Search

Your search keyword '"Korolev, V. S."' showing total 44 results
Start Over Author "Korolev, V. S."
44 results on '"Korolev, V. S."'

1. Coniferest: a complete active anomaly detection framework

Author

Kornilov, M. V., Korolev, V. S., Malanchev, K. L., Lavrukhina, A. D., Russeil, E., Semenikhin, T. A., Gangler, E., Ishida, E. E. O., Pruzhinskaya, M. V., Volnova, A. A., and Sreejith, S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package performance are evaluated on a series of synthetic datasets. We also describe a few success cases which resulted from applying the package to real astronomical data in active anomaly detection tasks within the SNAD project., Comment: 13 pages, 1 figure

Published
2024

2. Real-bogus scores for active anomaly detection

Author

Semenikhin, T. A., Kornilov, M. V., Pruzhinskaya, M. V., Lavrukhina, A. D., Russeil, E., Gangler, E., Ishida, E. E. O., Korolev, V. S., Malanchev, K. L., Volnova, A. A., and Sreejith, S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

In the task of anomaly detection in modern time-domain photometric surveys, the primary goal is to identify astrophysically interesting, rare, and unusual objects among a large volume of data. Unfortunately, artifacts -- such as plane or satellite tracks, bad columns on CCDs, and ghosts -- often constitute significant contaminants in results from anomaly detection analysis. In such contexts, the Active Anomaly Discovery (AAD) algorithm allows tailoring the output of anomaly detection pipelines according to what the expert judges to be scientifically interesting. We demonstrate how the introduction real-bogus scores, obtained from a machine learning classifier, improves the results from AAD. Using labeled data from the SNAD ZTF knowledge database, we train four real-bogus classifiers: XGBoost, CatBoost, Random Forest, and Extremely Randomized Trees. All the models perform real-bogus classification with similar effectiveness, achieving ROC-AUC scores ranging from 0.93 to 0.95. Consequently, we select the Random Forest model as the main model due to its simplicity and interpretability. The Random Forest classifier is applied to 67 million light curves from ZTF DR17. The output real-bogus score is used as an additional feature for two anomaly detection algorithms: static Isolation Forest and AAD. While results from Isolation Forest remained unchanged, the number of artifacts detected by the active approach decreases significantly with the inclusion of the real-bogus score, from 27 to 3 out of 100. We conclude that incorporating the real-bogus classifier result as an additional feature in the active anomaly detection pipeline significantly reduces the number of artifacts in the outputs, thereby increasing the incidence of astrophysically interesting objects presented to human experts., Comment: 8 pages, 6 figures

Published
2024

3. SNAD catalogue of M-dwarf flares from the Zwicky Transient Facility

Author

Voloshina, A. S., Lavrukhina, A. D., Pruzhinskaya, M. V., Malanchev, K. L., Ishida, E. E. O., Krushinsky, V. V., Aleo, P. D., Gangler, E., Kornilov, M. V., Korolev, V. S., Russeil, E., Semenikhin, T. A., Sreejith, S., and Volnova, A. A.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Most of the stars in the Universe are M spectral class dwarfs, which are known to be the source of bright and frequent stellar flares. In this paper, we propose new approaches to discover M-dwarf flares in ground-based photometric surveys. We employ two approaches: a modification of a traditional method of parametric fit search and a machine learning algorithm based on active anomaly detection. The algorithms are applied to Zwicky Transient Facility (ZTF) data release 8, which includes the data from the ZTF high-cadence survey, allowing us to reveal flares lasting from minutes to hours. We analyze over 35 million ZTF light curves and visually scrutinize 1168 candidates suggested by the algorithms to filter out artifacts, occultations of a star by an asteroid, and other types of known variable objects. The result of this analysis is the largest catalogue of ZTF flaring stars to date, representing 134 flares with amplitudes ranging from -0.2 to -4.6 magnitudes, including repeated flares. Using Pan-STARRS DR2 colors, we assign a spectral subclass to each object in the sample. For 13 flares with well-sampled light curves and available geometric distances from Gaia DR3, we estimate the bolometric energy. This research shows that the proposed methods combined with the ZTF's cadence strategy are suitable for identifying M-dwarf flares and other fast transients, allowing for the extraction of significant astrophysical information from their light curves., Comment: 19 pages, 6 figures, 1 table, 1 appendix

Published
2024
Full Text
View/download PDF

4. Rainbow: a colorful approach on multi-passband light curve estimation

Author

Russeil, E., Malanchev, K. L., Aleo, P. D., Ishida, E. E. O., Pruzhinskaya, M. V., Gangler, E., Lavrukhina, A. D., Volnova, A. A., Voloshina, A., Semenikhin, T., Sreejith, S., Kornilov, M. V., and Korolev, V. S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Data Analysis, Statistics and Probability
Abstract

We present Rainbow, a physically motivated framework which enables simultaneous multi-band light curve fitting. It allows the user to construct a 2-dimensional continuous surface across wavelength and time, even in situations where the number of observations in each filter is significantly limited. Assuming the electromagnetic radiation emission from the transient can be approximated by a black-body, we combined an expected temperature evolution and a parametric function describing its bolometric light curve. These three ingredients allow the information available in one passband to guide the reconstruction in the others, thus enabling a proper use of multi-survey data. We demonstrate the effectiveness of our method by applying it to simulated data from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) as well as real data from the Young Supernova Experiment (YSE DR1). We evaluate the quality of the estimated light curves according to three different tests: goodness of fit, time of peak prediction and ability to transfer information to machine learning (ML) based classifiers. Results confirm that Rainbow leads to equivalent (SNII) or up to 75% better (SN Ibc) goodness of fit when compared to the Monochromatic approach. Similarly, accuracy when using Rainbow best-fit values as a parameter space in multi-class ML classification improves for all classes in our sample. An efficient implementation of Rainbow has been publicly released as part of the light curve package at https://github.com/light-curve/light-curve-python. Our approach enables straight forward light curve estimation for objects with observations in multiple filters and from multiple experiments. It is particularly well suited for situations where light curve sampling is sparse., Comment: 14 pages, 15 figures, submitted to A&A

Published
2023

6. SNAD Transient Miner: Finding Missed Transient Events in ZTF DR4 using k-D trees

Author

Aleo, P. D., Malanchev, K. L., Pruzhinskaya, M. V., Ishida, E. E. O., Russeil, E., Kornilov, M. V., Korolev, V. S., Sreejith, S., Volnova, A. A., and Narayan, G. S.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We report the automatic detection of 11 transients (7 possible supernovae and 4 active galactic nuclei candidates) within the Zwicky Transient Facility fourth data release (ZTF DR4), all of them observed in 2018 and absent from public catalogs. Among these, three were not part of the ZTF alert stream. Our transient mining strategy employs 41 physically motivated features extracted from both real light curves and four simulated light curve models (SN Ia, SN II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which we calculate the 15 nearest neighbors. After pre-processing and selection cuts, our dataset contained approximately a million objects among which we visually inspected the 105 closest neighbors from seven of our brightest, most well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result illustrates the potential of coherently incorporating domain knowledge and automatic learning algorithms, which is one of the guiding principles directing the SNAD team. It also demonstrates that the ZTF DR is a suitable testing ground for data mining algorithms aiming to prepare for the next generation of astronomical data., Comment: 30 pages, 5 figures, 3 tables, preprint accepted to New Astronomy

Published
2021
Full Text
View/download PDF

7. Anomaly detection in the Zwicky Transient Facility DR3

Author

Malanchev, K. L., Pruzhinskaya, M. V., Korolev, V. S., Aleo, P. D., Kornilov, M. V., Ishida, E. E. O., Krushinsky, V. V., Mondon, F., Sreejith, S., Volnova, A. A., Belinski, A. A., Dodin, A. V., Tatarnikov, A. M., and Zheltoukhov, S. G.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of 3 stages: feature extraction, search of outliers with machine learning algorithms and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of 4 automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinised by an expert. From these, 188 (68%) were found to be bogus light curves -- including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24%) were previously reported sources whereas 23 (8%) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e. g. 1 spectroscopically confirmed RS Canum Venaticorum star, 4 supernovae candidates, 1 red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation which can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available at https://github.com/snad-space/zwad, Comment: 31 pages, 28 figures, 4 tables, accepted for publication in MNRAS

Published
2020
Full Text
View/download PDF

8. SNAD catalogue of M-dwarf flares from the Zwicky Transient Facility.

Author

Voloshina, A S, Lavrukhina, A D, Pruzhinskaya, M V, Malanchev, K L, Ishida, E E O, Krushinsky, V V, Aleo, P D, Gangler, E, Kornilov, M V, Korolev, V S, Russeil, E, Semenikhin, T A, Sreejith, S, Volnova, A A, and team), (The SNAD

Subjects
MACHINE learning, LIGHT curves, COOL stars (Astronomy), DATA release, DATA analysis
Abstract

Most of the stars in the Universe are M spectral class dwarfs, which are known to be the source of bright and frequent stellar flares. In this paper, we propose new approaches to discover M-dwarf flares in ground-based photometric surveys. We employ two approaches: a modification of a traditional method of parametric fit search and a machine learning algorithm based on active anomaly detection. The algorithms are applied to Zwicky Transient Facility (ZTF) data release 8, which includes the data from the ZTF high-cadence survey, allowing us to reveal flares lasting from minutes to hours. We analyse over 35 million ZTF light curves and visually scrutinize 1168 candidates suggested by the algorithms to filter out artefacts, occultations of a star by an asteroid, and other types of known variable objects. The result of this analysis is the largest catalogue of ZTF flaring stars to date, representing 134 flares with amplitudes ranging from −0.2 to −4.6 mag, including repeated flares. Using Pan-STARRS DR2 colours, we assign a spectral subclass to each object in the sample. For 13 flares with well-sampled light curves and available geometric distances from Gaia DR3, we estimate the bolometric energy. This research shows that the proposed methods combined with the ZTF's cadence strategy are suitable for identifying M-dwarf flares and other fast transients, allowing for the extraction of significant astrophysical information from their light curves. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

10. M-dwarf flares in the Zwicky Transient Facility data and what we can learn from them

Author

Voloshina, A. S., Lavrukhina, A. D., Pruzhinskaya, M. V., Malanchev, K. L., Ishida, E. E. O., Krushinsky, V. V., Aleo, P. D., Gangler, E., Kornilov, M. V., Korolev, V. S., Russeil, E., Semenikhin, T. A., Sreejith, S., Volnova, A. A., Voloshina, A. S., Lavrukhina, A. D., Pruzhinskaya, M. V., Malanchev, K. L., Ishida, E. E. O., Krushinsky, V. V., Aleo, P. D., Gangler, E., Kornilov, M. V., Korolev, V. S., Russeil, E., Semenikhin, T. A., Sreejith, S., and Volnova, A. A.

Abstract

In this paper, we explore the possibility of detecting M-dwarf flares using data from the Zwicky Transient Facility data releases (ZTF DRs). We employ two different approaches: the traditional method of parametric fit search and a machine learning algorithm originally developed for anomaly detection. We analyzed over 35 million ZTF light curves and visually scrutinized 1168 candidates suggested by the algorithms to filter out artifacts, occultations of a star by an asteroid, and known variable objects of other types. Our final sample comprises 134 flares with amplitude ranging from 0.2 to 4.6 magnitudes, including repeated flares and complex flares with multiple components. Using Pan-STARRS DR2 colors, we also assigned a corresponding spectral subclass to each object in the sample. For 13 flares with well-sampled light curves, we estimated the bolometric energy. Our results show that the ZTF's cadence strategy is suitable for identifying M-dwarf flares and other fast transients, allowing for the extraction of significant astrophysical information from their light curves., Comment: 34 pages, 5 figures, 2 tables, 2 appendices

Published
2024

12. Supernova search with active learning in ZTF DR3.

Author

Pruzhinskaya, M. V., Ishida, E. E. O., Novinskaya, A. K., Russeil, E., Volnova, A. A., Malanchev, K. L., Kornilov, M. V., Aleo, P. D., Korolev, V. S., Krushinsky, V. V., Sreejith, S., and Gangler, E.

Subjects
ACTIVE learning, ASTRONOMICAL surveys, ACTIVE galactic nuclei, LIGHT curves, BIG data
Abstract

Context. We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. Aims. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets. Methods. Our SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31, 2018 (58 194 = MJD= 58 483). We analysed 70 ZTF fields at a high galactic latitude and visually inspected 2100 outliers. Results. This resulted in 104 SN-like objects being found, 57 of which were reported to the Transient Name Server for the first time and with 47 having previously been mentioned in other catalogues, either as SNe with known types or as SN candidates. We visually inspected the multi-colour light curves of the non-catalogued transients and performed fittings with different supernova models to assign it to a probable photometric class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported slow-evolving transients that are good superluminous SN candidates, along with a few other non-catalogued objects, such as red dwarf flares and active galactic nuclei. Conclusions. Beyond confirming the effectiveness of human-machine integration underlying the AAD strategy, our results shed light on potential leaks in currently available pipelines. These findings can help avoid similar losses in future large-scale astronomical surveys. Furthermore, the algorithm enables direct searches of any type of data and based on any definition of an anomaly set by the expert. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

14. Active anomaly detection for time-domain discoveries

Author

Ishida, E. E. O., primary, Kornilov, M. V., additional, Malanchev, K. L., additional, Pruzhinskaya, M. V., additional, Volnova, A. A., additional, Korolev, V. S., additional, Mondon, F., additional, Sreejith, S., additional, Malancheva, A. A., additional, and Das, S., additional

Published
2021
Full Text
View/download PDF

15. Anomaly Detection in the Zwicky Transient Facility DR3

Author

Malanchev, K. L., Pruzhinskaya, M. V., Korolev, V. S., Aleo, P. D., Kornilov, M. V., Ishida, E. E. O., Krushinsky, V. V., Mondon, F., Sreejith, S., Volnova, A. A., Belinski, A. A., Dodin, A. V., Tatarnikov, A. M., Zheltoukhov, S. G., Malanchev, K. L., Pruzhinskaya, M. V., Korolev, V. S., Aleo, P. D., Kornilov, M. V., Ishida, E. E. O., Krushinsky, V. V., Mondon, F., Sreejith, S., Volnova, A. A., Belinski, A. A., Dodin, A. V., Tatarnikov, A. M., and Zheltoukhov, S. G.

Abstract

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of three stages: feature extraction, search of outliers with machine learning algorithms, and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of four automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinized by an expert. From these, 188 (68 per cent) were found to be bogus light curves - including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24 per cent) were previously reported sources whereas 23 (8 per cent) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e.g. one spectroscopically confirmed RS Canum Venaticorum star, four supernovae candidates, one red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation that can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available.1 © 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.

Published
2021

24. Anomaly detection in the Zwicky Transient Facility DR3.

Author

Malanchev, K L, Pruzhinskaya, M V, Korolev, V S, Aleo, P D, Kornilov, M V, Ishida, E E O, Krushinsky, V V, Mondon, F, Sreejith, S, Volnova, A A, Belinski, A A, Dodin, A V, Tatarnikov, A M, Zheltoukhov, S G, and Team), (The SNAD

Subjects
MACHINE learning, LIGHT curves, RECOMMENDER systems, ASTEROIDS, ASTRONOMY
Abstract

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of three stages: feature extraction, search of outliers with machine learning algorithms, and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of four automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinized by an expert. From these, 188 (68 per cent) were found to be bogus light curves – including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24 per cent) were previously reported sources whereas 23 (8 per cent) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e.g. one spectroscopically confirmed RS Canum Venaticorum star, four supernovae candidates, one red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation that can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available.1 [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

30. Superconducting Magnets for the NICA Accelerator Collider Complex

Author

Khodzhibagiyan, H. G., primary, Agapov, N. N., additional, Akishin, P. G., additional, Blinov, N. A., additional, Borisov, V. V., additional, Bychkov, A. V., additional, Galimov, A. R., additional, Donyagin, A. M., additional, Karpinskiy, V. N., additional, Korolev, V. S., additional, Kozlov, O. S., additional, Kunchenko, O. A., additional, Kuznetsov, G. L., additional, Meshkov, I. N., additional, Mikhaylov, V. A., additional, Nikiforov, D. N., additional, Pivin, R. V., additional, Shabunov, A. V., additional, Smirnov, A. V., additional, Starikov, A. Yu, additional, and Trubnikov, G. V., additional

Published
2014
Full Text
View/download PDF

31. The Data Acquisition of the Sphere 4$\pi$-Detector

Author

Afanasiev, S V, Maniakov, P K, Anisimov, Yu S, Arkhipov, V V, Bazylev, S N, Enkhbold, D, Kolesnikov, V I, Korolev, V S, Kuznetsov, V A, Kashirin, V A, Litvinenko, A G, Malakhov, A I, Nikiforov, S A, Peresedov, V F, Parfenov, A N, Reznikov, S G, Semenov, A Yu, Smirnov, V A, Zarubin, P I, Olejniczak, V, Slowinski, B, Cimpean, A, Sandu, M, and Kovtun, V E

Subjects
Detectors and Experimental Techniques
Published
1992

34. ALICE technical design report: Detector for high momentum PID

Author

Beole, S., Dellacasa, G., Ramello, L., Scalas, E., Sitta, M., Soave, C., Ahmad, N., Ahmad, S., Ahmad, T., Baritak, W., Irfan, M., Zafar, M., Angelis, A., Kapogiannis, A., Mavromanolakis, G., Panagiotou, A. D., Kalfas, K., Colonna, N., Cozza, D., Di Bari, D., Elia, D., Fini, R. A., Ghidini, B., Lenti, V., Liberti, L., Loconsole, R., Manzari, V., Nappi, E., Navach, F., Posa, F., Stucchi, S., Tomasicchio, G., Corsi, F., Venuto, D., Di Ciano, M., Gramegna, G., Grimaldi, A., Marzocca, C., Monno, E., Sun, Z., Andersen, E., Hetland, Kristin Fanebust, Helstrup, H., Holme, A. K., Klovning, A., Maland, O., Odland, O. H., Thorsteinsen, T. F., Mahapatra, D. P., Phatak, S. C., Bloodworth, I. J., Evans, D., Jones, G. T., Jovanovic, P., Kinson, J. B., Kirk, A., Villalobos Baillie, O., Votruba, M. F., Bracinik, J., Cerny, V., Ftacnik, J., Hlinka, V., Ivanov, M., Janik, R., Lietava, R., Pikna, M., Pisut, Jan, Pisutova, N., Sitar, B., Strmen, P., Szarka, I., Denes, Ervin, Eged, B., Fodor, Z., Harangozo, G., Kiss, T., Meggyesi, Z., Palla, G., Roka, T., Sulyan, J., Szendrei, L., Sziklai, J., Tarjan, D. L., Vissy, B. N., Zimanyi, J., Ceru, M., Cicalo, C., Falco, A., Lai, A., Macciotta-Serpi, M., Masoni, A., Mauro, S. A., Puddu, G., Serci, S., Siddi, E., Temnikov, P., Usai, G., Bhattacharya, P., Bose, S., Chattopadhyay, S., Majumdar, N., Sanyal, A., Sarkar, S., Sen, P., Sen, S. K., Sinha, B. C., Dutta Majumdar, M. R., Ganti, M. S., Nayak, T. K., Singaraju, R. N., Trivedi, M. D., Viyogi, Y. P., Badala, A., Barbera, R., Becciani, U., Caligiore, C., Gulino, M., Ingrassia, S., Insolia, A., Librizzi, F., Lo Presti, D., Palmeri, A., Panebianco, S., Pappalardo, G. S., Pappalardo, L., Petta, C., Randazzo, N., Reito, S., Riggi, F., Russo, A. C., Russo, G. V., Russo, M., Sacca, G., Sciutto, A., Bachler, J., Barwacz, L., Belikov, Yu A., Berezhnoi, V., Berset, J. C., Brun, R., Burns, M., Campbell, M., Cantatore, E., Carena, Wisla, Carminati, F., Christiansen, J., D Ambrosio, C., Davenport, M., Groot, J., Di Mauro, A., Divia, R., Eisenberg, C., Engster, C., Fischer, H. G., Flammier, M., Formenti, F., Fraissard, D., Futo, E., Gaumann, E., Goossens, M., Goret, B., Grassi, T., Gregory, C., Innocenti, P. G., Jacholkowski, A., Klempt, W., Knudson, K., Lecoeur, G., Legrand, J. C., Leistam, L., Lesenechal, Y., Lourenco, C., Macowicz, M., Marszal, T., Martinengo, P., Mast, M., Monnin, R., Morel, M., Morsch, A., Mota, M., Musa, L., Pigni, L., Piuz, F., Quercigh, E., Raynaud, J., Renshall, H., Rubin, G., Safarik, K., Santiard, J. C., Schukraft, J., Scomparin, E., Snoeys, W., Sobczynski, C., Sonderegger, P., Spegel, M., Swoboda, D., Szymanski, P., Tarle, J. C., Beelen, J., Velde, H., Vande Vyvre, P., Vascotto, A., Vranic, D., Wenig, S., Wertelaers, P., Williams, T., Aggarwal, M. M., Bhati, A. K., Bhatia, V. S., Alard, J. P., Babut, R., Baldit, A., Bastid, N., Blanchard, G., Brossard, M., Brun, N., Castor, J., Chambon, T., Daudon, F., Devaux, A., Dupieux, P., Espagnon, B., Fargeix, J., Force, P., Manso, F., Mayade, S., Ramillien, V., Roche, G., Roig, O., Royer, L., Saturnini, P., Savinel, G., Ferreira-Marques, R., Fonte, P., Policarpo, A., Hardtke, D., Humanic, T. O., Lisa, M., Lo Curto, G., Nilsen, B., Paic, G., Reichhold, D. M., Sugarbaker, E., Bearden, I., Boggild, H., Jens-Jorgen Gaardhoje, Nielsen, B., Bartke, J., Gladysz-Dziadus, E., Gornicki, E., Kowalski, M., Papiernik, K., Rybicki, A., Stefanski, P., Wlodarczyk, Z., Andronic, A., Averbeck, R., Blume, C., Braun-Munzinger, P., Crochet, P., Deusser, A., Devismes, A., Eschke, J., Herrmann, N., Kolb, B., Luhning, J., Lynen, U., Miskowiec, D., Muller, W. F. J., Neyer, C., Sandoval, A., Sann, H., Schmidt, H. R., Stelzer, H., Ruden, W., Worner, A., Bormann, C., Buncic, P., Foka, P., Gazdzicki, M., Gunther, J., Renfordt, R., Rohrich, D., Roland, G., Stock, R., Strobele, H., Komkov, B., Mylnikov, V., Nikulin, V., Samsonov, V., Volkov, S., Vorobev, A., Martin, M., Rosselet, L., Ceretto, F., Fuchs, C., Rak, J., Wurm, J. P., Appelshauser, H., Baur, R., Esumi, S., Glassel, P., Lenkeit, B., Messer, M., Pfeiffer, A., Schmitz, W., Stachel, J., Tilsner, H., Voigt, C., Voloshin, S., Wessels, J. P., Wienold, T., Aslanoglou, X., Bhalla, K. B., Bharti, A., Gupta, S. K., Kumar, V., Raniwala, R., Raniwala, S., Baba, P. V. K. S., Badyal, S. K., Bhasin, A., Gupta, A., Gupta, V. K., Mangotra, L. K., Rao, N. K., Sambyal, S. S., Akishin, P. G., Arefev, V. A., Arkhipov, E. V., Astakhov, V. I., Baldin, A., Bartenev, V. D., Batyunya, B. V., Blinov, N. A., Bondila, M., Boreiko, V. F., Borisovskaya, Z. V., Borzunov, Yu T., Shabunov, A. V., Shabratova, G. S., Shishov, Yu A., Datskov, V. I., Dodokhov, V. Kh, Efimov, L. G., Fedunov, A. G., Golovanov, L. B., Guskov, B. N., Yuldashev, O. I., Kadyshevsky, V. G., Korolev, V. S., Kovalenko, A. D., Lyuboshits, V. L., Lobanov, V. I., Lykasov, G. I., Matyushevsky, E. A., Maksimov, A. N., Mehdiyev, Rashid R., Mikhailov, K. V., Mikhalev, D. P., Minaev, Yu, Nomokonov, P. V., Parfenov, A. N., Puzynin, I. V., Sadovsky, A. B., Semashko, S. V., Senner, A. E., Shelaev, I. A., Sidorov, A. V., Slavin, N. V., Slepnev, R. V., Tsvineva, G. P., Vodopianov, A. S., Yuldasheva, M. B., Zaporozhets, S., Zinchenko, A. I., Demkin, V., Kuznetsov, V., Shestakov, V., Tkachuk, I., Baatar, Ts, Khurelbaatar, B., Togoo, R., Akhobadze, K. G., Dzhavrishvili, A. K., Grigalashvili, T., Ioramashvili, E. Sh, Kharadze, A. V., Khizanishvili, L., Khuskivadze, T. V., Shalamberidze, L. V., Shubitidze, N., Dzhordzhadze, V., Grigalashvili, N., Menteshashvili, Z., Nioradze, M., Tabidze, M., Tevzadze, Yu, Aysto, J., Trzaska, W., Ruuskanen, V., Bochek, G. L., Boldyshev, V. F., Chervonny, I. F., Dovbnya, A. N., Kulibaba, V. I., Maslov, N. I., Potin, S. M., Prokhorets, I. M., Starodubtsev, A. F., Borshchov, V. N., Kiprich, S. K., Listratenko, O. M., Starkov, V. E., Zamirets, M., Kharzeev, D. E., Pavlenko, O., Velytsky, A., Zinovev, H., Fedorisin, J., Hnatic, M., Jusko, A., Kocper, B., Kralik, I., Kravcakova, A., Krivan, F., Kulkova, I., Luptak, M., Martinska, G., Pastircak, B., Sandor, L., Urban, J., Vokal, S., Vrlakova, J., Ricci, R. A., Barbosa, J., Branco, P., Carvalho, R., Seixas, J., Vilela Mendes, R., Carlen, L., El Chenawi, K., Garpman, S. I. A., Gustafsson, H. A., Nilsson, P., Oskarsson, A., Osterman, Lennart, Otterlund, I., Silvermyr, D., Soderstrom, K., Stenlund, E. A., Chevallier, M., Cheynis, B., Grossiord, J. Y., Guichard, A., Guinet, D., Jacquet, G., Lautesse, P., Eckhardt, F., Friese, V., Puhlhofer, F., Fearnley, T., Herrera-Corral, G., Mendez, H., Lobko, A. A., Shileev, K. A., Filippov, S. N., Gavrilov, Yu K., Golubeva, M. B., Gotra, M. N., Guber, F. F., Karavicheva, T. L., Kurepin, A. B., Laptev, V. D., Maevskaya, A. I., Razin, V. I., Reshetin, A. I., Sobolevsky, N. M., Topilskaya, N. S., Akindinov, A. N., Boyarinov, S. V., Golovin, V., Grishuk, Yu G., Kaidalov, A. B., Kats, M. M., Kiselev, Yu T., Kiselev, S. M., Kiselevich, I., Lyublev, E., Martemyanov, A. N., Martemyanov, M., Polozov, P. A., Pozdnyakov, S. A., Serov, V. S., Smirnitsky, A. V., Chumakov, M. M., Vetlitsky, I. A., Voloshin, K. G., Vorobev, L. S., Zagreev, B. V., Antonenko, V., Belyaev, S., Shcherbachev, R., Dubovik, I., Fokin, S., Ippolitov, M., Karadzhev, K., Lebedev, A. L., Lebedev, V., Manko, V. I., Mgebrishvili, G., Mukhanova, T., Nyanin, A., Nikolaev, S., Patarakin, O., Peresunko, D., Sibiryak, I., Vasilev, A., Vinogradov, A., Volkov, M., Grigorev, V., Kaplin, V., Loginov, V., Strikhanov, M., Varma, R., Barlag, C., Bucher, D., Peitzmann, T., Santo, R., Schlagheck, H., Boucham, A., Bouvier, S., Cussonneau, J. P., Erazmus, B., Giliberto, S., Guillet, B., Gutbrod, H. H., Labalme, M. S., Lautridou, P., Lefevre, F., Luquin, L., Martin, L., Metivier, V., Rahmani, A., Ravel, O., Reposeur, T., Retiere, F., Roy, C. S., Roy, D., Frolov, A. R., Pestov, Yu N., Tyunov, M. A., Britton, C. L., Bryan, W. L., Ericson, M. N., Paulus, M. J., Simpson, M. L., Walker, J. W., Wintenberg, A. L., Bimbot, L., Comets, M. P., Courtat, P. F., Douet, R., Genolini, B., Harroch, H., Jouan, D., Kharmandarian, L., Le Bornec, Y., Maccormick, M., Peyre, J., Pouthas, J., Willis, N., Ingebrigtsen, L. M., Lovhoiden, G., Skaali, B., Wormald, D., Antinori, F., Brandolini, F., Carrer, N., Morando, M., Pepato, A., Scarlassara, F., Segato, G., Stanco, F., Zanoni, E., Mares, J., Mihokova, E., Nikl, M., Piska, K., Polak, K., Zavada, P., Blik, A. M., Bogolyubsky, M., Britvich, G., Erin, S., Khaustov, G. V., Kharlov, Yu V., Kolosov, V. N., Lishin, V., Sadovsky, S. A., Samoilenko, V. D., Semenov, P. A., Suzdalev, V. I., Tikhonov, V., Zvyagin, A., Hanzal, V., Hosek, J., Hrivnacova, I., Kushpil, V., Kugler, A., Petracek, V., Sumbera, M., Tlusty, A., Wagner, V., Zakoucky, D., Di Liberto, S., Mazzoni, M. A., Meddi, F., Prosperi, D., Rosa, G., Baldisseri, A., Borel, H., Gosset, J., Staley, F. M., Terrien, Y., Grella, G., Guida, M., Romano, G., Virgili, T., Abramovich, S., Basmanov, V., Fomushkin, E., Yanovsky, V., Ilkaev, R., Ilkaeva, L., Khlebnikov, A., Nazarenko, S., Punin, V., Putevskoi, S., Savkin, G. G., Selin, I., Vinogradov, I., Zhitnik, A., Abramova, L. Y., Aleksandrov, V. S., Baratov, V. M., Bolonin, A. A., Braun, M. A., Dobulevich, V. M., Feofilov, G. A., Godisov, O. N., Gerasimov, S., Igolkin, S. N., Yudkin, M. I., Kolozhvarii, A. A., Kondratev, V., Novikov, I. A., Potapov, S. V., Stolyarov, O. I., Tulina, T. A., Tsimbal, F. A., Valiev, F. F., Vechernin, V. V., Vinogradov, L. I., Vitushkin, L. F., Arnold, L., Ayachi, M., Baudot, J., Berst, J. D., Blonde, J. P., Coffin, J. P., Dulinski, W., Guillaume, G., Guthneck, L., Hebrard, L., Higueret, S., Hu, Y., Jundt, F., Kuhn, C., Lutz, J., Michalon, A., Rami, F., Suire, C., Bonvicini, V., Gregorio, A., Rashevsky, A., Vacchi, A., Valentincic, L., Zampa, N., Alessandro, B., Arnaldi, R., Astengo, R., Bonazzola, G., Botta, E., Busso, L., Cerello, P. G., Chiavassa, E., Cortese, P., Daudo, F., Marco, N., Farano, R., Feliciello, A., Gallian, S., Gallio, M., Giraudo, G., Giubellino, P., Hernandez-Montoya, A. R., Idzik, M., Marzari-Chiesa, A., Masera, M., Mazza, G., Minetti, B., Musso, A., Nouais, D., Piccotti, A., Riccati, L., Rivetti, A., Tosello, F., Vercellin, E., Botje, M., Buijs, A., Haas, A., Kamermans, R., Kuijer, P. G., Oskamp, C. J., Den Brink, A., Eijndhoven, N., Czerwinski, D., Deloff, A., Karpio, K., Kozak, S., Lukaszek, L., Malinowski, H., Siemiarczuk, T., Stefanek, G., Tykarski, L., Wilk, G., Cai, X., Liu, F., Liu, L., Wang, H., Zhou, D., Asatrian, R., Ataian, M., Avakian, R., Danielian, V., Gavalian, V., Grigorian, A., Grigorian, S., Gulkanian, H., Hakobian, R., Kakoian, V., Megrabian, S., Parlakian, L., Shahoian, R., Vardanian, H., Ferenc, D., Ljubicic, A., and Tustonic, T.

41. [Informative value of immunologic indicators at different periods of acute myocardial infarction].

Author

Korochkin IM, Chukaeva II, Korolev VS, Litvinova SN, and Klebanov GI

Subjects
Adult, Aged, Factor Analysis, Statistical, Female, Hemagglutination Tests methods, Humans, Immune Complex Diseases etiology, Luminescent Measurements, Male, Middle Aged, Myocardial Infarction etiology, Time Factors, Antigen-Antibody Complex analysis, Immune Complex Diseases immunology, Myocardial Infarction immunology
Abstract

To elucidate the mechanisms responsible for reorganization in the immune system in patients in the acute period of myocardial parameters for immune status were examined in 40 patients with acute myocardial infarction. The findings were processed by statistic methods of factor analysis. The value of factor exercise with the same parameters varied with the period of the disease. On the first day of myocardial infarction, immune complex concentrations were found to be mostly affected by this process. On day 7, chemiluminescence underwent changes, so did circulating antibody titers on day 30. Evidently, it is advisable to bear in mind the aforementioned relationships while studying the effect of immune responses on the course and prognosis of myocardial infarction.

Published
1989

Catalog

Books, media, physical & digital resources
See catalog results