Hydrogel-encapsulated extracellular vesicles for the regeneration of spinal cord injury.

Spinal cord injury (SCI) is a critical neurological condition that may impair motor, sensory, and autonomous functions. At the cellular level, inflammation, impairment of axonal regeneration, and neuronal death are responsible for SCI-related complications. Regarding the high mortality and morbidity rates associated with SCI, there is a need for effective treatment. Despite advances in SCI repair, an optimal treatment for complete recovery after SCI has not been found so far. Therefore, an effective strategy is needed to promote neuronal regeneration and repair after SCI. In recent years, regenerative treatments have become a potential option for achieving improved functional recovery after SCI by promoting the growth of new neurons, protecting surviving neurons, and preventing additional damage to the spinal cord. Transplantation of cells and cells-derived extracellular vesicles (EVs) can be effective for SCI recovery. However, there are some limitations and challenges related to cell-based strategies. Ethical concerns and limited efficacy due to the low survival rate, immune rejection, and tumor formation are limitations of cell-based therapies. Using EVs is a helpful strategy to overcome these limitations. It should be considered that short half-life, poor accumulation, rapid clearance, and difficulty in targeting specific tissues are limitations of EVs-based therapies. Hydrogel-encapsulated exosomes have overcome these limitations by enhancing the efficacy of exosomes through maintaining their bioactivity, protecting EVs from rapid clearance, and facilitating the sustained release of EVs at the target site. These hydrogel-encapsulated EVs can promote neuroregeneration through improving functional recovery, reducing inflammation, and enhancing neuronal regeneration after SCI. This review aims to provide an overview of the current research status, challenges, and future clinical opportunities of hydrogel-encapsulated EVs in the treatment of SCI.

Synchronizing chaos using reservoir computing.

We attempt to achieve complete synchronization between a drive system unidirectionally coupled with a response system, under the assumption that limited knowledge on the states of the drive is available at the response. Machine-learning techniques have been previously implemented to estimate the states of a dynamical system from limited measurements. We consider situations in which knowledge of the non-measurable states of the drive system is needed in order for the response system to synchronize with the drive. We use a reservoir computer to estimate the non-measurable states of the drive system from its measured states and then employ these measured states to achieve complete synchronization of the response system with the drive.

Algebraic Decomposition of Model Predictive Control Problems.

This paper is concerned with the application of model predictive control (MPC) to large-scale linear dynamical systems with linear inequality constraints. A decomposition is proposed of such problems into sets of independent MPCs of lower dimensions that preserves all information about the system, cost function, and constraints. Different from previous work, the constraints are incorporated in the decomposition procedure, which is attained by generalizing a previously developed technique to simultaneously block diagonalize a set of matrices. This approach is applied to practical examples involving large-scale systems with inequality constraints. It is shown that the computational complexity and the CPU time required to solve the transformed MPC problems are lower than those required by the solution of the original MPC problem.

Exact Decomposition of Optimal Control Problems via Simultaneous Block Diagonalization of Matrices.

In this paper, we consider optimal control problems (OCPs) applied to large-scale linear dynamical systems with a large number of states and inputs. We attempt to reduce such problems into a set of independent OCPs of lower dimensions. Our decomposition is 'exact' in the sense that it preserves all the information about the original system and the objective function. Previous work in this area has focused on strategies that exploit symmetries of the underlying system and of the objective function. Here, instead, we implement the algebraic method of simultaneous block diagonalization of matrices (SBD), which we show provides advantages both in terms of the dimension of the subproblems that are obtained and of the computation time. We provide practical examples with networked systems that demonstrate the benefits of applying the SBD decomposition over the decomposition method based on group symmetries.

Role of SARS-CoV-2-induced cytokine storm in multi-organ failure: Molecular pathways and potential therapeutic options.

Coronavirus disease 2019 (COVID-19) outbreak has become a global public health emergency and has led to devastating results. Mounting evidence proposes that the disease causes severe pulmonary involvement and influences different organs, leading to a critical situation named multi-organ failure. It is yet to be fully clarified how the disease becomes so deadly in some patients. However, it is proven that a condition called "cytokine storm" is involved in the deterioration of COVID-19. Although beneficial, sustained production of cytokines and overabundance of inflammatory mediators causing cytokine storm can lead to collateral vital organ damages. Furthermore, cytokine storm can cause post-COVID-19 syndrome (PCS), an important cause of morbidity after the acute phase of COVID-19. Herein, we aim to explain the possible pathophysiology mechanisms involved in COVID-19-related cytokine storm and its association with multi-organ failure and PCS. We also discuss the latest advances in finding the potential therapeutic targets to control cytokine storm wishing to answer unmet clinical demands for treatment of COVID-19.

Supermodal Decomposition of the Linear Swing Equation for Multilayer Networks.

We study the swing equation in the case of a multilayer network in which generators and motors are modeled differently; namely, the model for each generator is given by second order dynamics and the model for each motor is given by first order dynamics. We also remove the commonly used assumption of equal damping coefficients in the second order dynamics. Under these general conditions, we are able to obtain a decomposition of the linear swing equation into independent modes describing the propagation of small perturbations. In the process, we identify symmetries affecting the structure and dynamics of the multilayer network and derive an essential model based on a 'quotient network.' We then compare the dynamics of the full network and that of the quotient network and obtain a modal decomposition of the error dynamics. We also provide a method to quantify the steady-state error and the maximum overshoot error. Two case studies are presented to illustrate application of our method.

Matryoshka and disjoint cluster synchronization of networks.

The main motivation for this paper is to characterize network synchronizability for the case of cluster synchronization (CS), in an analogous fashion to Barahona and Pecora [Phys. Rev. Lett. 89, 054101 (2002)] for the case of complete synchronization. We find this problem to be substantially more complex than the original one. We distinguish between the two cases of networks with intertwined clusters and no intertwined clusters and between the two cases that the master stability function is negative either in a bounded range or in an unbounded range of its argument. Our proposed definition of cluster synchronizability is based on the synchronizability of each individual cluster within a network. We then attempt to generalize this definition to the entire network. For CS, the synchronous solution for each cluster may be stable, independent of the stability of the other clusters, which results in possibly different ranges in which each cluster synchronizes (isolated CS). For each pair of clusters, we distinguish between three different cases: Matryoshka cluster synchronization (when the range of the stability of the synchronous solution for one cluster is included in that of the other cluster), partially disjoint cluster synchronization (when the ranges of stability of the synchronous solutions partially overlap), and complete disjoint cluster synchronization (when the ranges of stability of the synchronous solutions do not overlap).

Hypoxia-sensitive miRNA regulation via CRISPR/dCas9 loaded in hybrid exosomes: A novel strategy to improve embryo implantation and prevent placental insufficiency during pregnancy.

Assisted reproductive techniques as a new regenerative medicine approach have significantly contributed to solving infertility problems that affect approximately 15% of couples worldwide. However, the success rate of an in vitro fertilization (IVF) cycle remains only about 20%-30%, and 75% of these losses are due to implantation failure (the crucial rate-limiting step of gestation). Implantation failure and abnormal placenta formation are mainly caused by defective adhesion, invasion, and angiogenesis. Placental insufficiency endangers both the mother's and the fetus's health. Therefore, we suggested a novel treatment strategy to improve endometrial receptivity and implantation success rate. In this strategy, regulating mir-30d expression as an upstream transcriptomic modifier of the embryo implantation results in modified expression of the involved genes in embryonic adhesion, invasion, and angiogenesis and consequently impedes implantation failure. For this purpose, "scaffold/matrix attachment regions (S/MARs)" are employed as non-viral episomal vectors, transfecting into trophoblasts by exosome-liposome hybrid carriers. These vectors comprise CRISPR/dCas9 with a guide RNA to exclusively induce miR-30d gene expression in hypoxic stress conditions. In order to avoid concerns about the fetus's genetic manipulation, our vector would be transfected specifically into the trophoblast layer of the blastocyst via binding to trophoblast Erb-B4 receptors without entering the inner cell mass. Additionally, S/MAR episomal vectors do not integrate with the original cell DNA. As an on/off regulatory switch, a hypoxia-sensitive promoter (HRE) is localized upstream of dCas9. The miR-30d expression increases before and during the implantation and placental insufficiency conditions and is extinguished after hypoxia elimination. This hypothesis emphasizes that improving the adhesion, invasion, and angiogenesis in the uterine microenvironment during pregnancy will result in increased implantation success and reduced placental insufficiency, as a new insight in translational medicine.

The contribution of gut-brain axis to development of neurological symptoms in COVID-19 recovered patients: A hypothesis and review of literature.

The gut microbiota undergoes significant alterations in response to viral infections, particularly the novel SARS-CoV-2. As impaired gut microbiota can trigger numerous neurological disorders, we suggest that the long-term neurological symptoms of COVID-19 may be related to intestinal microbiota disorders in these patients. Thus, we have gathered available information on how the virus can affect the microbiota of gastrointestinal systems, both in the acute and the recovery phase of the disease, and described several mechanisms through which this gut dysbiosis can lead to long-term neurological disorders, such as Guillain-Barre syndrome, chronic fatigue, psychiatric disorders such as depression and anxiety, and even neurodegenerative diseases such as Alzheimer's and Parkinson's disease. These mechanisms may be mediated by inflammatory cytokines, as well as certain chemicals such as gastrointestinal hormones (e.g., CCK), neurotransmitters (e.g., 5-HT), etc. (e.g., short-chain fatty acids), and the autonomic nervous system. In addition to the direct influences of the virus, repurposed medications used for COVID-19 patients can also play a role in gut dysbiosis. In conclusion, although there are many dark spots in our current knowledge of the mechanism of COVID-19-related gut-brain axis disturbance, based on available evidence, we can hypothesize that these two phenomena are more than just a coincidence and highly recommend large-scale epidemiologic studies in the future.