Novel SNPs and haplotypes identified in the CD4 gene and their influence on deregressed MACE EBV indexes of milk-related traits in Simmental breed

Cluster of differentiation 4 (CD4) is the accessory protein non-covalently bound to the T cell receptor that recognizes an invariant region of MHC class II on antigen presenting cells. Its cytoplasmic tail, physically associated with a protein tyrosine kinase, is important in the activation of helper/inducer T lymphocytes. In Bos taurus, CD4 gene is located on chromosome 5 from which two isoforms are transcribed, with a different number of amino acids due to splicing of exon 7 and variation in the reading frame. The aim of this study was to investigate the sequence of the entire CD4 gene in Simmental sires to evaluate the effects of genomic variants on the indexes of the bulls for milk, fat and protein yields, as well as somatic cell score. The associations among genomic variants and indexes were analysed using the Allele and GLM procedures of SAS 9.4. The analysis indicated that only four of the thirty-one identified SNPs influenced the considered traits. Identified variants insist on coding zones and intronic sequences, where we revealed the presence of sites for transcription factors. To evaluate the existence of haplotypic effects, combinations among the four genomic variants (SNP 3, SNP 8, SNP 11 and SNP 19) were investigated. Six different haplotypic alleles were identified, but only four of them were frequent enough to allow for an evaluation of any haplotypic effect (at least six copies in the examined sample): Hap1, Hap2, Hap3 and Hap6. The analysis of associations between the selected haplotypes in the CD4 gene with milk related indexes showed that bulls with Hap2 (T-A-C-C) had better indexes for milk and protein yields (P < 0.05), whereas the presence of the Hap1 haplotype (A-G-A-T) caused a significant decrease of the index for protein yield (P < 0.05). Frequencies of the two alleles Hap1 and Hap2 (9 and 36% respectively) make them of interest for their possible inclusion in breeding schemes and support the hypothesis of considering this gene as a candidate for the improvement of milk-related traits in the Simmental breed.

Model Predictive Control of COVID-19 Pandemic with Social Isolation and Vaccination Policies in Thailand

This study concerns the COVID-19 pandemic in Thailand related to social isolation and vaccination policies. The behavior of disease spread is described by an epidemic model via a system of ordinary differential equations. The invariant region and equilibrium point of the model, as well as the basic reproduction number, are also examined. Moreover, the model is fitted to real data for the second wave and the third wave of the pandemic in Thailand by a sum square error method in order to forecast the future spread of infectious diseases at each time. Furthermore, the model predictive control technique with quadratic programming is used to investigate the schedule of preventive measures over a time horizon. As a result, firstly, the plan results are proposed to solve the limitation of ICU capacity and increase the survival rate of patients. Secondly, the plan to control the outbreak without vaccination shows a strict policy that is difficult to do practically. Finally, the vaccination plan significantly prevents disease transmission, since the populations who get the vaccination have immunity against the virus. Moreover, the outbreak is controlled in 28 weeks. The results of a measurement strategy for preventing the disease are examined and compared with a control and without a control. Thus, the schedule over a time horizon can be suitably used for controlling.

Mathematical Modelling Co-existence of Diabetes and COVID-19 : Deterministic and Stochastic Approach

Diabetes affects the epidemiology of COVID-19 (Coronavirus Disease 2019), is correlated with the exorbitant threat of COVID-19 incidence and failure of treatment. Hence, studying the mechanisms behind the coexistence of diabetes and COVID-19 is useful for the development of better public health policies. In this article, we have developed a novel deterministic model for the co-infection of diabetes and COVID-19. It consists of five compartments corresponding to five population classes, namely, diabetes susceptible, diabetes patients, COVID-19 susceptible, COVID-19 infected and COVID19 recovered class. We have discussed the non-negativity and invariant region of the system. Next, the existence of equilibrium points and stabilities of different equilibria of the model are studied. Also, by using the next-generation matrix method the basic reproduction number (R0) is calculated. Further, we perform the sensitivity analysis of R0 and observed that reduction of transmission coefficient (α₁) from diabetes susceptible class to diabetes class is the most critical factor to control the co-infection. We endeavor to fit our model with the data given by the World Health Organization(WHO)[1] and it suits well with the data. Moreover, the deterministic model is extended into the stochastic model. And by using numerical simulations our results of stochastic and deterministic models are compared. Our numerical findings are performed through computer simulation, which illustrates the robustness of our model from the eco-epidemiological perspective. The results obtained highlight the burden of diabetes and COVID-19 coinfection and the role of the α₁ in the severity of the disease.

Adjuvant and immunomodulatory potential of Natural Killer T (NKT) activation by NKTT320

Invariant natural killer T-lymphocytes (iNKT) are a unique subset of immunomodulatory innate T-cells with an invariant TCRα chain recognizing glycolipids presented on the MHC class-I-like CD1d molecule. Activated iNKT rapidly secrete pro-and anti-inflammatory cytokines, potentiate innate and adaptive immunity, and modulate inflammation. While iNKT activation by glycolipid agonists are being explored as an adjuvant, their use depends on CD1d-restricted antigen presentation. Here, we report the effects of iNKT activation by a novel humanized monoclonal antibody, NKTT320, that binds to the invariant region of the iNKT TCR. A single dose of NKTT320 led to rapid iNKT activation, increased polyfunctionality, and elevation of multiple pro-inflammatory and chemotactic plasma analytes within 24 hours in cynomolgus macaques. Flow cytometry and RNA-Seq confirmed downstream effects of NKTT320 on multiple immune cell subsets. Inflammatory response, JAK/STAT and PI3K/AKT pathway genes were enriched along with upregulation of the inflammation-modulating genes CMKLR1, ARG2 and NLRP12. Finally, NKTT320 induced iNKT trafficking to adipose tissue and did not cause iNKT anergy. Our data indicate that NKTT320 has a sustained effect on in vivo iNKT activation, potentiation of innate and adaptive immunity, and resolution of inflammation, properties that support its future application as an immunotherapeutic and vaccine adjuvant.

Stochastic and deterministic mathematical model of cholera disease dynamics with direct transmission

In this paper we develop a stochastic mathematical model of cholera disease dynamics by considering direct contact transmission pathway. The model considers four compartments, namely susceptible humans, infectious humans, treated humans, and recovered humans. Firstly, we develop a deterministic mathematical model of cholera. Since the deterministic model does not consider the randomness process or environmental factors, we converted it to a stochastic model. Then, for both types of models, the qualitative behaviors, such as the invariant region, the existence of a positive invariant solution, the two equilibrium points (disease-free and endemic equilibrium), and their stabilities (local as well as global stability) of the model are studied. Moreover, the basic reproduction numbers are obtained for both models and compared. From the comparison, we obtained that the basic reproduction number of the stochastic model is much smaller than that of the deterministic one, which means that the stochastic approach is more realistic. Finally, we performed sensitivity analysis and numerical simulations. The numerical simulation results show that reducing contact rate, improving treatment rate, and environmental sanitation are the most crucial activities to eradicate cholera disease from the community.

A co-infection model of dengue and leptospirosis diseases

In this paper an SIR deterministic mathematical model for co-infection of dengue and leptospirosis is proposed. We use a compartment model by using ordinary differential equations. The positivity of future solution of the model, the invariant region, and the stability of disease-free equilibrium point as well as endemic equilibrium point are studied. To study the stability of the equilibria, a basic reproduction number is obtained by using the next generation matrix. The robustness of the model is also investigated. To identify the effect of each parameter on the expansion or control of the diseases, sensitivity analysis is performed. The effects of treating dengue infected only, leptospirosis infected only, and co-infected individuals have been identified by using the numerical simulation. Therefore, increasing the rate of recovery and decreasing the contact rate of dengue, leptospirosis, and their co-infection have a great influence in fighting dengue, leptospirosis, and their co-infection in the community.

Stochastic model of tuberculosis with vaccination of newborns

In this paper we have proposed a stochastic model for studying the dynamics of tuberculosis (TB) by incorporating vaccination of newly born babies. The total population in this model is subdivided in to four compartments, namely susceptible $S ( t )$ S ( t ) , infected $I ( t )$ I ( t ) , vaccinated newborns $V ( t ) $ V ( t ) , and recovered $R ( t ) $ R ( t ) . First, the developed model is expressed and analyzed by the deterministic approach. Since this approach neglects the randomness of the dynamics of the process, it has great limitations in the modeling process. To avoid this kind of issues, we transform the deterministic approach into a stochastic one, which is known to play a significant role by providing additional degree of realism compared to the deterministic approach. The analysis of the model is done employing both approaches. The invariant region, positivity of the solution, equilibrium points and their stability are checked. According to the analysis, we came to realize that the basic reproduction number for the stochastic approach is smaller than the deterministic one. We have conducted various numerical experiments and obtained interesting simulation results which indicate that a combination of increased newborn vaccination and appropriate treatment of infected individuals have a great contribution in combating TB. It is worth mentioning that the simulation results confirm the conclusion drawn from the qualitative analysis of the model.

Dynamic Study of SIQR-B Fractional-Order Epidemic Model of Cholera with Optimal Control Strategies in Mayo-Tsanaga Department of Cameroon Far North Region

In this paper, we investigated the dynamical behavior of a fractional-order model of the cholera epidemic in Mayo-Tsanaga Department. We extended the model of Lemos-Paião et al. [A. P. Lemos-Paião, C. J. Silva and D. F. M. Torres, J. Comput. Appl. Math. 16, 427 (2016)] by incorporating the contact rate [Formula: see text] by handling cholera death and optimal control strategies such as vaccination [Formula: see text], water sanitation [Formula: see text]. We provide a theoretical study of the model. We derive the basic reproduction number [Formula: see text] which determines the extinction and the persistence of the infection. We show that the disease-free equilibrium is globally asymptotically stable whenever [Formula: see text], while when [Formula: see text], the disease-free equilibrium is unstable and there exists a unique endemic equilibrium point which is locally asymptotically stable on a positively invariant region of the positive orthant. Using the sensitivity analysis, we find that the parameter related to vaccination and therapeutic treatment is more influencing the model. Theoretical results are supported by numerical simulations, which further suggest use of vaccination in endemic area. In case of a lack of necessary funding to fight again cholera, Figure 6 revealed that efforts should focus to keep contamination rate [Formula: see text] (susceptible-to-cholera death) in other to die out the disease.