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Boeri L, Hennig R, Hirschfeld P, Profeta G, Sanna A, Zurek E, Pickett WE, Amsler M, Dias R, Eremets MI, Heil C, Hemley RJ, Liu H, Ma Y, Pierleoni C, Kolmogorov AN, Rybin N, Novoselov D, Anisimov V, Oganov AR, Pickard CJ, Bi T, Arita R, Errea I, Pellegrini C, Requist R, Gross EKU, Margine ER, Xie SR, Quan Y, Hire A, Fanfarillo L, Stewart GR, Hamlin JJ, Stanev V, Gonnelli RS, Piatti E, Romanin D, Daghero D, Valenti R. The 2021 room-temperature superconductivity roadmap. J Phys Condens Matter 2022; 34:183002. [PMID: 34544070 DOI: 10.1088/1361-648x/ac2864] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/20/2021] [Indexed: 06/13/2023]
Abstract
Designing materials with advanced functionalities is the main focus of contemporary solid-state physics and chemistry. Research efforts worldwide are funneled into a few high-end goals, one of the oldest, and most fascinating of which is the search for an ambient temperature superconductor (A-SC). The reason is clear: superconductivity at ambient conditions implies being able to handle, measure and access a single, coherent, macroscopic quantum mechanical state without the limitations associated with cryogenics and pressurization. This would not only open exciting avenues for fundamental research, but also pave the road for a wide range of technological applications, affecting strategic areas such as energy conservation and climate change. In this roadmap we have collected contributions from many of the main actors working on superconductivity, and asked them to share their personal viewpoint on the field. The hope is that this article will serve not only as an instantaneous picture of the status of research, but also as a true roadmap defining the main long-term theoretical and experimental challenges that lie ahead. Interestingly, although the current research in superconductor design is dominated by conventional (phonon-mediated) superconductors, there seems to be a widespread consensus that achieving A-SC may require different pairing mechanisms.In memoriam, to Neil Ashcroft, who inspired us all.
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Affiliation(s)
- Lilia Boeri
- Physics Department, Sapienza University and Enrico Fermi Research Center, Rome, Italy
| | - Richard Hennig
- Deparment of Material Science and Engineering and Quantum Theory Project, University of Florida, Gainesville 32611, United States of America
| | - Peter Hirschfeld
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | | | - Antonio Sanna
- Max Planck Institute of Microstructure Physics, Halle, Germany
| | - Eva Zurek
- University at Buffalo, SUNY, United States of America
| | | | - Maximilian Amsler
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, CH-3012 Bern, Switzerland
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, United States of America
| | - Ranga Dias
- University of Rochester, United States of America
| | | | | | | | - Hanyu Liu
- Jilin University, People's Republic of China
| | - Yanming Ma
- Jilin University, People's Republic of China
| | - Carlo Pierleoni
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | | | | | | | | | | | | | - Tiange Bi
- University at Buffalo, SUNY, United States of America
| | | | - Ion Errea
- University of the Basque Country, Spain
| | | | - Ryan Requist
- Max Planck Institute of Microstructure Physics, Halle, Germany
- Hebrew University of Jerusalem, Israel
| | - E K U Gross
- Max Planck Institute of Microstructure Physics, Halle, Germany
- Hebrew University of Jerusalem, Israel
| | | | - Stephen R Xie
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | - Yundi Quan
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | - Ajinkya Hire
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | - Laura Fanfarillo
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
| | - G R Stewart
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | - J J Hamlin
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
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Kireeva G, Gubareva E, Maydin M, Osetnik V, Kruglov S, Panchenko A, Dorofeeva A, Tyndyk M, Fedoros E, Anisimov V. Efficacy and Safety of Systemic and Locoregional Cisplatin Chronotherapy in Rats with Ovarian Carcinoma. Onco Targets Ther 2021; 14:3373-3381. [PMID: 34079283 PMCID: PMC8163628 DOI: 10.2147/ott.s309285] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/28/2021] [Indexed: 12/15/2022] Open
Abstract
Aim Alterations in circadian rhythms caused by tumor growth are thought to be clinically relevant as they affect the prognosis and treatment response. We aimed to evaluate the chronotherapeutic approach in rats with ovarian cancer receiving cisplatin intravenously (IV) or with hyperthermic intraperitoneal chemoperfusion (HIPEC) and to assess daily variations in tumor and intestinal epithelium proliferation. Methods In the pilot study, we used 12 intact rats and 12 rats with transplantable ovarian cancer, which were euthanized at ZT0 (08:00, lights on), ZT6, ZT12 and ZT18. In the main study, we used 45 rats with transplantable ovarian cancer. Animals were randomized into five groups: control, HIPEC with cisplatin at ZT0 (08:00), HIPEC with cisplatin at ZT12 (20:00), IV cisplatin at ZT0 and IV cisplatin at ZT12. We assessed the proliferation rate of tumor and small intestinal epithelium, apoptosis in small intestinal epithelium, and levels of γ-H2AX (DNA damage/repair marker) in kidneys and liver. Survival was calculated in each group. Results Ascitic ovarian cancer disrupted daily variations in intestinal epithelium proliferation and DNA damage/repair in rats. Ovarian carcinoma exhibited no daily variation in mitotic activity. In animals receiving IV cisplatin, massive cell damage in the renal medulla and cystic changes within renal tubules were observed, unlike in rats receiving HIPEC. Tumor mitotic activity was lower in morning-treated groups. The median survival of rats in the control group was 8.5 days (95% CI 6.0–22.0), in HIPEC at ZT0 40.5 days (95% CI 28.0–47.0, p<0.001) and in HIPEC at ZT12 32.0 days (95% CI 28.0–37.0, p<0.001). Conclusion In a rat model, ovarian tumor growth disrupted daily variations in intestinal epithelium proliferation and caused genotoxic stress in tumor-free tissues. HIPEC with cisplatin at ZT0 had a better efficacy/toxicity profile than HIPEC with cisplatin at ZT12 and IV administration at both time points.
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Affiliation(s)
- Galina Kireeva
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
| | - Ekaterina Gubareva
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
| | - Mikhail Maydin
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
| | - Vladislav Osetnik
- Surgical Department, Saint-Petersburg State University Hospital, Saint-Petersburg, Russia
| | - Stepan Kruglov
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
| | - Andrey Panchenko
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
| | - Anastasia Dorofeeva
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
| | - Margarita Tyndyk
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
| | - Elena Fedoros
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
| | - Vladimir Anisimov
- Department of Carcinogenesis and Aging, N.N. Petrov National Medical Research Center of Oncology, Saint-Petersburg, Russia
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Anisimov V. Aging delay: of mice and men. Acta Biomed 2021; 92:e2021073. [PMID: 33682799 PMCID: PMC7975961 DOI: 10.23750/abm.v92i1.11273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 11/30/2022]
Abstract
The evaluation of the safety of a drug in rodents that may be used as geroprotectors is a challenge of current times. In the paper, we discuss approaches to long-term assays for selection of potent aging delay drugs for humans. Priority is given to methods combining evaluation of carcinogenic safety and life-spanning potential. The use of such methods will be time-efficient and economically feasible. (www.actabiomedica.it)
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Affiliation(s)
- Vladimir Anisimov
- Department of Carcinogenesis and Oncogerontology, N.N. Petrov National Medical Research Center of Oncology, Pesochny, St. Petersburg, Russia.
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Kovaleva A, Kvitchasty A, Anisimov V. PSYCHOPHYSIOLOGICAL INDICATORS OF FLOW STATE. hsm 2020. [DOI: 10.14529/hsm200206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Aim. The study aims to reveal objective psychophysiological indicators that are related to flow state. Materials and methods. Thirty-one (31) athletes of different competitive levels (20 females and 11 males, mean age 19.1 ± 4.59 years) participated in the study. The dynavision D2 training device was applied for creating optimal flow state conditions during the experiment. Physiological indicators were recorded by the Thought Technology hardware and software system. Heart rate variability, respiration rate (thoracic and abdominal), finger temperature, skin conductance were analyzed. Flow state depth was estimated based on the participants’ answers during the semi-structured interview that followed the experiment. Results. According to the results of the interview, all athletes were divided into two groups: the first group included athletes experiencing flow state (“flow” group), the second group composed of athletes who did not manage to experience flow state (“no-flow” group). When comparing these two groups after the experiment, it was revealed that the first group had higher levels of standard deviations of heart rate compared with the second group. In the first group (flow), the following indicators were significantly higher after the experiment: standard deviation of the RR-intervals (SDRR), skin conductance, and finger temperature. In the second group (no-flow), only skin conductance increased significantly. Conclusion. The results allow us to conclude that the flow state is characterized by a marked increase in the sympathetic nervous system (a higher level of stress compared to the same activity without flow).
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Aprà E, Bylaska EJ, de Jong WA, Govind N, Kowalski K, Straatsma TP, Valiev M, van Dam HJJ, Alexeev Y, Anchell J, Anisimov V, Aquino FW, Atta-Fynn R, Autschbach J, Bauman NP, Becca JC, Bernholdt DE, Bhaskaran-Nair K, Bogatko S, Borowski P, Boschen J, Brabec J, Bruner A, Cauët E, Chen Y, Chuev GN, Cramer CJ, Daily J, Deegan MJO, Dunning TH, Dupuis M, Dyall KG, Fann GI, Fischer SA, Fonari A, Früchtl H, Gagliardi L, Garza J, Gawande N, Ghosh S, Glaesemann K, Götz AW, Hammond J, Helms V, Hermes ED, Hirao K, Hirata S, Jacquelin M, Jensen L, Johnson BG, Jónsson H, Kendall RA, Klemm M, Kobayashi R, Konkov V, Krishnamoorthy S, Krishnan M, Lin Z, Lins RD, Littlefield RJ, Logsdail AJ, Lopata K, Ma W, Marenich AV, Martin Del Campo J, Mejia-Rodriguez D, Moore JE, Mullin JM, Nakajima T, Nascimento DR, Nichols JA, Nichols PJ, Nieplocha J, Otero-de-la-Roza A, Palmer B, Panyala A, Pirojsirikul T, Peng B, Peverati R, Pittner J, Pollack L, Richard RM, Sadayappan P, Schatz GC, Shelton WA, Silverstein DW, Smith DMA, Soares TA, Song D, Swart M, Taylor HL, Thomas GS, Tipparaju V, Truhlar DG, Tsemekhman K, Van Voorhis T, Vázquez-Mayagoitia Á, Verma P, Villa O, Vishnu A, Vogiatzis KD, Wang D, Weare JH, Williamson MJ, Windus TL, Woliński K, Wong AT, Wu Q, Yang C, Yu Q, Zacharias M, Zhang Z, Zhao Y, Harrison RJ. NWChem: Past, present, and future. J Chem Phys 2020; 152:184102. [PMID: 32414274 DOI: 10.1063/5.0004997] [Citation(s) in RCA: 275] [Impact Index Per Article: 68.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.
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Affiliation(s)
- E Aprà
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - E J Bylaska
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - W A de Jong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - N Govind
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - K Kowalski
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - T P Straatsma
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - M Valiev
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - H J J van Dam
- Brookhaven National Laboratory, Upton, New York 11973, USA
| | - Y Alexeev
- Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, USA
| | - J Anchell
- Intel Corporation, Santa Clara, California 95054, USA
| | - V Anisimov
- Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, USA
| | - F W Aquino
- QSimulate, Cambridge, Massachusetts 02139, USA
| | - R Atta-Fynn
- Department of Physics, The University of Texas at Arlington, Arlington, Texas 76019, USA
| | - J Autschbach
- Department of Chemistry, University at Buffalo, State University of New York, Buffalo, New York 14260, USA
| | - N P Bauman
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - J C Becca
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - D E Bernholdt
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | | | - S Bogatko
- 4G Clinical, Wellesley, Massachusetts 02481, USA
| | - P Borowski
- Faculty of Chemistry, Maria Curie-Skłodowska University in Lublin, 20-031 Lublin, Poland
| | - J Boschen
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - J Brabec
- J. Heyrovský Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, 18223 Prague 8, Czech Republic
| | - A Bruner
- Department of Chemistry and Physics, University of Tennessee at Martin, Martin, Tennessee 38238, USA
| | - E Cauët
- Service de Chimie Quantique et Photophysique (CP 160/09), Université libre de Bruxelles, B-1050 Brussels, Belgium
| | - Y Chen
- Facebook, Menlo Park, California 94025, USA
| | - G N Chuev
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Science, Pushchino, Moscow Region 142290, Russia
| | - C J Cramer
- Department of Chemistry, Chemical Theory Center, and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - J Daily
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - M J O Deegan
- SKAO, Jodrell Bank Observatory, Macclesfield SK11 9DL, United Kingdom
| | - T H Dunning
- Department of Chemistry, University of Washington, Seattle, Washington 98195, USA
| | - M Dupuis
- Department of Chemistry, University at Buffalo, State University of New York, Buffalo, New York 14260, USA
| | - K G Dyall
- Dirac Solutions, Portland, Oregon 97229, USA
| | - G I Fann
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - S A Fischer
- Chemistry Division, U. S. Naval Research Laboratory, Washington, DC 20375, USA
| | - A Fonari
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - H Früchtl
- EaStCHEM and School of Chemistry, University of St. Andrews, St. Andrews KY16 9ST, United Kingdom
| | - L Gagliardi
- Department of Chemistry, Chemical Theory Center, and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - J Garza
- Departamento de Química, División de Ciencias Básicas e Ingeniería, Universidad Autónoma Metropolitana-Iztapalapa, Col. Vicentina, Iztapalapa, C.P. 09340 Ciudad de México, Mexico
| | - N Gawande
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - S Ghosh
- Department of Chemistry, Chemical Theory Center, and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 5545, USA
| | - K Glaesemann
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - A W Götz
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, California 92093, USA
| | - J Hammond
- Intel Corporation, Santa Clara, California 95054, USA
| | - V Helms
- Center for Bioinformatics, Saarland University, D-66041 Saarbrücken, Germany
| | - E D Hermes
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551, USA
| | - K Hirao
- Next-generation Molecular Theory Unit, Advanced Science Institute, RIKEN, Saitama 351-0198, Japan
| | - S Hirata
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - M Jacquelin
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - L Jensen
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - B G Johnson
- Acrobatiq, Pittsburgh, Pennsylvania 15206, USA
| | - H Jónsson
- Faculty of Physical Sciences, University of Iceland, Reykjavík, Iceland and Department of Applied Physics, Aalto University, FI-00076 Aalto, Espoo, Finland
| | - R A Kendall
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - M Klemm
- Intel Corporation, Santa Clara, California 95054, USA
| | - R Kobayashi
- ANU Supercomputer Facility, Australian National University, Canberra, Australia
| | - V Konkov
- Chemistry Program, Florida Institute of Technology, Melbourne, Florida 32901, USA
| | - S Krishnamoorthy
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - M Krishnan
- Facebook, Menlo Park, California 94025, USA
| | - Z Lin
- Department of Physics, University of Science and Technology of China, Hefei, China
| | - R D Lins
- Aggeu Magalhaes Institute, Oswaldo Cruz Foundation, Recife, Brazil
| | | | - A J Logsdail
- Cardiff Catalysis Institute, School of Chemistry, Cardiff University, Cardiff, Wales CF10 3AT, United Kingdom
| | - K Lopata
- Department of Chemistry, Louisiana State University, Baton Rouge, Louisiana 70803, USA
| | - W Ma
- Institute of Software, Chinese Academy of Sciences, Beijing, China
| | - A V Marenich
- Department of Chemistry, Chemical Theory Center, and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - J Martin Del Campo
- Departamento de Física y Química Teórica, Facultad de Química, Universidad Nacional Autónoma de México, México City, Mexico
| | - D Mejia-Rodriguez
- Quantum Theory Project, Department of Physics, University of Florida, Gainesville, Florida 32611, USA
| | - J E Moore
- Intel Corporation, Santa Clara, California 95054, USA
| | - J M Mullin
- DCI-Solutions, Aberdeen Proving Ground, Maryland 21005, USA
| | - T Nakajima
- Computational Molecular Science Research Team, RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan
| | - D R Nascimento
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - J A Nichols
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - P J Nichols
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - J Nieplocha
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - A Otero-de-la-Roza
- Departamento de Química Física y Analítica, Facultad de Química, Universidad de Oviedo, 33006 Oviedo, Spain
| | - B Palmer
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - A Panyala
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - T Pirojsirikul
- Department of Chemistry, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - B Peng
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - R Peverati
- Chemistry Program, Florida Institute of Technology, Melbourne, Florida 32901, USA
| | - J Pittner
- J. Heyrovský Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, v.v.i., 18223 Prague 8, Czech Republic
| | - L Pollack
- StudyPoint, Boston, Massachusetts 02114, USA
| | | | - P Sadayappan
- School of Computing, University of Utah, Salt Lake City, Utah 84112, USA
| | - G C Schatz
- Department of Chemistry, Northwestern University, Evanston, Illinois 60208, USA
| | - W A Shelton
- Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, Louisiana 70803, USA
| | | | - D M A Smith
- Intel Corporation, Santa Clara, California 95054, USA
| | - T A Soares
- Dept. of Fundamental Chemistry, Universidade Federal de Pernambuco, Recife, Brazil
| | - D Song
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - M Swart
- ICREA, 08010 Barcelona, Spain and Universitat Girona, Institut de Química Computacional i Catàlisi, Campus Montilivi, 17003 Girona, Spain
| | - H L Taylor
- CD-adapco/Siemens, Melville, New York 11747, USA
| | - G S Thomas
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - V Tipparaju
- Cray Inc., Bloomington, Minnesota 55425, USA
| | - D G Truhlar
- Department of Chemistry, Chemical Theory Center, and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - T Van Voorhis
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Á Vázquez-Mayagoitia
- Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, USA
| | - P Verma
- 1QBit, Vancouver, British Columbia V6E 4B1, Canada
| | - O Villa
- NVIDIA, Santa Clara, California 95051, USA
| | - A Vishnu
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - K D Vogiatzis
- Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, USA
| | - D Wang
- College of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250014, China
| | - J H Weare
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - M J Williamson
- Department of Chemistry, Cambridge University, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - T L Windus
- Department of Chemistry, Iowa State University and Ames Laboratory, Ames, Iowa 50011, USA
| | - K Woliński
- Faculty of Chemistry, Maria Curie-Skłodowska University in Lublin, 20-031 Lublin, Poland
| | - A T Wong
- Qwil, San Francisco, California 94107, USA
| | - Q Wu
- Brookhaven National Laboratory, Upton, New York 11973, USA
| | - C Yang
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Q Yu
- AMD, Santa Clara, California 95054, USA
| | - M Zacharias
- Department of Physics, Technical University of Munich, 85748 Garching, Germany
| | - Z Zhang
- Stanford Research Computing Center, Stanford University, Stanford, California 94305, USA
| | - Y Zhao
- State Key Laboratory of Silicate Materials for Architectures, International School of Materials Science and Engineering, Wuhan University of Technology, Wuhan 430070, China
| | - R J Harrison
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, New York 11794, USA
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Semiglazova T, Osipov M, Krivorotko P, Semiglazov V, Protsenko S, Berstein L, Tsirlina E, Klimenko V, Donskih R, Anisimov V, Belyaev A. Melatonin and metformin in neoadjuvant chemotherapy in locally advanced breast cancer. Ann Oncol 2019. [DOI: 10.1093/annonc/mdz241.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Semiglazova T, Osipov M, Krivorotko P, Protsenko S, Semiglazov V, Donskih R, Klimenko V, Tsirlina E, Berstein L, Anisimov V, Semiglazov V, Belyaev A. Neoadjuvant endocrine therapy in combination with melatonin and metformin in locally advanced breast cancer. Ann Oncol 2019. [DOI: 10.1093/annonc/mdz241.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Myasnyankin M, Anisimov V, Drozd Y. Final results of neoadjuvant photodynamic therapy (PDT) on the indices of T- and B-cellar immune answer in the surgical treatment of patients with gastric cancer. Ann Oncol 2019. [DOI: 10.1093/annonc/mdz155.386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Konstantinova M, Anisimov V, Latanov A. Subjective estimation of time intervals has EEG-correlates in 13-30 frequency band. Int J Psychophysiol 2018. [DOI: 10.1016/j.ijpsycho.2018.07.418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Golubev A, Panchenko A, Anisimov V. Applying parametric models to survival data: tradeoffs between statistical significance, biological plausibility, and common sense. Biogerontology 2018; 19:341-365. [PMID: 29869230 DOI: 10.1007/s10522-018-9759-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 05/30/2018] [Indexed: 12/18/2022]
Abstract
Parametric models for survival data help to differentiate aging from other lifespan determinants. However, such inferences suffer from small sizes of experimental animal samples and variable animals handling by different labs. We analyzed control data from a single laboratory where interventions in murine lifespan were studied over decades. The minimal Gompertz model (GM) was found to perform best with most murine strains. However, when several control datasets related to a particular strain are fitted to GM, strikingly rigid interdependencies between GM parameters emerge, consistent with the Strehler-Mildvan correlation (SMC). SMC emerges even when survival patterns do not conform to GM, as with cancer-prone HER2/neu mice, which die at a log-normally distributed age. Numerical experiments show that SMC includes an artifact whose magnitude depends on dataset deviation from conformance to GM irrespectively of the noisiness of small datasets, another contributor to SMC. Still another contributor to SMC is the compensation effect of mortality (CEM): a real tradeoff between the physiological factors responsible for initial vitality and the rate of its decline. To avoid misinterpretations, we advise checking experimental results against a SMC based on historical controls or on subgroups obtained by randomization of control animals. An apparent acceleration of aging associated with a decrease in the initial mortality is invalid if it is not greater than SMC suggests. This approach applied to published data suggests that the effects of calorie restriction and of drugs believed to mimic it are different. SMC and CEM relevance to human survival patterns is discussed.
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Affiliation(s)
- Alexey Golubev
- N.N. Petrov Research Institute of Oncology, Pesochny-2, Saint-Petersburg, 197758, Russia.
| | - Andrei Panchenko
- N.N. Petrov Research Institute of Oncology, Pesochny-2, Saint-Petersburg, 197758, Russia
| | - Vladimir Anisimov
- N.N. Petrov Research Institute of Oncology, Pesochny-2, Saint-Petersburg, 197758, Russia
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Minois N, Lauwers-Cances V, Savy S, Attal M, Andrieu S, Anisimov V, Savy N. Using Poisson-gamma model to evaluate the duration of recruitment process when historical trials are available. Stat Med 2017; 36:3605-3620. [PMID: 28608361 DOI: 10.1002/sim.7365] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 04/24/2017] [Accepted: 05/05/2017] [Indexed: 11/07/2022]
Abstract
At the design of clinical trial operation, a question of a paramount interest is how long it takes to recruit a given number of patients. Modelling the recruitment dynamics is the necessary step to answer this question. Poisson-gamma model provides very convenient, flexible and realistic approach. This model allows predicting the trial duration using data collected at an interim time with very good accuracy. A natural question arises: how to evaluate the parameters of recruitment model before the trial begins? The question is harder to handle as there are no recruitment data available for this trial. However, if there exist similar completed trials, it is appealing to use data from these trials to investigate feasibility of the recruitment process. In this paper, the authors explore the recruitment data of two similar clinical trials (Intergroupe Francais du Myélome 2005 and 2009). It is shown that the natural idea of plugging the historical rates estimated from the completed trial in the same centres of the new trial for predicting recruitment is not a relevant strategy. In contrast, using the parameters of a gamma distribution of the rates estimated from the completed trial in the recruitment dynamic model of the new trial provides reasonable predictive properties with relevant confidence intervals. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Nathan Minois
- University of Toulouse III, Toulouse, F-31073, France.,INSERM, Toulouse, U1027, F-31073, France
| | | | | | - Michel Attal
- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, F-31059, France
| | - Sandrine Andrieu
- University of Toulouse III, Toulouse, F-31073, France.,INSERM, Toulouse, U1027, F-31073, France.,Epidemiology Unit, CHU Toulouse, Toulouse, F-31073, France
| | - Vladimir Anisimov
- School of Mathematics and Statistics, University of Glasgow, Glasglow, U.K
| | - Nicolas Savy
- University of Toulouse III, Toulouse, F-31073, France.,Toulouse Institute of Mathematics, Toulouse, UMR C5583, F-31062, France
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Parke T, Marchenko O, Anisimov V, Ivanova A, Jennison C, Perevozskaya I, Song G. Comparing oncology clinical programs by use of innovative designs and expected net present value optimization: Which adaptive approach leads to the best result? J Biopharm Stat 2017; 27:457-476. [PMID: 28281911 DOI: 10.1080/10543406.2017.1289949] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Designing an oncology clinical program is more challenging than designing a single study. The standard approaches have been proven to be not very successful during the last decade; the failure rate of Phase 2 and Phase 3 trials in oncology remains high. Improving a development strategy by applying innovative statistical methods is one of the major objectives of a drug development process. The oncology sub-team on Adaptive Program under the Drug Information Association Adaptive Design Scientific Working Group (DIA ADSWG) evaluated hypothetical oncology programs with two competing treatments and published the work in the Therapeutic Innovation and Regulatory Science journal in January 2014. Five oncology development programs based on different Phase 2 designs, including adaptive designs and a standard two parallel arm Phase 3 design were simulated and compared in terms of the probability of clinical program success and expected net present value (eNPV). In this article, we consider eight Phase2/Phase3 development programs based on selected combinations of five Phase 2 study designs and three Phase 3 study designs. We again used the probability of program success and eNPV to compare simulated programs. For the development strategies, we considered that the eNPV showed robust improvement for each successive strategy, with the highest being for a three-arm response adaptive randomization design in Phase 2 and a group sequential design with 5 analyses in Phase 3.
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Affiliation(s)
- Tom Parke
- a Berry Consultants , Abingdon , Oxfordshire , UK
| | - Olga Marchenko
- b Advisory Services Analytics, Quintiles , Durham , North Carolina , USA
| | - Vladimir Anisimov
- c School of Mathematics and Statistics, University of Glasgow , Glasgow , UK
| | - Anastasia Ivanova
- d Department of Biostatistics , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA
| | | | - Inna Perevozskaya
- f Statistical Research and Consulting Center, Pfizer, Inc. , Collegeville , Pennsylvania , USA
| | - Guochen Song
- b Advisory Services Analytics, Quintiles , Durham , North Carolina , USA
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Moskalev A, Anisimov V, Aliper A, Artemov A, Asadullah K, Belsky D, Baranova A, de Grey A, Dixit VD, Debonneuil E, Dobrovolskaya E, Fedichev P, Fedintsev A, Fraifeld V, Franceschi C, Freer R, Fülöp T, Feige J, Gems D, Gladyshev V, Gorbunova V, Irincheeva I, Jager S, Jazwinski SM, Kaeberlein M, Kennedy B, Khaltourina D, Kovalchuk I, Kovalchuk O, Kozin S, Kulminski A, Lashmanova E, Lezhnina K, Liu GH, Longo V, Mamoshina P, Maslov A, Pedro de Magalhaes J, Mitchell J, Mitnitski A, Nikolsky Y, Ozerov I, Pasyukova E, Peregudova D, Popov V, Proshkina E, Putin E, Rogaev E, Rogina B, Schastnaya J, Seluanov A, Shaposhnikov M, Simm A, Skulachev V, Skulachev M, Solovev I, Spindler S, Stefanova N, Suh Y, Swick A, Tower J, Gudkov AV, Vijg J, Voronkov A, West M, Wagner W, Yashin A, Zemskaya N, Zhumadilov Z, Zhavoronkov A. A review of the biomedical innovations for healthy longevity. Aging (Albany NY) 2017. [PMCID: PMC5310653 DOI: 10.18632/aging.101163] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Alexey Moskalev
- Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia
- Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
- Vavilov Institute of General Genetics RAS, Moscow, Russia
| | - Vladimir Anisimov
- Department of Carcinogenesis and Oncogerontology, N.N. Petrov Research Institute of Oncology, St. Petersburg, Russia
| | - Aleksander Aliper
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, ETC, B301, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Artem Artemov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, ETC, B301, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | | | - Daniel Belsky
- Social Science Research Institute, Duke University, Durham, NC 27708, USA
| | - Ancha Baranova
- School of Systems Biology, George Mason University, VA 20110, USA
| | - Aubrey de Grey
- SENS Research Foundation, 1 Beaconsfield Terrace, Cambridge, CB2 3EH, UK
| | - Vishwa Deep Dixit
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
| | | | - Eugenia Dobrovolskaya
- Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
| | - Peter Fedichev
- Gero Limited, International Commerce Center, Kowloon, Hong Kong
| | | | - Vadim Fraifeld
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Center for Multidisciplinary Research on Aging, Ben-Gurion University of the Negev, POB 653, 8410501, Beer-Sheva, Israel
| | - Claudio Franceschi
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Rosie Freer
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
| | - Tamas Fülöp
- Department of Medicine, Research Center on Aging, Graduate Program in Immunology, Faculty of Medicine and Health Sciences, University of Sherbrooke, Sherbrooke, Quebec, Canada
| | - Jerome Feige
- Nestlé Institute of Health Sciences, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | - David Gems
- Institute of Healthy Ageing, and Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Vadim Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Vera Gorbunova
- Department of Biology, University of Rochester, Rochester, NY 14627, USA
| | - Irina Irincheeva
- Nutrition and Metabolic Health Group, Nestlé Institute of Health Sciences, CH-1015 Lausanne, Switzerland
| | - Sibylle Jager
- Open Research Department, L’Oreal, 93600 Aulnay-sous-Bois, France
| | - S. Michal Jazwinski
- Tulane Center for Aging and Department of Medicine, Tulane University Health Sciences Center, SL-12, New Orleans, LA 70112, USA
| | - Matt Kaeberlein
- Department of Pathology, University of Washington, Seattle, WA 98195, USA
| | - Brian Kennedy
- Buck Institute for Research on Aging, Novato, CA 94945, USA
| | - Daria Khaltourina
- Department of the Integrated Prevention Programs, Federal State Institution "National Research Center for Preventive Medicine" of the Ministry of Healthcare of the Russian Federation, 101990, Moscow, Russia
| | - Igor Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, Alberta, T1K 3M4, Canada
| | - Olga Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, Alberta, T1K 3M4, Canada
| | - Sergey Kozin
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | | | | | - Ksenia Lezhnina
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, ETC, B301, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Guang-Hui Liu
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Valter Longo
- Longevity Institute and Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Polina Mamoshina
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, ETC, B301, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Alexander Maslov
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Joao Pedro de Magalhaes
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - James Mitchell
- Department of Genetics and Complex Diseases, Harvard School of Public Health, Boston, MA 02115, USA
| | - Arnold Mitnitski
- Department of Medicine, Dalhousie University, Centre for Health Care of the Elderly-Suite 1305, 5955 Veterans' Memorial Lane Halifax, Nova Scotia, B3H 2E1 Canada
| | - Yuri Nikolsky
- Biomedical Cluster, Skolkovo Foundation, Skolkovo, Russia
| | - Ivan Ozerov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, ETC, B301, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Elena Pasyukova
- Institute of Molecular Genetics of Russian Academy of Sciences, Moscow, Russia
| | - Darya Peregudova
- Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
| | | | - Ekaterina Proshkina
- Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
| | - Evgeny Putin
- Computer Technologies Lab, ITMO University, St. Petersburg 197101, Russia
| | - Evgeny Rogaev
- Vavilov Institute of General Genetics RAS, Moscow, Russia
| | - Blanka Rogina
- Institute for Systems Genomics, School of Medicine, University of Connecticut Health, Farmington, CT 06030, USA
| | - Jane Schastnaya
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, ETC, B301, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Andrey Seluanov
- Department of Biology, University of Rochester, Rochester, NY 14627, USA
| | - Mikhail Shaposhnikov
- Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
| | - Andreas Simm
- Centre of Medical Basic Research, Medical Faculty, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Vladimir Skulachev
- Department of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Maxim Skulachev
- Department of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Ilya Solovev
- Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
| | - Stephen Spindler
- Department of Biochemistry, University of California at Riverside, Riverside, CA 92521, USA
| | - Natalia Stefanova
- Institute of Cytology and Genetics of Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
| | - Yousin Suh
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | | | - John Tower
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrei V. Gudkov
- Department of Cell Stress Biology, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Andrey Voronkov
- Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia
| | | | - Wolfgang Wagner
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen University, Aachen, Germany
| | - Anatoliy Yashin
- Social Science Research Institute, Duke University, Durham, NC 27708, USA
| | - Nadezhda Zemskaya
- Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
| | | | - Alex Zhavoronkov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, ETC, B301, Johns Hopkins University, Baltimore, Maryland 21218, USA
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O'Kelly M, Anisimov V, Campbell C, Hamilton S. Proposed best practice for projects that involve modelling and simulation. Pharm Stat 2016; 16:107-113. [PMID: 27809406 DOI: 10.1002/pst.1789] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Revised: 07/28/2016] [Accepted: 09/15/2016] [Indexed: 11/09/2022]
Abstract
Modelling and simulation has been used in many ways when developing new treatments. To be useful and credible, it is generally agreed that modelling and simulation should be undertaken according to some kind of best practice. A number of authors have suggested elements required for best practice in modelling and simulation. Elements that have been suggested include the pre-specification of goals, assumptions, methods, and outputs. However, a project that involves modelling and simulation could be simple or complex and could be of relatively low or high importance to the project. It has been argued that the level of detail and the strictness of pre-specification should be allowed to vary, depending on the complexity and importance of the project. This best practice document does not prescribe how to develop a statistical model. Rather, it describes the elements required for the specification of a project and requires that the practitioner justify in the specification the omission of any of the elements and, in addition, justify the level of detail provided about each element. This document is an initiative of the Special Interest Group for modelling and simulation. The Special Interest Group for modelling and simulation is a body open to members of Statisticians in the Pharmaceutical Industry and the European Federation of Statisticians in the Pharmaceutical Industry. Examples of a very detailed specification and a less detailed specification are included as appendices.
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Anisimov V. Metformin for prevention and treatment of colon cancer: a reappraisal of experimental and clinical data. Curr Drug Targets 2015. [DOI: 10.2174/1389450116666150304102858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Knerr S, Anisimov V, Baret O, Gorski N, Price D, Simon JC. The A2iA Intercheque System: Courtesy Amount and Legal Amount Recognition for French Checks. INT J PATTERN RECOGN 2011. [DOI: 10.1142/s0218001497000226] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We developed a check reading system, termed INTERCHEQUE, which recognizes both the legal (LAR) and the courtesy amount (CAR) on bank checks. The version presented here is designed for the recognition of French, omni-bank, omni-scriptor, handwritten bank checks, and meets industrial requirements, such as high processing speed, robustness, and extremely low error rates. We give an overview of our recognition system and discuss some of the pattern recognition techniques used. We also describe an installation which processes of the order of 70,000 checks per day. Results on a data base of about 170,000 checks show a recognition rate of about 75% for an error rate of the order of 1/10,000 checks.
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Affiliation(s)
- S. Knerr
- A2iA, Tour CIT, BP 59, 75749 Paris Cedex 15, France
| | - V. Anisimov
- A2iA, Tour CIT, BP 59, 75749 Paris Cedex 15, France
- SPIIAS, 39, 14 Line, St. Petersburg, 199178, Russia
| | - O. Baret
- A2iA, Tour CIT, BP 59, 75749 Paris Cedex 15, France
| | - N. Gorski
- A2iA, Tour CIT, BP 59, 75749 Paris Cedex 15, France
- SPIIAS, 39, 14 Line, St. Petersburg, 199178, Russia
| | - D. Price
- A2iA, Tour CIT, BP 59, 75749 Paris Cedex 15, France
| | - J. C. Simon
- A2iA, Tour CIT, BP 59, 75749 Paris Cedex 15, France
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Monakhov A, Anisimov V. 7010 POSTER Characteristics of skin melanoma and determination of efficacy of its treatment by cytogenetic criteria. EJC Suppl 2007. [DOI: 10.1016/s1359-6349(07)71464-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Zyubina TS, Razumov VF, Brichkin SB, Anisimov V, Lin SH, Mebel AM. Quantum-chemical study of crystal formation of supramolecular silver compounds with trans-1,2-Bis(4-pyridyl)ethylene and their electronic absorption spectra. RUSS J INORG CHEM+ 2006. [DOI: 10.1134/s0036023606060131] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Kossoy G, Zandbank J, Tendler E, Anisimov V, Khavinson V, Popovich I, Zabezhinski M, Zusman I, Ben-Hur H. Epitalon and colon carcinogenesis in rats: proliferative activity and apoptosis in colon tumors and mucosa. Int J Mol Med 2003; 12:473-7. [PMID: 12964022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023] Open
Abstract
The effect of the synthetic pineal peptide Epitalon (Ala-Glu-Asp-Gly) on proliferative activity in colon tumors, and in mucosal epithelial cells adjacent to and located far from tumors was studied in rats. To evaluate the effect of Epitalon on different stages of carcinogenesis, different treatment regimens were used: during the tumor initiation stage, during the tumor-promotion stage, or during the entire process of tumor development. Eighty 2-month-old male LIO rats were exposed weekly to five subcutaneous injections of 1,2-dimethylhydrazine (DMH) at a single dose of 21 mg/kg body weight. Rats were divided into four groups. Control rats (group 1) received saline at a dose of 0.1 ml during the entire experiment. Rats in group 2 were treated with Epitalon at a dose of 1 micro g, five times a week, for 6 months, from the first injection of DMH till the end of the experiment. Rats in group 3 were treated with Epitalon after termination of the carcinogen injections. Rats in group 4 were treated with Epitalon only during the period of DMH exposure (for the first 5 weeks of the experiment). DMH induced proliferation of the secretory epithelium, and this phenomenon was accompanied by a decrease in the size of the stromal area and the area of lymph infiltration in colon tumors and in the colon mucosa adjacent to the tumors (group 1). Epitalon attenuated this effect, especially when the treatment was continued throughout the experiment (group 2). It increased the stromal areas, as well as that of lymphoid infiltration in the colon mucosa adjacent to the tumors. The intensity of lymphoid infiltration was activated in both the colon mucosa adjacent to a tumor and in the tumor. Mitotic activity of tumor cells was significantly inhibited by Epitalon when the treatment was given throughout the experiment (group 2). In parallel, a high level of apoptosis was seen in the same group. Thus, the strongest inhibitory effect of Epitalon on carcinogenesis in the colon mucosa was manifested when the treatment was continued throughout the experiment.
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Affiliation(s)
- George Kossoy
- Laboratory of Experimental Medicine, Rehovot, Israel
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Kossoy G, Zandbank J, Tendler E, Anisimov V, Khavinson V, Popovich I, Zabezhinski M, Zusman I, Ben-Hur H. Epitalon and colon carcinogenesis in rats: Proliferative activity and apoptosis in colon tumors and mucosa. Int J Mol Med 2003. [DOI: 10.3892/ijmm.12.4.473] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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Kvetnoy I, Popuichiev V, Mikhina L, Anisimov V, Yuzhakov V, Konovalov S, Pogudina N, Franceschi C, Piantanelli L, Rossolini G, Zaia A, Kvetnaia T, Hernandez-Yago J, Blesa JR. Gut neuroendocrine cells: relationship to the proliferative activity and apoptosis of mucous epitheliocytes in aging. Neuro Endocrinol Lett 2001; 22:337-41. [PMID: 11600875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/15/2001] [Accepted: 08/08/2001] [Indexed: 02/21/2023]
Abstract
OBJECTIVES Diffuse neuroendocrine system (DNES) cells regulate homeostasis via neurocrine, endocrine and paracrine mechanisms. Extensive effects of peptide hormones and biogenic amines necessitate studying of DNES cell biology in aging. In this connection, the functional morphology of gut neuroendocrine cells (NEC), proliferative activity and apoptosis of mucous epithelial cells in aging have been studied. MATERIAL AND METHODS The study was performed on BALB/c-nu mice of 4, 21 and 34 months of age. NEC, proliferative activity and apoptosis of mucous epitheliocytes in stomach and duodenum have been studied by histochemical, immunohistochemical and morphometrical methods. RESULTS The total number of NEC shows an increasing trend with advancing age. However, the different types of NEC elicit differential patterns. The total number of epithelial cell nuclei does not show any statistically significant difference during aging. The proliferative activity of mucous epitheliocytes also shows no difference among the three animal groups studied. On the contrary, the apoptotic index increases with advancing age. CONCLUSIONS The results demonstrate that various gut NEC show differential behavior with age and their time-courses are dependent on the site of location (stomach or duodenum). The picture seems quite complex to allow a comprehensive interpretation, nonetheless it gives us some useful indications for further investigation. In fact, since the gut does not show evident gross age-related physiological changes, modifications with age in specific biological parameters can suggest the key mechanisms of compensative regulatory processes possibly acting during aging.
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Affiliation(s)
- I Kvetnoy
- St. Petersburg Institute of Bioregulation and Gerontology, St. Petersburg, Russia.
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Kossoy G, Ben-Hur H, Popovich I, Zabezhinski M, Anisimov V, Zusman I. Melatonin and colon carcinogenesis. IV. Effect of melatonin on proliferative activity and expression of apoptosis-related proteins in the spleen of rats exposed to 1,2-dimethylhydrazine. Oncol Rep 2000; 7:1401-5. [PMID: 11032952 DOI: 10.3892/or.7.6.1401] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The suppression of 1,2-dimethylhydrazine (DMH)-induced colon carcinogenesis by melatonin was previously demonstrated. The objective of the present work was to evaluate histologically and immunohistochemically the splenic immune response to the induced cancer and to melatonin. Spleens from rats, either untreated, injected with DMH, fed with melatonin or treated with both carcinogen and melatonin, were studied. The exposure to the carcinogen and the consequential carcinogenesis resulted in splenic changes that reflected the insufficiency of the immune response, as manifested in significant reduction of the white pulp and the simultaneous expansion of the red pulp. The effects of melatonin on most splenic components were inverse to those of DMH. The anti-carcinogenic properties of melatonin were evidenced from the reversal of the inhibitory effects of DMH, especially when the densities of lymphocytes in different parts of the spleen were compared. The combined treatment of the rats with DMH and melatonin resulted in the expansion of the splenic zones by 106% to 125%, compared to those from DMH-treated rats, and the numbers of CD8+ lymphocytes and Fas-positive cells increased sharply. Therefore we conclude that anti-carcinogenic effects of melatonin are related to activation of several elements of the host's lymphatic system.
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Affiliation(s)
- G Kossoy
- Laboratory of Experimental Oncology, Koret School of Veterinary Medicine, Faculty of Agricultural, Food and Environmental Sciences, The Hebrew University of Jerusalem, Rehovot, Israel
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Mila F, Shiina R, Zhang F, Joshi A, Ma M, Anisimov V, Rice TM. Orbitally degenerate spin-1 model for insulating V2O3. Phys Rev Lett 2000; 85:1714-1717. [PMID: 10970596 DOI: 10.1103/physrevlett.85.1714] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2000] [Revised: 05/17/2000] [Indexed: 05/23/2023]
Abstract
Motivated by recent neutron, x-ray absorption, and resonant scattering experiments, we revisit the electronic structure of V2O3. We propose a model in which S = 1 V3+ ions are coupled in the vertical V-V pairs forming twofold orbitally degenerate configurations with S = 2. Ferro-orbital ordering of the V-V pairs gives a description which is consistent with all experiments in the antiferromagnetic insulating phase.
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Affiliation(s)
- F Mila
- Laboratoire de Physique Quantique, Universite Paul Sabatier, 31062 Toulouse, France
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Anisimov V, Zabezhinski M, Popovich I, Muratov E, Nikitina V, Kalinina N. Effect of video display terminal irradiation on urethane-induced lung carcinogenesis in mice. Oncol Rep 1996; 3:401-4. [DOI: 10.3892/or.3.2.401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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Likhachev A, Anisimov V, Zhukovskaya N, Petrov A. Inactivation of O6-alkylguanine-DNA alkyltransferase in the rat liver due to an intraperitoneal administration of methylated DNA. Carcinogenesis 1988; 9:1139-42. [PMID: 3383333 DOI: 10.1093/carcin/9.7.1139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Intraperitoneal administration of 6.5 mg of in vitro methylated DNA (meDNA) containing 1.5 x 10(-4) mM of O6-methylguanine (6MG) to male outbred rats weighing 150 g led to a considerable decrease in the activity of liver O6-alkylguanine-DNA alkyltransferase (AT). One hour after treatment there occurred a 4- to 5-fold decrease in the AT activity followed by its slow recovery. However, after 48 h, AT activity considerably exceeded control levels. A 5-fold decrease in the amount of administered meDNA resulted in the absence of its effect, whereas administration of higher amounts produced a further AT inactivation. A similar treatment with non-methylated DNA did not change AT activity. The possibility of AT exhaustion under in vivo conditions and thereby inhibition of repair of O6-alkylguanine in DNA, playing a key role in mutagenic, carcinogenic and antineoplastic effects of certain alkylating agents, might be helpful in increasing susceptibility of animals to such compounds.
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Affiliation(s)
- A Likhachev
- N.N.Petrov Research Institute of Oncology, Leningrad, USSR
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Napalkov N, Loktionov A, Likhachev A, Anisimov V, Zabezhinski M, Tomatis L. Persistence of carcinogenic effect in intact progeny of mice treated transplacentally with 7,12-dimethylbenz[a]anthracene. Cancer Lett 1987; 38:231-41. [PMID: 3121167 DOI: 10.1016/0304-3835(87)90219-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Pregnant SHR mice were treated once with 7,12-dimethylbenz[a]anthracene (DMBA) on days 17-19 of gestation. F1 and F2 descendants of these mice received multiple skin applications of 12-O-tetradecanoylphorbol-13-acetate (TPA) twice a week for 24 weeks beginning at 12 weeks of age, or applications of solvent alone. The increase in the frequency of skin tumours in F1 and F2 descendants was reported elsewhere. In addition, we report here an increase in overall numbers of tumor-bearing animals, independently of TPA treatment both in F1 and F2 groups compared to respective control groups. Separate statistical analyses were performed for lung tumours, mammary gland tumours, leukaemias and lymphomas. In both generations of descendants of DMBA-treated mothers lung tumour incidence was considerably increased and differed significantly (maximal P-value = 0.003) from control values. Local applications of TPA resulting in strong skin tumour promoting effect described in our previous paper (Napalkov et al., Carcinogenesis, 8(3) (1987) 381) did not produce any significant change in the rates of other types of tumours. The results of the present study provide additional evidence in support of the hypothesis on possibility of hereditary transmission of carcinogenic action of certain chemical compounds.
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Affiliation(s)
- N Napalkov
- N.N. Petrov Research Institute of Oncology, Leningrad, U.S.S.R
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Napalkov N, Likhachev A, Anisimov V, Loktionov A, Zabezhinski M, Ovsyannikov A, Wahrendorf J, Becher H, Tomatis L. Promotion of skin tumours by TPA in the progeny of mice exposed pre-natally to DMBA. Carcinogenesis 1987; 8:381-5. [PMID: 3102097 DOI: 10.1093/carcin/8.3.381] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Pregnant SHR mice were treated once with 7,12-dimethylbenz[a]anthracene (DMBA) on day 17-19 of gestation, and F1 and F2 descendants received multiple skin applications of 12-O-tetradecanoylphorbol-13-acetate (TPA) twice a week for 24 weeks beginning at 12 weeks of age. Post-natal promoter treatment resulted in a high incidence of skin tumours in F1 and F2 mice (37.3 and 19.7%, respectively), whereas only 6.6% of control animals treated with TPA only developed skin tumours. DMBA was shown previously to be capable of initiating skin carcinogenesis transplacentally; however, our results on the second generation provide suggestive evidence of hereditary transmission of at least part of the initiating action of this carcinogen.
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Likhachev A, Anisimov V, Parvanova L, Pozharisski K. Effect of exogenous beta-glucuronidase on the carcinogenicity of 1,2-dimethylhydrazine in rats: evidence that carcinogenic intermediates form conjugates and act through their subsequent enzymatic release. Carcinogenesis 1985; 6:679-81. [PMID: 4006053 DOI: 10.1093/carcin/6.5.679] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Albino, outbred 3-month-old rats were given a single s.c. dose of 1,2-dimethylhydrazine dihydrochloride (DMH; 100 mg/kg) and, 6 or 24 h later, an i.v. dose of bovine liver beta-glucuronidase (3 X 10(4) Fishman units). After this treatment, the incidence of tumours of the large intestine and Zymbal gland, and of cystocholangiomas was similar to that found in rats treated with DMH alone; the incidence of malignancies in various other tissues was considerably higher than that in rats treated only with DMH, especially in animals exposed to beta-glucuronidase 24 h after administration of DMH. beta-Glucuronidase itself had no carcinogenic activity. The broadening of the spectrum of malignant tumours produced in DMH-treated rats by administration of beta-glucuronidase indicates that the carcinogenic effect of DMH may be exerted through formation of comparatively stable conjugates of its metabolites and their enzymic release in target tissues. The approach used in this study could be helpful in investigating the formation of conjugates from other carcinogens.
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