Quantitative annotations of T-Cell repertoire specificity.

The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. We created deep-learning-based, epitope-aware vector embeddings to infer individual TCR specificity. Then we aggregated clonotype frequencies of TCRs to obtain a quantitative profile of repertoire specificity at epitope, antigen and organism levels. Applying TCRanno to 4195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of severe acute respiratory syndrome coronavirus 2 specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of coronavirus disease 2019. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno also demonstrated usefulness to single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.

Epidemiological and etiological analysis of a case cluster caused by norovirus in a nursing home in Lanzhou

Objective: To understand the epidemiology and etiology of a cluster of cases with gastroenteritis in a nursing home in Anning district of Lanzhou, and to provide a scientific evidence for the prevention and control of norovirus diarrhea in community nursing centers. Methods: From January 28 to February 4 2021, an epidemiological investigation was conducted on all diarrhea cases, nursing staff and chefs in a nursing home in Anning district, Lanzhou city. Samples of patients' anal swabs, feces, vomitus were collected for norovirus detection by real-time fluorescent PCR. ORF1/ORF2 junction region of norovirus in some selected positive samples(Ct value 25) was sequenced. MEGA-X software was used to construct a phylogenetic tree for genetic evolution analysis using the neighboring method. Results: The first case was confirmed on January20,2021, and the number of cases peaked during January 25and 29.A total of 58 clinically diagnosed cases were reported,57were older people, with an incidence of(57/360,15.83%). Diarrhea(50/58,86.21%),vomiting(35/58,60.34%),nausea(13/58,22.41%)and abdominal pain(6/58,10.34%)were common symptoms, all cases were mild. Fifty-three asymptomatic cases were detected among chefs, housekeepers and nurses.A total of 163specimens were tested, the positive rate of norovirus GII was 49.08%(80/163). The positive rate of fecal samples collected from nurses, chefs and housekeepers was 48.62%(53/109), and was11.11%(2/18)in environmental surface swabs. The possibility of other pathogenic infections such as SARS-CoV-2was ruled out by further tests. Thirteen positive samples were selected for sequencing, and 9were successfully sequenced, they were all recombinant GII.4Sydney_2012 [P16]genotypes, forming an independent cluster, while in a large evolutionary branch with the 2020GII.10 [P16]and 2019GII.2 [P16]virus strains in Lanzhou city, showing a relative close genetic connection. Conclusions: GII .4Sydney_2012[P16]genotype of norovirus is found to be causative pathogen of this outbreak, and close contact is the main reason of the outbreak and persistence of the infection,so asymptomatic infections of norovirus play an important role in the disease spreading. Therefore, public health management in nursing homes and other centralized nursing facilities should be strengthened especially for asymptomatic workers in order to prevent virus transmission.

A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US.

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.

A Multiscale Model of COVID-19 Dynamics.

COVID-19, caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a global pandemic and created unprecedented public health challenges throughout the world. Despite significant progresses in understanding the disease pathogenesis and progression, the epidemiological triad of pathogen, host, and environment remains unclear. In this paper, we develop a multiscale model to study the coupled within-host and between-host dynamics of COVID-19. The model includes multiple transmission routes (both human-to-human and environment-to-human) and connects multiple scales (both the population and individual levels). A detailed analysis on the local and global dynamics of the fast system, slow system and full system shows that rich dynamics, including both forward and backward bifurcations, emerge with the coupling of viral infection and epidemiological models. Model fitting to both virological and epidemiological data facilitates the evaluation of the influence of a few infection characteristics and antiviral treatment on the spread of the disease. Our work underlines the potential role that the environment can play in the transmission of COVID-19. Antiviral treatment of infected individuals can delay but cannot prevent the emergence of disease outbreaks. These results highlight the implementation of comprehensive intervention measures such as social distancing and wearing masks that aim to stop airborne transmission, combined with surface disinfection and hand hygiene that can prevent environmental transmission. The model also provides a multiscale modeling framework to study other infectious diseases when the environment can serve as a reservoir of pathogens.

Effects of vaccination on mitigating COVID-19 outbreaks: a conceptual modeling approach.

This paper is devoted to investigating the impact of vaccination on mitigating COVID-19 outbreaks. In this work, we propose a compartmental epidemic ordinary differential equation model, which extends the previous so-called SEIRD model [1,2,3,4] by incorporating the birth and death of the population, disease-induced mortality and waning immunity, and adding a vaccinated compartment to account for vaccination. Firstly, we perform a mathematical analysis for this model in a special case where the disease transmission is homogeneous and vaccination program is periodic in time. In particular, we define the basic reproduction number $ \mathcal{R}_0 $ for this system and establish a threshold type of result on the global dynamics in terms of $ \mathcal{R}_0 $. Secondly, we fit our model into multiple COVID-19 waves in four locations including Hong Kong, Singapore, Japan, and South Korea and then forecast the trend of COVID-19 by the end of 2022. Finally, we study the effects of vaccination again the ongoing pandemic by numerically computing the basic reproduction number $ \mathcal{R}_0 $ under different vaccination programs. Our findings indicate that the fourth dose among the high-risk group is likely needed by the end of the year.

Epidemiological and etiological analysis of a case cluster caused by norovirus in a nursing home in Lanzhou

Objective To understand the epidemiology and etiology of a cluster of cases with gastroenteritis in a nursing home in Anning district of Lanzhou, and to provide a scientific evidence for the prevention and control of norovirus diarrhea in community nursing centers. Methods From January 28 to February 4 2021, an epidemiological investigation was conducted on all diarrhea cases, nursing staff and chefs in a nursing home in Anning district, Lanzhou city.Samples of patients′ anal swabs, feces, vomitus were collected for norovirus detection by real-time fluorescent PCR.ORF1/ORF2 junction region of norovirus in some selected positive samples（Ct value ≤ 25） was sequenced.MEGA-X software was used to construct a phylogenetic tree for genetic evolution analysis using the neighboring method. Results The first case was confirmed on January20,2021,and the number of cases peaked during January 25and 29.A total of 58clinically diagnosed cases were reported,57were older people,with an incidence of（57/360,15.83%）.Diarrhea（50/58,86.21%）,vomiting（35/58,60.34%）,nausea（13/58,22.41%）and abdominal pain（6/58,10.34%）were common symptoms,all cases were mild.Fifty-three asymptomatic cases were detected among chefs,housekeepers and nurses.A total of 163specimens were tested,the positive rate of norovirus GⅡwas 49.08%（80/163）.The positive rate of fecal samples collected from nurses,chefs and housekeepers was 48.62%（53/109）,and was11.11%（2/18）in environmental surface swabs.The possibility of other pathogenic infections such as SARS-CoV-2was ruled out by further tests.Thirteen positive samples were selected for sequencing,and 9were successfully sequenced,they were all recombinant GⅡ.4Sydney＿2012 [P16]genotypes,forming an independent cluster,while in a large evolutionary branch with the 2020GⅡ.10 [P16]and 2019GⅡ.2 [P16]virus strains in Lanzhou city,showing a relative close genetic connection. Conclusions GⅡ.4Sydney＿2012[P16]genotype of norovirus is found to be causative pathogen of this outbreak,and close contact is the main reason of the outbreak and persistence of the infection,so asymptomatic infections of norovirus play an important role in the disease spreading.Therefore,public health management in nursing homes and other centralized nursing facilities should be strengthened especially for asymptomatic workers in order to prevent virus transmission.

Dynamic changes in production of antibodies against SARS-CoV-2 based on three serological kits

We wished to understand the dynamic changes in production of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibodies in sera collected from coronavirus disease 19 (COVID-19) patients. Fifty-eight serum samples from 33 patients confirmed to have COVID-19 in Gansu Province, China, were tested for three types of SARS-CoV-2-specific antibodies: immunoglobulin (Ig) M, IgG, and total antibodies. The positive rate of IgM, IgG and total antibodies increased gradually with COVID-19 progression. Within the first 3 days, the positive rate of detection of SARS-CoV-2-specific antibody using the three kits was 13.6%-31.8%. whereas, within 4-7 days, it was 36.4%-45.5%, within 8-14 days it was 55.6%-77.8%, and after 15 days, it was 100%. In addition, the three kits were used to measure antibodies from serum samples collected from healthy people, and the specificity was 99%-100%. Statistical analyses indicated no significant difference among the results of the three kits (P > 0.05 for all). In summary, the three SARS-CoV-2 antibody-detection kits had good sensitivity and specificity for detection of antibodies against SARS-CoV-2, and could aid the clinical diagnosis of COVID-19. The dynamic characteristics of production of SARS-CoV-2- specific antibodies could provide important scientific bases for epidemiologic investigations.

Effects of recombinant porcine Lactobacillus reuteri secreting expression of bovine lactoferrin peptides on growth performance of newborn piglets and protective effect against diarrhea virus infection

This experiment was conducted to investigate the effects of recombinant porcine Lactobacillus reuteri secreting bovine lactoferrin peptide (LFCA) on growth performance of newborn piglets and the protective effect on porcine transmissible gastroenteritis virus (TGEV)infection which caused piglet diarrhea. Experiment 1:thirty-six one-day-old newborn piglets with an average body weight of about 1.5 kg were randomly divided into 3 groups, which were pPG-LFCA/LR-CO21 group, pPG/LR-CO21 group and control group, each group with 12 piglets. Piglets in each group were orally administered recombinant porcine Lactobacillus reuteri expressing LFCA pPG-LFCA/LR-CO21, containing empty vector plasmid PPG/LR-CO21 and equal volume phosphate buffer (PBS);oral administration continued for 3 days, and the observation time after oral administration was 14 d. During the period, piglets were fed freely, and the changes of body weight and diarrhea were recorded. Experiment 2:thirty one-day-old newborn piglets with an average body weight of about 1.5 kg were randomly divided into 5 groups and given TGEV with a half tissue culture infection dose (TCID50) of 10-7.50/mL by oral administration of 1, 3, 6, 9 and 12 mL, respectively. The observation period of 7 d was set to analyze the conditions of half lethal dose. Experiment 3:another thirty-two newborn piglets with an average body weight of about 1.5 kg were selected as experimental animals and randomly divided into 4 groups, with 8 piglets in each group. The groups were pPG-LFCA/LR-CO21 group, pPG/LR-CO21 group, control group and TGEV infect group. There were 8 replicates in each group and 1 piglet in each replicate. Each head of the experimental group was orally fed ppG-LFCA/LR-CO21, pPG/LR-CO21 and equal volume of PBS at a dose of 2..1010 CFU per day for 1 consecutive week. At 8 days of age, TGEV was infected by oral administration at half lethal dose, and samples were collected after 7 days of infection. The weight change and diarrhea of each group of piglets were recorded;hematoxylin-eosin staining was used to detect the length of intestinal villi and the depth of crypts;enzyme linked immunosorbent assay (ELISA) was used to determine total serum total immunoglobulin G (IgG) and total secretory immunoglobulin A (sIgA) antibody contents. RT-qPCR was used to detect the mRNA relative expression levels of Claudin-1, Occludin, tight junction protein-1 (ZO-1), inflammatory cytokines interleukin-6 (IL-6), interleukin-8 (IL-8), interferon-P (IFN-P), tumor necrosis factor-a (TNF-a) and Toll-like receptor 2 (TLR2). The flora structure of the contents of the piglet's cecum was analyzed. After oral recombinant porcine Lactobacillusreuteri, compared with the control group, the average daily gain of newborn piglets in the pPG-LFCA/LR-CO21 group was significantly increased (P < 0.01), while the diarrhea rate was significantly decreased (P < 0.01). Compared with TGEV infection group, the average daily gain of piglets in pPG-LFCA/LR-CO21 group was increased and diarrhea rate was decreased, and the differences were significant (P < 0.05). Villus height and the ratio of villus height to crypt depth in jejunum and ileum were significantly increased (P < 0.05). The contents of total IgG and intestinal mucosal total sIgA antibody in serum of piglets were significantly increased (P < 0.05);the mRNA relative expression levels of tight junction protein-related genes Claudin-1, Occludin and ZO-1 in intestinal mucosal tissue were extremely significantly increased (P < 0.01), and the serum TNF-a content was extremely significantly decreased (P < 0.01). Serum IFN-P, IL-6, IL-8 and TLR2 contents were significantly increased (P < 0.01), and the survival rate of piglets was improved. The analysis of the bacterial diversity in the contents of the piglets' cecum showed that the proportion of normal intestinal flora of piglets decreased after TGEV infection. Compared with the TGEV infect group, the proportion of pathogenic bacteria Bacteroides in piglet's intestinal flora decreased by o

The Heterogeneous Severity of COVID-19 in African Countries: A Modeling Approach.

The COVID-19 pandemic has had a considerable impact on global health and economics. The impact in African countries has not been investigated thoroughly via fitting epidemic models to the reported COVID-19 deaths. We downloaded the data for the 12 most-affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying basic reproductive number ([Formula: see text]) and infection attack rate. We develop a simple epidemic model and fitted it to reported COVID-19 deaths in 12 African countries using iterated filtering and allowing a flexible transmission rate. We observe high heterogeneity in the case-fatality rate across the countries, which may be due to different reporting or testing efforts. South Africa, Tunisia, and Libya were most affected, exhibiting a relatively higher [Formula: see text] and infection attack rate. Thus, to effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvements in socioeconomic well-being, healthcare systems, the water supply, and awareness campaigns).

The Second Wave of COVID-19 in South and Southeast Asia and the Effects of Vaccination.

Background: By February 2021, the overall impact of coronavirus disease 2019 (COVID-19) in South and Southeast Asia was relatively mild. Surprisingly, in early April 2021, the second wave significantly impacted the population and garnered widespread international attention. Methods: This study focused on the nine countries with the highest cumulative deaths from the disease as of August 17, 2021. We look at COVID-19 transmission dynamics in South and Southeast Asia using the reported death data, which fits a mathematical model with a time-varying transmission rate. Results: We estimated the transmission rate, infection fatality rate (IFR), infection attack rate (IAR), and the effects of vaccination in the nine countries in South and Southeast Asia. Our study suggested that the IAR is still low in most countries, and increased vaccination is required to prevent future waves. Conclusion: Implementing non-pharmacological interventions (NPIs) could have helped South and Southeast Asia keep COVID-19 under control in 2020, as demonstrated in our estimated low-transmission rate. We believe that the emergence of the new Delta variant, social unrest, and migrant workers could have triggered the second wave of COVID-19.

Effects of environmental variability on superspreading transmission events in stochastic epidemic models.

Superspreaders (individuals with a high propensity for disease spread) have played a pivotal role in recent emerging and re-emerging diseases. In disease outbreak studies, host heterogeneity based on demographic (e.g. age, sex, vaccination status) and environmental (e.g. climate, urban/rural residence, clinics) factors are critical for the spread of infectious diseases, such as Ebola and Middle East Respiratory Syndrome (MERS). Transmission rates can vary as demographic and environmental factors are altered naturally or due to modified behaviors in response to the implementation of public health strategies. In this work, we develop stochastic models to explore the effects of demographic and environmental variability on human-to-human disease transmission rates among superspreaders in the case of Ebola and MERS. We show that the addition of environmental variability results in reduced probability of outbreak occurrence, however the severity of outbreaks that do occur increases. These observations have implications for public health strategies that aim to control environmental variables.

Deterministic and stochastic models for the epidemic dynamics of COVID-19 in Wuhan, China.

In this paper, deterministic and stochastic models are proposed to study the transmission dynamics of the Coronavirus Disease 2019 (COVID-19) in Wuhan, China. The deterministic model is formulated by a system of ordinary differential equations (ODEs) that is built upon the classical SEIR framework. The stochastic model is formulated by a continuous-time Markov chain (CTMC) that is derived based on the ODE model with constant parameters. The nonlinear CTMC model is approximated by a multitype branching process to obtain an analytical estimate for the probability of a disease outbreak. The local and global dynamics of the disease are analyzed by using the deterministic model with constant parameters, and the result indicates that the basic reproduction number $ \mathcal{R}_0 $ serves as a sharp disease threshold: the disease dies out if $ \mathcal{R}_0\le 1 $ and persists if $ \mathcal{R}_0 > 1 $. In contrast to the deterministic dynamics, the stochastic dynamics indicate that the disease may not persist when $ \mathcal{R}_0 > 1 $. Parameter estimation and validation are performed to fit our ODE model to the public reported data. Our result indicates that both the exposed and infected classes play an important role in shaping the epidemic dynamics of COVID-19 in Wuhan, China. In addition, numerical simulations indicate that a second wave of the ongoing pandemic is likely to occur if the prevention and control strategies are not implemented properly.

Coronavirus disease 2019 (COVID-19): diagnosis and prognosis.

Coronavirus disease 2019 (COVID-19) has become a global pandemic with a high rate of transmission. Currently, there is a lack of vaccines and specific drugs for this newly-emerged virus. Timely diagnosis and treatment, as well as isolation of patients and virus carriers, contribute to the effective prevention and control of this epidemic. This review focuses on early stage COVID-19 diagnosis methods and strategies, highlighting the guiding role of laboratory indicators on treatment strategy formulation, and prognosis assessments.

Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model.

While the Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten public health and safety, every state has strategically reopened the business in the United States. It is urgent to evaluate the effect of reopening policies on the COVID-19 pandemic to help with the decision-making on the control measures and medical resource allocations. In this study, a novel SEIR model was developed to evaluate the effect of reopening policies based on the real-world reported COVID-19 data in Texas. The earlier reported data before the reopening were used to develop the SEIR model; data after the reopening were used for evaluation. The simulation results show that if continuing the "stay-at-home order" without reopening the business, the COVID-19 pandemic could end in December 2020 in Texas. On the other hand, the pandemic could be controlled similarly as the case of no-reopening only if the contact rate was low and additional high magnitude of control measures could be implemented. If the control measures are only slightly enhanced after reopening, it could flatten the curve of the COVID-19 epidemic with reduced numbers of infections and deaths, but it might make the epidemic last longer. Based on the reported data up to July 2020 in Texas, the real-world epidemic pattern is between the cases of the low and high magnitude of control measures with a medium risk of contact rate after reopening. In this case, the pandemic might last until summer 2021 to February 2022 with a total of 4-10 million infected cases and 20,080-58,604 deaths.

Clinical HDAC Inhibitors Are Effective Drugs to Prevent the Entry of SARS-CoV2.

The outbreak of COVID-19 by the end of 2019 has posed serious health threats to humanity and jeopardized the global economy. However, no effective drugs are available to treat COVID-19 currently and there is a great demand to fight against it. Here, we combined computational screening and an efficient cellular pseudotyped virus system, confirming that clinical HDAC inhibitors can efficiently prevent SARS-CoV-2 and potentially be used to fight against COVID-19.