The Stochastic Dance of Early HIV Infection

The stochastic nature of early HIV infection is described in a series of models, each of which captures aspects of the dance of HIV during the early stages of infection. It is to this highly variable target that the immune response must respond. The adaptability of the various components of the immune response is an important aspect of the system\u27s operation, as the nature of the pathogens that the response will be required to respond to and the order in which those responses must be made cannot be known beforehand. As HIV infection has direct influence over cells responsible for the immune response, the dance predicts that the immune response will be also in a variable state of readiness and capability for this task of adaptation. The description of the stochastic dance of HIV here will use the tools of stochastic models, and for the most part, simulation. The justification for this approach is that the early stages and the development of HIV diversity require that the model to be able to describe both individual sample path and patient-to-patient variability. In addition, as early viral dynamics are best described using branching processes, the explosive growth of these models both predicts high variability and rapid response of HIV to changes in system parameters. In this paper, a basic viral growth model based on a time dependent continuous-time branching process is used to describe the growth of HIV infected cells in the macrophage and lymphocyte populations. Immigration from the reservoir population is added to the basic model to describe the incubation time distribution. This distribution is deduced directly from the modeling assumptions and the model of viral growth. A system of two branching processes, one in the infected macrophage population and one in the infected lymphocyte population is used to provide a description of the relationship between the development of HIV diversity as it relates to tropism (host cell preference). The role of the immune response to HIV and HIV infected cells is used to describe the movement of the infection from a few infected macrophages to a disease of infected CD4+ role= presentation style= box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; font-style: normal; font-weight: normal; line-height: normal; font-size: 14.4px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative; CD4+ T lymphocytes

Untangling the Most Probable Role for Vitamin D\u3csub\u3e3\u3c/sub\u3e in Autism

Recent studies indicate an important role for vitamin D3 in autism spectrum disorder (ASD), although its mechanism is not completely understood. The most puzzling aspect of ASD is that identical twins, who share identical DNA, do not have 100% concordance rates (∼88% for identical and ∼31% for fraternal twins). These findings provide major clues into the etiology: ASD must involve an environmental factor present in the prenatal milieu that both identical twins are not always exposed to because they do not always share it (i.e., placentas). Combined with the exponential increasing rates of ASD around the world, these observations suggest a contagious disease is probably transferred to the fetus via the placenta becoming infected by a cervical virus. Vitamin D3 boosts immune responses clearing viral infections and increases serotonin and estrogen brain levels. Here we review the different roles and untangle the most probable one vitamin D3 plays in ASD

An Ensemble Model of QSAR Tools for Regulatory Risk Assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study

Untangling thyroid autoimmunity through modeling and simulation

Thyroid autoimmunity is characterized by a large number of identified factors, and determining the relative importance of genetics and environment, for instance, can be difficult. In addition, the definition and progression of the individual diseases can also be challenging, and questions such as “when to begin treatment” or even “should treatment be begun” can be problematic. One approach to handling situations in which there are many factors is utilizing mathematical modeling. In a model, quantities that are clinically measurable are related through equations, based on known and inferred relationships between the systems involved. In situations where these relationships are complicated, the resulting simulations can provide information not previous recognized as logically resulting from those relationships. One advantage of this approach is that patient-specific parameter estimates can be used to personalize disease monitoring and treatment. In this paper, models involving Hashimoto’s (autoimmune) thyroiditis, Graves’ disease, and the roles of leptin, vitamin D3, and adipose tissue are described. In the case of Hashimoto’s, a model consisting of a system of differential equations is presented which allows a patient specific description of the progression of the disease. The conditions leading to Hashitoxicosis are also described through that model. The patient specific model of the treatment of Graves’ disease is also described. Finally, the roles of the inflammatory adipokines, especially leptin, and vitamin D3 is explored as it relates to the initiation of thyroid autoimmunity. The result of this approach is an enhanced view of the initiation and progression of autoimmunity in the thyroid

Paper Session I-A - Magnetic Shielding For Interplanetary Spacecraft

The protection of spacecraft crews from the radiation produced by high energy electrons, protons and heavier ions in the space environment is a major health concern on long duration missions. Conventional approaches to radiation shielding in space have relied on thicker spacecraft walls to stop the high energy charged particles and to absorb the resulting gamma and bremsstrahlung photons. The shielding concept described here uses superconducting magnets to deflect charged particles before they collide with the spacecraft, thus avoiding the production of secondary particles. A number of spacecraft configurations and sizes have been analyzed, ranging from a small \u27storm cellar\u27 for use during solar flares to continuous shielding for space stations having a crew of 15-25. The effectiveness of the magnetic shielding has been analyzed using a Monte Carlo program with incident proton energies from 0.5 to 1000 MeV. Typically the shield deflects 35-99 percent of the incident particles, depending, of course on particle energy and magnetic field strength. Further evaluation studies have been performed to assess weight comparisons between magnetic and conventional shielding; to determine magnet current distributions which minimize the magnetic field within the spacecraft itself; and to assess the potential role of ceramic superconductors

A Patient-Specific Treatment Model for Graves’ Hyperthyroidism

Background: Graves’ is disease an autoimmune disorder of the thyroid gland caused by circulating anti-thyroid receptor antibodies (TRAb) in the serum. TRAb mimics the action of thyroid stimulating hormone (TSH) and stimulates the thyroid hormone receptor (TSHR), which results in hyperthyroidism (overactive thyroid gland) and goiter. Methimazole (MMI) is used for hyperthyroidism treatment for patients with Graves’ disease. Methods: We have developed a model using a system of ordinary differential equations for hyperthyroidism treatment with MMI. The model has four state variables, namely concentration of MMI (in mg/L), concentration of free thyroxine - FT4 (in pg/mL), and concentration of TRAb (in U/mL) and the functional size of the thyroid gland (in mL) with thirteen parameters. With a treatment parameter, we simulate the time-course of patients’ progression from hyperthyroidism to euthyroidism (normal condition). We validated the model predictions with data from four patients. Results: When there is no MMI treatment, there is a unique asymptotically stable hyperthyroid state. After the initiation of MMI treatment, the hyperthyroid state moves towards subclinical hyperthyroidism and then euthyroidism. Conclusion: We can use the model to describe or test and predict patient treatment schedules. More specifically, we can fit the model to individual patients’ data including loading and maintenance doses and describe the mechanism, hyperthyroidism → euthyroidism. The model can be used to predict when to discontinue the treatment based on FT4 levels within the physiological range, which in turn help maintain the remittance of euthyroidism and avoid relapses of hyperthyroidism. Basically, the model can guide with decision-making on oral intake of MMI based on FT4 levels

\u3cem\u3eDe Novo\u3c/em\u3e [PSI\u3csup\u3e+\u3c/sup\u3e] Prion Formation Involves Multiple Pathways to Form Infectious Oligomers

Prion and other neurodegenerative diseases are associated with misfolded protein assemblies called amyloid. Research has begun to uncover common mechanisms underlying transmission of amyloids, yet how amyloids form in vivo is still unclear. Here, we take advantage of the yeast prion, [PSI +], to uncover the early steps of amyloid formation in vivo. [PSI +] is the prion form of the Sup35 protein. While [PSI +] formation is quite rare, the prion can be greatly induced by overexpression of the prion domain of the Sup35 protein. This de novo induction of [PSI +] shows the appearance of fluorescent cytoplasmic rings when the prion domain is fused with GFP. Our current work shows that de novoinduction is more complex than previously thought. Using 4D live cell imaging, we observed that fluorescent structures are formed by four different pathways to yield [PSI +] cells. Biochemical analysis of de novo induced cultures indicates that newly formed SDS resistant oligomers change in size over time and lysates made from de novo induced cultures are able to convert [psi −] cells to [PSI +] cells. Taken together, our findings suggest that newly formed prion oligomers are infectious

Understanding Schools and Schooling. (Book Review)

A review of a book written by Clive Chitty (2002 with a useful focus on issues of equity and social justice, including prejudice, discrimination and bullying in secondary schools. Education policy makers need to explore the extent to which it is important to produce interested, motivated and socially balanced young adults. It is well researched and documented

Simplified Models of Vector Control Impact upon Malaria Transmission by Zoophagic Mosquitoes

BACKGROUND\ud \ud High coverage of personal protection measures that kill mosquitoes dramatically reduce malaria transmission where vector populations depend upon human blood. However, most primary malaria vectors outside of sub-Saharan Africa can be classified as "very zoophagic," meaning they feed occasionally (<10% of blood meals) upon humans, so personal protection interventions have negligible impact upon their survival.\ud \ud METHODS AND FINDINGS\ud \ud We extended a published malaria transmission model to examine the relationship between transmission, control, and the baseline proportion of bloodmeals obtained from humans (human blood index). The lower limit of the human blood index enables derivation of simplified models for zoophagic vectors that (1) Rely on only three field-measurable parameters. (2) Predict immediate and delayed (with and without assuming reduced human infectivity, respectively) impacts of personal protection measures upon transmission. (3) Illustrate how appreciable indirect communal-level protection for non-users can be accrued through direct personal protection of users. (4) Suggest the coverage and efficacy thresholds required to attain epidemiological impact. The findings suggest that immediate, indirect, community-wide protection of users and non-users alike may linearly relate to the efficacy of a user's direct personal protection, regardless of whether that is achieved by killing or repelling mosquitoes. High protective coverage and efficacy (≥80%) are important to achieve epidemiologically meaningful impact. Non-users are indirectly protected because the two most common species of human malaria are strict anthroponoses. Therefore, the small proportion of mosquitoes that are killed or diverted while attacking humans can represent a large proportion of those actually transmitting malaria.\ud \ud CONCLUSIONS\ud \ud Simplified models of malaria transmission by very zoophagic vectors may be used by control practitioners to predict intervention impact interventions using three field-measurable parameters; the proportion of human exposure to mosquitoes occurring when an intervention can be practically used, its protective efficacy when used, and the proportion of people using it

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data