A computational model of cellular response to modulated radiation fields

Purpose:
To develop a model to describe the response of cell populations to spatially modulated radiation exposures of relevance to advanced radiotherapies.

Materials and Methods:
A Monte Carlo model of cellular radiation response was developed. This model incorporated damage from both direct radiation and intercellular communication including bystander signaling. The predictions of this model were compared to previously measured survival curves for a normal human fibroblast line (AGO1522) and prostate tumor cells (DU145) exposed to spatially modulated fields.

Results:
The model was found to be able to accurately reproduce cell survival both in populations which were directly exposed to radiation and those which were outside the primary treatment field. The model predicts that the bystander effect makes a significant contribution to cell killing even in uniformly irradiated cells. The bystander effect contribution varies strongly with dose, falling from a high of 80% at low doses to 25% and 50% at 4 Gy for AGO1522 and DU145 cells, respectively. This was verified using the inducible nitric oxide synthase inhibitor aminoguanidine to inhibit the bystander effect in cells exposed to different doses, which showed significantly larger reductions in cell killing at lower doses.

Conclusions:
The model presented in this work accurately reproduces cell survival following modulated radiation exposures, both in and out of the primary treatment field, by incorporating a bystander component. In addition, the model suggests that the bystander effect is responsible for a significant portion of cell killing in uniformly irradiated cells, 50% and 70% at doses of 2 Gy in AGO1522 and DU145 cells, respectively. This description is a significant departure from accepted radiobiological models and may have a significant impact on optimization of treatment planning approaches if proven to be applicable in vivo.

Using the hidden Markov model to capture quality of care in Lombardy geriatric wards

Quality of care is an important aspect of healthcare monitoring, which is used to ensure that the healthcare system is delivering care of the highest standard. With populations growing older there is an increased urgency in making sure that the healthcare delivered is of the highest standard. Healthcare providers are under increased pressure to ensure that this is the case with public and government demand expecting a healthcare system of the highest quality. Modelling quality of care is difficult to measure due to the many ways of defining it. This paper introduces a potential model which could be used to take quality of care into account when modelling length of stay. The Coxian phase-type distribution is used to model length of stay and the associated quality of care incorporated into the Coxian using a Hidden Markov model. Covariates are also introduced to determine their impact on the hidden level to find out what potentially can affect quality of care. This model is applied to geriatic patient data from the Lombardy region of Italy. The results obtained highlighted that bed numbers and the type of hospital (public or private) can have an effect on the quality of care delivered.

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.

Which screening strategy using BMD measurements would be most cost effective for hip fracture prevention in elderly women? A decision analysis based on a Markov model.

INTRODUCTION: Hip fractures are responsible for excessive mortality, decreasing the 5-year survival rate by about 20%. From an economic perspective, they represent a major source of expense, with direct costs in hospitalization, rehabilitation, and institutionalization. The incidence rate sharply increases after the age of 70, but it can be reduced in women aged 70-80 years by therapeutic interventions. Recent analyses suggest that the most efficient strategy is to implement such interventions in women at the age of 70 years. As several guidelines recommend bone mineral density (BMD) screening of postmenopausal women with clinical risk factors, our objective was to assess the cost-effectiveness of two screening strategies applied to elderly women aged 70 years and older. METHODS: A cost-effectiveness analysis was performed using decision-tree analysis and a Markov model. Two alternative strategies, one measuring BMD of all women, and one measuring BMD only of those having at least one risk factor, were compared with the reference strategy "no screening". Cost-effectiveness ratios were measured as cost per year gained without hip fracture. Most probabilities were based on data observed in EPIDOS, SEMOF and OFELY cohorts. RESULTS: In this model, which is mostly based on observed data, the strategy "screen all" was more cost effective than "screen women at risk." For one woman screened at the age of 70 and followed for 10 years, the incremental (additional) cost-effectiveness ratio of these two strategies compared with the reference was 4,235 euros and 8,290 euros, respectively. CONCLUSION: The results of this model, under the assumptions described in the paper, suggest that in women aged 70-80 years, screening all women with dual-energy X-ray absorptiometry (DXA) would be more effective than no screening or screening only women with at least one risk factor. Cost-effectiveness studies based on decision-analysis trees maybe useful tools for helping decision makers, and further models based on different assumptions should be performed to improve the level of evidence on cost-effectiveness ratios of the usual screening strategies for osteoporosis.

Decomposition of surface electromyographic signal using Hidden Markov Model

The detection of physiological signals from the motor system (electromyographic signals) is being utilized in the practice clinic to guide the therapist in a more precise and accurate diagnosis of motor disorders. In this context, the process of decomposition of EMG (electromyographic) signals that includes the identification and classification of MUAP (Motor Unit Action Potential) of a EMG signal, is very important to help the therapist in the evaluation of motor disorders. The EMG decomposition is a complex task due to EMG features depend on the electrode type (needle or surface), its placement related to the muscle, the contraction level and the health of the Neuromuscular System. To date, the majority of researches on EMG decomposition utilize EMG signals acquired by needle electrodes, due to their advantages in processing this type of signal. However, relatively few researches have been conducted using surface EMG signals. Thus, this article aims to contribute to the clinical practice by presenting a technique that permit the decomposition of surface EMG signal via the use of Hidden Markov Models. This process is supported by the use of differential evolution and spectral clustering techniques. The developed system presented coherent results in: (1) identification of the number of Motor Units actives in the EMG signal; (2) presentation of the morphological patterns of MUAPs in the EMG signal; (3) identification of the firing sequence of the Motor Units. The model proposed in this work is an advance in the research area of decomposition of surface EMG signals.

Analysis of contagion from the constant conditional correlation model with Markov regime switching

Over the last decades, the analysis of the transmissions of international nancial events has become the subject of many academic studies focused on multivariate volatility models volatility. The goal of this study is to evaluate the nancial contagion between stock market returns. The econometric approach employed was originally presented by Pelletier (2006), named Regime Switching Dynamic Correlation (RSDC). This methodology involves the combination of Constant Conditional Correlation Model (CCC) proposed by Bollerslev (1990) with Markov Regime Switching Model suggested by Hamilton and Susmel (1994). A modi cation was made in the original RSDC model, the introduction of the GJR-GARCH model formulated in Glosten, Jagannathan e Runkle (1993), on the equation of the conditional univariate variances to allow asymmetric e ects in volatility be captured. The database was built with the series of daily closing stock market indices in the United States (SP500), United Kingdom (FTSE100), Brazil (IBOVESPA) and South Korea (KOSPI) for the period from 02/01/2003 to 09/20/2012. Throughout the work the methodology was compared with others most widespread in the literature, and the model RSDC with two regimes was de ned as the most appropriate for the selected sample. The set of results provide evidence for the existence of nancial contagion between markets of the four countries considering the de nition of nancial contagion from the World Bank called very restrictive. Such a conclusion should be evaluated carefully considering the wide diversity of de nitions of contagion in the literature.

Cellular recruitment and cytokine generation in a rat model of allergic lung inflammation are differentially modulated by progesterone and estradiol

We evaluated the role of estradiol and progesterone in allergic lung inflammation. Rats were ovariectomized (Ovx) and, 7 days later, were sensitized with ovalbumin (OA) and challenged after 2 wk with inhaled OA; experiments were performed 1 day thereafter. Ovx-allergic rats showed reduced cell recruitment into the bronchoalveolar lavage (BAL) fluid relative to sham-Ovx allergic rats, as was observed in intact allergic rats treated with ICI-182,780. Estradiol increased the number of cells in the BAL of Ovx-allergic rats, whereas progesterone induced an additional reduction. Cells of BAL and bone marrow (BM) of Ovx-allergic rats released elevated amounts of IL-10 and reduced IL-1 beta and TNF-alpha. BM cells of Ovx-allergic rats released increased amounts of IL-10 and lower amounts of IL-4. Estradiol treatment of Ovx-allergic rats decreased the release of IL-10 but increased that of IL-4 by BM cells. Estradiol also caused an increased release of IL-1 beta and TNF-alpha by BAL cells. Progesterone significantly increased the release of IL-10, IL-1 beta, and TNF-alpha by BAL cells and augmented that of IL-4 by BM cells. Degranulation of bronchial mast cells from Ovx rats was reduced after in vitro challenge, an effect reverted by estradiol but not by progesterone. We suggest that the serum estradiol-to-progesterone ratio might drive cellular recruitment, modulating the pulmonary allergy and profile of release of anti-inflammatory or inflammatory cytokines. The existence of such dual hormonal effects suggests that the hormone therapy of asthmatic postmenopausal women and of those suffering of premenstrual asthma should take into account the possibility of worsening the pulmonary conditions.