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103. Theory of localized synfire chain

Author

Hamaguchi, Kosuke, Okada, Masato, and Aihara, Kazuyuki

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Neuron is a noisy information processing unit and conventional view is that information in the cortex is carried on the rate of neurons spike emission. More recent studies on the activity propagation through the homogeneous network have demonstrated that signals can be transmitted with millisecond fidelity; this model is called the Synfire chain and suggests the possibility of the spatio-temporal coding. However, the more biologically realistic, structured feedforward network generates spatially distributed inputs. It results in the difference of spike timing. This poses a question on how the spatial structure of a network effect the stability of spatio-temporal spike patterns, and the speed of a spike packet propagation. By formulating the Fokker-Planck equation for the feedforwardly coupled network with Mexican-Hat type connectivity, we show the stability of localized spike packet and existence of Multi-stable phase where both uniform and localized spike packets are stable depending on the initial input structure. The Multi-stable phase enables us to show that a spike pattern, or the information of its own, determines the propagation speed., Comment: 9 pages, 4 figures

Published
2004

104. Fine Discrimination of Analog Patterns by Nonlinear Dendritic Inhibition

Author

Morita, Kenji and Aihara, Kazuyuki

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Recent experiments revealed that a certain class of inhibitory neurons in the cerebral cortex make synapses not onto cell bodies but at distal parts of dendrites of the target neurons, mediating highly nonlinear dendritic inhibition. We propose a novel form of competitive neural network model that realizes such dendritic inhibition. Contrary to the conventional lateral inhibition in neural networks, our dendritic inhibition models don't always show winner-take-all behaviors; instead, they converge to "I don't know" states when unknown input patterns are presented. We derive reduced two-dimensional dynamics for the network, showing that a drastic shift of the fixed point from a winner-take-all state to an "I don't know" state occurs in accordance with the increase in noise added to the stored patterns. By preventing misrecognition in such a way, dendritic inhibition networks achieve fine pattern discrimination, which could be one of the basic computations by inhibitory connected recurrent neural networks in the brain., Comment: 4 pages, 3 figures

Published
2004

105. Transmission of severe acute respiratory syndrome in dynamical small-world networks

Author

Masuda, Naoki, Konno, Norio, and Aihara, Kazuyuki

Subjects
Condensed Matter - Other Condensed Matter, Quantitative Biology - Populations and Evolution
Abstract

The outbreak of severe acute respiratory syndrome (SARS) is still threatening the world because of a possible resurgence. In the current situation that effective medical treatments such as antiviral drugs are not discovered yet, dynamical features of the epidemics should be clarified for establishing strategies for tracing, quarantine, isolation, and regulating social behavior of the public at appropriate costs. Here we propose a network model for SARS epidemics and discuss why superspreaders emerged and why SARS spread especially in hospitals, which were key factors of the recent outbreak. We suggest that superspreaders are biologically contagious patients, and they may amplify the spreads by going to potentially contagious places such as hospitals. To avoid mass transmission in hospitals, it may be a good measure to treat suspected cases without hospitalizing them. Finally, we indicate that SARS probably propagates in small-world networks associated with human contacts and that the biological nature of individuals and social group properties are factors more important than the heterogeneous rates of social contacts among individuals. This is in marked contrast with epidemics of sexually transmitted diseases or computer viruses to which scale-free network models often apply., Comment: 4 figures

Published
2004
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108. Coherent Response in a Chaotic Neural Network

Author

Nishimura, Haruhiko, Katada, Naofumi, and Aihara, Kazuyuki

Subjects
Nonlinear Sciences - Chaotic Dynamics, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Quantitative Biology
Abstract

We set up a signal-driven scheme of the chaotic neural network with the coupling constants corresponding to certain information, and investigate the stochastic resonance-like effects under its deterministic dynamics, comparing with the conventional case of Hopfield network with stochastic noise. It is shown that the chaotic neural network can enhance weak subthreshold signals and have higher coherence abilities between stimulus and response than those attained by the conventional stochastic model., Comment: 9 pages, 5 figures, to be published in Neural Processing Letters

Published
2000

111. Network Structure Identification from a Small Number of Inputs/Outputs

Author

Suzuki, Masayasu, Imura, Jun-ichi, Aihara, Kazuyuki, Wakayama, Masato, Editor-in-chief, Anderssen, Robert S., Series editor, Bauschke, Heinz H., Series editor, Broadbridge, Philip, Series editor, Cheng, Jin, Series editor, Chyba, Monique, Series editor, Cottet, Georges-Henri, Series editor, Cuminato, José Alberto, Series editor, Ei, Shin-ichiro, Series editor, Fukumoto, Yasuhide, Series editor, Hosking, Jonathan R. M., Series editor, Jofré, Alejandro, Series editor, Landman, Kerry, Series editor, McKibbin, Robert, Series editor, Mercer, Geoff, Series editor, Parmeggiani, Andrea, Series editor, Pipher, Jill, Series editor, Polthier, Konrad, Series editor, Schilders, Wil, Series editor, Shen, Zuowei, Series editor, Toh, Kim-Chuan, Series editor, Verbitskiy, Evgeny, Series editor, Yoshida, Nakahiro, Series editor, Aihara, Kazuyuki, editor, Imura, Jun-ichi, editor, and Ueta, Tetsushi, editor

Published
2015
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112. Feedback Control Method Based on Predicted Future States for Controlling Chaos

Author

Kobayashi, Miki U., Ueta, Tetsushi, Aihara, Kazuyuki, Wakayama, Masato, Editor-in-chief, Anderssen, Robert S., Series editor, Bauschke, Heinz H., Series editor, Broadbridge, Philip, Series editor, Cheng, Jin, Series editor, Chyba, Monique, Series editor, Cottet, Georges-Henri, Series editor, Cuminato, José Alberto, Series editor, Ei, Shin-ichiro, Series editor, Fukumoto, Yasuhide, Series editor, Hosking, Jonathan R. M., Series editor, Jofré, Alejandro, Series editor, Landman, Kerry, Series editor, McKibbin, Robert, Series editor, Mercer, Geoff, Series editor, Parmeggiani, Andrea, Series editor, Pipher, Jill, Series editor, Polthier, Konrad, Series editor, Schilders, Wil, Series editor, Shen, Zuowei, Series editor, Toh, Kim-Chuan, Series editor, Verbitskiy, Evgeny, Series editor, Yoshida, Nakahiro, Series editor, Aihara, Kazuyuki, editor, Imura, Jun-ichi, editor, and Ueta, Tetsushi, editor

Published
2015
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113. Clustered Model Reduction of Large-Scale Bidirectional Networks

Author

Ishizaki, Takayuki, Kashima, Kenji, Imura, Jun-ichi, Aihara, Kazuyuki, Wakayama, Masato, Editor-in-chief, Anderssen, Robert S., Series editor, Bauschke, Heinz H., Series editor, Broadbridge, Philip, Series editor, Cheng, Jin, Series editor, Chyba, Monique, Series editor, Cottet, Georges-Henri, Series editor, Cuminato, José Alberto, Series editor, Ei, Shin-ichiro, Series editor, Fukumoto, Yasuhide, Series editor, Hosking, Jonathan R. M., Series editor, Jofré, Alejandro, Series editor, Landman, Kerry, Series editor, McKibbin, Robert, Series editor, Mercer, Geoff, Series editor, Parmeggiani, Andrea, Series editor, Pipher, Jill, Series editor, Polthier, Konrad, Series editor, Schilders, Wil, Series editor, Shen, Zuowei, Series editor, Toh, Kim-Chuan, Series editor, Verbitskiy, Evgeny, Series editor, Yoshida, Nakahiro, Series editor, Aihara, Kazuyuki, editor, Imura, Jun-ichi, editor, and Ueta, Tetsushi, editor

Published
2015
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114. Threshold Control for Stabilization of Unstable Periodic Orbits in Chaotic Hybrid Systems

Author

Ito, Daisuke, Ueta, Tetsushi, Kousaka, Takuji, Imura, Jun-ichi, Aihara, Kazuyuki, Wakayama, Masato, Editor-in-chief, Anderssen, Robert S., Series editor, Bauschke, Heinz H., Series editor, Broadbridge, Philip, Series editor, Cheng, Jin, Series editor, Chyba, Monique, Series editor, Cottet, Georges-Henri, Series editor, Cuminato, José Alberto, Series editor, Ei, Shin-ichiro, Series editor, Fukumoto, Yasuhide, Series editor, Hosking, Jonathan R. M., Series editor, Jofré, Alejandro, Series editor, Landman, Kerry, Series editor, McKibbin, Robert, Series editor, Mercer, Geoff, Series editor, Parmeggiani, Andrea, Series editor, Pipher, Jill, Series editor, Polthier, Konrad, Series editor, Schilders, Wil, Series editor, Shen, Zuowei, Series editor, Toh, Kim-Chuan, Series editor, Verbitskiy, Evgeny, Series editor, Yoshida, Nakahiro, Series editor, Aihara, Kazuyuki, editor, Imura, Jun-ichi, editor, and Ueta, Tetsushi, editor

Published
2015
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115. Parametric Control to Avoid Bifurcation Based on Maximum Local Lyapunov Exponent

Author

Fujimoto, Ken’ichi, Yoshinaga, Tetsuya, Ueta, Tetsushi, Aihara, Kazuyuki, Wakayama, Masato, Editor-in-chief, Anderssen, Robert S., Series editor, Bauschke, Heinz H., Series editor, Broadbridge, Philip, Series editor, Cheng, Jin, Series editor, Chyba, Monique, Series editor, Cottet, Georges-Henri, Series editor, Cuminato, José Alberto, Series editor, Ei, Shin-ichiro, Series editor, Fukumoto, Yasuhide, Series editor, Hosking, Jonathan R. M., Series editor, Jofré, Alejandro, Series editor, Landman, Kerry, Series editor, McKibbin, Robert, Series editor, Mercer, Geoff, Series editor, Parmeggiani, Andrea, Series editor, Pipher, Jill, Series editor, Polthier, Konrad, Series editor, Schilders, Wil, Series editor, Shen, Zuowei, Series editor, Toh, Kim-Chuan, Series editor, Verbitskiy, Evgeny, Series editor, Yoshida, Nakahiro, Series editor, Aihara, Kazuyuki, editor, Imura, Jun-ichi, editor, and Ueta, Tetsushi, editor

Published
2015
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116. Robust Bifurcation Analysis Based on Degree of Stability

Author

Kitajima, Hiroyuki, Yoshinaga, Tetsuya, Imura, Jun-ichi, Aihara, Kazuyuki, Wakayama, Masato, Editor-in-chief, Anderssen, Robert S., Series editor, Bauschke, Heinz H., Series editor, Broadbridge, Philip, Series editor, Cheng, Jin, Series editor, Chyba, Monique, Series editor, Cottet, Georges-Henri, Series editor, Cuminato, José Alberto, Series editor, Ei, Shin-ichiro, Series editor, Fukumoto, Yasuhide, Series editor, Hosking, Jonathan R. M., Series editor, Jofré, Alejandro, Series editor, Landman, Kerry, Series editor, McKibbin, Robert, Series editor, Mercer, Geoff, Series editor, Parmeggiani, Andrea, Series editor, Pipher, Jill, Series editor, Polthier, Konrad, Series editor, Schilders, Wil, Series editor, Shen, Zuowei, Series editor, Toh, Kim-Chuan, Series editor, Verbitskiy, Evgeny, Series editor, Yoshida, Nakahiro, Series editor, Aihara, Kazuyuki, editor, Imura, Jun-ichi, editor, and Ueta, Tetsushi, editor

Published
2015
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117. Dynamic Robust Bifurcation Analysis

Author

Inoue, Masaki, Imura, Jun-ichi, Kashima, Kenji, Aihara, Kazuyuki, Wakayama, Masato, Editor-in-chief, Anderssen, Robert S., Series editor, Bauschke, Heinz H., Series editor, Broadbridge, Philip, Series editor, Cheng, Jin, Series editor, Chyba, Monique, Series editor, Cottet, Georges-Henri, Series editor, Cuminato, José Alberto, Series editor, Ei, Shin-ichiro, Series editor, Fukumoto, Yasuhide, Series editor, Hosking, Jonathan R. M., Series editor, Jofré, Alejandro, Series editor, Landman, Kerry, Series editor, McKibbin, Robert, Series editor, Mercer, Geoff, Series editor, Parmeggiani, Andrea, Series editor, Pipher, Jill, Series editor, Polthier, Konrad, Series editor, Schilders, Wil, Series editor, Shen, Zuowei, Series editor, Toh, Kim-Chuan, Series editor, Verbitskiy, Evgeny, Series editor, Yoshida, Nakahiro, Series editor, Aihara, Kazuyuki, editor, Imura, Jun-ichi, editor, and Ueta, Tetsushi, editor

Published
2015
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122. HIERARCHICAL SPATIO-TEMPORAL DYNAMICS OF A CHAOTIC NEURAL NETWORK FOR MULTISTABLE BINOCULAR RIVALRY

Author

KAKIMOTO, YUTA and AIHARA, KAZUYUKI

Subjects
Chaotic neuron model, Neural network, Binocular rivalry
Abstract

Binocular rivalry is perceptual alternation that occurs when different visual images are presented to each eye. Despite the intensive studies, the mechanism of binocular rivalry still remains unclear. In multistable binocular rivalry, which is a special case of binocular rivalry, it is known that the perceptual alternation between paired patterns is more frequent than that between unpaired patterns. This result suggests that perceptual transition in binocular rivalry is not a simple random process, and the memories stored in the brain can play an important role in the perceptual transition. In this study, we propose a hierarchical chaotic neural network model for multistable binocular rivalry and show that our model reproduces some characteristic features observed in multistable binocular rivalry.

Published
2009

127. Chaotic Simulated Annealing by A Neural Network Model with Transient Chaos

Author

Chen, Luonan and Aihara, Kazuyuki

Subjects
Nonlinear Sciences - Chaotic Dynamics
Abstract

We propose a neural network model with transient chaos, or a transiently chaotic neural network (TCNN) as an approximation method for combinatorial optimization problem, by introducing transiently chaotic dynamics into neural networks. Unlike conventional neural networks only with point attractors, the proposed neural network has richer and more flexible dynamics, so that it can be expected to have higher ability of searching for globally optimal or near-optimal solutions. A significant property of this model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing, and eventually vanishes with autonomous decreasing of a bifurcation parameter corresponding to the "temperature" in usual annealing process. Therefore, the neural network gradually approaches, through the transient chaos, to dynamical structure similar to such conventional models as the Hopfield neural network which converges to a stable equilibrium point. Since the optimization process of the transiently chaotic neural network is similar to simulated annealing, not in a stochastic way but in a deterministically chaotic way, the new method is regarded as chaotic simulated annealing (CSA). Fundamental characteristics of the transiently chaotic neurodynamics are numerically investigated with examples of a single neuron model and the Traveling Salesman Problem (TSP). Moreover, a maintenance scheduling problem for generators in a practical power system is also analysed to verify practical efficiency of this new method., Comment: the theoretical results related to this paper should be referred to "Chaos and Asymptotical Stability in Discrete-time Neural Networks" by L.Chen and K.Aihara, Physica D (in press). Journal ref.: Neural Networks, Vol.8, No.6, pp.915-930, 1995

Published
1997

128. Chaos and Asymptotical Stability in Discrete-time Neural Networks

Author

Chen, Luonan and Aihara, Kazuyuki

Subjects
Nonlinear Sciences - Chaotic Dynamics
Abstract

This paper aims to theoretically prove by applying Marotto's Theorem that both transiently chaotic neural networks (TCNN) and discrete-time recurrent neural networks (DRNN) have chaotic structure. A significant property of TCNN and DRNN is that they have only one fixed point, when absolute values of the self-feedback connection weights in TCNN and the difference time in DRNN are sufficiently large. We show that this unique fixed point can actually evolve into a snap-back repeller which generates chaotic structure, if several conditions are satisfied. On the other hand, by using the Lyapunov functions, we also derive sufficient conditions on asymptotical stability for symmetrical versions of both TCNN and DRNN, under which TCNN and DRNN asymptotically converge to a fixed point. Furthermore, generic bifurcations are also considered in this paper. Since both of TCNN and DRNN are not special but simple and general, the obtained theoretical results hold for a wide class of discrete-time neural networks. To demonstrate the theoretical results of this paper better, several numerical simulations are provided as illustrating examples., Comment: This paper will be published in Physica D. Figures should be requested to the first author

Published
1997
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129. Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation to analyze extracellular viral markers.

Author

Kitagawa, Kosaku, Kim, Kwang Su, Iwamoto, Masashi, Hayashi, Sanae, Park, Hyeongki, Nishiyama, Takara, Nakamura, Naotoshi, Fujita, Yasuhisa, Nakaoka, Shinji, Aihara, Kazuyuki, Perelson, Alan S., Allweiss, Lena, Dandri, Maura, Watashi, Koichi, Tanaka, Yasuhito, and Iwami, Shingo

Subjects
HEPATITIS B, BIOMARKERS, MULTISCALE modeling, CHRONIC hepatitis B, VIRUS diseases
Abstract

Chronic infection with hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. Thus, quantifying and understanding the dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, such study requires repeated liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because liver biopsy is potentially morbid and not common during hepatitis B treatment. We here aimed to develop a noninvasive method for quantifying cccDNA in the liver using surrogate markers in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations, integrates experimental data from in vitro and in vivo investigations. By applying this model, we roughly predicted the amount and dynamics of intrahepatic cccDNA within a certain range using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The noninvasive quantification of cccDNA using our proposed method holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions. Author summary: In viral infectious diseases, evaluating the quantity and dynamics of the virus within target cells is crucial for understanding disease progression caused by the virus and predicting the efficacy of anti-viral drugs. HBV, which infects the human liver, increases the risk of development of hepatocellular carcinoma with chronic infection. To avoid the risk, viral elimination by anti-viral treatment is needed. The final goal of HBV treatment is the elimination of cccDNA known as a viral reservoir, although it is challenging with current treatment. One of the barriers to this lies in the fact that cccDNA remains within the liver without entering the peripheral blood, making it difficult to assess the quantity and dynamics of cccDNA within the patient. To quantitate cccDNA, an invasive method called liver biopsy is necessary. This method involves inserting a large needle through the abdomen into the liver, which is basically not accepted. In this study, we constructed a multiscale mathematical model based on the experimental data, which allowed us to evaluate the quantity and dynamics of cccDNA using viral markers in the peripheral blood, if the baseline cccDNA amount prior to treatment has been quantified. The method we have developed is expected to significantly contribute to the improvement of future HBV treatment strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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131. Computational Performance of Echo State Networks with Dynamic Synapses

Author

Mori, Ryota, Tanaka, Gouhei, Nakane, Ryosho, Hirose, Akira, Aihara, Kazuyuki, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hirose, Akira, editor, Ozawa, Seiichi, editor, Doya, Kenji, editor, Ikeda, Kazushi, editor, Lee, Minho, editor, and Liu, Derong, editor

Published
2016
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132. A Hybrid Pooling Method for Convolutional Neural Networks

Author

Tong, Zhiqiang, Aihara, Kazuyuki, Tanaka, Gouhei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hirose, Akira, editor, Ozawa, Seiichi, editor, Doya, Kenji, editor, Ikeda, Kazushi, editor, Lee, Minho, editor, and Liu, Derong, editor

Published
2016
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136. Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation for analyzing extracellular viral markers

Author

Kitagawa, Kosaku, primary, Kim, Kwang Su, additional, Iwamoto, Masashi, additional, Hayashi, Sanae, additional, Park, Hyeongki, additional, Nishiyama, Takara, additional, Nakamura, Naotoshi, additional, Fujita, Yasuhisa, additional, Nakaoka, Shinji, additional, Aihara, Kazuyuki, additional, Perelson, Alan S., additional, Allweiss, Lena, additional, Dandri, Maura, additional, Watashi, Koichi, additional, Tanaka, Yasuhito, additional, and Iwami, Shingo, additional

Published
2023
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139. A coherent Ising machine for 2000-node optimization problems

Author

Inagaki, Takahiro, Haribara, Yoshitaka, Igarashi, Koji, Sonobe, Tomohiro, Tamate, Shuhei, Honjo, Toshimori, Marandi, Alireza, McMahon, Peter L., Umeki, Takeshi, Enbutsu, Koji, Tadanaga, Osamu, Takenouchi, Hirokazu, Aihara, Kazuyuki, Kawarabayashi, Ken-ichi, Inoue, Kyo, Utsunomiya, Shoko, and Takesue, Hiroki

Published
2016

147. Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Author

Iwanami, Shoya, Ejima, Keisuke, Kim, Kwang Su, Noshita, Koji, Fujita, Yasuhisa, Miyazaki, Taiga, Kohno, Shigeru, Miyazaki, Yoshitsugu, Morimoto, Shimpei, Nakaoka, Shinji, Koizumi, Yoshiki, Asai, Yusuke, Aihara, Kazuyuki, Watashi, Koichi, Thompson, Robin N., Shibuya, Kenji, Fujiu, Katsuhito, Perelson, Alan S., Iwami, Shingo, and Wakita, Takaji

Subjects
Antiviral agents -- Testing, Experimental design -- Analysis, Clinical trials -- Methods, Biological sciences
Abstract

Background Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. Methods and findings A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d.sup.-1 (95% CI: 1.06 to 1.27 d.sup.-1 ), 0.777 d.sup.-1 (0.716 to 0.838 d.sup.-1 ), and 0.450 d.sup.-1 (0.378 to 0.522 d.sup.-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. Conclusions In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model., Author(s): Shoya Iwanami 1,2, Keisuke Ejima 3,*, Kwang Su Kim 1,2, Koji Noshita 1, Yasuhisa Fujita 1,2, Taiga Miyazaki 4, Shigeru Kohno 5, Yoshitsugu Miyazaki 6, Shimpei Morimoto 7, Shinji [...]

Published
2021
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150. Infectious virus shedding duration reflects secretory IgA antibody response latency after SARS-CoV-2 infection

Author

Miyamoto, Sho, Nishiyama, Takara, Ueno, Akira, Park, Hyeongki, Kanno, Takayuki, Nakamura, Naotoshi, Ozono, Seiya, Aihara, Kazuyuki, Takahashi, Kenichiro, Tsuchihashi, Yuuki, Ishikane, Masahiro, Arashiro, Takeshi, Saito, Shinji, Ainai, Akira, Hirata, Yuichiro, Iida, Shun, Katano, Harutaka, Tobiume, Minoru, Tokunaga, Kenzo, Fujimoto, Tsuguto, Suzuki, Michiyo, Nagashima, Maki, Nakagawa, Hidenori, Narita, Masashi, Kato, Yasuyuki, Igari, Hidetoshi, Fujita, Kaori, Kato, Tatsuo, Hiyama, Kazutoshi, Shindou, Keisuke, Adachi, Takuya, Fukushima, Kazuaki, Nakamura-Uchiyama, Fukumi, Hase, Ryota, Yoshimura, Yukihiro, Yamato, Masaya, Nozaki, Yasuhiro, Ohmagari, Norio, Suzuki, Motoi, Saito, Tomoya, Iwami, Shingo, Suzuki, Tadaki, Miyamoto, Sho, Nishiyama, Takara, Ueno, Akira, Park, Hyeongki, Kanno, Takayuki, Nakamura, Naotoshi, Ozono, Seiya, Aihara, Kazuyuki, Takahashi, Kenichiro, Tsuchihashi, Yuuki, Ishikane, Masahiro, Arashiro, Takeshi, Saito, Shinji, Ainai, Akira, Hirata, Yuichiro, Iida, Shun, Katano, Harutaka, Tobiume, Minoru, Tokunaga, Kenzo, Fujimoto, Tsuguto, Suzuki, Michiyo, Nagashima, Maki, Nakagawa, Hidenori, Narita, Masashi, Kato, Yasuyuki, Igari, Hidetoshi, Fujita, Kaori, Kato, Tatsuo, Hiyama, Kazutoshi, Shindou, Keisuke, Adachi, Takuya, Fukushima, Kazuaki, Nakamura-Uchiyama, Fukumi, Hase, Ryota, Yoshimura, Yukihiro, Yamato, Masaya, Nozaki, Yasuhiro, Ohmagari, Norio, Suzuki, Motoi, Saito, Tomoya, Iwami, Shingo, and Suzuki, Tadaki

Abstract

Infectious virus shedding from individuals infected with severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is used to estimate human-to-human transmission risk. Control of SARS-CoV-2 transmission requires identifying the immune correlates that protect infectious virus shedding. Mucosal immunity prevents infection by SARS-CoV-2, which replicates in the respiratory epithelium and spreads rapidly to other hosts. However, whether mucosal immunity prevents the shedding of the infectious virus in SARS-CoV-2-infected individuals is unknown. We examined the relationship between viral RNA shedding dynamics, duration of infectious virus shedding, and mucosal antibody responses during SARS-CoV-2 infection. Anti-spike secretory IgA antibodies (S-IgA) reduced viral RNA load and infectivity more than anti-spike IgG/IgA antibodies in infected nasopharyngeal samples. Compared with the IgG/IgA response, the anti-spike S-IgA post-infection responses affected the viral RNA shedding dynamics and predicted the duration of infectious virus shedding regardless of the immune history. These findings highlight the importance of anti-spike S-IgA responses in individuals infected with SARS-CoV-2 for preventing infectious virus shedding and SARS-CoV-2 transmission. Developing medical countermeasures to shorten S-IgA response time may help control human-to-human transmission of SARS-CoV-2 infection and prevent future respiratory virus pandemics.

Published
2023

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