Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming

This work presents a new algorithm for solving the explicit/multi-parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step.     

Development of a Topical Insulin Polymeric Nanoformulation for Skin Burn Regeneration: An Experimental Approach

Insulin is a peptide hormone with many physiological functions, besides its use in diabetes treatment. An important role of insulin is related to the wound healing process—however, insulin itself is too sensitive to the external environment requiring the protective of a nanocarrier. Polymer-based nanoparticles can protect, deliver, and retain the protein in the target area. This study aims to produce and characterize a topical treatment for wound healing consisting of insulin-loaded poly-DL-lactide/glycolide (PLGA) nanoparticles. Insulin-loaded nanoparticles present a mean size of approximately 500 nm and neutral surface charge. Spherical shaped nanoparticles are observed by scanning electron microscopy and confirmed by atomic force microscopy. SDS-PAGE and circular dichroism analysis demonstrated that insulin preserved its integrity and secondary structure after the encapsulation process. In vitro release studies suggested a controlled release profile. Safety of the formulation was confirmed using cell lines, and cell viability was concentration and time-dependent. Preliminary safety in vivo assays also revealed promising results.     

Assessing the Effect of Loop Mutations in the Folding Space of β2-Microglobulin with Molecular Dynamics Simulations

We use molecular dynamics simulations of a full atomistic Gō model to explore the impact of selected DE-loop mutations (D59P and W60C) on the folding space of protein human β₂-microglobulin (Hβ₂m), the causing agent of dialysis-related amyloidosis, a conformational disorder characterized by the deposition of insoluble amyloid fibrils in the osteoarticular system. Our simulations replicate the effect of mutations on the thermal stability that is observed in experiments in vitro. Furthermore, they predict the population of a partially folded state, with 60% of native internal free energy, which is akin to a molten globule. In the intermediate state, the solvent accessible surface area increases up to 40 times relative to the native state in 38% of the hydrophobic core residues, indicating that the identified species has aggregation potential. The intermediate state preserves the disulfide bond established between residue Cys25 and residue Cys80, which helps maintain the integrity of the core region, and is characterized by having two unstructured termini. The movements of the termini dominate the essential modes of the intermediate state, and exhibit the largest displacements in the D59P mutant, which is the most aggregation prone variant. PROPKA predictions of pK_a suggest that the population of the intermediate state may be enhanced at acidic pH explaining the larger amyloidogenic potential observed in vitro at low pH for the WT protein and mutant forms.     

A Step Forward in Breast Cancer Research: From a Natural-Like Experimental Model to a Preliminary Photothermal Approach

Breast cancer is one of the most frequently diagnosed malignancies and common causes of cancer death in women. Recent studies suggest that environmental exposures to certain chemicals, such as 7,12-Dimethylbenzanthracene (DMBA), a chemical present in tobacco, may increase the risk of developing breast cancer later in life. The first-line treatments for breast cancer (surgery, chemotherapy or a combination of both) are generally invasive and frequently associated with severe side effects and high comorbidity. Consequently, novel approaches are strongly required to find more natural-like experimental models that better reflect the tumors’ etiology, physiopathology and response to treatments, as well as to find more targeted, efficient and minimally invasive treatments. This study proposes the development and an in deep biological characterization of an experimental model using DMBA-tumor-induction in Sprague-Dawley female rats. Moreover, a photothermal therapy approach using a near-infrared laser coupled with gold nanoparticles was preliminarily assessed. The gold nanoparticles were functionalized with Epidermal Growth Factor, and their physicochemical properties and in vitro effects were characterized. DMBA proved to be a very good and selective inductor of breast cancer, with 100% incidence and inducing an average of 4.7 tumors per animal. Epigenetic analysis showed that tumors classified with worst prognosis were hypomethylated. The tumor-induced rats were then subjected to a preliminary treatment using functionalized gold nanoparticles and its activation by laser (650–900 nm). The treatment outcomes presented very promising alterations in terms of tumor histology, confirming the presence of necrosis in most of the cases. Although this study revealed encouraging results as a breast cancer therapy, it is important to define tumor eligibility and specific efficiency criteria to further assess its application in breast cancer treatment on other species.     

An algorithm for robust explicit/multi-parametric model predictive control

A new algorithm for robust explicit/multi-parametric Model Predictive Control (MPC) for uncertain, linear discrete-time systems is proposed. Based on previous work on Dynamic Programming (DP), multi-parametric Programming and Robust Optimization, the proposed algorithm features, (i) a DP reformulations of the MPC optimization problem, (ii) a robust reformulation of the constraints, and (iii) a multi-parametric programming step, where the control variables are obtained as explicit functions of the state variable, such that the state and input constraints are satisfied for all admissible values of the uncertainty. A key feature of the proposed procedure is that, as opposed to previous methods, it only solves a convex multi-parametric programming problem for each stage of the DP procedure.