On the prediction of SAV transmission among Norwegian aquaculture sites.

Pancreas Disease (PD) is a viral disease that affects Atlantic salmon (Salmo salar) in Norwegian, Scottish and Irish aquaculture. It is caused by salmonid alphavirus (SAV) and represents a significant problem in salmonid farming. Infection with SAV leads to reduced growth, mortality, product downgrading, and has a significant financial impact for the farms. The overall aim of this study is to evaluate the effect of various factors on the transmission of SAV and to create a predictive model capable of providing an early warning system for salmon farms within the Norwegian waters. Using a combination of publicly available databases, specifically BarentsWatch, and privately held PCR analyses a feature set consisting of 11 unique features was created based on the input parameters of the databases. An ensemble model was developed based on this feature set using XG-Boost, Ada-Boost, Random Forest and a Multilayer Perceptron. It was possible to successfully predict SAV transmission with 94.4% accuracy. Moreover, it was possible to predict SAV transmission 8 weeks in advance of a 'PD registration' at individual aquaculture salmon farming sites. Important predictors included well boat movement, environmental factors, proximity to sites with a 'PD registration' and seasonality.

A novel mouse model of rhabdomyosarcoma underscores the dichotomy of MDM2-ALT1 function in vivo.

Alternative splicing of the oncogene murine double minute 2 (MDM2) is induced in response to genotoxic stress. MDM2-ALT1, the major splice variant generated, is known to activate the p53 pathway and impede full-length MDM2's negative regulation of p53. Despite this perceptible tumor-suppressive role, MDM2-ALT1 is also associated with several cancers. Furthermore, expression of MDM2-ALT1 has been observed in aggressive metastatic disease in pediatric rhabdomyosarcoma (RMS), irrespective of histological subtype. Therefore, we generated a transgenic MDM2-ALT1 mouse model that would allow us to investigate the effects of this splice variant on the progression of tumorigenesis. Here we show that when MDM2-ALT1 is ubiquitously expressed in p53 null mice it leads to increased incidence of spindle cell sarcomas, including RMS. Our data provide evidence that constitutive MDM2-ALT1 expression is itself an oncogenic lesion that aggravates the tumorigenesis induced by p53 loss. On the contrary, when MDM2-ALT1 is expressed solely in B-cells in the presence of homozygous wild-type p53 it leads to significantly increased lymphomagenesis (56%) when compared with control mice (27%). However, this phenotype is observable only at later stages in life (⩾18 months). Moreover, flow cytometric analyses for B-cell markers revealed an MDM2-ALT1-associated decrease in the B-cell population of the spleens of these animals. Our data suggest that the B-cell loss is p53 dependent and is a response mounted to persistent MDM2-ALT1 expression in a wild-type p53 background. Overall, our findings highlight the importance of an MDM2 splice variant as a critical modifier of both p53-dependent and -independent tumorigenesis, underscoring the complexity of MDM2 posttranscriptional regulation in cancer. Furthermore, MDM2-ALT1-expressing p53 null mice represent a novel mouse model of fusion-negative RMS.

Integrating habits and practices data for soaps, cosmetics and air care products into an existing aggregate exposure model.

In order to accurately assess aggregate exposure to a fragrance material in consumers, data are needed on consumer habits and practices, as well as the concentration of the fragrance material in those products. The present study describes the development of Phase 2 Creme RIFM model by expanding the previously developed Phase 1 model to include an additional six product types. Using subject-matching algorithms, the subjects in the Phase 1 Creme RIFM database were paired with subjects in the SUPERB and BodyCare surveys based on age and gender. Consumption of the additional products was simulated to create a seven day diary allowing full data integration in a consistent format. The inhalation route was also included for air care and other products where a fraction of product used is inhaled, derived from the RIFM 2-box model. The expansion of the Phase 1 Creme RIFM model has resulted in a more extensive and refined model, which covers a broader range of product categories and now, includes all relevant routes of exposure. An evaluation of the performance of the model has been carried out in an accompanying publication to this one.

Application of the expanded Creme RIFM consumer exposure model to fragrance ingredients in cosmetic, personal care and air care products.

As part of a joint project between the Research Institute for Fragrance Materials (RIFM) and Creme Global, a Monte Carlo model (here named the Creme RIFM model) has been developed to estimate consumer exposure to ingredients in personal care products. Details of the model produced in Phase 1 of the project have already been published. Further data on habits and practises have been collected which enable the model to estimate consumer exposure from dermal, oral and inhalation routes for 25 product types. . In addition, more accurate concentration data have been obtained which allow levels of fragrance ingredients in these product types to be modelled. Described is the use of this expanded model to estimate aggregate systemic exposure for eight fragrance ingredients. Results are shown for simulated systemic exposure (expressed as µg/kg bw/day) for each fragrance ingredient in each product type, along with simulated aggregate exposure. Highest fragrance exposure generally occurred from use of body lotions, body sprays and hydroalcoholic products. For the fragrances investigated, aggregate exposure calculated using this model was 11.5-25 fold lower than that calculated using deterministic methodology. The Creme RIFM model offers a very comprehensive and powerful tool for estimating aggregate exposure to fragrance ingredients.

Use of an aggregate exposure model to estimate consumer exposure to fragrance ingredients in personal care and cosmetic products.

Ensuring the toxicological safety of fragrance ingredients used in personal care and cosmetic products is essential in product development and design, as well as in the regulatory compliance of the products. This requires an accurate estimation of consumer exposure which, in turn, requires an understanding of consumer habits and use of products. Where ingredients are used in multiple product types, it is important to take account of aggregate exposure in consumers using these products. This publication investigates the use of a newly developed probabilistic model, the Creme RIFM model, to estimate aggregate exposure to fragrance ingredients using the example of 2-phenylethanol (PEA). The output shown demonstrates the utility of the model in determining systemic and dermal exposure to fragrances from individual products, and aggregate exposure. The model provides valuable information not only for risk assessment, but also for risk management. It should be noted that data on the concentrations of PEA in products used in this article were obtained from limited sources and not the standard, industry wide surveys typically employed by the fragrance industry and are thus presented here to illustrate the output and utility of the newly developed model. They should not be considered an accurate representation of actual exposure to PEA.

Novel database for exposure to fragrance ingredients in cosmetics and personal care products.

Exposure of fragrance ingredients in cosmetics and personal care products to the population can be determined by way of a detailed and robust survey. The frequency and combinations of products used at specific times during the day will allow the estimation of aggregate exposure for an individual consumer, and to the sample population. In the present study, habits and practices of personal care and cosmetic products have been obtained from market research data for 36,446 subjects across European countries and the United States in order to determine the exposure to fragrance ingredients. Each subject logged their product uses, time of day and body application sites in an online diary for seven consecutive days. The survey data did not contain information on the amount of product used per occasion or body measurements, such as weight and skin surface area. Nevertheless, this was found from the literature where the likely amount of product used per occasion or body measurement could be probabilistically chosen from distributions of data based on subject demographics. The daily aggregate applied consumer product exposure was estimated based on each subject's frequency of product use, and Monte Carlo simulations of their likely product amount per use and body measurements. Statistical analyses of the habits and practices and consumer product exposure are presented, which show the robustness of the data and the ability to estimate aggregate consumer product exposure. Consequently, the data and modelling methods presented show potential as a means of performing ingredient safety assessments for personal care and cosmetics products.

Predicting the external formation of callus tissues in oblique bone fractures: idealised and clinical case studies.

It is proposed that the external asymmetric formation of callus tissues that forms naturally about an oblique bone fracture can be predicted computationally. We present an analysis of callus formation for two cases of bone fracture healing: idealised and subject-specific oblique bone fractures. Plane strain finite element (FE) models of the oblique fractures were generated to calculate the compressive strain field experienced by the immature callus tissues due to interfragmentary motion. The external formations of the calluses were phenomenologically simulated using an optimisation style algorithm that iteratively removes tissue that experiences low strains from a large domain. The resultant simulated spatial formation of the healing tissues for the two bone fracture cases showed that the calluses tended to form at an angle equivalent to the angle of the oblique fracture line. The computational results qualitatively correlated with the callus formations found in vivo. Consequently, the proposed methods show potential as a means of predicting callus formation in pre-clinical testing.

Predicting the external formation of a bone fracture callus: an optimisation approach.

The formation of a fracture callus in vivo tends to form in a structurally efficient manner distributing tissues where mechanical stimulus persists. Therefore, it is proposed that the formation of a fracture callus can be modelled in silico by way of an optimisation algorithm. This was tested by generating a finite element model of a transversal bone fracture embedded in a large tissue domain which was subjected to axial, bending and torsional loads. It was found that the relative fragment motion induced a compressive strain field in the early callus tissue which could be utilised to simulate the formation of external callus structures through an iterative optimisation process of tissue maintenance and removal. The phenomenological results showed a high level of congruence with in vivo healing patterns found in the literature. Consequently, the proposed strategy shows potential as a means of predicting spatial bone healing phenomena for pre-clinical testing.

The role of interfragmentary strain on the rate of bone healing-a new interpretation and mathematical model.

It is postulated that there is a causal relationship between mechanical stimulus and the rate of bone healing post fracture. However, despite numerous experimental studies in the literature, no quantifiable relationship has been proposed. It is hypothesized in the present study that the temporal rate of bone fracture healing, measured in terms of callus stiffening per week, can be described mathematically based on the relative motions between bone fragments at the initial stage of the healing process. To test this, a comparative reanalysis of experimental data found in the literature was conducted. These individual data sets described a relationship between an initial intermittently applied peak interfragmentary strain and the change in interfragmentary motion or the increase in callus stiffness over time. The data were converted into a relative increase in stiffness, which normalised the results and reduced inter-study variability. The rates of healing for the various initial strains were compared, and based on this a mathematical phenomenological model was derived. Error analyses were then performed, which showed a high level of congruence between the in-vivo and simulated rates of healing. The results of the comparative analysis revealed that there is a positive correlation between the rate of callus stiffening and interfragmentary strain. Finally, the proposed model has shown for the first time that a quantifiable cause-and-effect relationship exists between the rate of bone healing and mechanical stimulus.

Fixation of humeral intercondylar fractures using a lateral plate in 14 dogs supported by finite element analysis of repair.

Fourteen Spaniels that presented with an intercondylar fracture of the distal humerus were managed using a lateral plate and an additional pin in twelve cases. Fixation of the fracture was achieved using a plate applied laterally which incorporated the transcondylar lag screw in the most distal hole. Of the 14 cases, two had poor results, one of which was a bilateral case, whilst the remaining 12 cases had good or very good results with only occasional stiffness or lameness. Finite element (FE) modelling of a distal humerus was generated, and loading of fracture repairs using a lateral plate and caudal plate was completed in a comparative study to determine which fixation method resisted micro-motion most effectively. Finite element analysis revealed that the lateral plate fixation provided significantly more resistance to micro movement at the fracture site that the caudal plate fixation, with 40% more micro-motion in the latter.

Use of transilial pinning for the treatment of sacroiliac separation in 25 dogs and finite element analysis of repair methods.

A retrospective study of 25 cases of sacroiliac separation showed that transilial pinning is an effective method of repair for sacroiliac separations. Only 8% of cases of sacroiliac separation had transilial pinning as the sole surgical intervention as other concomitant minor injuries, such as fractured ischium or pubis, did not require surgery. Even though pin loosening and migration along with local soft tissue irritation occurred in all cases, 92% of the cases had 'good' or 'excellent' outcomes. Sacroiliac separation heals by fibrosis, not directly by bone healing, and therefore can heal sufficiently in four weeks to allow pain free weight bearing in four weeks. Additionally, finite element modelling was undertaken to analyse micro-movement of repaired sacroiliac separations. The micro-motion analysis showed that the lag screw fixation method was more stable than the transilial fixation method since the relative motion between the two indicated that the latter allowed more unsupported iliac movement.