Structure, function, and control of the human musculoskeletal network.

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle's role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.

Recent Advances in Smart Biomaterials for the Detection and Treatment of Autoimmune Diseases.

Autoimmune diseases are a group of debilitating illnesses that are often idiopathic in nature. The steady rise in the prevalence of these conditions warrants new approaches for diagnosis and treatment. Stimuli-responsive biomaterials also known as "smart", "intelligent" or "recognitive" biomaterials are widely studied for their applications in drug delivery, biosensing and tissue engineering due to their ability to produce thermal, optical, chemical, or structural changes upon interacting with the biological environment. This critical analysis highlights studies within the last decade that harness the recognitive capabilities of these biomaterials towards the development of novel detection and treatment options for autoimmune diseases.

Engineered microscale hydrogels for drug delivery, cell therapy, and sequencing.

Engineered microscale hydrogels have emerged as promising therapeutic approaches for the treatment of various diseases. These microgels find wide application in the biomedical field because of the ease of injectability, controlled release of therapeutics, flexible means of synthesis, associated tunability, and can be engineered as stimuli-responsive. While bulk hydrogels of several length-scale dimensions have been used for over two decades in drug delivery applications, their use as microscale carriers of drug and cell-based therapies is relatively new. Herein, we critically summarize the fundamentals of hydrogels based on their equilibrium and dynamics of their molecular structure, as well as solute diffusion as it relates to drug delivery. In addition, examples of common microgel synthesis techniques are provided. The ability to tune microscale hydrogels to obtain controlled release of therapeutics is discussed, along with microgel considerations for cell encapsulation as it relates to the development of cell-based therapies. We conclude with an outlook on the use of microgels for cell sequencing, and the convergence of the use of microscale hydrogels for drug delivery, cell therapy, and cell sequencing based systems.

Brain state flexibility accompanies motor-skill acquisition.

Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging - and to assess their dynamics during learning - remain limited. Here, we describe an approach based on a distinct application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning.

A highly tuneable inverse emulsion polymerization for the synthesis of stimuli-responsive nanoparticles for biomedical applications.

Polymeric nanomaterials have seen widespread use in biomedical applications as they are highly tuneable to achieve the desired stimuli-responsiveness, targeting, biocompatibility, and degradation needed for fields such as drug delivery and biosensing. However, adjustments to composition and the introduction of new monomers often necessitate reoptimization of the polymer synthesis to achieve the target parameters. In this study, we explored the use of inverse emulsion polymerization to prepare a library of polymeric nanoparticles with variations in pH and temperature response and examined the impact of overall batch volume and the volume of the aqueous phase on nanoparticle size and composition. We were able to prepare copolymeric nanoparticles using three different nonionic and three different anionic comonomers. Varying the non-ionizable comonomers, acrylamide (AAm), 2-hydroxyethyl methacrylate, and N-isopropylacrylamide (NIPAM), was found to alter the mass percentage of methacrylic acid (MAA) incorporated (from 26.7 ± 3.5 to 45.8 ± 1.8 mass%), the critical swelling pH (from 5.687 ± 0.194 to 6.637 ± 0.318), and the volume swelling ratio (from 1.389 ± 0.064 to 2.148 ± 0.037). Additionally, the use of NIPAM was found to allow for temperature-responsive behavior. Varying the ionizable comonomers, MAA, itaconic acid, and 2-acrylamido-2-methylpropane sulfonic acid (AMPSA), was found to significantly alter the critical swelling pH and, in the case of AMPSA, remove the pH-responsive behavior entirely. Finally, we found that for the base P(AAm-co-MAA) formulation, the pH-responsive swelling behavior was independent of the scale of the reaction; however, variations in the aqueous volume relative to the volume of the continuous phase significantly affected both the nanoparticle size and the critical swelling pH.

Recent Advancements in Biosensing Approaches for Screening and Diagnostic Applications.

Recent advancements in molecular recognition have provided additional diagnostic and treatment approaches for multiple diseases, including autoimmune disorders and cancers. Research investigating how the composition of biological fluids is altered during disease progression, including differences in the expression of the small molecules, proteins, RNAs, and other components present in patient tears, saliva, blood, urine, or other fluids, has provided a wealth of potential candidates for early disease screening; however, adoption of biomarker screening into clinical settings has been challenged by the need for more robust, low-cost, and high-throughput assays. This review examines current approaches in molecular recognition and biosensing for the quantification of biomarkers for disease screening and diagnostic outcomes.

Multimodal network dynamics underpinning working memory.

Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.

A Network Neuroscience of Neurofeedback for Clinical Translation.

In the emerging field of network neuroscience, the brain is represented as a network of discrete yet functionally and structurally interconnected areas. Mathematical and computational tools to characterize the organization of this network can provide insights into the principles guiding brain structure and function, and can pinpoint differences between healthy individuals and individuals suffering from psychiatric disease or neurological disorders. The field is now faced with the question of how to devise clinical interventions that target these network alterations. Potential solutions to this question include the combination of emerging theories of network control with cutting-edge interventions such as neurofeedback. Each of these techniques may now be mature enough to combine to obtain a theoretically-motivated framework informing viable neuropsychiatric therapies.

Implementation of an accelerated mobilization protocol following primary total hip arthroplasty: impact on length of stay and disposition.

With improvements in surgical techniques, implant design, and patient caremaps, surgeons have sought to accelerate early rehabilitation after total hip arthroplasty. Many authors have reported results of fundamentally similar protocols to achieve this end. These protocols focus on multi-modal pain management, early therapy, tissue-preserving surgical technique, and careful blood management. We present the implementation and results of such a protocol involving a different surgical approach, and highlight the published literature on this topic.