Caffeic Acid Enhances the Anti-Leukemic Effect of Imatinib on Chronic Myeloid Leukemia Cells and Triggers Apoptosis in Cells Sensitive and Resistant to Imatinib

Among the phenolic acids tested on the K562 cell line, a model of chronic myeloid leukemia (CML), caffeic acid (CA) was biologically active on sensitive and imatinib (IM)-resistant cells at micro-molar concentration, either in terms of reduction of cell proliferation or triggering of apoptosis. The CA treatment provoked mitochondrial membrane depolarization, genomic DNA fragmentation and phosphatidylserine exposure, hallmarks of apoptosis. Cell cycle analysis following the treatment with comparable cytotoxic concentrations of IM or CA showed marked differences in the distribution profiles. The reduction of cell proliferation by CA administration was associated with increased expression of two cell cycle repressor genes, CDKN1A and CHES1, while IM at a cytotoxic concentration increased the CHES1 but not the CDKN1A expression. In addition, CA treatment affected the proliferation and triggered the apoptosis in IM-resistant cells. Taken together, these data suggested that CA induced the anti-proliferative effect and triggered apoptosis of CML cells by a different mechanism than IM. Finally, the combined administration of IM and CA at suboptimal concentrations evidenced a synergy of action in determining the anti-proliferative effect and triggering apoptosis. The ability of CA to potentiate the anti-leukemic effect of IM highlighted the nutraceutical potential of CA in CML.     

Process noise covariance estimation via stochastic approximation

Kalman filtering for linear systems is known to provide the minimum variance estimation error, under the assumption that the model dynamics is known. While many system identification tools are available for computing the system matrices from experimental data, estimating the statistics of the output and process noises is still an open problem. Correlation-based approaches are very fast and sufficiently accurate, but there are typically restrictions on the number of noise covariance elements that can be estimated. On the other hand, maximum likelihood methods estimate all elements with high accuracy, but they are computationally expensive, and they require the use of external optimization solvers. In this paper, we propose an alternative solution, tailored for process noise covariance estimation and based on stochastic approximation and gradient-free optimization, that provides a good trade-off in terms of performance and computational load, and is also easy to implement. The effectiveness of the method as compared to the state of the art is shown on a number of recently proposed benchmark examples.     

Anelastic reorganisation of fibre-reinforced biological tissues

In this work, we contribute to the study of the structural reorganisation of biological tissues in response to mechanical stimuli. We specialise our investigation to a class of hydrated soft tissues, whose internal structure features reinforcing fibres. These are oriented statistically within the tissue, and their pattern of orientation is such that, at each material point, the tissue is anisotropic. From its natural, stress-free state, the tissue can be distorted anelastically into a global reference configuration, and then deformed under the action of external mechanical loads. The anelastic distortions are responsible for changing irreversibly the internal structure of the tissue, which, in the present context, occurs through both the rearrangement of the bonds among the tissue cells and the deformation-driven reorientation of the fibres. The anelastic strains, in addition, are assumed to model the onset and evolution of microcracks in the tissue, which may be triggered by the mechanical loads applied to the tissue in the case of traumatic events, or diseases. For our purposes, we formulate an anisotropic model of remodelling and we consider a fully isotropic model of structural reorganisation for comparison, with the aim to study if, how, and to what extent the evolution of anelastic distortions is influenced by the tissue’s anisotropy.

     

Influence of Pulsed Electric Field Protocols on the Reversible Permeabilization of Rucola Leaves

Reversible electropermeabilization of plant tissues with heterogeneous structure represents a technological challenge as the response of the different structures within the same specimen to the application of electric field may differ due to different cell sizes, extracellular space configurations, and electrical properties. The influence of five different pulsed electric field protocols with different pulse polarity, number of pulses (25, 50, 75, 100, 250, and 500), and intervals between pulses (no intervals and 1- and 2-ms intervals) on the reversible permeabilization of rucola (Eruca sativa) leaves was investigated. The electric field intensity was 600 V/cm. Electrical resistance of the bulk tissue was measured before and after electroporation, and propidium iodide was used to analyze the electroporation at the surface of the leaf. Leaf viability was assessed from survival in storage, and cell viability was investigated with fluorescein diacetate. Results indicate that the viability of the leaves could not be predicted by measurements of electrical resistance or permeabilization levels of the leaf surface. Higher survival rate was demonstrated when applying bipolar pulses compared with monopolar pulses, but the latter proved to be more effective than bipolar pulses for permeabilizing the surface of the leaves. Longer intervals between bipolar pulses resulted in increased viability preservation, while the number of electroporated cells on the leaf surface was comparable for all tested protocols.     

SafeNet: A methodology for integrating general-purpose unsafe devices in safe-robot rehabilitation systems

Robot-assisted neurorehabilitation often involves networked systems of sensors (“sensory rooms”) and powerful devices in physical interaction with weak users. Safety is unquestionably a primary concern. Some lightweight robot platforms and devices designed on purpose include safety properties using redundant sensors or intrinsic safety design (e.g. compliance and backdrivability, limited exchange of energy). Nonetheless, the entire “sensory room” shall be required to be fail-safe and safely monitored as a system at large. Yet, sensor capabilities and control algorithms used in functional therapies require, in general, frequent updates or re-configurations, making a safety-grade release of such devices hardly sustainable in cost-effectiveness and development time. As such, promising integrated platforms for human-in-the-loop therapies could not find clinical application and manufacturing support because of lacking in the maintenance of global fail-safe properties.     

A genetic algorithm for energy-efficiency in job-shop scheduling

Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.