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Article: Mitochondrial ribosomal protein S24 is associated with immunosuppressive microenvironment and cold tumor in lung adenocarcinoma.

Gao, Yanni / Yu, Yilin / Wu, Haixia / Xiao, Zhenzhou / Li, Jiancheng

2024 Volume 10, Issue 7, Page(s) e29171

Abstract: Objective: MRPS24: Methods: The analysis of : Results: MRPS24: Conclusions: This study systematically explored ... ...

Abstract	Objective: MRPS24 Methods: The analysis of Results: MRPS24 Conclusions: This study systematically explored that
Language	English
Publishing date	2024-04-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e29171
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Article: Optimal strategies for coordinating infection control and socio-economic activities.

Li, Tangjuan / Xiao, Yanni

Mathematics and computers in simulation

2023 Volume 207, Page(s) 533–555

Abstract: It becomes challenging to identify feasible control strategies for simultaneously relaxing the countermeasures and containing the Covid-19 pandemic, given China's huge population size, high susceptibility, persist vaccination waning, and relatively weak ... ...

Abstract	It becomes challenging to identify feasible control strategies for simultaneously relaxing the countermeasures and containing the Covid-19 pandemic, given China's huge population size, high susceptibility, persist vaccination waning, and relatively weak strength of health systems. We propose a novel mathematical model with waning of immunity and solve the optimal control problem, in order to provide an insight on how much detecting and social distancing are required to coordinate socio-economic activities and epidemic control. We obtain the optimal intensity of countermeasures, i.e., the dynamic nucleic acid screening and social distancing, under which the health system is functioning normally and people can engage in a certain level of socio-economic activities. We find that it is the isolation capacity or the restriction of the case fatality rate (CFR) rather than the hospital capacity that mainly determines the optimal strategies. And the solved optimal controls under quarterly CFR restrictions exhibit oscillations. It is worth noticing that, if without considering booster or very low booster rate, the optimal strategy is a "on-off" mode, alternating between lock down and opening with certain social distancing, which reflects the importance and necessity of China's static management on a certain area during Covid-19 outbreak. The findings suggest some feasible paths to smoothly transit from the Covid-19 pandemic to an endemic phase.
Language	English
Publishing date	2023-01-20
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	161242-6
ISSN	0378-4754
ISSN	0378-4754
DOI	10.1016/j.matcom.2023.01.017
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Article ; Online: Analysis of a diffusive epidemic system with spatial heterogeneity and lag effect of media impact.

Song, Pengfei / Xiao, Yanni

Journal of mathematical biology

2022 Volume 85, Issue 2, Page(s) 17

Abstract: We considered an SIS functional partial differential model cooperated with spatial heterogeneity and lag effect of media impact. The wellposedness including existence and uniqueness of the solution was proved. We defined the basic reproduction number and ...

Abstract	We considered an SIS functional partial differential model cooperated with spatial heterogeneity and lag effect of media impact. The wellposedness including existence and uniqueness of the solution was proved. We defined the basic reproduction number and investigated the threshold dynamics of the model, and discussed the asymptotic behavior and monotonicity of the basic reproduction number associated with the diffusion rate. The local and global Hopf bifurcation at the endemic steady state was investigated theoretically and numerically. There exists numerical cases showing that the larger the number of basic reproduction number, the smaller the final epidemic size. The meaningful conclusion generalizes the previous conclusion of ordinary differential equation.
MeSH term(s)	Basic Reproduction Number ; Epidemics ; Models, Biological
Language	English
Publishing date	2022-08-01
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	187101-8
ISSN	1432-1416 ; 0303-6812
ISSN (online)	1432-1416
ISSN	0303-6812
DOI	10.1007/s00285-022-01780-w
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Zs.A 1495: Show issues

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Article: Complex dynamics of an epidemic model with saturated media coverage and recovery.

Li, Tangjuan / Xiao, Yanni

Nonlinear dynamics

2022 Volume 107, Issue 3, Page(s) 2995–3023

Abstract: During the outbreak of emerging infectious diseases, media coverage and medical resource play important roles in affecting the disease transmission. To investigate the effects of the saturation of media coverage and limited medical resources, we proposed ...

Abstract	During the outbreak of emerging infectious diseases, media coverage and medical resource play important roles in affecting the disease transmission. To investigate the effects of the saturation of media coverage and limited medical resources, we proposed a mathematical model with extra compartment of media coverage and two nonlinear functions. We theoretically and numerically investigate the dynamics of the proposed model. Given great difficulties caused by high nonlinearity in theoretical analysis, we separately considered subsystems with only nonlinear recovery or with only saturated media impact. For the model with only nonlinear recovery, we theoretically showed that backward bifurcation can occur and multiple equilibria may coexist under certain conditions in this case. Numerical simulations reveal the rich dynamic behaviors, including forward-backward bifurcation, Hopf bifurcation, saddle-node bifurcation, homoclinic bifurcation and unstable limit cycle. So the limitation of medical resources induces rich dynamics and causes much difficulties in eliminating the infectious diseases. We then investigated the dynamics of the system with only saturated media impact and concluded that saturated media impact hardly induces the complicated dynamics. Further, we parameterized the proposed model on the basis of the COVID-19 case data in mainland China and data related to news items, and estimated the basic reproduction number to be 2.86. Sensitivity analyses were carried out to quantify the relative importance of parameters in determining the cumulative number of infected individuals at the end of the first month of the outbreak. Combining with numerical analyses, we suggested that providing adequate medical resources and improving media response to infection or individuals' response to mass media may reduce the cumulative number of the infected individuals, which mitigates the transmission dynamics during the early stage of the COVID-19 pandemic.
Language	English
Publishing date	2022-01-16
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2012600-1
ISSN	1573-269X ; 0924-090X
ISSN (online)	1573-269X
ISSN	0924-090X
DOI	10.1007/s11071-021-07096-6
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Article ; Online: Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.

Mengqi He / Sanyi Tang / Yanni Xiao

PLoS Computational Biology, Vol 19, Iss 10, p e

2023 Volume 1011535

Abstract: During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and ... ...

Abstract	During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi'an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.
Keywords	Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
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Article ; Online: Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic.

He, Mengqi / Tang, Sanyi / Xiao, Yanni

PLoS computational biology

2023 Volume 19, Issue 10, Page(s) e1011535

Abstract	During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi'an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.
MeSH term(s)	Humans ; COVID-19/epidemiology ; COVID-19/prevention & control ; SARS-CoV-2 ; Pandemics/prevention & control ; Quarantine ; Contact Tracing ; Neural Networks, Computer
Language	English
Publishing date	2023-10-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011535
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Article ; Online: Transmission dynamics informed neural network with application to COVID-19 infections.

He, Mengqi / Tang, Biao / Xiao, Yanni / Tang, Sanyi

Computers in biology and medicine

2023 Volume 165, Page(s) 107431

Abstract: Since the end of 2019 the COVID-19 repeatedly surges with most countries/territories experiencing multiple waves, and mechanism-based epidemic models played important roles in understanding the transmission mechanism of multiple epidemic waves. However, ... ...

Abstract	Since the end of 2019 the COVID-19 repeatedly surges with most countries/territories experiencing multiple waves, and mechanism-based epidemic models played important roles in understanding the transmission mechanism of multiple epidemic waves. However, capturing temporal changes of the transmissibility of COVID-19 during the multiple waves keeps ill-posed problem for traditional mechanism-based epidemic compartment models, because that the transmission rate is usually assumed to be specific piecewise functions and more parameters are added to the model once multiple epidemic waves involved, which poses a huge challenge to parameter estimation. Meanwhile, data-driven deep neural networks fail to discover the driving factors of repeated outbreaks and lack interpretability. In this study, aiming at developing a data-driven method to project time-dependent parameters but also merging the advantage of mechanism-based models, we propose a transmission dynamics informed neural network (TDINN) by encoding the SEIRD compartment model into deep neural networks. We show that the proposed TDINN algorithm performs very well when fitting the COVID-19 epidemic data with multiple waves, where the epidemics in the United States, Italy, South Africa, and Kenya, and several outbreaks the Omicron variant in China are taken as examples. In addition, the numerical simulation shows that the trained TDINN can also perform as a predictive model to capture the future development of COVID-19 epidemic. We find that the transmission rate inferred by the TDINN frequently fluctuates, and a feedback loop between the epidemic shifting and the changes of transmissibility drives the occurrence of multiple waves. We observe a long response delay to the implementation of control interventions in the four countries, while the decline of the transmission rate in the outbreaks in China usually happens once the implementation of control interventions. The further simulation show that 17 days' delay of the response to the implementation of control interventions lead to a roughly four-fold increase in daily reported cases in one epidemic wave in Italy, which suggest that a rapid response to policies that strengthen control interventions can be effective in flattening the epidemic curve or avoiding subsequent epidemic waves. We observe that the transmission rate in the outbreaks in China is already decreasing before enhancing control interventions, providing the evidence that the increasing of the epidemics can drive self-conscious behavioural changes to protect against infections.
MeSH term(s)	Humans ; COVID-19/epidemiology ; SARS-CoV-2 ; Neural Networks, Computer ; Computer Simulation
Language	English
Publishing date	2023-09-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107431
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Article ; Online: Incidence of collagen-induced arthritis is elevated by a high-fat diet without influencing body weight in mice.

Liang, Jianhui / Yang, Kuangyang / Shen, Yanni / Peng, Xiao / Tan, Hao / Liu, Lichu / Xie, Qian / Wang, Yan

RMD open

2024 Volume 10, Issue 2

MeSH term(s)	Mice ; Humans ; Animals ; Arthritis, Experimental/etiology ; Arthritis, Experimental/chemically induced ; Diet, High-Fat/adverse effects ; Incidence ; Body Weight ; Arthritis, Rheumatoid/etiology ; Arthritis, Rheumatoid/chemically induced
Language	English
Publishing date	2024-04-04
Publishing country	England
Document type	Letter
ZDB-ID	2812592-7
ISSN	2056-5933 ; 2056-5933
ISSN (online)	2056-5933
ISSN	2056-5933
DOI	10.1136/rmdopen-2023-003869
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Article ; Online: Linking the disease transmission to information dissemination dynamics: An insight from a multi-scale model study.

Li, Tangjuan / Xiao, Yanni

Journal of theoretical biology

2021 Volume 526, Page(s) 110796

Abstract: During the outbreak of emerging infectious diseases, information dissemination dynamics significantly affects the individuals' psychological and behavioral changes, and consequently influences on the disease transmission. To investigate the interaction ... ...

Abstract	During the outbreak of emerging infectious diseases, information dissemination dynamics significantly affects the individuals' psychological and behavioral changes, and consequently influences on the disease transmission. To investigate the interaction of disease transmission and information dissemination dynamics, we proposed a multi-scale model which explicitly models both the disease transmission with saturated recovery rate and information transmission to evaluate the effect of information transmission on dynamic behaviors. Considering time variation between information dissemination, epidemiological and demographic processes, we obtained a slow-fast system by reasonably introducing a sufficiently small quantity. We carefully examined the dynamics of proposed system, including existence and stability of possible equilibria and existence of backward bifurcation, by using the fast-slow theory and directly investigating the full system. We then compared the dynamics of the proposed system and the essential thresholds based on two methods, and obtained the similarity between the basic dynamical behaviors of the slow system and that of the full system. Finally, we parameterized the proposed model on the basis of the COVID-19 case data in mainland China and data related to news items, and estimated the basic reproduction number to be 3.25. Numerical analysis suggested that information transmission about COVID-19 pandemic caused by media coverage can reduce the peak size, which mitigates the transmission dynamics during the early stage of the COVID-19 pandemic.
MeSH term(s)	COVID-19 ; China ; Humans ; Information Dissemination ; Pandemics ; SARS-CoV-2
Language	English
Publishing date	2021-06-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2972-5
ISSN	1095-8541 ; 0022-5193
ISSN (online)	1095-8541
ISSN	0022-5193
DOI	10.1016/j.jtbi.2021.110796
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Article ; Online: The association of sun-cured tobacco and cigarette use with global cognitive function, verbal fluency and memory in patients with chronic obstructive pulmonary disease: A cross-sectional study.

Chen, Xiaomei / Li, Jie / Liu, Jia / Liu, Xiao / Deng, Menghui / Dong, Xunhu / Yang, Yanni

Tobacco induced diseases

2024 Volume 22

Abstract: Introduction: Some elderly people in China prefer sun-cured tobacco to cigarettes, and the composition of sun-cured tobacco and cigarettes is inconsistent. The influence of cigarettes on the cognitive function of COPD patients has been widely reported, ... ...

Abstract	Introduction: Some elderly people in China prefer sun-cured tobacco to cigarettes, and the composition of sun-cured tobacco and cigarettes is inconsistent. The influence of cigarettes on the cognitive function of COPD patients has been widely reported, but the research on sun-cured tobacco is relatively rare. Our study explored the association of sun-cured tobacco and cigarette use with cognitive decline in COPD patients. Methods: This was a cross-sectional study. A total of 401 COPD patients were included, and 190, 103, and 108 participants were included in non-smoking, cigarette-smoking, and sun-cured tobacco groups, respectively. We evaluated the global cognitive function using the Beijing version of the Montreal Cognitive Assessment, verbal fluency function using an animal fluency test, and memory function using ten unrelated words. Results: The participants of both cigarette-smoking (AOR=11.18; 95% CI: 1.28- 97.5) and sun-cured tobacco (AOR=10.46; 95% CI: 1.14-96.4) groups were more likely to develop mild cognitive impairment compared to the non-smoking group. The mean z scores of global cognitive function, verbal fluency, and memory were lower in cigarette-smoking and sun-cured tobacco groups than those in a non-smoking group; Multivariable linear regression showed that global cognitive function (β= -0.61; 95% CI: -1.04 - -0.18; and β= -0.48; 95% CI: -0.91 - -0.05) and verbal fluency (β= -0.79; 95% CI: -1.33 - -0.26; and β= -0.69; 95% CI: -1.23 - -0.16) of the sun-cured tobacco group and the cigarette-smoking group were significantly lower than those of the non-smoking group when adjusting for demographic and disease-related characteristics. However, there was no significant difference between the cigarette-smoking and sun-cured tobacco groups in global cognitive function, verbal fluency, and memory. Conclusions: Compared with non-smokers, the use of cigarettes and sun-cured tobacco may damage the cognitive function of COPD patients, especially in global cognitive function and verbal fluency.
Language	English
Publishing date	2024-01-16
Publishing country	Greece
Document type	Journal Article
ZDB-ID	2194616-4
ISSN	1617-9625 ; 1617-9625
ISSN (online)	1617-9625
ISSN	1617-9625
DOI	10.18332/tid/175973
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