Explore the vast range of titles available.

Uncontrolled and chronic inflammatory states in the Central Nervous System (CNS) are the hallmark of neurodegenerative pathology and every injury or stroke-related insult. The key mediators of these neuroinflammatory states are glial cells known as microglia, the resident immune cell at the core of the inflammatory event, and astroglia, which encapsulate inflammatory insults in proteoglycan-rich scar tissue. Since the majority of neuroinflammation is exclusively based on the responses of said glia, their phenotypes have been identified to be on an inflammatory spectrum encompassing developmental, homeostatic, and reparative behaviors as opposed to their ability to affect devastating cell death cascades and scar tissue formation. Recently, research groups have focused on peptide discovery to identify these phenotypes, find novel mechanisms, and mediate or re-engineer their actions. Peptides retain the diverse function of proteins but significantly reduce the activity dependence on delicate 3D structures. Several peptides targeting unique phenotypes of microglia and astroglia have been identified, along with several capable of mediating deleterious behaviors or promoting beneficial outcomes in the context of neuroinflammation. A comprehensive review of the peptides unique to microglia and astroglia will be provided along with their primary discovery methodologies, including top-down approaches using known biomolecules and naïve strategies using peptide and phage libraries.

Animal models of nerve injury are important for studying nerve injury and repair, particularly for interventions that cannot be studied in humans. However, the vast majority of gait analysis in animals has been limited to univariate analysis even though gait data is highly multi-dimensional. As a result, little is known about how various spatiotemporal components of the gait relate to each other in the context of peripheral nerve injury and trauma. We hypothesize that a multivariate characterization of gait will reveal relationships among spatiotemporal components of gait with biological relevance to peripheral nerve injury and trauma. We further hypothesize that legitimate relationships among said components will allow for more accurate classification among distinct gait phenotypes than if attempted with univariate analysis alone. DigiGait data was collected of mice across groups representing increasing degrees of damage to the neuromusculoskeletal sequence of gait; that is (a) healthy controls, (b) nerve damage only via total nerve transection + reconnection of the femoral and sciatic nerves, and (c) nerve, muscle, and bone damage via total hind-limb transplantation. Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. The identified features were then used to train classifier models and compared to a model trained with features identified using only univariate analysis. 10-15 features relevant to describing gait in the context of increasing degrees of traumatic peripheral nerve injury were identified. Factor analysis uncovered relationships among the identified features and enabled the extrapolation of a set of latent factors that further described the distinct gait phenotypes. The latent factors tied to biological differences among the groups (e.g. alterations to the anatomical configuration of the limb due to transplantation or aberrant fine motor function due to peripheral nerve injury). Models trained using the identified features generated values that could be used to distinguish among pathophysiological states with high statistical significance (p < .001) and accuracy (>80%) as compared to univariate analysis alone. This is the first performance evaluation of a multivariate approach to gait analysis and the first demonstration of superior performance as compared to univariate gait analysis in animals. It is also the first study to use multivariate statistics to characterize and distinguish among different gradations of gait deficit in animals. This study contributes a comprehensive, multivariate characterization pipeline for application in the study of any pathologies in which gait is a quantitative translational outcome metric.

Hundreds of rodent gait studies have been published over the past two decades, according to a PubMed search. Treadmill gait data, for example from the DigiGait system, generates over 30 + spatial and temporal measures. Despite this multi-dimensional data, all but a handful of the published literature on rodent gait has conducted univariate analysis that reveals limited information on the relationships that are characteristic of different gait states. This study conducted rigorous multivariate analysis in the form of sequential feature selection and factor analysis on gait data from a variety of gait deviations (due to injury i.e. peripheral nerve transection and transplantation, disease i.e. IUGR and hyperoxia, and age-related changes) and used machine learning to train a classifier to distinguish among and score different gait states. Treadmill gait data (DigiGait) of three different types of gait deviations were collected. Data were collected from B6 mice using the DigiGait system, with gait measurements taken at standardized treadmill speeds of 10, 17, and 24 cm/s over a period of 3–4 s per observation. Each mouse underwent at least two trials at each speed. Data were collected on B6 mice that were healthy and had various types of gait deficit due to: (a) a peripheral nerve injury model with increasing degrees of damage to the neuromusculoskeletal sequence of gait i.e. nerve transection, total hind limb transplantation, (b) a central nerve injury model of increasing degrees of damage to the motor regions responsible for gait i.e. IUGR, IUGR + hyperoxia, and (c) gait changes due to increasing age. Multivariate factor analysis (using MATLAB’s factoran) and forward feature selection (with ten-fold cross-validation) were conducted to identify those features and factors most descriptive of each gait state for comparison. Various machine learning classifier models were trained with ten-fold cross-validation and evaluated (e.g. random forest, regression, discriminant analysis, support vector machine, and ensemble) in a 70 − 30 training-testing split for their accuracy, precision, recall, and F-score. The highest performing model was used to score each type of gait for direct comparison on a scale of -0.5 to 0.5. The score distributions were plotted on a histogram for direct comparisons of score populations among various gait states. Multivariate feature selection revealed that not all 30 + features were relevant to describing the gait states. Plotting misclassification error (MCE) as a function of number of features included revealed that there was a critical number of features (~ 16) that minimized MCE (0.17 via univariate feature selection vs. 0.12 via multivariate feature selection). Incorporating more than 16 features led MCE to increase linearly indicating overfitting. Relationships among the identified features were understood via factor analysis. The factor analysis results were consistent with the biological differences between the groups (e.g. total hind limb transplantation was distinguishable via features descriptive of the positioning of the paw in relation to the body while nerve transection injury alone was distinguishable via features descriptive of changes to fine motor movements). Across all gait states, there was significant conservation of features and factors. This suggests certain relationships may be fundamental to rodent gait analysis regardless of the gait pathology in question. The highest performing machine learning classifier model (ensemble) was able to distinguish between gait deficits with high performance (F-score, recall, precision, and accuracy all > 0.90). This included the ability to distinguish between peripheral vs. central gait deficit, between individual types of peripheral deficit, between individual types of central deficit, and between younger vs. older animals. Using the classifier to score individual animals and plot the scores by group revealed score distributions that were consistent with biological phenomena. For example, the multivariate gait score trends as a result of increasing central nerve injury were consistent with the trends of white matter volume loss in relevant motor regions of the brain as measured via MRI. Finally, the degrees of separation between multivariate gait scores were consistent with the degree of biological difference between gaits (e.g. central injury had greater separation from healthy vs. peripheral injury; older and younger animals had more moderate, yet still statistically significant, separation in scores vs. any of the injury / disease states did with each other). In conclusion, this study establishes a new methodology to quantify and evaluate gait deviations across a variety of different models. Its novelty is in using multivariate statistics to describe the features and factors that characterize gait states due to injury, disease, and age for use in machine learning model training. This includes statistically describing the differences in gait between diseases with vastly different etiologies of gait deficits (peripheral vs. central). In doing so the methodology’s novelty includes accounting for relationships between groupings of features in model training; something that traditional univariate analysis is unable to do. It used multivariate statistics and machine learning to reveal gait as a quantifiable, preclinical biomarker of injury, disease, and age. It collapsed a multi-dimensional biological phenomena (gait) into a single score by encoding revealed biological relationships allowing for direct, quantifiable comparisons of function as it pertains to ambulation. It revealed how these multivariate gait scores can visualize biologically consistent separation and combined effects. Finally, we demonstrate the application of this methodology to already published univariate study that is representative of the hundreds of univariate treadmill gait analysis published over the last two decades. Thereby, opening the door to a new class of multivariate gait analyses that provides greater insight and value than the current state-of-the art.

Fibrosis is involved in 45% of deaths in the United States, and no treatment exists to reverse the progression of lung or kidney fibrosis. Myofibroblasts are key to the progression and maintenance of fibrosis. We investigated features of cell adhesion necessary for monocytes to differentiate into myofibroblasts, seeking to identify pathways key to myofibroblast differentiation. Blocking antibodies against integrins α3, αM, and αMβ2 de-differentiate myofibroblasts in vitro, lower the pro-fibrotic secretome of myofibroblasts, and treat lung fibrosis and inhibit kidney fibrosis in vivo. Decorin’s collagen-binding peptide can be used to direct functionalized blocking antibodies (against integrins-α3, -αM, -αMβ2) to both fibrotic lungs and fibrotic kidneys, reducing the dose of antibody necessary to treat fibrosis. This targeted immunotherapy blocking key integrins may be an effective therapeutic for the treatment of fibrosis.

Polymeric micelles are widely studied as nanocarriers for hydrophobic drugs, yet their structural stability under physiological conditions remains a major limitation. This review provides a comparative evaluation of synthetic and natural polymeric micelles with a focus on their stability under dilution and in protein-rich environments. The discussion integrates thermodynamic and kinetic factors governing micelle integrity and examines how molecular composition, hydrophobic segment length, and core–shell modifications influence disintegration behavior. While synthetic micelles commonly collapse below their critical micelle concentration during intravenous administration, natural polymeric micelles, such as those derived from chitosan, alginate, or heparin, exhibit improved resistance to dilution but remain vulnerable to protein-induced destabilization. Strategies such as core or shell cross-linking, surface functionalization, and natural polymer coatings are reviewed as promising approaches to enhance circulation stability and controlled drug release. The work provides a framework for designing micellar systems with balanced biocompatibility, biodegradability, and robustness suitable for clinical drug-delivery applications.

(Arginine-alanine-aspartic acid-alanine)4 ((RADA)4) nanoscaffolds are excellent candidates for use as peptide delivery vehicles: they are relatively easy to synthesize with custom bio-functionality, and assemble in situ to allow a focal point of release. This enables (RADA)4 to be utilized in multiple release strategies by embedding a variety of bioactive molecules in an all-in-one “construct”. One novel strategy focuses on the local, on-demand release of peptides triggered via proteolysis of tethered peptide sequences. However, the spatial-temporal morphology of self-assembling nanoscaffolds may greatly influence the ability of enzymes to both diffuse into as well as actively cleave substrates. Fine structure and its impact on the overall effect on peptide release is poorly understood. In addition, fractal networks observed in nanoscaffolds are linked to the fractal nature of diffusion in these systems. Therefore, matrix morphology and fractal dimension of virgin (RADA)4 and mixtures of (RADA)4 and matrix metalloproteinase 2 (MMP-2) cleavable substrate modified (RADA)4 were characterized over time. Sites of high (glycine-proline-glutamine-glycine+isoleucine-alanine-serine-glutamine (GPQG+IASQ), CP1) and low (glycine-proline-glutamine-glycine+proline-alanine-glycine-glutamine (GPQG+PAGQ), CP2) cleavage activity were chosen. Fine structure was visualized using transmission electron microscopy. After 2 h of incubation, nanofiber networks showed an established fractal nature; however, nanofibers continued to bundle in all cases as incubation times increased. It was observed that despite extensive nanofiber bundling after 24 h of incubation time, the CP1 and CP2 nanoscaffolds were susceptible to MMP-2 cleavage. The properties of these engineered nanoscaffolds characterized herein illustrate that they are an excellent candidate as an enzymatically initiated peptide delivery platform.

Abstract Stem cells are enabling an improved understanding of the peripheral arterial disease, and patient-specific stem cell-derived endothelial cells (ECs) present major advantages as a therapeutic modality. However, applications of patient-specific induced pluripotent stem cell (iPSC)-derived ECs are limited by rapid loss of mature cellular function in culture. We hypothesized that changes in autophagy impact the phenotype and cellular proliferation of iPSC-ECs. Endothelial cells were differentiated from distinct induced pluripotent stem cell lines in 2D culture and purified for CD144 positive cells. Autophagy, mitochondrial morphology, and proliferation were characterized during differentiation and over serial passages in culture. We found that autophagy activity was stimulated during differentiation but stagnated in mature iPSC-ECs. Mitochondria remodeled through mitophagy during differentiation and demonstrated increasing membrane potential and mass through serial passages; however, these plateaued, coinciding with decreased proliferation. To evaluate for oxidative damage, iPSC-ECs were alternatively grown under hypoxic culture conditions; however, hypoxia only transiently improved the proliferation. Stimulating mTOR-independent ULK1-mediated autophagy with a plant derivative AMP kinase activator Rg2 significantly improved proliferative capacity of iPSC-ECs over multiple passages. Therefore, autophagy, a known mediator of longevity, played an active role in remodeling mitochondria during maturation from pluripotency to a terminally differentiated state. Autophagy failed to compensate for increasing mitochondrial mass over serial passages, which correlated with loss of proliferation in iPSC-ECs. Stimulating ULK1-kinase-driven autophagy conferred improved proliferation and longevity over multiple passages in culture. This represents a novel approach to overcoming a major barrier limiting the use of iPSC-ECs for clinical and research applications. Graphical Abstract Graphical Abstract

ObjectiveAnimal models of nerve injury are important for studying nerve injury and repair, particularly for interventions that cannot be studied in humans. However, the vast majority of gait analysis in animals has been limited to univariate analysis even though gait data is highly multi-dimensional. As a result, little is known about how various spatiotemporal components of the gait relate to each other in the context of peripheral nerve injury and trauma. We hypothesize that a multivariate characterization of gait will reveal relationships among spatiotemporal components of gait with biological relevance to peripheral nerve injury and trauma. We further hypothesize that legitimate relationships among said components will allow for more accurate classification among distinct gait phenotypes than if attempted with univariate analysis alone.MethodsDigiGait data was collected of mice across groups representing increasing degrees of damage to the neuromusculoskeletal sequence of gait; that is (a) healthy controls, (b) nerve damage only via total nerve transection + reconnection of the femoral and sciatic nerves, and (c) nerve, muscle, and bone damage via total hind-limb transplantation. Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. The identified features were then used to train classifier models and compared to a model trained with features identified using only univariate analysis.Results10-15 features relevant to describing gait in the context of increasing degrees of traumatic peripheral nerve injury were identified. Factor analysis uncovered relationships among the identified features and enabled the extrapolation of a set of latent factors that further described the distinct gait phenotypes. The latent factors tied to biological differences among the groups (e.g. alterations to the anatomical configuration of the limb due to transplantation or aberrant fine motor function due to peripheral nerve injury). Models trained using the identified features generated values that could be used to distinguish among pathophysiological states with high statistical significance (p < .001) and accuracy (>80%) as compared to univariate analysis alone.ConclusionThis is the first performance evaluation of a multivariate approach to gait analysis and the first demonstration of superior performance as compared to univariate gait analysis in animals. It is also the first study to use multivariate statistics to characterize and distinguish among different gradations of gait deficit in animals. This study contributes a comprehensive, multivariate characterization pipeline for application in the study of any pathologies in which gait is a quantitative translational outcome metric.

Fibrosis is involved in 45% of deaths in the United States, and no treatment exists to reverse the progression of the disease. Myofibroblasts are key to the progression and maintenance of fibrosis. We investigated features of cell adhesion necessary for monocytes to differentiate into myofibroblasts, seeking to identify pathways key to myofibroblast differentiation. Blocking antibodies against integrins α3, αM, and αMβ2 de-differentiate myofibroblasts in vitro, lower the pro-fibrotic secretome of myofibroblasts, and reverse lung and kidney fibrosis in vivo. Decorin’s collagen-binding peptide directs blocking antibodies (against integrins-α3, -αM, -αMβ2) to both fibrotic lungs and fibrotic kidneys, reducing the dose of antibody necessary to reverse fibrosis. This targeted immunotherapy blocking key integrins may be an effective therapeutic for the treatment and reversal of fibrosis. Blocking antibodies against integrins-α3, -αM, and -αMβ2 can be targeted to sites of fibrosis, reverse lung and kidney fibroses, and offer the potential to bring immunotherapy to fibrosis

Fibrosis is involved in 45% of deaths in the United States, and no treatment exists to reverse the progression of the disease. Myofibroblasts are key to the progression and maintenance of fibrosis. We investigated features of cell adhesion necessary for monocytes to differentiate into myofibroblasts, seeking to identify pathways key to myofibroblast differentiation. Blocking antibodies against integrins α3, αM, and αMβ2 de-differentiate myofibroblasts in vitro, lower the pro-fibrotic secretome of myofibroblasts, and reverse fibrosis in vivo. Blocking key integrins may be an effective therapeutic for the treatment and reversal of fibrosis. Competing Interest Statement MJVW and JH are authors on a patent regarding this submission Footnotes * This manuscript has been revised to include more data about kidney fibrosis, and to include a greater number of histology images. I've also cleaned up the explanatory language a bit This is the final version of the manuscript, and minor changes have been made for clarity, including adding 2 new authors