Comparison of mechanical properties and host tissue response to OviTex™ and Strattice™ surgical meshes.

PURPOSE: This study compared the in vitro/benchtop and in vivo mechanical properties and host biologic response to ovine rumen-derived/polymer mesh hybrid OviTex™ with porcine-derived acellular dermal matrix Strattice™ Firm. METHODS: OviTex 2S Resorbable (OviTex 2S-R) and Strattice morphology were examined in vitro using histology and scanning electron microscopy; mechanical properties were assessed via tensile test; in vivo host biologic response and explant mechanics were evaluated in a rodent subcutaneous model. Separately, OviTex 1S Permanent (OviTex 1S-P) and Strattice were evaluated in a primate abdominal wall repair model. RESULTS: OviTex 2S-R demonstrated layer separation, whereas Strattice retained its structural integrity and demonstrated higher maximum load than OviTex 2S-R out-of-package (124.8 ± 11.1 N/cm vs 37.9 ± 5.5 N/cm, p < 0.001), 24 h (55.7 ± 7.4 N/cm vs 5.6 ± 3.8 N/cm, p < 0.001), 48 h (45.3 ± 14.8 N/cm vs 2.8 ± 2.6 N/cm, p = 0.003), and 72 h (29.2 ± 10.5 N/cm vs 3.2 ± 3.1 N/cm, p = 0.006) following collagenase digestion. In rodents, inflammatory cell infiltration was observed between OviTex 2S-R layers, while Strattice induced a minimal inflammatory response. Strattice retained higher maximum load at 3 (46.3 ± 27.4 N/cm vs 9.5 ± 3.2 N/cm, p = 0.041) and 6 weeks (28.6 ± 14.1 N/cm vs 7.0 ± 3.0 N/cm, p = 0.029). In primates, OviTex 1S-P exhibited loss of composite mesh integrity whereas Strattice integrated into host tissue with minimal inflammation and retained higher maximum load at 1 month than OviTex 1S-P (66.8 ± 43.4 N/cm vs 9.6 ± 4.4 N/cm; p = 0.151). CONCLUSIONS: Strattice retained greater mechanical strength as shown by lower susceptibility to collagenase degradation than OviTex 2S-R in vitro, as well as higher maximum load and improved host biologic response than OviTex 2S-R in rodents and OviTex 1S-P in primates.

Budget impact of the Oncotype DX® test compared to other gene expression tests in patients with early breast cancer in Germany.

PURPOSE: Gene expression tests can inform decisions on whether to recommend chemotherapy for patients with HR+, HER2- early breast cancer. The goal of this analysis was to compare treatment costs by an expanded budget impact model of reimbursed gene expression tests in Germany. METHODS: A cost comparison was constructed as an expanded budget impact model to calculate average total costs per patient covered by public health insurance. Based on the strong clinical evidence from the prospective randomized controlled trial TAILORx including more than 10,000 patients with HR+ and node negative breast cancer, the assumption was made that the Oncotype DX® test accurately predicts chemotherapy benefit and clinical outcomes. For the further reimbursed tests (EndoPredict®, MammaPrint®, Prosigna®), results from comparative studies - aligned with prognosis studies - as analyzed in IQWiG Rapid Report D19-01 were applied. RESULTS: The use of the Oncotype DX test led to estimated average savings per patient of 2,500 € vs. EndoPredict, 1,936 € vs. MammaPrint, and 649 € vs. Prosigna. Savings were achieved by reduction of unnecessary chemotherapy use, a consequence of false-positive test results (EndoPredict 73%, MammaPrint 42%, Prosigna 20%). False-negative test results (EndoPredict 5%, MammaPrint 22%, Prosigna 49%) reduced necessary chemotherapies, which initially results in cost savings, but may lead to increased long-term costs associated with management of progressive disease. CONCLUSION: The results from this model suggest that the use of the Oncotype DX test reduces the cost of health care in Germany making it the most cost effective test compared to the further tests.

Impact of inflammation-mediated response on pan-coronary plaque vulnerability, myocardial viability and ventricular remodeling in the postinfarction period - the VIABILITY study: Protocol for a non-randomized prospective clinical study.

INTRODUCTION: While the role of inflammation in acute coronary events is well established, the impact of inflammatory-mediated vulnerability of coronary plaques from the entire coronary tree, on the extension of ventricular remodeling and scaring, has not been clarified yet. MATERIALS AND METHODS: The present manuscript describes the procedures of the VIABILITY trial, a descriptive prospective single-center cohort study. The main purpose of this trial is to assess the link between systemic inflammation, pan-coronary plaque vulnerability (referring to the plaque vulnerability within the entire coronary tree), myocardial viability and ventricular remodeling in patients who had suffered a recent ST-segment elevation acute myocardial infarction (STEMI). One hundred patients with STEMI who underwent successful revascularization of the culprit lesion in the first 12 hours after the onset of symptoms will be enrolled in the study. The level of systemic inflammation will be evaluated based on the serum biomarker levels (hs-CRP, matrix metalloproteinases, interleukin-6) in the acute phase of the myocardial infarction (MI) and at 1 month. Pan-coronary plaque vulnerability will be assessed based on serum biomarkers known to be associated with increased plaque vulnerability (V-CAM or I-CAM) and at 1 month after infarction, based on computed tomographic angiography analysis of vulnerability features of all coronary plaques. Myocardial viability and remodeling will be assessed based on 3D speckle tracking echocardiography associated with dobutamine infusion and LGE-CMR associated with post-processing imaging methods. The study population will be categorized in 2 subgroups: subgroup 1 - subjects with STEMI and increased inflammatory response at 7 days after the acute event (hs-CRP ≥ 3 mg/dl), and subgroup 2 - subjects with STEMI and no increased inflammatory response at 7 days (hs-CRP < 3 mg/dl). Study outcomes will consist in the rate of post-infarction heart failure development and the major adverse events (MACE) rate. CONCLUSION: VIABILITY is the first prospective study designed to evaluate the influence of infarct-related inflammatory response on several major determinants of post-infarction outcomes, such as coronary plaque vulnerability, myocardial viability, and ventricular remodeling.

Predictive control of intersegmental tarsal movements in an insect.

In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes.

Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson's hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits.

A modified two-stage Markov clustering algorithm for large and sparse networks.

BACKGROUND: Graph-based hierarchical clustering algorithms become prohibitively costly in both execution time and storage space, as the number of nodes approaches the order of millions. OBJECTIVE: A fast and highly memory efficient Markov clustering algorithm is proposed to perform the classification of huge sparse networks using an ordinary personal computer. METHODS: Improvements compared to previous versions are achieved through adequately chosen data structures that facilitate the efficient handling of symmetric sparse matrices. Clustering is performed in two stages: the initial connected network is processed in a sparse matrix until it breaks into isolated, small, and relatively dense subgraphs, which are then processed separately until convergence is obtained. An intelligent stopping criterion is also proposed to quit further processing of a subgraph that tends toward completeness with equal edge weights. The main advantage of this algorithm is that the necessary number of iterations is separately decided for each graph node. RESULTS: The proposed algorithm was tested using the SCOP95 and large synthetic protein sequence data sets. The validation process revealed that the proposed method can reduce 3-6 times the processing time of huge sequence networks compared to previous Markov clustering solutions, without losing anything from the partition quality. CONCLUSIONS: A one-million-node and one-billion-edge protein sequence network defined by a BLAST similarity matrix can be processed with an upper-class personal computer in 100 minutes. Further improvement in speed is possible via parallel data processing, while the extension toward several million nodes needs intermediary data storage, for example on solid state drives.

Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident.

A Proactive Innovation for Health Care Transformation: Health and Wellness Nurse Coaching.

A cohort of holistic nurses, recognizing opportunities inherent in health care transformation, organized and worked together from 2009 to 2012. The goal was to hold space for holistic nursing by developing a health and wellness coaching role and certification program for holistic nurses. The intent was to ensure that holistic nurses could work to the fullest of their ability within the evolving health care system, and others could discover the merit of holistic nursing as they explored the possibilities of nurse coaching. Challenges emerged that required the cohort plan strategies that would hold the space for nursing while also moving toward the intended goal. As they worked, this cohort demonstrated leadership skills, knowledge, values, and attitudes of holistic nursing that provide an example for others who follow in the wake of health care transformation. The American Holistic Credentialing Corporation's perspective of the events that unfolded and of the related decisions made by the coalition provides a record of the evolution of holistic nursing.

A fast hierarchical clustering algorithm for large-scale protein sequence data sets.

TRIBE-MCL is a Markov clustering algorithm that operates on a graph built from pairwise similarity information of the input data. Edge weights stored in the stochastic similarity matrix are alternately fed to the two main operations, inflation and expansion, and are normalized in each main loop to maintain the probabilistic constraint. In this paper we propose an efficient implementation of the TRIBE-MCL clustering algorithm, suitable for fast and accurate grouping of protein sequences. A modified sparse matrix structure is introduced that can efficiently handle most operations of the main loop. Taking advantage of the symmetry of the similarity matrix, a fast matrix squaring formula is also introduced to facilitate the time consuming expansion. The proposed algorithm was tested on protein sequence databases like SCOP95. In terms of efficiency, the proposed solution improves execution speed by two orders of magnitude, compared to recently published efficient solutions, reducing the total runtime well below 1min in the case of the 11,944proteins of SCOP95. This improvement in computation time is reached without losing anything from the partition quality. Convergence is generally reached in approximately 50 iterations. The efficient execution enabled us to perform a thorough evaluation of classification results and to formulate recommendations regarding the choice of the algorithm׳s parameter values.

Efficient Markov clustering algorithm for protein sequence grouping.

In this paper we propose an efficient reformulation of a Markov clustering algorithm, suitable for fast and accurate grouping of protein sequences, based on pairwise similarity information. The proposed modification consists of optimal reordering of rows and columns in the similarity matrix after every iteration, transforming it into a matrix with several compact blocks along the diagonal, and zero similarities outside the blocks. These blocks are treated separately in later iterations, thus reducing the computational burden of the algorithm. The proposed algorithm was tested on protein sequence databases like SCOP95. In terms of efficiency, the proposed solution achieves a speed-up factor in the range 15-50 compared to the conventional Markov clustering, depending on input data size and parameter settings. This improvement in computation time is reached without losing anything from the partition accuracy. The convergence is usually reached in 40-50 iterations. Combining the proposed method with sparse matrix representation and parallel execution will certainly lead to a significantly more efficient solution in future.

The holistic worldview in action: evolution of holistic nurses certification programs.

The American Holistic Nurses Credentialing Corporation (AHNCC), the only national credentialing body for holistic nurses, has a responsibility to offer valid, reliable, and rigorous certification examinations and to grow and evolve as indicated by social and professional changes. This article describes four major changes in the work of AHNCC since 2004: a detection of an evolution in the domain of holistic nursing through review of the literature; clarification and specification of levels of practice by educational level; development of the nurse coach role in nursing, designed within the precepts of holistic nursing; and AHNCC's response to the social paradigm shift for health care, and nursing's advanced practice registered nurse consensus model. Each of these is discussed in detail describing the circumstances that perpetuated AHNCC's consideration and the actions taken by AHNCC.

Comparative study of non-invasive monitoring via infrared spectroscopy for mammalian cell cultivations.

Process analytical technology (PAT) is a guide to improve process development in biotech industry. Optical sensors such as near and mid infrared spectrometers fulfill an essential part for PAT. NIRS and MIRS were investigated as non-invasive on line monitoring tools for animal cell cultivations in order to predict critical process parameters, like cell parameters as well as substrate and metabolite concentrations. Eight cultivations were performed with frequent sampling. Variances between cultivations were induced by spiking experiments with intent to break correlations between analytes; to keep causality of the models; and to increase model robustness. Calibration models were built for each analyte using partial least-squares regression method. Cultivations chosen for validation were not part of the calibration set. Glucose concentration, cell density and viability were predicted by NIRS with a root mean square error of prediction (RMSEP) of 0.36 g/L, 3.9 10(6)cells/mL and 3.62% respectively. Based on MIR spectra glucose and lactate concentrations were predicted with a RMSEP of 0.16 and 0.14 g/L respectively. Results show that MIRS has higher accuracy regarding the prediction of single analytes. For prediction of a main course of a cultivation, NIRS is much better suited than MIRS.

Validation of holistic nursing competencies: role-delineation study, 2012.

The American Holistic Nurses Credentialing Corporation (AHNCC), certifying body for nurses practicing within the precepts of holistic nursing, uses a systematic process to guide program development. A previous publication described their early work that distinguished basic and advanced holistic nursing and development of related examinations. A more recent publication described the work of AHNCC from 2004 to 2012, including a role-delineation study (RDS) that was undertaken to identify and validate competencies currently used by holistic nurses. A final report describes the RDS design, methods, and raw data information. This article discusses AHNCC's goals for undertaking the 2012 Holistic Nursing RDS and the implications for the certification programs.

Anelasticity-induced increase of the Al-centered local symmetry in the metallic glass La50Ni15Al35.

The mechanism of anelastic deformation of metallic glasses is a fundamental issue of materials physics. A critical step toward atomic level understanding is the identification of measurable atomic level structural parameters that respond to anelastic deformation. We demonstrate that the electric-field-gradient tensor measured by means of (27)Al nuclear magnetic resonance in glassy La50Ni15Al35 is such a parameter and it reveals that anelasticity induces atomic processes that lead to increases of local site symmetry at Al sites. Such atomic processes could play an important role in the reversible slow ß process.

Zirconium and hafnium complexes based on 2-aryl-8-arylaminoquinoline ligands: synthesis, molecular structure, and catalytic performance in ethylene copolymerization.

A general and efficient approach toward new zirconium and hafnium complexes based on 2-aryl-8-arylaminoquinoline ligands was developed. These precursors, when activated with MAO/borate cocatalyst and supported on silica, result in active olefin polymerization catalysts. The ethylene copolymers produced under industrially relevant conditions show very high molecular weights and unique microstructures defined by the multisite nature of the catalyst. A site-diversification mechanism is proposed to explain the presence of at least five individual sites, as deduced from 3D-TREF analysis of ethylene-butene copolymers.

Efficient inhomogeneity compensation using fuzzy c-means clustering models.

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into classification or clustering algorithms, they generally have difficulties when INU reaches high amplitudes and usually suffer from high computational load. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels. The theoretical assumptions are demonstrated using the fuzzy c-means algorithm, but the proposed modification is compatible with a various range of c-means clustering based INU compensation and MR image segmentation algorithms. Experiments carried out using synthetic phantoms and real MR images indicate that the proposed approach produces practically the same segmentation accuracy as the conventional formulation, but 20-30 times faster.

CONTINUOUS REPOPULATION OF LYMPHOCYTE SUBSETS IN TRANSPLANTED MYCOBACTERIAL GRANULOMAS.

Granulomas are the interface between host and mycobacteria, and are crucial for the surivival of both species. While macrophages are the main cellular component of these lesions, different lymphocyte subpopulations within the lesions also play important roles. Lymphocytes are continuously recruited into these inflammatory lesions via local vessels to replace cells that are either dying or leaving; however, their rate of replacement is not known. Using a model of granuloma transplantation and fluorescently labeled cellular compartments we report that, depending on the subpopulation, 10-80%, of cells in the granuloma are replaced within one week after transplantation. CD4(+) T cells specific for Mycobacterium antigen entered transplanted granulomas at a higher frequency than Foxp3(+) CD4(+) T cells by one week. Interestingly, a small number of T lymphocytes migrated out of the granuloma to secondary lymphoid organs. The mechanisms that define the differences in recruitment and efflux behind each subpopulation requires further studies. Ultimately, a better understanding of lymphoid traffic may provide new ways to modulate, regulate, and treat granulomatous diseases.

Bonding characters of Al-containing bulk metallic glasses studied by 27Al NMR.

We report very small (27)Al metallic shifts in a series of Cu-Zr-Al bulk metallic glasses. This observation and the Korringa type of spin-lattice relaxation behavior suggest that s-character wavefunctions weakly participate in bonding and opens the possibility of enhanced covalency (pd hybridization) with increasing Al concentration, in good agreement with elastic constants and hardness measurements. Moreover, ab initio calculations show that this bonding character originates from the strong Al 3p band and Zr 4d band hybridization since their atomic energy levels are closer to each other while the Al 3s band is localized far below the Fermi level. This study might provide a chemical view for understanding flow and fracture mechanisms of these bulk glass-forming alloys.