The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates.
Methods
This study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t.
Results
The study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas.
Conclusions
The study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.
A significant amount of prior research has been conducted to investigate type 2 diabetes, the most prevalent form afflicting over 90% of diabetic individuals [6]. Yet, gestational diabetes is an understudied form of diabetes that is thought to share various attributes with type 2 diabetes. It was the aim of this project to investigate a proposed mechanism of the disease, the contra-insulin effect, through a cell-culture experiment. To address the question of whether glycemic and hormonal conditions of cell-culture media affect Hs 795.Pl morphology, cellular growth, and glucose uptake, immunocytochemistry (ICC) and a glucose uptake assay was performed. It was hypothesized that higher the presence of hormones, specifically lactogen, in cell culture media will exacerbate the contra-insulin effect, decreasing the glucose uptake of the Hs 795.Pl cells and inducing abhorrent cell morphology. Qualitatively, estradiol and cortisol had a severe impact on cellular morphology indicative of stress and death. As for glucose uptake, it was decreased when the hormones were isolated compared to all together with estradiol thought to be majorly inhibitory to insulin’s proper functioning. It was concluded that cell morphology, growth, and glucose uptake were detrimentally impacted by the gestational hormones, especially those of cortisol and estrogen.
A significant amount of prior research has been conducted to investigate type 2 diabetes, the most prevalent form afflicting over 90% of diabetic individuals [6]. Yet, gestational diabetes is an understudied form of diabetes that is thought to share various attributes with type 2 diabetes. It was the aim of this project to investigate a proposed mechanism of the disease, the contra-insulin effect, through a cell-culture experiment. To address the question of whether glycemic and hormonal conditions of cell-culture media affect Hs 795.Pl morphology, cellular growth, and glucose uptake, immunocytochemistry (ICC) and a glucose uptake assay was performed. It was hypothesized that higher the presence of hormones, specifically lactogen, in cell culture media will exacerbate the contra-insulin effect, decreasing the glucose uptake of the Hs 795.Pl cells and inducing abhorrent cell morphology. Qualitatively, estradiol and cortisol had a severe impact on cellular morphology indicative of stress and death. As for glucose uptake, it was decreased when the hormones were isolated compared to all together with estradiol thought to be majorly inhibitory to insulin’s proper functioning. It was concluded that cell morphology, growth, and glucose uptake were detrimentally impacted by the gestational hormones, especially those of cortisol and estrogen.
Influenza virus A (IVA) poses a serious threat to human health, killing over 25,000 Americans in the 2022 flu season alone. In the past 10 years, vaccine efficacy has varied significantly, ranging from 20-60% each season. Because IVA is subject to high antigenic shift and strain cocirculation, more effective IVA vaccines are needed to reduce the incidence of disease. Herein we report the production of a recombinant immune complex (RIC) vaccine “4xM2e” in Nicotiana benthamiana plants using agroinfiltration for use as a potential universal IVA vaccine candidate. RICs fuse antigen to the C-terminus of an immunoglobulin heavy chain with an epitope tag cognate to the antibody, promoting immune complex formation to increase immunogenicity. IVA matrix protein 2 ectodomain (M2e) is selected to serve as vaccine antigen for its high sequence conservation, as only a small number of minor mutations have occurred since its discovery in 1981 in the human sequence. However, there is some divergence in zoonotic IVA strains, and to account for this, we designed a combination of human consensus, swine, and avian M2e variants, 4xM2e. This was fused to the C terminus of the RIC platform to improve M2e immunogenicity and IVA strain coverage. The 4xM2e RIC was produced in N. benthamiana and verified with SDS-PAGE and Western blot assays, along with an analysis of complex formation and the potential for complement activation via complement C1q ELISA. With this work, we demonstrate the potential of RICs and plant-expression systems to generate universal IVA vaccine candidates.
The burden of dementia and its primary cause, Alzheimer’s disease, continue to devastate many with no available cure although present research has delivered methods for risk calculation and models of disease development that promote preventative strategies. Presently Alzheimer’s disease affects 1 in 9 people aged 65 and older amounting to a total annual healthcare cost in 2023 in the United States of $345 billion between Alzheimer’s disease and other dementias making dementia one of the costliest conditions to society (“2023 Alzheimer’s Disease Facts and Figures,” 2023). This substantial cost can be dramatically lowered in addition to a reduction in the overall burden of dementia through the help of risk prediction models, but there is still a need for models to deliver an individual’s predicted time of onset that supplements risk prediction in hopes of improving preventative care. The aim of this study is to develop a model used to predict the age of onset for all-cause dementias and Alzheimer’s disease using demographic, comorbidity, and genetic data from a cohort sample. This study creates multiple regression models with methods of ordinary least squares (OLS) and least absolute shrinkage and selection operator (LASSO) regression methods to understand the capacity of predictor variables that estimate age of onset for all-cause dementia and Alzheimer’s disease. This study is unique in its use of a diverse cohort containing 346 participants to create a predictive model that originates from the All of Us Research Program database and seeks to represent an accurate sampling of the United States population. The regression models generated had no predictive capacity for the age of onset but outline a simplified approach for integrating public health data into a predictive model. The results from the generated models suggest a need for continued research linking risk factors that estimate time of onset.