Cell migration in paediatric glioma; characterisation and potential therapeutic targeting.

BACKGROUND: Paediatric high grade glioma (pHGG) and diffuse intrinsic pontine glioma (DIPG) are highly aggressive brain tumours. Their invasive phenotype contributes to their limited therapeutic response, and novel treatments that block brain tumour invasion are needed. METHODS: Here, we examine the migratory characteristics and treatment effect of small molecule glycogen synthase kinase-3 inhibitors, lithium chloride (LiCl) and the indirubin derivative 6-bromoindirubin-oxime (BIO), previously shown to inhibit the migration of adult glioma cells, on two pHGG cell lines (SF188 and KNS42) and one patient-derived DIPG line (HSJD-DIPG-007) using 2D (transwell membrane, immunofluorescence, live cell imaging) and 3D (migration on nanofibre plates and spheroid invasion in collagen) assays. RESULTS: All lines were migratory, but there were differences in morphology and migration rates. Both LiCl and BIO reduced migration and instigated cytoskeletal rearrangement of stress fibres and focal adhesions when viewed by immunofluorescence. In the presence of drugs, loss of polarity and differences in cellular movement were observed by live cell imaging. CONCLUSIONS: Ours is the first study to demonstrate that it is possible to pharmacologically target migration of paediatric glioma in vitro using LiCl and BIO, and we conclude that these agents and their derivatives warrant further preclinical investigation as potential anti-migratory therapeutics for these devastating tumours.

Non-Fickian diffusion and the accumulation of methane bubbles in deep-water sediments.

In the absence of fractures, methane bubbles in deep-water sediments can be immovably trapped within a porous matrix by surface tension. The dominant mechanism of transfer of gas mass therefore becomes the diffusion of gas molecules through porewater. The accurate description of this process requires non-Fickian diffusion to be accounted for, including both thermal diffusion and gravitational action. We evaluate the diffusive flux of aqueous methane considering non-Fickian diffusion and predict the existence of extensive bubble mass accumulation zones within deep-water sediments. The limitation on the hydrate deposit capacity is revealed; too weak deposits cannot reach the base of the hydrate stability zone and form any bubbly horizon.

Stability and stabilization of the lattice Boltzmann method.

We revisit the classical stability versus accuracy dilemma for the lattice Boltzmann methods (LBM). Our goal is a stable method of second-order accuracy for fluid dynamics based on the lattice Bhatnager-Gross-Krook method (LBGK). The LBGK scheme can be recognized as a discrete dynamical system generated by free flight and entropic involution. In this framework the stability and accuracy analysis are more natural. We find the necessary and sufficient conditions for second-order accurate fluid dynamics modeling. In particular, it is proven that in order to guarantee second-order accuracy the distribution should belong to a distinguished surface--the invariant film (up to second order in the time step). This surface is the trajectory of the (quasi)equilibrium distribution surface under free flight. The main instability mechanisms are identified. The simplest recipes for stabilization add no artificial dissipation (up to second order) and provide second-order accuracy of the method. Two other prescriptions add some artificial dissipation locally and prevent the system from loss of positivity and local blowup. Demonstration of the proposed stable LBGK schemes are provided by the numerical simulation of a one-dimensional (1D) shock tube and the unsteady 2D flow around a square cylinder up to Reynolds number Re approximately 20,000.

Stabilization of the lattice Boltzmann method using the Ehrenfests' coarse-graining idea.

The lattice Boltzmann method (LBM) and its variants have emerged as promising, computationally efficient and increasingly popular numerical methods for modeling complex fluid flow. However, it is acknowledged that the method can demonstrate numerical instabilities, e.g., in the vicinity of shocks. We propose a simple technique to stabilize the LBM by monitoring the difference between microscopic and macroscopic entropy. Populations are returned to their equilibrium states if a threshold value is exceeded. We coin the name Ehrenfests' steps for this procedure in homage to the vehicle that we use to introduce the procedure, namely, the Ehrenfests' coarse-graining idea.

Handling missing data in large healthcare dataset: A case study of unknown trauma outcomes.

Handling of missed data is one of the main tasks in data preprocessing especially in large public service datasets. We have analysed data from the Trauma Audit and Research Network (TARN) database, the largest trauma database in Europe. For the analysis we used 165,559 trauma cases. Among them, there are 19,289 cases (11.35%) with unknown outcome. We have demonstrated that these outcomes are not missed 'completely at random' and, hence, it is impossible just to exclude these cases from analysis despite the large amount of available data. We have developed a system of non-stationary Markov models for the handling of missed outcomes and validated these models on the data of 15,437 patients which arrived into TARN hospitals later than 24h but within 30days from injury. We used these Markov models for the analysis of mortality. In particular, we corrected the observed fraction of death. Two naïve approaches give 7.20% (available case study) or 6.36% (if we assume that all unknown outcomes are 'alive'). The corrected value is 6.78%. Following the seminal paper of Trunkey (1983 [15]) the multimodality of mortality curves has become a much discussed idea. For the whole analysed TARN dataset the coefficient of mortality monotonically decreases in time but the stratified analysis of the mortality gives a different result: for lower severities the coefficient of mortality is a non-monotonic function of the time after injury and may have maxima at the second and third weeks. The approach developed here can be applied to various healthcare datasets which experience the problem of lost patients and missed outcomes.

A neural network approach to the biopsy diagnosis of early acute renal transplant rejection.

AIMS: To develop and test a neural network to assist in the histological diagnosis of early acute renal allograft rejection. METHODS AND RESULTS: We used three sets of biopsies to train and test the network: 100 'routine' biopsies from Leicester; 21 selected difficult biopsies which had already been evaluated by most of the renal transplant pathologists in the UK, in a study of the Banff classification of allograft pathology and 25 cases which had been classified as 'borderline' according to the Banff classification in a review of transplant biopsies from Oxford. The correct diagnosis for each biopsy was defined by careful retrospective clinical review. Biopsies where this review did not provide a clear diagnosis were excluded. Each biopsy was graded for 12 histological features and the data was entered into a simple single layer perception network, designed using the MATLAB neural network toolbox. Results were compared with logistic regression using the same data, and with 'conventional' histological diagnosis. If the network was trained only with the 100 'routine' cases, its performance with either of the other sets was poor. However, if either of the 'difficult' sets was added to the training group, testing with the other 'difficult' group improved dramatically; 19 of the 21 'Banff' study cases were diagnosed correctly. This was achieved using observations made by a trainee pathologist. The result is better than was achieved by any of the many experienced pathologists who had previously seen these biopsies (maximum 18/21 correct), and is considerably better than that achieved by using logistic regression with the same data. CONCLUSION: A neural network can provide a considerable improvement in the diagnosis of early acute allograft rejection, though further development work will be needed before this becomes a routine diagnostic tool. The selection of cases used to train the network is crucial to the quality of its performance. There is scope to improve the system further by incorporating clinical information. Other related areas where this approach is likely to be of value are discussed.