Use of an Individual-based Model to Control Transmission Pathways of Mycobacterium avium Subsp. paratuberculosis Infection in Cattle Herds.

Johne's disease (JD) is a chronic enteric disease in cattle caused by Mycobacterium avian subsp. paratuberculosis (MAP). Eradicating JD is a difficult task due to the long incubation period of MAP, inefficient diagnostic tests, and delayed clinical signs. Effective control strategies can help farmers to reduce prevalence, but those most acceptable to farmers combine specific information about lactation performance and testing results, which existing models do not provide. This paper presents an individual-based model of MAP infection dynamics and assesses the relative performance of the applied alternative control strategies. The base dairy herd model included the daily life events of a dairy cow and reflects several current dairy management processes. We then integrated MAP infection dynamics into the model. The model adopted four different test-based control strategies based on risk-based culling decisions and three hygiene scenarios. The model tracked the source of each infection and quantified the efficacy of each control strategy in reducing the risks of different transmission routes. The results suggest that risk-based culling can reduce prevalence compared with no control, but cannot eliminate the infection. Overall, this work provides not only a valuable tool to investigate MAP transmission dynamics but also offers adaptability to model similar infectious diseases.

Economic consequences of paratuberculosis control in dairy cattle: A stochastic modeling study.

The cost of paratuberculosis to dairy herds, through decreased milk production, early culling, and poor reproductive performance, has been well-studied. The benefit of control programs, however, has been debated. A recent stochastic compartmental model for paratuberculosis transmission in US dairy herds was modified to predict herd net present value (NPV) over 25 years in herds of 100 and 1000 dairy cattle with endemic paratuberculosis at initial prevalence of 10% and 20%. Control programs were designed by combining 5 tests (none, fecal culture, ELISA, PCR, or calf testing), 3 test-related culling strategies (all test-positive, high-positive, or repeated positive), 2 test frequencies (annual and biannual), 3 hygiene levels (standard, moderate, or improved), and 2 cessation decisions (testing ceased after 5 negative whole-herd tests or testing continued). Stochastic dominance was determined for each herd scenario; no control program was fully dominant for maximizing herd NPV in any scenario. Use of the ELISA test was generally preferred in all scenarios, but no paratuberculosis control was highly preferred for the small herd with 10% initial prevalence and was frequently preferred in other herd scenarios. Based on their effect on paratuberculosis alone, hygiene improvements were not found to be as cost-effective as test-and-cull strategies in most circumstances. Global sensitivity analysis found that economic parameters, such as the price of milk, had more influence on NPV than control program-related parameters. We conclude that paratuberculosis control can be cost effective, and multiple control programs can be applied for equivalent economic results.

Relationship between Serum Iron Profile and Blood Groups among the Voluntary Blood Donors of Bangladesh.

Blood donation results in a substantial iron loss and subsequent mobilization from body stores. Chronic iron deficiency is a well-recognized complication of regular blood donation. The present study conducted to compare the level of serum ferritin, serum iron, total iron binding capacity (TIBC) and percentage transferrin saturation in different ABO and Rhesus type blood groups among the voluntary blood donors of Bangladesh. The present prospective study included 100 healthy voluntary donors attending at Department of Blood Transfusion, Dhaka Medical College, Dhaka between the periods of July 2013 to Jun 2014. From each donor 10mL venous blood sample was taken and divided into heparinized and non-heparinized tubes for determination of hemoglobin (Hb), hematocrit (Hct), serum iron (SI), total iron binding capacity (TIBC) and serum ferritin by standard laboratory methods. Percentage of transferrin saturation (TS) calculated from serum iron and TIBC. Data were analyzed with SPSS (version 16) software and comparisons between groups were made using student's t-test and one way ANOVA. In the present study mean±SD of age of the respondents was 27.2±6.5 years with a range of 18 to 49 years and 81.0% were male and 19.0% were female. Among the donors 18.0% had blood group A, 35.0% had blood group B, 14.0% had blood group AB and 33.0% had blood group O. Among the donors 91.0% had rhesus positive and 9.0% had rhesus negative. Donors with blood group O had lowest haemoglobin, serum iron and transferring saturation levels. Donors with blood group A had highest TIBC level. Donors with blood group B had lowest serum ferritin level. An independent samples 't' test showed statistically significant difference in serum ferritin and percentage transferrin saturation between blood group AB and blood group O and in percentage transferrin saturation between blood group B and blood group O. One way ANOVA showed that there is no significant difference in haemoglobin, serum iron, serum ferritin and percentage transferring saturation in different ABO and Rh blood grouping categories. Blood donors with blood group O had lowest haemoglobin, serum iron and transferring saturation levels and donors with blood group A had highest TIBC level. Blood donors with blood group B had lowest serum ferritin level. The understanding of the different blood groups ability to retain iron in their system can give an insight into their ability to handle the disease iron deficiency anaemia.

An in silico model to demonstrate the effects of Maspin on cancer cell dynamics.

Most cancer treatments efficacy depends on tumor metastasis suppression, where tumor suppressor genes play an important role. Maspin (Mammary Serine Protease Inhibitor), an non-inhibitory serpin has been reported as a potential tumor suppressor to influence cell migration, adhesion, proliferation and apoptosis in in vitro and in vivo experiments in last two decades. Lack of computational investigations hinders its ability to go through clinical trials. Previously, we reported first computational model for maspin effects on tumor growth using artificial neural network and cellular automata paradigm with in vitro data support. This paper extends the previous in silico model by encompassing how maspin influences cell migration and the cell-extracellular matrix interaction in subcellular level. A feedforward neural network was used to define each cell behavior (proliferation, quiescence, apoptosis) which followed a cell-cycle algorithm to show the microenvironment impacts over tumor growth. Furthermore, the model concentrates how the in silico experiments results can further confirm the fact that maspin reduces cell migration using specific in vitro data verification method. The data collected from in vitro and in silico experiments formulates an unsupervised learning problem which can be solved by using different clustering algorithms. A density based clustering technique was developed to measure the similarity between two datasets based on the number of links between instances. Our proposed clustering algorithm first finds the nearest neighbors of each instance, and then redefines the similarity between pairs of instances in terms of how many nearest neighbors share the two instances. The number of links between two instances is defined as the number of common neighbors they have. The results showed significant resemblances with in vitro experimental data. The results also offer a new insight into the dynamics of maspin and establish as a metastasis suppressor gene for further molecular research.

A hybrid computational model for the effects of maspin on cancer cell dynamics.

Cancer metastasis is a complex multistep process which allows cancer cells to establish new tumours in distant organs. The process of metastasis involves cell migration and invasion; it is what makes cancer a fatal disease. The efficiency of most cancer treatments depends on metastasis suppression. Maspin is a type II tumour metastasis suppressor which has multiple cellular effects. It has been described as a key regulatory protein in both the intracellular and extracellular environments. Maspin has been shown to reduce cell migration, invasion, proliferation and angiogenesis, and increase apoptosis and cell-cell adhesion in in vitro and in vivo experiments. The clinical data regarding the predictive effects of maspin expression are variable. To date, the whole cellular mechanisms that maspin uses to influence tumour cell behaviours have not been clearly defined. The diversity of the effects of maspin motivated us to develop an intelligent model to investigate its effects on cellular proliferation and migration. This paper reports a hybrid model of solid tumour growth in order to investigate the impact of maspin on the growth and evolutionary dynamics of the cancer cell. A feed-forward neural network was used to model the behaviours (proliferation, quiescence, apoptosis and/or movement) of each cell, which has been suggested as a suitable model of cell signalling pathways. Results show that maspin reduces migration by 10-40%, confirmed by published in vitro data. The model also shows a reduction in cell proliferation by 20-30% in the presence of maspin. So far, this is the first attempt to model the effect of maspin in a computational model to verify in vitro data. This will provide new insights into the tumour suppressive properties of maspin and inform the development of novel cancer therapies.

Avascular tumour growth dynamics and the constraints of protein binding for drug transportation.

The potential for the use of in-silico models of disease in progression monitoring is becoming increasingly recognised, as well as its contribution to the development of complete curative processes. In this paper we report the development of a hybrid cellular automaton model to mimic the growth of avascular tumours, including the infusion of a bioreductive drug to study the effects of protein binding on drug transportation. The growth model is operated within an extracellular tumour microenvironment. An artificial Neural Network based scheme was implemented that modelled the behaviours of each cell (proliferation, quiescence, apoptosis and/or movement) based on the complex heterogeneous microenvironment; consisting of oxygen, glucose, hydrogen ions, inhibitory factors and growth factors. To validate the growth model results, we conducted experiments with multicellular tumour spheroids. These results showed good agreement with the predicted growth dynamics. The outcome of the avascular tumour growth model suggested that tumour microenvironments have a strong impact on cell behaviour. To address the problem of cellular proteins acting as resistive factors preventing efficient drug penetration, a bioreactive drug (tirapazamine) was added to the system. This allowed us to study the drug penetration through multicellular layers of tissue after its binding to cellular proteins. The results of the in vitro model suggested that the proteins reduce the toxicity of the drug, reducing its efficacy for the most severely hypoxic fractions furthest from a functional blood vessel. Finally this research provides a unique comparison of in vitro tumour growth with an intelligent in silico model to measure bioreductive drug availability inside tumour tissue through a set of experiments.