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Despite a half-century-long global eradication effort, polio continues to have a devastating impact on individuals and communities worldwide, especially in low-income countries affected by conflict or geographic barriers to immunization programs. In response, the World Health Organization (WHO) Global Polio Eradication Initiative (GPEI) employs disease surveillance and vaccination campaigns coordinated through the WHO Regional Office for Africa (AFRO) Geographic Information System (GIS) Centre. Established in 2017, the AFRO GIS Centre played a key role in the eradication of wild-type polioviruses (WPVs) in 2020, but the COVID-19 pandemic, emergence of circulating vaccine-derived polioviruses, and transmission of WPV1 from Central Asia have led to a resurgence of polio in Sub-Saharan Africa. The AFRO GIS comprises a set of mobile device or cloud-based tools for geospatial data collection, analysis, and visualization. Using tools such as Auto-Visual Acute Flaccid Paralysis Detection and Reporting, electronic surveillance, and Integrated Supportive Supervision, GIS personnel collect polio case numbers and locations, track field worker activities, follow the movements of nomadic populations vulnerable to polio and other diseases, and determine needs for further healthcare deployments. The system is location specific and operates in real time, enabling the AFRO GIS to promptly target its responses to polio, COVID-19, Ebola virus disease, and other public health crises and natural disasters. The present review describes the components of the AFRO GIS and how the AFRO GIS Centre coordinated on-the-ground polio eradication efforts to help secure Africa's certification as WPV free. It also examines current and prospective challenges regarding other disease outbreaks in the COVID-19 era and how the AFRO GIS Centre is addressing these ongoing public health needs.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders' development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females.

We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others.

ObjectiveTo explore the nature of genetic-susceptibility to multiple sclerosis (MS) in African-Americans.BackgroundRecently, the number of genetic-associations with MS has exploded although the MS-associations of specific haplotypes within the major histocompatibility complex (MHC) have been known for decades. For example, the haplotypes HLA-DRB1*15:01~HLA-DQB1*06:02, and HLA-DRB1*03:01~ HLA-DQB1*02:01 have odds ratios (ORs) for an MS-association orders of magnitude stronger than many of these newly-discovered associations. Nevertheless, all these haplotypes are part of much larger conserved extended haplotypes (CEHs), which span both the Class I and Class II MHC regions. African-Americans are at greater risk of developing MS compared to a native Africans but at lesser risk compared to Europeans. It is the purpose of this manuscript to explore the relationship between MS-susceptibility and the CEH make-up of our African-American cohort.Design/methodsThe African-American (AA) cohort consisted of 1,305 patients with MS and 1,155 controls, who self-identified as being African-American. For comparison, we used the 18,492 controls and 11,144 MS-cases from the predominantly European Wellcome Trust Case Control Consortium (WTCCC) and the 28,557 phased native Africans from the multinational "Be the Match" registry. The WTCCC and the African-Americans were phased at each of five HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRB1 and HLA-DQB1) and the at 11 SNPs (10 of which were in non-coding regions) surrounding the Class II region of the DRB1 gene using previously-published probabilistic phasing algorithms.ResultsOf the 32 most frequent CEHs, 18 (56%) occurred either more frequently or exclusively in Africans) whereas 9 (28%) occurred more frequently or exclusively in Europeans. The remaining 5 CEHs occurred in neither control group although, likely, these were African in origin. Eight of these CEHs carried the DRB1*15:03~DQB1*06:02~a36 haplotype and three carried the DRB1*15:01~DQB1*06:02~a1 haplotype. In African Americans, a single-copy of the European CEH (03:01_07:02_07:02_15:01_06:02_a1) was associated with considerable MS-risk (OR = 3.30; p = 0.0001)-similar to that observed in the WTCCC (OR = 3.25; p

IntroductionArtificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI.MethodsWe followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis.ResultsBased on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship.ConclusionOur findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care.

ObjectiveTo explore and describe the basis and implications of genetic and environmental susceptibility to multiple sclerosis (MS) using the Canadian population-based data.BackgroundCertain parameters of MS-epidemiology are directly observable (e.g., the recurrence-risk of MS in siblings and twins, the proportion of women among MS patients, the population-prevalence of MS, and the time-dependent changes in the sex-ratio). By contrast, other parameters can only be inferred from the observed parameters (e.g., the proportion of the population that is "genetically susceptible", the proportion of women among susceptible individuals, the probability that a susceptible individual will experience an environment "sufficient" to cause MS, and if they do, the probability that they will develop the disease).Design/methodsThe "genetically susceptible" subset (G) of the population (Z) is defined to include everyone with any non-zero life-time chance of developing MS under some environmental conditions. The value for each observed and non-observed epidemiological parameter is assigned a "plausible" range. Using both a Cross-sectional Model and a Longitudinal Model, together with established parameter relationships, we explore, iteratively, trillions of potential parameter combinations and determine those combinations (i.e., solutions) that fall within the acceptable range for both the observed and non-observed parameters.ResultsBoth Models and all analyses intersect and converge to demonstrate that probability of genetic-susceptibitly, P(G), is limited to only a fraction of the population {i.e., P(G) ≤ 0.52)} and an even smaller fraction of women {i.e., P(G│F) < 0.32)}. Consequently, most individuals (particularly women) have no chance whatsoever of developing MS, regardless of their environmental exposure. However, for any susceptible individual to develop MS, requires that they also experience a "sufficient" environment. We use the Canadian data to derive, separately, the exponential response-curves for men and women that relate the increasing likelihood of developing MS to an increasing probability that a susceptible individual experiences an environment "sufficient" to cause MS. As the probability of a "sufficient" exposure increases, we define, separately, the limiting probability of developing MS in men (c) and women (d). These Canadian data strongly suggest that: (c < d ≤ 1). If so, this observation establishes both that there must be a "truly" random factor involved in MS pathogenesis and that it is this difference, rather than any difference in genetic or environmental factors, which primarily accounts for the penetrance difference between women and men.ConclusionsThe development of MS (in an individual) requires both that they have an appropriate genotype (which is uncommon in the population) and that they have an environmental exposure "sufficient" to cause MS given their genotype. Nevertheless, the two principal findings of this study are that: P(G) ≤ 0.52)} and: (c < d ≤ 1). Threfore, even when the necessary genetic and environmental factors, "sufficient" for MS pathogenesis, co-occur for an individual, they still may or may not develop MS. Consequently, disease pathogenesis, even in this circumstance, seems to involve an important element of chance. Moreover, the conclusion that the macroscopic process of disease development for MS includes a "truly" random element, if replicated (either for MS or for other complex diseases), provides empiric evidence that our universe is non-deterministic.

Despite a half-century-long global eradication effort, polio continues to have a devastating impact on individuals and communities worldwide, especially in low-income countries affected by conflict or geographic barriers to immunization programs. In response, the World Health Organization (WHO) Global Polio Eradication Initiative (GPEI) employs disease surveillance and vaccination campaigns coordinated through the WHO Regional Office for Africa (AFRO) Geographic Information System (GIS) Centre. Established in 2017, the AFRO GIS Centre played a key role in the eradication of wild-type polioviruses (WPVs) in 2020, but the COVID-19 pandemic, emergence of circulating vaccine-derived polioviruses, and transmission of WPV1 from Central Asia have led to a resurgence of polio in Sub-Saharan Africa. The AFRO GIS comprises a set of mobile device or cloud-based tools for geospatial data collection, analysis, and visualization. Using tools such as Auto-Visual Acute Flaccid Paralysis Detection and Reporting, electronic surveillance, and Integrated Supportive Supervision, GIS personnel collect polio case numbers and locations, track field worker activities, follow the movements of nomadic populations vulnerable to polio and other diseases, and determine needs for further healthcare deployments. The system is location specific and operates in real time, enabling the AFRO GIS to promptly target its responses to polio, COVID-19, Ebola virus disease, and other public health crises and natural disasters. The present review describes the components of the AFRO GIS and how the AFRO GIS Centre coordinated on-the-ground polio eradication efforts to help secure Africa's certification as WPV free. It also examines current and prospective challenges regarding other disease outbreaks in the COVID-19 era and how the AFRO GIS Centre is addressing these ongoing public health needs. [ABSTRACT FROM AUTHOR]

Purpose: To create and validate an automated pipeline for detection of early signs of irreversible ischemic change from admission CTA in patients with large vessel occlusion (LVO) stroke. Methods: We retrospectively included 368 patients for training and 143 for external validation. All patients had anterior circulation LVO stroke, endovascular therapy with successful reperfusion, and follow-up diffusion-weighted imaging (DWI). We devised a pipeline to automatically segment Alberta Stroke Program Early CT Score (ASPECTS) regions and extracted their relative Hounsfield unit (rHU) values. We determined the optimal rHU cut points for prediction of final infarction in each ASPECT region, performed 10-fold cross-validation in the training set, and measured the performance via external validation in patients from another institute. We compared the model with an expert neuroradiologist for prediction of final infarct volume and poor functional outcome. Results: We achieved a mean area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of 0.69±0.13, 0.69±0.09, 0.61±0.23, and 0.72±0.11 across all regions and folds in cross-validation. In the external validation cohort, we achieved a median [interquartile] AUC, accuracy, sensitivity, and specificity of 0.71 [0.68–0.72], 0.70 [0.68–0.73], 0.55 [0.50–0.63], and 0.74 [0.73–0.77], respectively. The rHU-based ASPECTS showed significant correlation with DWI-based ASPECTS (rS = 0.39, p<0.001) and final infarct volume (rS = -0.36, p<0.001). The AUC for predicting poor functional outcome was 0.66 (95%CI: 0.57–0.75). The predictive capabilities of rHU-based ASPECTS were not significantly different from the neuroradiologist's visual ASPECTS for either final infarct volume or functional outcome. Conclusions: Our study demonstrates the feasibility of an automated pipeline and predictive model based on relative HU attenuation of ASPECTS regions on baseline CTA and its non-inferior performance in predicting final infarction on post-stroke DWI compared to an expert human reader. [ABSTRACT FROM AUTHOR]

ObjectiveTo determine the relationship between highly-conserved extended-haplotypes (CEHs) in the major histocompatibility complex (MHC) and MS-susceptibility.BackgroundAmong the ~200 MS-susceptibility regions, which are known from genome-wide analyses of single nucleotide polymorphisms (SNPs), the MHC accounts for roughly a third of the currently explained variance and the strongest MS-associations are for certain Class II alleles (e.g., HLA-DRB1*15:01; HLA-DRB1*03:01; and HLA-DRB1*13:03), which frequently reside on CEHs within the MHC.Design/methodsAutosomal SNPs (441,547) from 11,376 MS cases and 18,872 controls in the WTCCC dataset were phased. The most significant MS associated SNP haplotype was composed of 11 SNPs in the MHC Class II region surrounding the HLA-DRB1 gene. We also phased alleles at the HLA-A, HLA-C, HLA-B, HLA-DRB1, and HLA-DQB1 loci. This data was used to probe the relationship between CEHs and MS susceptibility.ResultsWe phased a total of 59,884 extended haplotypes (HLA-A, HLA-C, HLA-B, HLA-DRB1, HLA-DQB1 and SNP haplotypes) from 29,942 individuals. Of these, 10,078 unique extended haplotypes were identified. The 10 most common CEHs accounted for 22% (13,302) of the total. By contrast, the 8,446 least common extended haplotypes also accounted for approximately 20% (12,298) of the total. This extreme frequency-disparity among extended haplotypes necessarily complicates interpretation of reported disease-associations with specific HLA alleles. In particular, the HLA motif HLA-DRB1*15:01~HLA-DQB1*06:02 is strongly associated with MS risk. Nevertheless, although this motif is almost always found on the a1 SNP haplotype, it can rarely be found on others (e.g., a27 and a36), and, in these cases, it seems to have no apparent disease-association (OR = 0.7; CI = 0.3-1.3 and OR = 0.7; CI = 0.2-2.2, respectively). Furthermore, single copy carriers of the a1 SNP-haplotype without this HLA motif still have an increased disease risk (OR = 2.2; CI = 1.2-3.8). In addition, even among the set of CEHs, which carry the Class II motif of HLA-DRB1*15:01~HLA-DQB1*06:02~a1, different CEHs have differing strengths in their MS-associations.ConclusionsThe MHC in diverse human populations consists, primarily, of a very small collection of very highly-selected CEHs. Our findings suggest that the MS-association with the HLA-DRB1*15:01~HLA-DQB1*06:02 haplotype may be due primarily to the combined attributes of the CEHs on which this particular HLA-motif often resides.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders' development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females. [ABSTRACT FROM AUTHOR]

Glaucoma is the leading cause of irreversible blindness worldwide. Therapies for glaucoma are directed toward reducing intraocular pressure (IOP), the leading risk factor and only reliable therapeutic target via topical medications or with procedural intervention including laser or surgery. Though topical therapeutics are typically first line, less than 50% of patients take drops as prescribed. Sustained release technologies that decrease IOP for extended periods of time are being examined for clinical use. We recently identified Stanniocalcin-1, a naturally occurring hormone, as an IOP-lowering agent. Here, we show that a single injection into the anterior chamber of mice with an adeno-associated viral vector containing the transgene of stanniocalcin-1 results in diffuse and sustained expression of the protein and produces IOP reduction for up to 6 months. As the treatment effect begins to wane, IOP-lowering can be rescued with a repeat injection. Aqueous humor dynamic studies revealed an increase in outflow facility as the mechanism of action. This first-in-class therapeutic approach has the potential to improve care and reduce the rates of vision loss in the 80 million people worldwide currently affected by glaucoma.

BackgroundPlay is an important childhood occupation and a medium for development. Parents' attitudes towards play, knowledge about play and its benefits, and their experiences in facilitating effective play are key factors that determine the experiences of play in children. These factors related to parent's understanding and experiences of play gain additional significance when the child has a disability. The aim of this systematic review is to synthesize the available evidence on awareness, knowledge, and experience of play among parents and to summarise the findings.MethodMEDLINE, CINAHL, APA PsycINFO, Psychology Database, Sociological Abstracts, EMBASE, and Cochrane Collection Plus will be searched for studies of any design that investigate awareness, knowledge, and experience of play among parents and its benefits to child development. Manual searches from reference lists of relevant papers will also be completed. The primary outcomes are parents' knowledge (what constitutes play), experiences (what parents feel about play) and awareness (benefits of play) about play. Three independent reviewers will screen identified papers with pre-defined eligibility criteria and extract data using a customized extraction form. Discrepancies will be resolved in discussion with a fourth reviewer. A synthesis of eligible studies and summary will be provided.DiscussionA systematic review of quantitative and qualitative research evidence of parents' awareness, knowledge, and experiences in play will be carried out. This will highlight parents' own views on play among their children, current theories/domains related to parents' awareness, knowledge, and experience in play, and outcome measures that have been used. In addition, comparison among views of parents of children with disabilities and parents of typically developing children will be made. The results will be presented as a summary of key findings under the themes of awareness, knowledge, and experience of parents in play.

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs’ predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network’s learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject's trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Objective To explore the nature of genetic-susceptibility to multiple sclerosis (MS) in African-Americans. Background Recently, the number of genetic-associations with MS has exploded although the MS-associations of specific haplotypes within the major histocompatibility complex (MHC) have been known for decades. For example, the haplotypes HLA-DRB1*15:01~HLA-DQB1*06:02, and HLA-DRB1*03:01~ HLA-DQB1*02:01 have odds ratios (ORs) for an MS-association orders of magnitude stronger than many of these newly-discovered associations. Nevertheless, all these haplotypes are part of much larger conserved extended haplotypes (CEHs), which span both the Class I and Class II MHC regions. African-Americans are at greater risk of developing MS compared to a native Africans but at lesser risk compared to Europeans. It is the purpose of this manuscript to explore the relationship between MS-susceptibility and the CEH make-up of our African-American cohort. Design/methods The African-American (AA) cohort consisted of 1,305 patients with MS and 1,155 controls, who self-identified as being African-American. For comparison, we used the 18,492 controls and 11,144 MS-cases from the predominantly European Wellcome Trust Case Control Consortium (WTCCC) and the 28,557 phased native Africans from the multinational “Be the Match” registry. The WTCCC and the African-Americans were phased at each of five HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRB1 and HLA-DQB1) and the at 11 SNPs (10 of which were in non-coding regions) surrounding the Class II region of the DRB1 gene using previously-published probabilistic phasing algorithms. Results Of the 32 most frequent CEHs, 18 (56%) occurred either more frequently or exclusively in Africans) whereas 9 (28%) occurred more frequently or exclusively in Europeans. The remaining 5 CEHs occurred in neither control group although, likely, these were African in origin. Eight of these CEHs carried the DRB1*15:03~DQB1*06:02~a36 haplotype and three carried the DRB1*15:01~DQB1*06:02~a1 haplotype. In African Americans, a single-copy of the European CEH (03:01_07:02_07:02_15:01_06:02_a1) was associated with considerable MS-risk (OR = 3.30; p = 0.0001)–similar to that observed in the WTCCC (OR = 3.25; pConclusions The common CEHs in African Americans are divisible into those that are either African or European in origin, which are derived without modification from their source population. European CEHs, linked to MS-risk, in general, had similar impacts in African-Americans as they did in Europeans. By contrast, African CEHs had mixed MS-risks. For a few, the MS-risk exceeded that in a neutral-reference group whereas, for many others, these CEHs were “protective”–perhaps providing a partial rationale for the lower MS-risk in African-Americans compared to European-Americans.

ObjectiveTo understand the nature of genetic and environmental susceptibility to multiple sclerosis (MS) and, by extension, susceptibility to other complex genetic diseases.BackgroundCertain basic epidemiological parameters of MS (e.g., population-prevalence of MS, recurrence-risks for MS in siblings and twins, proportion of women among MS patients, and the time-dependent changes in the sex-ratio) are well-established. In addition, more than 233 genetic-loci have now been identified as being unequivocally MS-associated, including 32 loci within the major histocompatibility complex (MHC), and one locus on the X chromosome. Despite this recent explosion in genetic associations, however, the association of MS with the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 (H+) haplotype has been known for decades.Design/methodsWe define the "genetically-susceptible" subset (G) to include everyone with any non-zero life-time chance of developing MS. Individuals who have no chance of developing MS, regardless of their environmental experiences, belong to the mutually exclusive "non-susceptible" subset (G-). Using these well-established epidemiological parameters, we analyze, mathematically, the implications that these observations have regarding the genetic-susceptibility to MS. In addition, we use the sex-ratio change (observed over a 35-year interval in Canada), to derive the relationship between MS-probability and an increasing likelihood of a sufficient environmental exposure.ResultsWe demonstrate that genetic-susceptibitly is confined to less than 7.3% of populations throughout Europe and North America. Consequently, more than 92.7% of individuals in these populations have no chance whatsoever of developing MS, regardless of their environmental experiences. Even among carriers of the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype, far fewer than 32% can possibly be members the (G) subset. Also, despite the current preponderance of women among MS patients, women are less likely to be in the susceptible (G) subset and have a higher environmental threshold for developing MS compared to men. Nevertheless, the penetrance of MS in susceptible women is considerably greater than it is in men. Moreover, the response-curves for MS-probability in susceptible individuals increases with an increasing likelihood of a sufficient environmental exposure, especially among women. However, these environmental response-curves plateau at under 50% for women and at a significantly lower level for men.ConclusionsThe pathogenesis of MS requires both a genetic predisposition and a suitable environmental exposure. Nevertheless, genetic-susceptibility is rare in the population (< 7.3%) and requires specific combinations of non-additive genetic risk-factors. For example, only a minority of carriers of the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype are even in the (G) subset and, thus, genetic-susceptibility to MS in these carriers must result from the combined effect this haplotype together with the effects of certain other (as yet, unidentified) genetic factors. By itself, this haplotype poses no MS-risk. By contrast, a sufficient environmental exposure (however many events are involved, whenever these events need to act, and whatever these events might be) is common, currently occurring in, at least, 76% of susceptible individuals. In addition, the fact that environmental response-curves plateau well below 50% (especially in men), indicates that disease pathogenesis is partly stochastic. By extension, other diseases, for which monozygotic-twin recurrence-risks greatly exceed the disease-prevalence (e.g., rheumatoid arthritis, diabetes, and celiac disease), must have a similar genetic basis.

State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods.

The impacts of urbanization, such as urban heat island (UHI) and nutrient loads, can influence tree function through altered physiology and metabolism and stress response, which has implications for urban forest health in cities across the world. Our goal was to compare growth-stimulating and stress-mitigating acclimation patterns of red maple (Acer rubrum) trees in deciduous forests embedded in a small (Newark, DE, US) and a large (Philadelphia, PA, US) city. The study was conducted in a long-term urban forest network on seventy-nine mature red maple trees spanning ten forests across Newark and Philadelphia. We hypothesized that red maples in Philadelphia forests compared to Newark forests will be healthier and more acclimated to warmer temperatures, elevated CO2 concentrations and reactive nitrogen (Nr) deposition, and higher nutrient/heavy metal loads. Therefore, these red maples will have higher foliar pigments, nutrients, and stress-indicating elements, enriched δ15N isotopes and increased free polyamines and amino acids to support a growth-stimulating and stress-induced response to urbanization. Our results indicate red maples are potentially growth-stimulated and stress-acclimated in Philadelphia forests experiencing a greater magnitude of urban intensity. Red maples in Philadelphia forests contained higher concentrations of foliar chlorophyll, %N, δ15N, and nutrients than those in Newark forests. Similarly, lower foliar magnesium and manganese, and higher foliar zinc, cadmium, lead, and aluminum reflected the difference in soil biogeochemistry in Philadelphia forests. Accumulation patterns of foliar free amino acids, polyamines, phosphorous, and potassium ions in red maples in Philadelphia forests shows a reallocation in cellular metabolism and nutrient uptake pathways responsible for physiological acclimation. Our results suggest the approach used here can serve as a model for investigating 'plant physiology' and the use of urban trees as a biomonitor of the impacts of 'urban pollution' on urban forests. The results suggest that cellular oxidative stress in trees caused by pollutant uptake is mitigated by the accumulation of free amino acids, polyamines, and nutrients in a larger city. Our study provides a framework for determining whether trees respond to complex urban environments through stress memory and/or acclimation.

While it is still not possible to describe the neuronal-level connections of the human brain, we can map the human connectome with several hundred vertices, by the application of diffusion-MRI based techniques. In these graphs, the nodes correspond to anatomically identified gray matter areas of the brain, while the edges correspond to the axonal fibers, connecting these areas. In our previous contributions, we have described numerous graph-theoretical phenomena of the human connectomes. Here we map the frequent complete subgraphs of the human brain networks: in these subgraphs, every pair of vertices is connected by an edge. We also examine sex differences in the results. The mapping of the frequent subgraphs gives robust substructures in the graph: if a subgraph is present in the 80% of the graphs, then, most probably, it could not be an artifact of the measurement or the data processing workflow. We list here the frequent complete subgraphs of the human braingraphs of 413 subjects (238 women and 175 men), each with 463 nodes, with a frequency threshold of 80%, and identify 812 complete subgraphs, which are more frequent in male and 224 complete subgraphs, which are more frequent in female connectomes.

In the study of the human connectome, the vertices and the edges of the network of the human brain are analyzed: the vertices of the graphs are the anatomically identified gray matter areas of the subjects; this set is exactly the same for all the subjects. The edges of the graphs correspond to the axonal fibers, connecting these areas. In the biological applications of graph theory, it happens very rarely that scientists examine numerous large graphs on the very same, labeled vertex set. Exactly this is the case in the study of the connectomes. Because of the particularity of these sets of graphs, novel, robust methods need to be developed for their analysis. Here we introduce the new method of the Frequent Network Neighborhood Mapping for the connectome, which serves as a robust identification of the neighborhoods of given vertices of special interest in the graph. We apply the novel method for mapping the neighborhoods of the human hippocampus and discover strong statistical asymmetries between the connectomes of the sexes, computed from the Human Connectome Project. We analyze 413 braingraphs, each with 463 nodes. We show that the hippocampi of men have much more significantly frequent neighbor sets than women; therefore, in a sense, the connections of the hippocampi are more regularly distributed in men and more varied in women. Our results are in contrast to the volumetric studies of the human hippocampus, where it was shown that the relative volume of the hippocampus is the same in men and women.

Cancer cells harness immune checkpoints such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1) and indoleamine 2,3-dioxygenase 1 (IDO1) to evade immune control. Checkpoint inhibitors have demonstrated durable anti-tumor efficacy in human and preclinical models. Liver toxicity is one of the common immune-related adverse events associated with checkpoint inhibitors (CPIs) and its frequency and severity often increase significantly during CPI combination therapies. We aim to develop a mouse model to elucidate the immune mechanisms of CPI-associated liver toxicity. Co-administration of CTLA-4 blocking antibody, 9D9, and/or an IDO1 inhibitor, epacadostat in wild-type and PD-1-/- mice (to simulate the effect of PD1 blockade) synergistically induced liver injury and immune cell infiltration. Infiltrated cells were primarily composed of CD8+ T cells and positively associated with hepatocyte necrosis. Strikingly, sites of hepatocyte necrosis were frequently surrounded by clusters of mononuclear immune cells. CPI treatments resulted in increased expression of genes associated with hepatocyte cell death, leukocyte migration and T cell activation in the liver. In conclusion, blockade of immune checkpoints PD-1, CTLA-4, and IDO1 act synergistically to enhance T cell infiltration and activity in the liver, leading to hepatocyte death.

Here we show a method of directing the edges of the connectomes, prepared from HARDI datasets from the human brain. Before the present work, no high-definition directed braingraphs were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the "Consensus Connectome Dynamics", described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed connectomes publicly available at the site http://braingraph.org. We also show the robustness of our edge directing method in four independently chosen connectome datasets: we have found that 86% of the edges, which were present in all four datasets, get the same directions in all datasets; therefore the direction method is robust. While our new edge-directing method still needs more empirical validation, we think that our present contribution opens up new possibilities in the analysis of the high-definition human connectome.

The phenomenon of 'microdosing', that is, regular ingestion of very small quantities of psychedelic substances, has seen a rapid explosion of popularity in recent years. Individuals who microdose report minimal acute effects from these substances yet claim a range of long-term general health and wellbeing benefits. There have been no published empirical studies of microdosing and the current legal and bureaucratic climate makes direct empirical investigation of the effects of psychedelics difficult. In Study One we conducted a systematic, observational investigation of individuals who microdose. We tracked the experiences of 98 microdosing participants, who provided daily ratings of psychological functioning over a six week period. 63 of these additionally completed a battery of psychometric measures tapping mood, attention, wellbeing, mystical experiences, personality, creativity, and sense of agency, at baseline and at completion of the study. Analyses of daily ratings revealed a general increase in reported psychological functioning across all measures on dosing days but limited evidence of residual effects on following days. Analyses of pre and post study measures revealed reductions in reported levels of depression and stress; lower levels of distractibility; increased absorption; and increased neuroticism. To better understand these findings, in Study Two we investigated pre-existing beliefs and expectations about the effects of microdosing in a sample of 263 naïve and experienced microdosers, so as to gauge expectancy bias. All participants believed that microdosing would have large and wide-ranging benefits in contrast to the limited outcomes reported by actual microdosers. Notably, the effects believed most likely to change were unrelated to the observed pattern of reported outcomes. The current results suggest that dose controlled empirical research on the impacts of microdosing on mental health and attentional capabilities are needed.

From a large clinical blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) study, we report several interrelated findings involving transient supra-network brainwide states characterized by a saturation phenomenon we are referring to as "polarization." These are whole-brain states in which the voxelwise-normalized BOLD (vn-BOLD) activation of a large proportion of voxels is simultaneously either very high or very low. The presence of such states during a resting-state fMRI (rs-fMRI) scan is significantly anti-correlated with diagnosed schizophrenia, significantly anti-correlated with connectivity between subcortical networks and auditory, visual and sensorimotor networks and also significantly anti-correlated with contemporaneous occupancy of transient functional network connectivity states featuring broad disconnectivity or strong inhibitory connections between the default mode and other networks. Conversely, the presence of highly polarized vn-BOLD states is significantly correlated with connectivity strength between auditory, visual and sensorimotor networks and with contemporaneous occupancy of transient whole-brain patterns of strongly modularized network connectivity and diffuse hyperconnectivity. Despite their consistency with well-documented effects of schizophrenia on static and time-varying functional network connectivity, the observed relationships between polarization and network connectivity are with very few exceptions unmediated by schizophrenia diagnosis. Many differences observed between patients and controls are echoed within the patient population itself in the effect patterns of positive symptomology (e.g. hallucinations, delusions, grandiosity). Our findings highlight a particular whole-brain spatiotemporal BOLD activation phenomenon that differs markedly between healthy subjects and schizophrenia patients, one that also strongly informs time-resolved network connectivity patterns that are associated with this serious clinical disorder.

Semaphorins are a diverse family of immunoregulators recently recognized to play a major role in various phases of immune responses. Their role in chronic viral hepatitis C (CHC) and contribution to the progression of liver disease is unknown. The aim of this study was to analyse the association of secreted semaphorins with the severity of liver disease in patients with CHC. Serum concentrations of semaphorins were measured in 114 treatment-naive CHC patients and 36 healthy controls. Serum concentrations of SEMA3A, SEMA3C, SEMA5A, SEMA6B and SEMA6D were significantly increased in patients with CHC compared to controls. While serum concentrations of SEMA3C and SEMA6D significantly increased with fibrosis stage in both HCV-g1 and HCV-g3 infections, the concentration of SEMA5A inversely correlated with fibrosis stage in both HCV genotypes. ROC analysis showed that serum concentrations of SEMA3C (>4.0ng/mL, AUC 0.88) and SEMA6D (>4.5, AUC 0.82) had higher AUC than widely used APRI (AUC 0.71) and FIB-4 (AUC 0.74) scores. Serum concentrations of SEMA3C and SEMA6D significantly decreased after DAA and PEG IFN-α/ribavirin therapy, while the serum concentration of SEMA5A significantly increased after DAAs therapy. Immunohistochemistry confirmed the expression of SEMA3C and SEMA5A in hepatocytes, endothelial cells and lymphocytes of cirrhotic livers from CHC patients but not in controls. In conclusion, we provide the first evidence that SEMA3C, SEMA5A and SEMA6D can be considered as markers of liver injury in CHC.

To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping.A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis.Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance.The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization.

To determine the relationship between highly-conserved extended-haplotypes (CEHs) in the major histocompatibility complex (MHC) and MS-susceptibility.Among the ~200 MS-susceptibility regions, which are known from genome-wide analyses of single nucleotide polymorphisms (SNPs), the MHC accounts for roughly a third of the currently explained variance and the strongest MS-associations are for certain Class II alleles (e.g., HLA-DRB1*15:01; HLA-DRB1*03:01; and HLA-DRB1*13:03), which frequently reside on CEHs within the MHC.Autosomal SNPs (441,547) from 11,376 MS cases and 18,872 controls in the WTCCC dataset were phased. The most significant MS associated SNP haplotype was composed of 11 SNPs in the MHC Class II region surrounding the HLA-DRB1 gene. We also phased alleles at the HLA-A, HLA-C, HLA-B, HLA-DRB1, and HLA-DQB1 loci. This data was used to probe the relationship between CEHs and MS susceptibility.We phased a total of 59,884 extended haplotypes (HLA-A, HLA-C, HLA-B, HLA-DRB1, HLA-DQB1 and SNP haplotypes) from 29,942 individuals. Of these, 10,078 unique extended haplotypes were identified. The 10 most common CEHs accounted for 22% (13,302) of the total. By contrast, the 8,446 least common extended haplotypes also accounted for approximately 20% (12,298) of the total. This extreme frequency-disparity among extended haplotypes necessarily complicates interpretation of reported disease-associations with specific HLA alleles. In particular, the HLA motif HLA-DRB1*15:01~HLA-DQB1*06:02 is strongly associated with MS risk. Nevertheless, although this motif is almost always found on the a1 SNP haplotype, it can rarely be found on others (e.g., a27 and a36), and, in these cases, it seems to have no apparent disease-association (OR = 0.7; CI = 0.3-1.3 and OR = 0.7; CI = 0.2-2.2, respectively). Furthermore, single copy carriers of the a1 SNP-haplotype without this HLA motif still have an increased disease risk (OR = 2.2; CI = 1.2-3.8). In addition, even among the set of CEHs, which carry the Class II motif of HLA-DRB1*15:01~HLA-DQB1*06:02~a1, different CEHs have differing strengths in their MS-associations.The MHC in diverse human populations consists, primarily, of a very small collection of very highly-selected CEHs. Our findings suggest that the MS-association with the HLA-DRB1*15:01~HLA-DQB1*06:02 haplotype may be due primarily to the combined attributes of the CEHs on which this particular HLA-motif often resides.

We have previously demonstrated that arginine-rich and poly-arginine peptides possess potent neuroprotective properties, with poly-arginine peptide R18 identified as being highly effective at reducing infarct volume following middle cerebral artery occlusion (MCAO) in the Sprague Dawley rat. Since peptides synthesised using D-isoform amino acids have greater stability than L-isoform peptides due to increased resistance to proteolytic degradation, they represent potentially more effective peptide therapeutics. Therefore we compared the neuroprotective efficacy of R18 and its D-enantiomer R18D following permanent MCAO in the Wistar rat. Furthermore, as increased peptide stability may also increase peptide toxicity, we examined the effects of R18 and R18D on cultured cortical neurons, astrocytes, brain endothelial cells (bEND.3), and embryonic kidney cells (HEK293) following a 10-minute or 24-hour peptide exposure duration. The in vivo studies demonstrated that R18D resulted in a greater reduction in mean infarct volume compared to R18 (33%, p = 0.004 vs 12%, p = 0.27) after intravenous administration at 300 nmol/kg 30 minutes after MCAO. Both R18D and R18 reduced cerebral hemisphere swelling to a comparable degree (27%, p = 0.03 and 30%, p = 0.02), and improved neurological assessment scores (1.5, p = 0.02 and 2, p = 0.058 vs 3 for vehicle). No abnormal histological findings specific to peptide treatments were observed in hematoxylin and eosin stained sections of kidney, liver, spleen, lung and heart. In vitro studies demonstrated that R18 and R18D were most toxic to neurons, followed by astrocytes, HEK293 and bEND.3 cells, but only at high concentrations and/or following 24-hour exposure. These findings further highlight the neuroprotective properties of poly-arginine peptides, and indicate that R18D at the dose examined is more potent than R18 in Wistar rats, and justify continued investigation of the R18 peptide as a novel neuroprotective agent for stroke.

This study examined the types of discordance occurring in the diagnosis of soft tissue and visceral sarcomas, gastrointestinal stromal tumors (GIST), and desmoid tumors, as well as the economic impact of diagnostic discrepancies.We carried out a retrospective, multicenter analysis using prospectively implemented databases performed on a cohort of patients within the French RRePS network in 2010. Diagnoses were deemed to be discordant based on the 2013 World Health Organization (WHO) classification. Predictive factors of discordant diagnoses were explored. A decision tree was used to assess the expected costs of two strategies of disease management: one based on revised diagnoses after centralized histological review (option 1), the other on diagnoses without centralized review (option 2). Both were defined based on the patient and the disease characteristics, according to national or international guidelines. The time horizon was 12 months and the perspective of the French National Health Insurance (NHI) was retained. Costs were expressed in Euros for 2013. Sensitivity analyses were performed using low and high scenarios that included ± 20% estimates for cost.A total of 2,425 patients were included. Three hundred forty-one patients (14%) had received discordant diagnoses. These discordances were determined to mainly be benign tumors diagnosed as sarcomas (n = 124), or non-sarcoma malignant tumors diagnosed as sarcomas (n = 77). The probability of discordance was higher for a final diagnosis of desmoid tumors when compared to liposarcomas (odds ratio = 5.1; 95%CI [2.6-10.4]). The expected costs per patient for the base-case analysis (low- and high-case scenarios) amounted to €8,791 (€7,033 and €10,549, respectively) for option 1 and €8,904 (€7,057 and €10,750, respectively) for option 2.Our findings highlight misdiagnoses of sarcomas, which were found to most often be confused with benign tumors. Centralized histological reviews are likely to provide cost-savings for the French NHI.

PURPOSE:The outcome of sarcoma has been suggested in retrospective and non-exhaustive studies to be better through management by a multidisciplinary team of experts and adherence to clinical practice guidelines (CPGs). The aim of this prospective and exhaustive population based study was to confirm the impact of adherence to CPGs on survival in patients with localized sarcoma. EXPERIMENTAL DESIGN:Between 2005 and 2007, all evaluable adult patients with a newly diagnosis of localized sarcoma located in Rhone Alpes region (n = 634), including 472 cases of soft-tissue sarcoma (STS), were enrolled. The prognostic impact of adherence to CPGs on progression-free survival (PFS) and overall survival (OS) was assessed by multivariate Cox model in this cohort. RESULTS:The median age was 61 years (range 16-92). The most common subtypes were liposarcoma (n = 133, 28%), unclassified sarcoma (n = 98, 20.7%) and leiomyosarcoma (n = 69, 14.6%). In the initial management phase, from diagnosis to adjuvant treatment, the adherence to CPGs for patients with localized STS was 36% overall, corresponding to 56%, 85%, 96% and 84% for initial surgery, radiation therapy, chemotherapy and follow-up, respectively. Adherence to CPGs for surgery was the strongest independent prognostic factor of PFS, along with age, gender, grade, and tumor size. For OS, multivariate analysis adherence to CPGs for surgery was a strong independent prognostic factor, with an important interaction with a management in the regional expert centers. CONCLUSIONS:This study demonstrates impact of CPGs and treatment within an expert center on survival for STS patients in a whole population-based cohort.

Brain oscillations and synchronicity among brain regions (brain connectivity) have been studied in resting-state (RS) and task-induced settings. RS-connectivity which captures brain functional integration during an unconstrained state is shown to vary with the frequency of oscillations. Indeed, high temporal resolution modalities have demonstrated both between and cross-frequency connectivity spanning across frequency bands such as theta and gamma. Despite high spatial resolution, functional magnetic resonance imaging (fMRI) suffers from low temporal resolution due to modulation with slow-varying hemodynamic response function (HRF) and also relatively low sampling rate. This limits the range of detectable frequency bands in fMRI and consequently there has been no evidence of cross-frequency dependence in fMRI data. In the present work we uncover recurring patterns of spectral power in network timecourses which provides new insight on the actual nature of frequency variation in fMRI network activations. Moreover, we introduce a new measure of dependence between pairs of rs-fMRI networks which reveals significant cross-frequency dependence between functional brain networks specifically default-mode, cerebellar and visual networks. This is the first strong evidence of cross-frequency dependence between functional networks in fMRI and our subject group analysis based on age and gender supports usefulness of this observation for future clinical applications.

The human braingraph or the connectome is the object of an intensive research today. The advantage of the graph-approach to brain science is that the rich structures, algorithms and definitions of graph theory can be applied to the anatomical networks of the connections of the human brain. In these graphs, the vertices correspond to the small (1-1.5 cm2) areas of the gray matter, and two vertices are connected by an edge, if a diffusion-MRI based workflow finds fibers of axons, running between those small gray matter areas in the white matter of the brain. One main question of the field today is discovering the directions of the connections between the small gray matter areas. In a previous work we have reported the construction of the Budapest Reference Connectome Server http://connectome.pitgroup.org from the data recorded in the Human Connectome Project of the NIH. The server generates the consensus braingraph of 96 subjects in Version 2, and of 418 subjects in Version 3, according to selectable parameters. After the Budapest Reference Connectome Server had been published, we recognized a surprising and unforeseen property of the server. The server can generate the braingraph of connections that are present in at least k graphs out of the 418, for any value of k = 1, 2, …, 418. When the value of k is changed from k = 418 through 1 by moving a slider at the webserver from right to left, certainly more and more edges appear in the consensus graph. The astonishing observation is that the appearance of the new edges is not random: it is similar to a growing shrub. We refer to this phenomenon as the Consensus Connectome Dynamics. We hypothesize that this movement of the slider in the webserver may copy the development of the connections in the human brain in the following sense: the connections that are present in all subjects are the oldest ones, and those that are present only in a decreasing fraction of the subjects are gradually the newer connections in the individual brain development. An animation on the phenomenon is available at https://youtu.be/yxlyudPaVUE. Based on this observation and the related hypothesis, we can assign directions to some of the edges of the connectome as follows: Let Gk + 1 denote the consensus connectome where each edge is present in at least k+1 graphs, and let Gk denote the consensus connectome where each edge is present in at least k graphs. Suppose that vertex v is not connected to any other vertices in Gk+1, and becomes connected to a vertex u in Gk, where u was connected to other vertices already in Gk+1. Then we direct this (v, u) edge from v to u.

Colorectal cancer is a leading cause of cancer related deaths in the U.S., with African-Americans having higher incidence and mortality rates than Caucasian-Americans. Recent studies have demonstrated that anti-tumor cytotoxic T lymphocytes provide protection to patients with colon cancer while patients deficient in these responses have significantly worse prognosis. To determine if differences in cytotoxic immunity might play a role in racial disparities in colorectal cancer 258 microsatellite-stable colon tumors were examined for infiltrating immune biomarkers via immunohistochemistry. Descriptive summary statistics were calculated using two-sample Wilcoxon rank sum tests, while linear regression models with log-transformed data were used to assess differences in race and Pearson and Spearman correlations were used to correlate different biomarkers. The association between different biomarkers was also assessed using linear regression after adjusting for covariates. No significant differences were observed in CD8+ (p = 0.83), CD57+ (p = 0.55), and IL-17-expressing (p = 0.63) cell numbers within the tumor samples tested. When infiltration of granzyme B+ cells was analyzed, however, a significant difference was observed, with African Americans having lower infiltration of cells expressing this cytotoxic marker than Caucasians (p

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject's trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Usher syndrome type III (USH3A) is an autosomal recessive disorder caused by mutations in clarin-1 (CLRN1) gene, leading to progressive retinal degeneration and sensorineural deafness. Efforts to develop therapies for preventing photoreceptor cell loss are hampered by the lack of a retinal phenotype in the existing USH3 mouse models and by conflicting reports regarding the endogenous retinal localization of clarin-1, a transmembrane protein of unknown function. In this study, we used an AAV-based approach to express CLRN1 in the mouse retina in order to determine the pattern of its subcellular localization in different cell types. We found that all major classes of retinal cells express AAV-delivered CLRN1 driven by the ubiquitous, constitutive small chicken β-actin promoter, which has important implications for the design of future USH3 gene therapy studies. Within photoreceptor cells, AAV-expressed CLRN1 is mainly localized at the inner segment region and outer plexiform layer, similar to the endogenous expression of other usher proteins. Subretinal delivery using a full strength viral titer led to significant loss of retinal function as evidenced by ERG analysis, suggesting that there is a critical limit for CLRN1 expression in photoreceptor cells. Taken together, these results suggest that CLRN1 expression is potentially supported by a variety of retinal cells, and the right combination of AAV vector dose, promoter, and delivery method needs to be selected to develop safe therapies for USH3 disorder.

Apoptosis mediated by Bax or Bak is usually thought to be triggered by BH3-only members of the Bcl-2 protein family. BH3-only proteins can directly bind to and activate Bax or Bak, or indirectly activate them by binding to anti-apoptotic Bcl-2 family members, thereby relieving their inhibition of Bax and Bak. Here we describe a third way of activation of Bax/Bak dependent apoptosis that does not require triggering by multiple BH3-only proteins. In factor dependent myeloid (FDM) cell lines, cycloheximide induced apoptosis by a Bax/Bak dependent mechanism, because Bax-/-Bak-/- lines were profoundly resistant, whereas FDM lines lacking one or more genes for BH3-only proteins remained highly sensitive. Addition of cycloheximide led to the rapid loss of Mcl-1 but did not affect the expression of other Bcl-2 family proteins. In support of these findings, similar results were observed by treating FDM cells with the CDK inhibitor, roscovitine. Roscovitine reduced Mcl-1 abundance and caused Bax/Bak dependent cell death, yet FDM lines lacking one or more genes for BH3-only proteins remained highly sensitive. Therefore Bax/Bak dependent apoptosis can be regulated by the abundance of anti-apoptotic Bcl-2 family members such as Mcl-1, independently of several known BH3-only proteins.

Deep graph-theoretic ideas in the context with the graph of the World Wide Web led to the definition of Google's PageRank and the subsequent rise of the most popular search engine to date. Brain graphs, or connectomes, are being widely explored today. We believe that non-trivial graph theoretic concepts, similarly as it happened in the case of the World Wide Web, will lead to discoveries enlightening the structural and also the functional details of the animal and human brains. When scientists examine large networks of tens or hundreds of millions of vertices, only fast algorithms can be applied because of the size constraints. In the case of diffusion MRI-based structural human brain imaging, the effective vertex number of the connectomes, or brain graphs derived from the data is on the scale of several hundred today. That size facilitates applying strict mathematical graph algorithms even for some hard-to-compute (or NP-hard) quantities like vertex cover or balanced minimum cut. In the present work we have examined brain graphs, computed from the data of the Human Connectome Project, recorded from male and female subjects between ages 22 and 35. Significant differences were found between the male and female structural brain graphs: we show that the average female connectome has more edges, is a better expander graph, has larger minimal bisection width, and has more spanning trees than the average male connectome. Since the average female brain weighs less than the brain of males, these properties show that the female brain has better graph theoretical properties, in a sense, than the brain of males. It is known that the female brain has a smaller gray matter/white matter ratio than males, that is, a larger white matter/gray matter ratio than the brain of males; this observation is in line with our findings concerning the number of edges, since the white matter consists of myelinated axons, which, in turn, roughly correspond to the connections in the brain graph. We have also found that the minimum bisection width, normalized with the edge number, is also significantly larger in the right and the left hemispheres in females: therefore, the differing bisection widths are independent from the difference in the number of edges.

Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's) emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33%) and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%), with accuracies ranging from 52.9% (Silver Linings Playbook) to 11.75% (Seinfeld) within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz) primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

Functional magnetic resonance imaging (fMRI) studies traditionally use general linear model-based analysis (GLM-BA) and regularly report task-related activation, deactivation, or no change in activation in separate brain regions. However, several recent fMRI studies using spatial independent component analysis (sICA) find extensive overlap of functional networks (FNs), each exhibiting different task-related modulation (e.g., activation vs. deactivation), different from the dominant findings of GLM-BA. This study used sICA to assess overlap of FNs extracted from four datasets, each related to a different cognitive task. FNs extracted from each dataset overlapped with each other extensively across most or all brain regions and showed task-related concurrent increases, decreases, or no changes in activity. These findings indicate that neural substrates showing task-related concurrent but different modulations in activity intermix with each other and distribute across most of the brain. Furthermore, spatial correlation analyses found that most FNs were highly consistent in spatial patterns across different datasets. This finding indicates that these FNs probably reflect large-scale patterns of task-related brain activity. We hypothesize that FN overlaps as revealed by sICA might relate to functional heterogeneity, balanced excitation and inhibition, and population sparseness of neuron activity, three fundamental properties of the brain. These possibilities deserve further investigation.