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IntroductionImmune dysfunction is important in aging and neurodegeneration; lacking clinically available tools limits research translation. We tested associations of cerebral spinal fluid (CSF) monocyte-to-lymphocyte ratio (MLR)-innate immune activation surrogate-with cognition in an aging and dementia cohort, hypothesizing that elevated MLR is associated with poorer executive functioning.MethodsCSF MLR was calculated in well-characterized, genotyped participants enrolled in studies of aging and dementia at University of California, San Francisco Memory and Aging Center (n = 199, mean age 57.5 years, SD 11.9). Linear models tested associations with episodic memory and executive function (verbal fluency, speeded set-shifting).ResultsAging was associated with higher CSF monocyte, lower lymphocyte counts, and higher MLRs (p

[This corrects the article DOI: 10.1371/journal.pone.0265254.].

Interplanetary space travel poses many hazards to the human body. To protect astronaut health and performance on critical missions, there is first a need to understand the effects of deep space hazards, including ionizing radiation, confinement, and altered gravity. Previous studies of rodents exposed to a single such stressor document significant deficits, but our study is the first to investigate possible cumulative and synergistic impacts of simultaneous ionizing radiation, confinement, and altered gravity on behavior and cognition. Our cohort was divided between 6-month-old female and male mice in group, social isolation, or hindlimb unloading housing, exposed to 0 or 50 cGy of 5 ion simplified simulated galactic cosmic radiation (GCRsim). We report interactions and independent effects of GCRsim exposure and housing conditions on behavioral and cognitive performance. Exposure to GCRsim drove changes in immune cell populations in peripheral blood collected early after irradiation, while housing conditions drove changes in blood collected at a later point. Female mice were largely resilient to deficits observed in male mice. Finally, we used principal component analysis to represent total deficits as principal component scores, which were predicted by general linear models using GCR exposure, housing condition, and early blood biomarkers.

Spinal cord injuries (SCIs) frequently occur in combination with other major organ injuries, such as traumatic brain injury (TBI) and injuries to the chest, abdomen, and musculoskeletal system (e.g., extremity, pelvic, and spine fractures). However, the effects of appendicular fractures on SCI recovery are poorly understood. We investigated whether the presence of SCI-concurrent appendicular fractures is predictive of a less robust SCI recovery. Patients enrolled in the Transforming Research and Clinical Knowledge in SCI (TRACK-SCI) prospective cohort study were identified and included in this secondary analysis study. Inclusion criteria resulted in 147 patients, consisting of 120 with isolated SCIs and 27 with concomitant appendicular fracture. The primary outcome was American Spinal Injury Association (ASIA) Impairment Scale (AIS) neurological grades at hospital discharge. Secondary outcomes included hospital length of stay, intensive care unit (ICU) length of stay, and AIS grade improvement during hospitalization. Multivariable binomial logistical regression analyses assessed whether SCI-concomitant appendicular fractures associate with SCI function and secondary outcomes. These analyses were adjusted for age, gender, injury severity, and non-fracture polytrauma. Appendicular fractures were associated with more severe AIS grades at hospital discharge, though covariate adjustments diminished statistical significance of this effect. Notably, non-fracture injuries to the chest and abdomen were influential covariates. Secondary analyses suggested that appendicular fractures also increased hospital length of stay. Our study indicated that SCI-associated polytrauma is important for predicting SCI functional outcomes. Further statistical evaluation is required to disentangle the effects of appendicular fractures, non-fracture solid organ injury, and SCI physiology to improve health outcomes among SCI patients.

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a 'data dump' that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on 'long-tail' preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields.

Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.

Diagnosis of spinal cord injury (SCI) severity at the ultra-acute stage is of great importance for emergency clinical care of patients as well as for potential enrollment into clinical trials. The lack of a diagnostic biomarker for SCI has played a major role in the poor results of clinical trials. We analyzed global gene expression in peripheral white blood cells during the acute injury phase and identified 197 genes whose expression changed after SCI compared with healthy and trauma controls and in direct relation to SCI severity. Unsupervised coexpression network analysis identified several gene modules that predicted injury severity (AIS grades) with an overall accuracy of 72.7% and included signatures of immune cell subtypes. Specifically, for complete SCIs (AIS A), ROC analysis showed impressive specificity and sensitivity (AUC: 0.865). Similar precision was also shown for AIS D SCIs (AUC: 0.938). Our findings indicate that global transcriptomic changes in peripheral blood cells have diagnostic and potentially prognostic value for SCI severity.

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. 'Syndromics' refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. 'Syndromics' refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients.

BackgroundPredicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.MethodsIntra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.ResultsNetwork analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery.ConclusionsWe show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention.FundingNIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).

Traumatic brain injury (TBI) is of particular concern for the aging community since there is both increased incidence of TBI and decreased functional recovery in this population. In addition, TBI is the strongest environmental risk factor for development of Alzheimer's disease and other dementia-related neurodegenerative disorders. Critical changes that affect cognition take place over time following the initial insult. Our previous work identified immune system activation as a key contributor to cognitive deficits observed in aged animals. Using a focal contusion model in the current study, we demonstrate a brain lesion and cavitation formation, as well as prolonged blood⁻brain barrier breakdown. These changes were associated with a prolonged inflammatory response, characterized by increased microglial cell number and phagocytic activity 30 days post injury, corresponding to significant memory deficits. We next aimed to identify the injury-induced cellular and molecular changes that lead to chronic cognitive deficits in aged animals, and measured increases in complement initiation components C1q, C3, and CR3, which are known to regulate microglial⁻synapse interactions. Specifically, we found significant accumulation of C1q on synapses within the hippocampus, which was paralleled by synapse loss 30 days post injury. We used genetic and pharmacological approaches to determine the mechanistic role of complement initiation on cognitive loss in aging animals after TBI. Notably, both genetic and pharmacological blockade of the complement pathway prevented memory deficits in aged injured animals. Thus, therapeutically targeting early components of the complement cascade represents a significant avenue for possible clinical intervention following TBI in the aging population.

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Traumatic brain injury (TBI) is a leading cause for neurological disabilities world-wide. TBI occurs most frequently among the elderly population, and elderly TBI survivors suffer from reduced recovery and poorer quality of life. The effect of age on the pathophysiology of TBI is still poorly understood. We previously established that peripherally-derived monocytes (CCR2⁺) infiltrate the injured brain and contribute to chronic TBI-induced cognitive deficits in young animals. Furthermore, age was shown to amplify monocyte infiltration acutely after injury. In the current study, we investigated the impact of age on the subchronic response of peripherally-derived monocytes (CD45hi; CCR2⁺) and their role in the development of chronic cognitive deficits. In the aged brain, there was a significant increase in the number of peripherally-derived monocytes after injury compared to young, injured animals. The infiltration rate of peripherally-derived monocytes remained elevated subchronically and corresponded with enhanced expression of CCR2 chemotactic ligands. Interestingly, the myeloid cell populations observed in injured aged brains had impaired anti-inflammatory responses compared to those in young animals. Additionally, in the aged animals, there was an expansion of the blood CCR2⁺ monocyte population after injury that was not present in the young animals. Importantly, knocking out CCR2 to inhibit infiltration of peripherally-derived monocytes prevented chronic TBI-induced spatial memory deficits in the aged mice. Altogether, these results demonstrate the critical effects of age on the peripherally-derived monocyte response during the progression of TBI pathophysiology.

Microglia are the main immune component in the brain that can regulate neuronal health and synapse function. Exposure to cosmic radiation can cause long-term cognitive impairments in rodent models thereby presenting potential obstacles for astronauts engaged in deep space travel. The mechanism/s for how cosmic radiation induces cognitive deficits are currently unknown. We find that temporary microglia depletion, one week after cosmic radiation, prevents the development of long-term memory deficits. Gene array profiling reveals that acute microglia depletion alters the late neuroinflammatory response to cosmic radiation. The repopulated microglia present a modified functional phenotype with reduced expression of scavenger receptors, lysosome membrane protein and complement receptor, all shown to be involved in microglia-synapses interaction. The lower phagocytic activity observed in the repopulated microglia is paralleled by improved synaptic protein expression. Our data provide mechanistic evidence for the role of microglia in the development of cognitive deficits after cosmic radiation exposure.

Complex alterations in cerebral energetic metabolism arise after traumatic brain injury (TBI). To date, methods allowing for metabolic evaluation are highly invasive, limiting our understanding of metabolic impairments associated with TBI pathogenesis. We investigated whether 13C MRSI of hyperpolarized (HP) [1-13C] pyruvate, a non-invasive metabolic imaging method, could detect metabolic changes in controlled cortical injury (CCI) mice (n = 57). Our results show that HP [1-13C] lactate-to-pyruvate ratios were increased in the injured cortex at acute (12/24 hours) and sub-acute (7 days) time points after injury, in line with decreased pyruvate dehydrogenase (PDH) activity, suggesting impairment of the oxidative phosphorylation pathway. We then used the colony-stimulating factor-1 receptor inhibitor PLX5622 to deplete brain resident microglia prior to and after CCI, in order to confirm that modulations of HP [1-13C] lactate-to-pyruvate ratios were linked to microglial activation. Despite CCI, the HP [1-13C] lactate-to-pyruvate ratio at the injury cortex of microglia-depleted animals at 7 days post-injury remained unchanged compared to contralateral hemisphere, and PDH activity was not affected. Altogether, our results demonstrate that HP [1-13C] pyruvate has great potential for in vivo non-invasive detection of cerebral metabolism post-TBI, providing a new tool to monitor the effect of therapies targeting microglia/macrophages activation after TBI.

Traumatic brain injury (TBI) is a leading cause of long-term neurological disability, yet the mechanisms underlying the chronic cognitive deficits associated with TBI remain unknown. Consequently, there are no effective treatments for patients suffering from the long-lasting symptoms of TBI. Here, we show that TBI persistently activates the integrated stress response (ISR), a universal intracellular signaling pathway that responds to a variety of cellular conditions and regulates protein translation via phosphorylation of the translation initiation factor eIF2α. Treatment with ISRIB, a potent drug-like small-molecule inhibitor of the ISR, reversed the hippocampal-dependent cognitive deficits induced by TBI in two different injury mouse models-focal contusion and diffuse concussive injury. Surprisingly, ISRIB corrected TBI-induced memory deficits when administered weeks after the initial injury and maintained cognitive improvement after treatment was terminated. At the physiological level, TBI suppressed long-term potentiation in the hippocampus, which was fully restored with ISRIB treatment. Our results indicate that ISR inhibition at time points late after injury can reverse memory deficits associated with TBI. As such, pharmacological inhibition of the ISR emerges as a promising avenue to combat head trauma-induced chronic cognitive deficits.

Traumatic brain injury (TBI) is a major cause of chronic disability in the world. Moderate to severe TBI often results in damage to the frontal lobe region and leads to cognitive, emotional, and social behavioral sequelae that negatively affect quality of life. More specifically, TBI patients often develop persistent deficits in social behavior, anxiety, and executive functions such as attention, mental flexibility, and task switching. These deficits are intrinsically associated with prefrontal cortex (PFC) functionality. Currently, there is a lack of analogous, behaviorally characterized TBI models for investigating frontal lobe injuries despite the prevalence of focal contusions to the frontal lobe in TBI patients. We used the controlled cortical impact (CCI) model in mice to generate a frontal lobe contusion and studied behavioral changes associated with PFC function. We found that unilateral frontal lobe contusion in mice produced long-term impairments to social recognition and reversal learning while having only a minor effect on anxiety and completely sparing rule shifting and hippocampal-dependent behavior.

In Huntington disease (HD), immune cells are activated before symptoms arise; however, it is unclear how the expression of mutant huntingtin (htt) compromises the normal functions of immune cells. Here we report that primary microglia from early postnatal HD mice were profoundly impaired in their migration to chemotactic stimuli, and expression of a mutant htt fragment in microglial cell lines was sufficient to reproduce these deficits. Microglia expressing mutant htt had a retarded response to a laser-induced brain injury in vivo. Leukocyte recruitment was defective upon induction of peritonitis in HD mice at early disease stages and was normalized upon genetic deletion of mutant htt in immune cells. Migration was also strongly impaired in peripheral immune cells from pre-manifest human HD patients. Defective actin remodeling in immune cells expressing mutant htt likely contributed to their migration deficit. Our results suggest that these functional changes may contribute to immune dysfunction and neurodegeneration in HD, and may have implications for other polyglutamine expansion diseases in which mutant proteins are ubiquitously expressed.

The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients. [ABSTRACT FROM AUTHOR]

It remains important to maximize energy density of wearable batteries. In addition, such batteries should be compliant, safe, and environmentally sustainable. Intrinsically safe zinc–manganese dioxide (Zn/MnO2) batteries are great candidates for powering wearables. However, achieving flexibility of these systems is hindered by the absence of a binder that ensures mechanical integrity of the MnO2 electrode composite. Herein, a unique approach to fabricate a mechanically robust MnO2 electrode is presented. Polyvinyl alcohol (PVA)/polyacrylic acid (PAA) gel cross‐linked in situ via thermal treatment is used as a binder for the electrode. Furthermore, energy density and rate capability of the printed battery electrodes are improved by replacing graphite with single‐walled carbon nanotubes (CNTs). The batteries retain 93% capacity when the discharge rate is increased from C/10 to C/3, as well as 97% of their capacity after being flexed. In contrast, batteries based on conventional composition retain 60% and 23% of the capacity, respectively. Finally, the battery with the modified electrode has high areal energy density of 4.8 mWh cm−2 and volumetric energy density of 320 mWh cm−3. [ABSTRACT FROM AUTHOR]

Background: Retinal atrophy in CNS neurodegenerative disease (NDD) has been reported by a growing literature base, suggesting that retinal changes may mirror, or even precede, neurodegeneration in the brain. However, understanding of how retinal and brain atrophy are associated in NDD is limited. In a large cohort of healthy aging controls (HC) and patients with confident diagnoses of NDD, we investigated if neuronal tissue loss in the unmyelinated retina is associated with whole brain grey (GM) and white (WM) matter volumes. Method: We performed optical coherence tomography (OCT) within six months of structural brain imaging (MRI) in 373 patients with NDD (256 frontotemporal lobar degeneration spectrum (FTLD), 87 Alzheimer's Disease (AD), 16 Lewy Body Disease (LBD), 12 prion disease, 2 with mixed etiology; age [years, mean±SD]:65.6±8.8, gender [male %]:51%, clinical dementia rating (CDR) [median (IQR)]:1 (0.5)) and 117 HC (age:73.8±9.5, gender:48%). 123 subjects (81 FTLD, 20 AD, 5 prion, 17 HC) had at least one follow up exam with both brain and retinal imaging (follow up time[years, median (IQR)]:1 (0.5)). Linear models correcting for age and total intracranial volume (TIV) were used to investigate the relationship between measurements of retinal thickness (µm), and whole brain GM and WM volumes (mm3). Associations between annualized rate of change were modeled for patients with follow up, and were not corrected for TIV. Result: Cross sectionally, macular thickness was associated with GM (0.62±0.20, p = 0.003) and WM (0.30±0.14, p = 0.030). Inner plexiform layer (IPL; 2.00±0.98, p = 0.038) and outer nuclear layer (ONL; 1.10±0.40, p = 0.005) were associated with GM, but not WM. Annualized rate of IPL (4.1±1.7, p = 0.016) and ONL (1.1±0.5, p = 0.034) change were associated with annualized rate of GM change, but not WM. In FTLD patients alone, rates of macular (2.7±0.6, p<0.001) and GCL (6.1±2, p = 0.005) change were also associated with GM change. Conclusion: Whole macular thickness and structurally relevant sublayers representing neuron cell bodies (GCL, ONL) and dendritic arbors (IPL, ONL), were associated with GM, but not WM volume changes in a cohort of HC and NDD, with overrepresentation of FTLD. While findings may be limited to FTLD, they support retinal imaging as a biomarker for CNS neurodegeneration. [ABSTRACT FROM AUTHOR]

Huntington's disease (HD) is caused by an expanded polyglutamine tract in the protein huntingtin (htt). Although HD has historically been viewed as a brain-specific disease, htt is expressed ubiquitously, and recent studies indicate that mutant htt might cause changes to the immune system that could contribute to pathogenesis. Monocytes from HD patients and mouse models are hyperactive in response to stimulation, and increased levels of inflammatory cytokines and chemokines are found in pre-manifest patients that correlate with pathogenesis. In this study, wild-type (WT) bone marrow cells were transplanted into two lethally irradiated transgenic mouse models of HD that ubiquitously express full-length htt (YAC128 and BACHD mice). Bone marrow transplantation partially attenuated hypokinetic and motor deficits in HD mice. Increased levels of synapses in the cortex were found in HD mice that received bone marrow transplants. Importantly, serum levels of interleukin-6, interleukin-10, CXC chemokine ligand 1, and interferon-&ggr; were significantly higher in HD than WT mice but were normalized in mice that received a bone marrow transplant. These results suggest that immune cell dysfunction might be an important modifier of pathogenesis in HD. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) initiates a robust innate immune response, which has been shown to persist for years following the initial event, ultimately affecting cognitive function. Surviving patients of TBI have increased cerebrospinal levels of the chemokine CCL2, suggesting a brain-peripheral dialogue involving CCR2 competent cells. We have recently shown that TBI induces the recruitment of peripheral CCR2+ macrophages to the brain at discreet time points following injury. However, themolecular signaling contribution of recruited macrophages to the TBI-damaged brain remains largely unknown. Herein, we used parabiontes consisting of a wildtype B.6 mouse paired to a CX3CR1GFP/+ CCR2RFP/+ mouse to accurately delineate the accumulation of three subpopulations of monocytes/macrophages; CX3CR1+CCR2- CX3CR1+ CCR2+, and CX3CR1- CCR2+ at multiple time points following injury. This approach allowed us to unambiguously track these donor (GFP+ or RFP+) monocyte/macrophage subpopulations in the injured WT animal. Data indicate that following TBI acrossmultiple time points, there is not a robust recruitment of peripheral CX3CR1+CCR2- monocytes/macrophages to the injured parenchyma. Next, we followed these experiments using FACS in the CX3CR1GFP/+ CCR2RFP/+ mouse at 24 hours post injury, which represented the largest influx of peripheral macrophages to the injured brain. FACS enriched microglia (CX3CR1+CCR2-), and two peripheral macrophage populations (CX3CR1+CCR2+ and CX3CR1-CCR2+) were analyzed for gene expression by RNA sequencing. RNAseq revealed unique heterogeneous signatures of these three enriched subpopulations. Importantly, among these three subpopulations of microglia/macrophages there was not a clear delineation of the M1/M2 polarization phenotypes, as each displayed simultaneous expression profiles. We next examined how deletion of CX3CR1 and CCR2 chemokine receptors altered these molecular signatures in tissue 24 hours after TBI. Lastly, we examined how age at the time of TBI as well as Cenicivoric, a novel small molecule antagonist of CCR2 affected these molecular signatures. Cumulatively, these data point toward a unique molecular contribution of peripheral-derivedmacrophages in the etiology of TBI-induced neuroinflammatory sequelae. [ABSTRACT FROM AUTHOR]

Background: Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients., Methods: Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods., Results: Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery., Conclusions: We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention., Funding: NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB)., Competing Interests: AT, JH, RE, DT, CA, JH, AC, DM, NS, BD, CS, JN, NK, DH, JT, GM, SD, WW, JB, MB, SM, JP, AF No competing interests declared, (© 2021, Torres-Espín et al.)