normal Stress Echocardiography is Associated With Increased Subclinical Coronary Atherosclerosis: Insights From the Project Baseline Health Study

Author

Duran, Jessica M, Nguyen, Maggie, Haddad, Francois, Cauwenberghs, Nicholas, Kuznetsova, Tatiana, Bloomfield, Gerald S, Mahaffey, Kenneth, Hernandez, Adrian F, Mega, Jessica L, Short, Sarah, Douglas, Pamela S, Shah, Svati H, and Daubert, Melissa A

Published
2022
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42. Erratum: Genetic loci associated with heart rate variability and their effects on cardiac disease risk.

Author

Nolte, Ilja, Munoz, M, Tragante, Vinicius, Amare, Azmeraw, Jansen, Rick, Vaez, Ahmad, von der Heyde, Benedikt, Avery, Christy, Bis, Joshua, Dierckx, Bram, van Dongen, Jenny, Gogarten, Stephanie, Goyette, Philippe, Hernesniemi, Jussi, Huikari, Ville, Hwang, Shih-Jen, Jaju, Deepali, Kerr, Kathleen, Kluttig, Alexander, Krijthe, Bouwe, Kumar, Jitender, van der Laan, Sander, Lyytikäinen, Leo-Pekka, Maihofer, Adam, Minassian, Arpi, van der Most, Peter, Müller-Nurasyid, Martina, Nivard, Michel, Salvi, Erika, Stewart, James, Thayer, Julian, Verweij, Niek, Wong, Andrew, Zabaneh, Delilah, Zafarmand, Mohammad, Abdellaoui, Abdel, Albarwani, Sulayma, Albert, Christine, Alonso, Alvaro, Ashar, Foram, Auvinen, Juha, Axelsson, Tomas, Baker, Dewleen, de Bakker, Paul, Barcella, Matteo, Bayoumi, Riad, Bieringa, Rob, Boomsma, Dorret, Boucher, Gabrielle, Britton, Annie, Christophersen, Ingrid, Dietrich, Andrea, Ehret, George, Ellinor, Patrick, Eskola, Markku, Felix, Janine, Floras, John, Franco, Oscar, Friberg, Peter, Gademan, Maaike, Geyer, Mark, Giedraitis, Vilmantas, Hartman, Catharina, Hemerich, Daiane, Hofman, Albert, Hottenga, Jouke-Jan, Huikuri, Heikki, Hutri-Kähönen, Nina, Jouven, Xavier, Junttila, Juhani, Juonala, Markus, Kiviniemi, Antti, Kors, Jan, Kumari, Meena, Kuznetsova, Tatiana, Laurie, Cathy, Lefrandt, Joop, Li, Yong, Li, Yun, Liao, Duanping, Limacher, Marian, Lin, Henry, Lindgren, Cecilia, Lubitz, Steven, Mahajan, Anubha, McKnight, Barbara, Zu Schwabedissen, Henriette, Milaneschi, Yuri, Mononen, Nina, Morris, Andrew, Nalls, Mike, Navis, Gerjan, Neijts, Melanie, Nikus, Kjell, North, Kari, OConnor, Daniel, Ormel, Johan, Perz, Siegfried, Peters, Annette, and Psaty, Bruce

Abstract

This corrects the article DOI: 10.1038/ncomms15805.

Published
2017

43. Circulating Biomarkers to Identify Responders in Cardiac Cell therapy.

Author

Jokerst, Jesse V, Cauwenberghs, Nicholas, Kuznetsova, Tatiana, Haddad, Francois, Sweeney, Timothy, Hou, Jiayi, Rosenberg-Hasson, Yael, Zhao, Eric, Schutt, Robert, Bolli, Roberto, Traverse, Jay H, Pepine, Carl J, Henry, Timothy D, Schulman, Ivonne H, Moyé, Lem, Taylor, Doris A, and Yang, Phillip C

Abstract

Bone marrow mononuclear cell (BM-MNC) therapy in ST-elevation acute myocardial infarction (STEMI) has no biological inclusion criteria. Here, we analyzed 63 biomarkers and cytokines in baseline plasma samples from 77 STEMI patients treated with BM-MNCs in the TIME and Late-TIME trials as well as 61 STEMI patients treated with placebo. Response to cell therapy was defined by changes in left ventricular ejection fraction, systolic/diastolic volumes, and wall motion indexes. We investigated the clinical value of circulating proteins in outcome prediction using significance testing, partial least squares discriminant analysis, and receiver operating characteristic (ROC) analysis. Responders had higher biomarker levels (76-94% elevated) than non-responders. Several biomarkers had values that differed significantly (P 0.70 including interleukin 15. These biomarkers were not involved in the placebo-treated subjects suggesting that they may have predictive power. We conclude that plasma profiling after STEMI may help identify patients with a greater likelihood of response to cell-based treatment. Prospective trials are needed to assess the predictive value of the circulating biomarkers.

Published
2017

44. Genetic loci associated with heart rate variability and their effects on cardiac disease risk.

Author

Nolte, Ilja M, Munoz, M Loretto, Tragante, Vinicius, Amare, Azmeraw T, Jansen, Rick, Vaez, Ahmad, von der Heyde, Benedikt, Avery, Christy L, Bis, Joshua C, Dierckx, Bram, van Dongen, Jenny, Gogarten, Stephanie M, Goyette, Philippe, Hernesniemi, Jussi, Huikari, Ville, Hwang, Shih-Jen, Jaju, Deepali, Kerr, Kathleen F, Kluttig, Alexander, Krijthe, Bouwe P, Kumar, Jitender, van der Laan, Sander W, Lyytikäinen, Leo-Pekka, Maihofer, Adam X, Minassian, Arpi, van der Most, Peter J, Müller-Nurasyid, Martina, Nivard, Michel, Salvi, Erika, Stewart, James D, Thayer, Julian F, Verweij, Niek, Wong, Andrew, Zabaneh, Delilah, Zafarmand, Mohammad H, Abdellaoui, Abdel, Albarwani, Sulayma, Albert, Christine, Alonso, Alvaro, Ashar, Foram, Auvinen, Juha, Axelsson, Tomas, Baker, Dewleen G, de Bakker, Paul IW, Barcella, Matteo, Bayoumi, Riad, Bieringa, Rob J, Boomsma, Dorret, Boucher, Gabrielle, Britton, Annie R, Christophersen, Ingrid, Dietrich, Andrea, Ehret, George B, Ellinor, Patrick T, Eskola, Markku, Felix, Janine F, Floras, John S, Franco, Oscar H, Friberg, Peter, Gademan, Maaike GJ, Geyer, Mark A, Giedraitis, Vilmantas, Hartman, Catharina A, Hemerich, Daiane, Hofman, Albert, Hottenga, Jouke-Jan, Huikuri, Heikki, Hutri-Kähönen, Nina, Jouven, Xavier, Junttila, Juhani, Juonala, Markus, Kiviniemi, Antti M, Kors, Jan A, Kumari, Meena, Kuznetsova, Tatiana, Laurie, Cathy C, Lefrandt, Joop D, Li, Yong, Li, Yun, Liao, Duanping, Limacher, Marian C, Lin, Henry J, Lindgren, Cecilia M, Lubitz, Steven A, Mahajan, Anubha, McKnight, Barbara, Zu Schwabedissen, Henriette Meyer, Milaneschi, Yuri, Mononen, Nina, Morris, Andrew P, Nalls, Mike A, Navis, Gerjan, Neijts, Melanie, Nikus, Kjell, North, Kari E, O'Connor, Daniel T, Ormel, Johan, Perz, Siegfried, Peters, Annette, and Psaty, Bruce M

Subjects
Humans, Heart Diseases, Genetic Predisposition to Disease, RGS Proteins, Potassium Channels, Muscle Proteins, Risk Factors, Cohort Studies, Blood Pressure, Heart Rate, Polymorphism, Single Nucleotide, Quantitative Trait Loci, European Continental Ancestry Group, Genome-Wide Association Study, Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels, Cardiovascular, Genetics, Heart Disease
Abstract

Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4). Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (-0.74g

Published
2017

47. Increasing Risks to the Health of the Invertebrates—Balancing between Harm and Benefit.

Author

Kuznetsova, Tatiana V., Kudryavtseva, Valentina A., and Kapranova, Larisa L.

Subjects
*EVIDENCE gaps, *POLLUTANTS, *INVERTEBRATES, *BIOLOGICAL membranes, *HEAVY metals
Abstract

Simple Summary: Emerging challenges associated with the COVID-19 (SARS-CoV-2) pandemic include the unpredictable biological effects of highly recommended detergents and disinfectants to reduce the risk of human infection. How these classes of substances affect living organisms, especially the health of the invertebrates as common representatives of biota, is still unclear. Due to the exceptional ability of disinfectants and detergents to penetrate biological membranes and alter the pH of the environment, these classes of substances, together with other environmental pollutants of concern (e.g., heavy metals), can have pronounced negative effects on macrobenthic invertebrates. The review analyzes the current state of research in this area and discusses ways to reduce risks to invertebrates. The article discusses the issue of extensive use of detergents and sanitizers in the time of new challenges associated with the COVID-19 (SARS-CoV-2) pandemic. These agents could pose threats to the existence of free-living invertebrates as essential components of the ecosystem. The biological effects of the mentioned classes of substances, their metabolites, and combined effects in the mixture have not been studied enough. The main challenges in trying to balance the threats and benefits of using such substances are the lack of knowledge of the biological effects of these products, the gaps in testing invertebrates' responses, and changes in environment-related regulations to minimize risks to animals and humans. Numerous studies in this field still leave research gaps, particularly concerning the combined toxicity of well-known and widely used disinfectants, surfactants, and heavy metals, posing potential future challenges. Additionally, the review identified the need for additional testing of invertebrates for their sensitivity to disinfectants and surfactants of different compositions, including improved (non-invasive) methods, studies for early life stages, and comparative studies of species resilience. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Hemodynamic Mechanisms

Author

Kuznetsova, Tatiana, Cauwenberghs, Nicholas, Mancia, Giuseppe, Series Editor, Rosei, Enrico Agabiti, Series Editor, Dorobantu, Maria, editor, Grassi, Guido, editor, and Voicu, Victor, editor

Published
2019
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