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Animal models are crucial to preclinical oncological research and drug development. Animal experiments must be performed in accordance with the 3R principles of replacement and reduction, if possible, and refinement where these procedures remain crucial. In addition, European Union legislations demand a continuous refinement approach, as well as pro- and retrospective severity assessment. In this study, an objective databased severity assessment was performed in murine models for pancreatic cancer induced by orthotopic, subcutaneous, or intravenous injection of Panc02 cells. Parameters such as body weight change, distress score, perianal temperature, mouse grimace scale, burrowing, nesting behavior, and the concentration of corticosterone in plasma and its metabolites in feces were monitored during tumor progression. The most important parameters were combined into a score and mapped against a reference data set by the Relative Severity Assessment procedure (RELSA) to obtain the maximum achieved severity for each animal (RELSAmax). This scoring revealed a significantly higher RELSAmax for the orthotopic model than for the subcutaneous and intravenous models. However, compared to animal models such as pancreatitis and bile duct ligation, the pancreatic cancer models are shown to be less severe. Data-based animal welfare assessment proved to be a valuable tool for comparing the severity of differently induced cancer models.

Pancreatic cancer is the fourth leading cause of cancer death, with a 5-year survival rate of 10%. A stagnant high mortality rate over the last decades highlights the need for innovative therapeutic approaches. Pancreatic tumors pursue an altered metabolism in order to maintain energy generation under low nutrient influx and hypoxic conditions. Targeting these metabolic strategies might therefore be a reasonable therapeutic approach for pancreatic cancer. One promising agent is CPI- 613, a potent inhibitor of two enzymes of the tricarboxylic acid cycle. The present study evaluated the anti-cancerous efficacy of CPI-613 in combination with galloflavin, a lactate dehydrogenase inhibitor or with alpha-cyano-4-hydroxycinnamic acid, an inhibitor of monocarboxylate transporters. The efficacy of both combination therapies was tested in vitro on one human and two murine pancreatic cancer cell lines and in vivo in an orthotopic pancreatic cancer model. Tumor progression was evaluated by MRI and 18F-FDG PET-CT. Both combinatorial treatments demonstrated in vitro a significant inhibition of pancreatic cancer cell proliferation and induction of cell death. In contrast to the in vitro results, both combination therapies did not significantly reduce tumor growth in vivo. The in vitro results suggest that a combined inhibition of different metabolic pathways might be a promising approach for cancer therapy. However, the in vivo experiments indicate that applying a higher dosage or using other drugs targeting these metabolic pathways might be more promising.

Abstract Background Membranes with increasing pore size are introduced to enhance removal of large uremic toxins with regular hemodialysis. These membranes might theoretically have higher permeability for bacterial degradation products. In this paper, permeability for bacterial degradation products of membranes of comparable composition with different pore size was investigated with a new in vitro set-up that represents clinical flow and pressure conditions. Methods Dialysis was simulated with an AK200 machine using a low-flux, high-flux, medium cut-off (MCO) or high cut-off (HCO) device (n = 6/type). A polyvinylpyrrolidone-solution (PVP) was recirculated at blood side. At dialysate side, a challenge solution containing a filtrated lysate of two water-borne bacteria (Pseudomonas aeruginosa and Pelomononas saccharophila) was infused in the dialysate flow (endotoxin ≥ 4EU/ml). Blood and dialysate flow were set at 400 and 500 ml/min for 60 min. PVP was sampled before (PVPpre) and after (PVPpost) the experiment and dialysate after 5 and 55 min. Limulus Amebocyte Lysate (LAL) test was performed. Additionally, samples were incubated with a THP-1 cell line (24 h) and IL-1β levels were measured evaluating biological activity. Results The LAL-assay confirmed presence of 9.5 ± 7.4 EU/ml at dialysate side. For none of the devices the LAL activity in PVPpre vs. PVPpost was significantly different. Although more blood side PVP solutions had a detectable amount of endotoxin using a highly sensitive LAL assay in the more open vs traditional membranes, the permeability for endotoxins of the 4 tested dialysis membranes was not significantly different but the number of repeats is small. None of the PVP solutions induced IL-1β in the THP-1 assay. Conclusions A realisitic in vitro dialysis was developed to assess membrane translocation of bacterial products. LAL activity on the blood side after endotoxin exposure did not change for all membranes. Also, none of the PVPpost solutions induced IL-1β in the THP-1 bio-assay.

This paper describes in detail the estimation algorithm of upper bound prediction of the time acquisition task. We use the specific hardware from ARM Cortex-A series and empirical approach of time values retrieval from the timer counter. The robust Measurement-Based Probabilistic Timing Analysis (MBPTA) method based on the Extreme Value Theory (EVT) has been used for experimental verification of the algorithm. The MBPTA method allows deriving a reliable and safe worst-case execution time (WCET) estimation based on the limited number of measurements on the target platform. However, it requires an appropriate complete set of statistical tests for verifying EVT applicability. In ongoing work, we intend to outline challenges behind EVT assumptions and parameter tuning for timing analysis, and provide more coherent approach for safe probabilistic WCET estimations in order to increase the confidence that timing constraints will be met.

The problem of similarity measurement of biological signals is considered on this article. The dynamic time warping algorithm is used as a possible solution. A short overview of this algorithm and its modifications are given. Testing procedure for different modifications of DTW, which are based on artificial test signals, are presented.

Research analysis of electrocardiograms (ECG) today is carried out mostly using time depending signals of different leads shown in the graphs. Definition of ECG parameters is performed by qualified personnel, and requiring particular skills. To support decoding the cardiac depolarization phase of ECG there are methods to analyze space-time convolution charts in three dimensions where the heartbeat is described by the trajectory of its electrical vector. Based on this, it can be assumed that all available options of the classical ECG analysis of this time segment can be obtained using this technique. Investigated ECG visualization techniques in three dimensions combined with quantitative methods giving additional features of cardiac depolarization and allow a better exploitation of the information content of the given ECG signals.

Selecting a set of relevant markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has become a challenging task given the wealth of regional pathologic information that can be extracted from multimodal imaging data. Here, we used regularized regression approaches with an elastic net penalty for best subset selection of multiregional information from AV45-PET, FDG-PET and volumetric MRI data to predict conversion from MCI to AD. The study sample consisted of 127 MCI subjects from ADNI-2 who had a clinical follow-up between 6 and 31 months. Additional analyses assessed the effect of partial volume correction on predictive performance of AV45- and FDG-PET data. Predictor variables were highly collinear within and across imaging modalities. Penalized Cox regression yielded more parsimonious prediction models compared to unpenalized Cox regression. Within single modalities, time to conversion was best predicted by increased AV45-PET signal in posterior medial and lateral cortical regions, decreased FDG-PET signal in medial temporal and temporobasal regions, and reduced gray matter volume in medial, basal, and lateral temporal regions. Logistic regression models reached up to 72% cross-validated accuracy for prediction of conversion status, which was comparable to cross-validated accuracy of non-linear support vector machine classification. Regularized regression outperformed unpenalized stepwise regression when number of parameters approached or exceeded the number of training cases. Partial volume correction had a negative effect on the predictive performance of AV45-PET, but slightly improved the predictive value of FDG-PET data. Penalized regression yielded more parsimonious models than unpenalized stepwise regression for the integration of multiregional and multimodal imaging information. The advantage of penalized regression was particularly strong with a high number of collinear predictors.