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Resumen: Introducción: La hormona de crecimiento (GH) y el factor de crecimiento similar a la insulina tipo 1 (IGF-1) tienen efecto modulador sobre la funcionalidad intestinal y la microbiota. Nuestro objetivo fue investigar si los pacientes con déficit de GH y, por lo tanto, con niveles bajos de GH e IGF-1 se asocian con cambios en la fisiología/integridad intestinal, así como en la composición de la microbiota intestinal. Material y métodos: Se realizó un estudio de casos y controles en 21 pacientes con déficit de GH previo al inicio y tras 6 meses de tratamiento con GH y en 20 controles sanos. Se estudiaron los cambios antropométricos, analíticos, de translocación bacteriana y también se determinó la composición del microbioma mediante secuenciación masiva del gen del ARNr 16S. Resultados: El déficit de GH se acompañó de un incremento significativo en los niveles séricos de sCD14, un marcador de translocación bacteriana (p

BACKGROUND: The novel optic neuritis (ON) diagnostic criteria include intereye differences (IED) of optical coherence tomography (OCT) parameters. IED has proven valuable for ON diagnosis in multiple sclerosis but has not been evaluated in aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders (AQP4+NMOSD). We evaluated the diagnostic accuracy of intereye absolute (IEAD) and percentage difference (IEPD) in AQP4+NMOSD after unilateral ON >6 months before OCT as compared with healthy controls (HC). METHODS: Twenty-eight AQP4+NMOSD after unilateral ON (NMOSD-ON), 62 HC and 45 AQP4+NMOSD without ON history (NMOSD-NON) were recruited by 13 centres as part of the international Collaborative Retrospective Study on retinal OCT in Neuromyelitis Optica study. Mean thickness of peripapillary retinal nerve fibre layer (pRNFL) and macular ganglion cell and inner plexiform layer (GCIPL) were quantified by Spectralis spectral domain OCT. Threshold values of the ON diagnostic criteria (pRNFL: IEAD 5 µm, IEPD 5%; GCIPL: IEAD: 4 µm, IEPD: 4%) were evaluated using receiver operating characteristics and area under the curve (AUC) metrics. RESULTS: The discriminative power was high for NMOSD-ON versus HC for IEAD (pRNFL: AUC 0.95, specificity 82%, sensitivity 86%; GCIPL: AUC 0.93, specificity 98%, sensitivity 75%) and IEPD (pRNFL: AUC 0.96, specificity 87%, sensitivity 89%; GCIPL: AUC 0.94, specificity 96%, sensitivity 82%). The discriminative power was high/moderate for NMOSD-ON versus NMOSD-NON for IEAD (pRNFL: AUC 0.92, specificity 77%, sensitivity 86%; GCIP: AUC 0.87, specificity 85%, sensitivity 75%) and for IEPD (pRNFL: AUC 0.94, specificity 82%, sensitivity 89%; GCIP: AUC 0.88, specificity 82%, sensitivity 82%). CONCLUSIONS: Results support the validation of the IED metrics as OCT parameters of the novel diagnostic ON criteria in AQP4+NMOSD.

Abstract Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Coastal wetlands provide a range of ecosystem services, yet they are currently under threat from global change impacts. Thus, their monitoring and assessment is vital for evaluating their status, extent and distribution. Remote sensing provides an excellent tool for evaluating coastal ecosystems, whether with small-scale studies using drones or national-/regional-/global-scale studies using satellite-derived data. This study used a fine-scale plant community classification of coastal meadows in Estonia derived from a multispectral camera on board unoccupied aerial vehicles (UAVs) to calculate the plant fractional cover (PFC) in Sentinel-2 MultiSpectral Instrument (MSI) sensor grids. A random forest (RF) algorithm was trained and tested with vegetation indices (VIs) calculated from the spectral bands extracted from the MSI sensor to predict the PFC. Additional RF models were trained and tested after adding a digital elevation model (DEM). After comparing the models, results show that using DEM with VIs can increase the prediction accuracy of PFC up to 2 times (R2 58 %–70 %). This suggests the use of ancillary data such as DEM to improve the prediction of empirical machine learning models, providing an appropriate approach to upscale local studies to wider areas for management and conservation purposes.

Objectives: This study aimed to assess the real use of cefazolin for methicillin-susceptible Staphylococcus aureus (MSSA) infective endocarditis (IE) in the Spanish National Endocarditis Database (GAMES) and to compare it with antistaphylococcal penicillin (ASP). Methods: Prospective cohort study with retrospective analysis of a cohort of MSSA IE treated with cloxacillin and/or cefazolin. Outcomes assessed were relapse; intra-hospital, overall, and endocarditis-related mortality; and adverse events. Risk of renal toxicity with each treatment was evaluated separately. Results: We included 631 IE episodes caused by MSSA treated with cloxacillin and/or cefazolin. Antibiotic treatment was cloxacillin, cefazolin, or both in 537 (85%), 57 (9%), and 37 (6%) episodes, respectively. Patients treated with cefazolin had significantly higher rates of comorbidities (median Charlson Index 7, P

Abstract Herbivores can exert a controlling effect on the reproduction and growth of shrubs, thereby counter‐acting the climate‐driven encroachment of shrubs in the Arctic and the potential consequences. This control is particularly evident in the case of abundant herbivores, such as reindeer (Rangifer tarandus tarandus), whose grazing patterns are affected by management. Here, we tested how different reindeer grazing practices on the border between Finland and Norway impact the occurrence of willow (Salix spp.) dominated patches, their above‐ground biomass (AGB) and the ability of willows to form dense thickets. We used a combination of multispectral and RGB imagery obtained from unoccupied aerial vehicles field data and an ensemble of machine‐learning models, which allowed us to model the occurrence of plant community types (Overall accuracy = 0.80), AGB fractions (maximum R2 = 0.90) and topsoil moisture (maximum R2 = 0.89). With this combination of approaches, we show that willows are kept in a browsing‐trap under spring and early summer grazing by reindeer, growing mostly small and scattered in the landscape. In contrast, willows under the winter grazing regime formed dense stands, particularly within riparian areas. We confirm this pattern using a random forest willow habitat distribution model based on topographical parameters. The model shows that willow biomass correlated with parameters of optimal habitat quality only in the winter grazing regime and did not respond to the same parameters under spring and summer grazing of reindeer.

Con ocasión de la augusta visita que recientemente (el 18 de enero de 1959, festividad de la Cátedra de San Pedro en Roma) el Santo Padre Juan XXIII se dignó hacer a la Pontificia Universidad Gregoriana —la más universal de las Universidades, con sus 2.600 alumnos pertenecientes a 74 naciones— se recogen algunas noticias de otras visitas que los Sumos Pontífices hicieron al más antiguo —exceptuada «la Sapienza»— de los Ateneos eclesiásticos romanos, en las cuatro centurias, ya bien cumplidas, de su brillante historia. Casi todos los datos referentes a los siglos XVI, XVII y XVIII se han sacado de dos códices manuscritos, que se conservan en el archivo de la misma Universidad.

Semiconductor oxides belonging to various families are ideal candidates for application in photocatalytic processes. One of the challenges facing photocatalytic processes today is improving their efficiency under sunlight irradiation. In this study, the growth and characterization of semiconductor oxide nanostructures and composites based on the ZnO and CuO families are proposed. The selected growth method is the resistive heating of Zn and Cu wires to produce the corresponding oxides, combined with galvanic corrosion of Zn. An exhaustive characterization of the materials obtained has been carried out using techniques based on scanning electron microscopy and optical spectroscopies. The method we have followed and the conditions used in this study present promising results, not only from a degradation efficiency point of view but also because it is a cheap, easy, and fast growth method. These characteristics are essential in order to scale the process beyond the laboratory.

Abstract Oral microbiome is the second largest microbial community in humans after gut. Human immunodeficiency virus (HIV) infection triggers an impairment of the immune system which could favour the growth and the colonization of pathogens in the oral cavity, and this dysbiosis has been associated with oral manifestations that worsen the quality of life of these patients. Antiretroviral therapy (ART) could also drive changes in specific oral bacterial taxa associated with such periodontal diseases. Integrase strand transfer inhibitors (INSTIs), therapy of choice in the treatment of naive HIV-patients, are able to reverse the impact of HIV infection on systemic inflammation, gut permeability, and gut bacterial diversity/richness. The objective of this study was to analyse the effects of HIV infection per se and INSTIs on salivary bacteriome composition, taking into consideration other factors such as smoking, that could also have a significant impact on oral microbiome. To accomplish this objective, 26 non-HIV-infected volunteers and 30 HIV-infected patients (15 naive and 15 under INSTIs-regimen) were recruited. Salivary samples were collected to measure lysozyme levels. Oral bacteriome composition was analysed using 16S rRNA gene sequencing. Naive HIV-infected patients showed statistically higher levels of lysozyme compared to controls (p

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

The coupling reaction of carbon dioxide and highly-substituted epoxides derived from renewable resources such as fatty acids and waste vegetable oils (cooking oils derived from olive and sunflower) leads to the synthesis of new bio-derived cyclic carbonates using efficient metal-free bifunctional organocatalysts under mild and solvent-free reaction conditions. Once cyclic carbonates derived from biobased sources were synthesized, the design of non-isocyanate polyurethanes (NIPUs) with different chemical structures was investigated by their reaction with a broad substrate scope of diamines. The NIPUs materials were characterized by spectroscopic techniques (NMR and IR) and their molecular weights and polydispersities were determined by GPC studies. Finally, thermal properties of the polymers were studied by DSC and TGA analyses.

Key unmet needs in multiple sclerosis (MS) include detection of early pathology, disability worsening independent of relapses, and accurate monitoring of treatment response. Collaborative approaches to address these unmet needs have been driven in part by industry–academic networks and initiatives such as the Grant for Multiple Sclerosis Innovation (GMSI) and Multiple Sclerosis Leadership and Innovation Network (MS-LINK™) programs. We review the application of recent advances, supported by the GMSI and MS-LINK™ programs, in neuroimaging technology to quantify pathology related to central pathology and disease worsening, and potential for their translation into clinical practice/trials. GMSI-supported advances in neuroimaging methods and biomarkers include developments in magnetic resonance imaging, positron emission tomography, ocular imaging, and machine learning. However, longitudinal studies are required to facilitate translation of these measures to the clinic and to justify their inclusion as endpoints in clinical trials of new therapeutics for MS. Novel neuroimaging measures and other biomarkers, combined with artificial intelligence, may enable accurate prediction and monitoring of MS worsening in the clinic, and may also be used as endpoints in clinical trials of new therapies for MS targeting relapse-independent disease pathology.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

Abstract Background The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS). Methods We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center. Results We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later. Conclusions The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability.

Abstract Background Glutamate excitotoxicity contributes to oligodendrocyte and tissue damage in multiple sclerosis (MS). Intriguingly, glutamate level in plasma and cerebrospinal fluid of MS patients is elevated, a feature which may be related to the pathophysiology of this disease. In addition to glutamate transporters, levels of extracellular glutamate are controlled by cystine/glutamate antiporter xc-, an exchanger that provides intracellular cystine for production of glutathione, the major cellular antioxidant. The objective of this study was to analyze the role of the system xc- in glutamate homeostasis alterations in MS pathology. Methods Primary cultures of human monocytes and the cell line U-937 were used to investigate the mechanism of glutamate release. Expression of cystine glutamate exchanger (xCT) was quantified by quantitative PCR, Western blot, flow cytometry and immunohistochemistry in monocytes in vitro, in animals with experimental autoimmune encephalomyelitis (EAE), the animal model of MS, and in samples of MS patients. Results and discussion We show here that human activated monocytes release glutamate through cystine/glutamate antiporter xc- and that the expression of the catalytic subunit xCT is upregulated as a consequence of monocyte activation. In addition, xCT expression is also increased in EAE and in the disease proper. In the later, high expression of xCT occurs both in the central nervous system (CNS) and in peripheral blood cells. In particular, cells from monocyte-macrophage-microglia lineage have higher xCT expression in MS and in EAE, indicating that immune activation upregulates xCT levels, which may result in higher glutamate release and contribution to excitotoxic damage to oligodendrocytes. Conclusions Together, these results reveal that increased expression of the cystine/glutamate antiporter system xc- in MS provides a link between inflammation and excitotoxicity in demyelinating diseases.

Abstract OCS-05 (aka BN201) is a peptidomimetic that binds to serum glucocorticoid kinase-2 (SGK2), displaying neuroprotective activity. The objective of this randomized, double-blind 2-part study was to test safety and pharmacokinetics of OCS-05 administered by intravenous (i.v.) infusion in healthy volunteers. Subjects (n = 48) were assigned to receive placebo (n = 12) or OCS-05 (n = 36). , Doses tested were 0.05, 0.2, 0.4, 0.8, 1.6, 2.4 and 3.2 mg/kg in the single ascending dose (SAD) part. In the multiple ascending dose (MAD) part, 2.4 and 3.0 mg/kg doses were administered with 2 h i.v. infusion for 5 consecutive days. Safety assessments included adverse events, blood tests, ECG, Holter monitoring, brain MRI and EEG. No serious adverse events were reported in the OCS-05 group (there was one serious adverse event in the placebo group). Adverse events reported in the MAD part were not clinically significant, and no changes on the ECG, EEG or brain MRI were observed. Single-dose (0.05–3.2 mg/kg) exposure (Cmax and AUC) increased in a dose-proportional manner. Steady state was reached by Day 4 and no accumulation was observed. Elimination half-life ranged from 3.35 to 8.23 h (SAD) and 8.63 to 12.2 h (MAD). Mean individual Cmax concentrations in the MAD part were well below the safety thresholds. OCS-05 administered as 2-h i.v. infusions of multiple doses up to 3.0 mg/Kg daily for up to 5 consecutive days was safe and well tolerated. Based on this safety profile, OCS-05 is currently being tested in a phase 2 trial in patient with acute optic neuritis (NCT04762017, date registration 21/02/2021).

Abstract The relationship between brain diffusion microstructural changes and disability in multiple sclerosis (MS) remains poorly understood. We aimed to explore the predictive value of microstructural properties in white (WM) and grey matter (GM), and identify areas associated with mid-term disability in MS patients. We studied 185 patients (71% female; 86% RRMS) with the Expanded Disability Status Scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), and Symbol Digit Modalities Test (SDMT) at two time-points. We used Lasso regression to analyse the predictive value of baseline WM fractional anisotropy and GM mean diffusivity, and to identify areas related to each outcome at 4.1 years follow-up. Motor performance was associated with WM (T25FW: RMSE = 0.524, R2 = 0.304; 9HPT dominant hand: RMSE = 0.662, R2 = 0.062; 9HPT non-dominant hand: RMSE = 0.649, R2 = 0.139), and SDMT with GM diffusion metrics (RMSE = 0.772, R2 = 0.186). Cingulum, longitudinal fasciculus, optic radiation, forceps minor and frontal aslant were the WM tracts most closely linked to motor dysfunction, and temporal and frontal cortex were relevant for cognition. Regional specificity related to clinical outcomes provide valuable information that can be used to develop more accurate predictive models that could improve therapeutic strategies.

Joule heating of niobium (Nb) metal wires by running a high electric current density through them has been used to grow Nb2O5 nanowires. The formation of a micrometric oxide layer on the Nb wires has also been observed. The size and density of the nanowires are related to the current values applied, as well as the thickness of the oxide layer formed. Characterization of both nanowires and oxide layer has been performed using X-ray diffraction, scanning electron microscopy, energy dispersive X-ray microanalysis, and micro-Raman spectroscopy. It has been observed that this method allows the growth of Nb2O5 nanowires in times as short as tens of seconds.

Abstract Viruses are the most abundant components of the human gut microbiome with a significant impact on health and disease. The effects of human immunodeficiency virus (HIV) infection on gut virome has been scarcely analysed. Several studies suggested that integrase strand transfers inhibitors (INSTIs) are associated with a healthier gut. Thus, the objective of this work was to evaluate the effects of HIV infection and INSTIs on gut virome composition. 26 non-HIV-infected volunteers, 15 naive HIV-infected patients and 15 INSTIs-treated HIV-infected patients were recruited and their gut virome composition was analysed using shotgun sequencing. Bacteriophages were the most abundant and diverse viruses present in gut. HIV infection was accompanied by a decrease in phage richness which was reverted after INSTIs-based treatment. β-diversity of phages revealed that samples from HIV-infected patients clustered separately from those belonging to the control group. Differential abundant analysis showed an increase in phages belonging to Caudoviricetes class in the naive group and a decrease of Malgrandaviricetes class phages in the INSTIs-treated group compared to the control group. Besides, it was observed that INSTIs-based treatment was not able to reverse the increase of lysogenic phages associated with HIV infection or to modify the decrease observed on the relative abundance of Proteobacteria-infecting phages. Our study describes for the first time the impact of HIV and INSTIs on gut virome and demonstrates that INSTIs-based treatments are able to partially restore gut dysbiosis at the viral level, which opens several opportunities for new studies focused on microbiota-based therapies.

Abstract Coastal wetlands provide a range of important ecosystem services, yet they are under threat from a range of stressors including climate change. This is predominantly as a result of alterations to the hydroregime and associated edaphic factors. We used a three-year mesocosm experiment to assess changes in coastal plant community composition for three plant communities in response to altered water level and salinity scenarios. Species richness and abundance were calculated by year and abundance was plotted using rank abundance curves. The permutational multivariate analysis of variance with Bray–Curtis dissimilarity was used to examine differences among treatments in plant community composition. A Non-metric Multi-dimensional Scaling analysis (NMDS) was used to visualize the responses of communities to treatments by year. Results showed that all three plant communities responded differently to altered water levels and salinity. Species richness and abundance increased significantly in an Open Pioneer plant community while Lower and Upper Shore plant communities showed less change. Species abundances changed in all plant communities with shifts in species composition significantly influenced by temporal effects and treatment. The observed responses to experimentally altered conditions highlight the need for conservation of these important ecosystems in the face of predicted climate change, since these habitats are important for wading birds and livestock grazing.

Plant communities of mires can be linked to important ecological processes, such as carbon storage and gas fluxes. As indicators of ecosystem dynamics, knowledge about their distribution and condition can support ecosystem assessment. Mapping mire vegetation enables monitoring at ecosystem-scale, which can be done with UAVs (Unmanned Aerial Vehicles). Depending on the mounted sensor and the spectral signals recorded, various attributes of plant communities can be retrieved. However, it is uncertain to what extent plant communities can be derived, as mapping vegetation on detailed level remains challenging due to overlapping spectral signatures of plant species. Advancing technology offers the choice between low cost RGB and multispectral sensors as well as a variety of classification methods to overcome these challenges. Therefore, we used K-means unsupervised classification and Random Forest supervised classification with different input variables to map microtopographical patterns and plant communities of two aapa mires as resolved by hierarchical clustering. This extensive approach allowed the assessment of both classifier’s strength and weaknesses, as well as the criteria of selecting suitable input data. UAV- RGB and multispectral imagery with associated spectral and topographical indices of both 0.05 m and 0.30 m spatial resolution were used for the K-means method. We assessed the relationship between these generated spectral classes and plant community clusters. The clusters further served as training and validation labels for to classify the high resolution, multispectral UAV-data (0.05 m) using Random Forest. Our study demonstrates that maps reflecting microtopograpical patterns and a wetness gradient can be produced with low-cost RGB imagery and unsupervised classification. Despite this linkage to plant communities, enhanced and detailed maps of plant community distribution can only be achieved with multispectral data and robust machine learning techniques. Random forest classifications showed good overall accuracies (0.59 – 0.82) in mapping microtopographical patterns and plant communities based on hierarchical clustering of vegetation data. While the strength of both classifiers lies in the distinction of bog hummock communities, classification performance was weaker between different transition and lawn community types. Casual misclassifications occurred also for communities along the transition of microtopographical patterns. The main obstacle for accurate mapping remains the overlap of spectral signatures from species and spectral noise originating from wetness in mires that lead to misclassification. Future studies addressing plant community and diversity mapping should therefore consider the origin of spectral variation with further sensors.

ObjectiveTo evaluate the inter-rater reliability of semiautomated segmentation of spectral domain optical coherence tomography (OCT) macular volume scans.MethodsMacular OCT volume scans of left eyes from 17 subjects (8 patients with MS and 9 healthy controls) were automatically segmented by Heidelberg Eye Explorer (v1.9.3.0) beta-software (Spectralis Viewing Module v6.0.0.7), followed by manual correction by 5 experienced operators from 5 different academic centers. The mean thicknesses within a 6-mm area around the fovea were computed for the retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer, outer plexiform layer (OPL), and outer nuclear layer (ONL). Intraclass correlation coefficients (ICCs) were calculated for mean layer thickness values. Spatial distribution of ICC values for the segmented volume scans was investigated using heat maps.ResultsAgreement between raters was good (ICC > 0.84) for all retinal layers, particularly inner retinal layers showed excellent agreement across raters (ICC > 0.96). Spatial distribution of ICC showed highest values in the perimacular area, whereas the ICCs were poorer for the foveola and the more peripheral macular area. The automated segmentation of the OPL and ONL required the most correction and showed the least agreement, whereas differences were less prominent for the remaining layers.ConclusionsAutomated segmentation with manual correction of macular OCT scans is highly reliable when performed by experienced raters and can thus be applied in multicenter settings. Reliability can be improved by restricting analysis to the perimacular area and compound segmentation of GCL and IPL.

Abstract Artificial intelligence (AI)‐based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human‐led validated consensus quality control criteria (OSCAR‐IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI‐based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five‐point expansion of the OSCAR‐IB criteria to embrace AI (OSCAR‐AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

Abstract Background Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects. Methods Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies. Results Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-β activated kinase 1 (TAK1) kinase, involved in Transforming growth factor β-1 proprotein (TGF-β), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Conclusions Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

Biomass burning influences global atmospheric chemistry by releasing greenhouse gases and climate-forcing aerosols. There is controversy about the magnitude and timing of Holocene changes in biomass burning emissions from millennial to centennial timescales and, in particular, about the possible impact of ancient civilizations. Here we present a 5 kyr record of fire activity proxies levoglucosan, black carbon, and ammonium measured in the RECAP (Renland ice cap) ice core, drilled in coastal eastern Greenland, and therefore affected by processes occurring in the high North Atlantic region. Levoglucosan and ammonium fluxes are high from 5 to 4.5 kyr BP (thousand years before 2000 CE) followed by an abrupt decline, possibly due to monotonic decline in Northern Hemisphere summer insolation. Levoglucosan and black carbon show an abrupt decline at 1.1 kyr BP, suggesting a decline in the wildfire regime in Iceland due to the extensive land clearing caused by Viking colonizers. All fire proxies reach a minimum during the second half of the last century, after which levoglucosan and ammonium fluxes increase again, in particular over the last 200 years. We find that the fire regime reconstructed from RECAP fluxes seems mainly related to climatic changes; however over the last millennium human activities might have influenced wildfire frequency/occurrence substantially.

This paper briefly reviews different debates about approaches for paths out of poverty, considering several views, from the analysis of specific policies to more general or systemic considerations. The contribution of this paper is to present a broad outline of those debates and to serve as an illustration of the complexity of analyzing paths out of poverty. It discusses in sequence, the more microeconomic approach of evaluation of individual policies for poverty alleviation; then it moves to broader issues of growth and development strategies, and macroeconomic policies, and their links to the persistence or reduction of poverty; and finally discusses the topic of institutions, related to how policy decisions are made and enforced in societies at the previous three levels. Finally, the concluding section argues that a successful program to eliminate poverty must integrate all levels of individual policies, macroeconomic programs, development strategies and good institutions. This paper hopes to contribute to that crucial work.

The area covered by low-input agroecosystems (e.g. semi-natural and permanent grasslands) in Europe has considerably decreased throughout the last century. To support more sustainable management practices, and to promote biodiversity and ecosystem service values of such agroecosystems a decision support tool was developed. The tool aims to enhance the operationalization of ecosystem services and address the challenge of their integration into spatial planning. The Viva Grass tool aims to enhance the maintenance of ecosystem services delivered by low-input agroecosystems. It does so by providing spatially explicit decision support for land-use planning and sustainable management of agroecosystems. The Viva Grass tool is a multi-criteria decision analysis tool for integrated planning. It is designed for farmers, spatial planners and policy makers to support decisions of management of agroecosystems. The tool has been tested to assess spatial planning in eight case studies across the Baltic States.

Neuromyelitis optica (NMO) and spectrum disorder (NMO/SD) represent a vexing process and its clinical variants appear to have at their pathogenic core the loss of immune tolerance to the aquaporin-4 water channel protein. This process results in a characteristic pattern of astrocyte dysfunction, loss, and demyelination that predominantly affects the spinal cord and optic nerves. Although several empirical therapies are currently used in the treatment of NMO/SD, none has been proven effective in prospective, adequately powered, randomized trials. Furthermore, most of the current therapies subject patients to long-term immunologic suppression that can cause serious infections and development of cancers. The following is the first of a 2-part description of several key immune mechanisms in NMO/SD that might be amenable to therapeutic restoration of immune tolerance. It is intended to provide a roadmap for how potential immune tolerance restorative techniques might be applied to patients with NMO/SD. This initial installment provides a background rationale underlying attempts at immune tolerization. It provides specific examples of innovative approaches that have emerged recently as a consequence of technical advances. In several autoimmune diseases, these strategies have been reduced to practice. Therefore, in theory, the identification of aquaporin-4 as the dominant autoantigen makes NMO/SD an ideal candidate for the development of tolerizing therapies or cures for this increasingly recognized disease.

Neuromyelitis optica spectrum disorder (NMO/SD) and its clinical variants have at their core the loss of immune tolerance to aquaporin-4 and perhaps other autoantigens. The characteristic phenotype is disruption of astrocyte function and demyelination of spinal cord, optic nerves, and particular brain regions. In this second of a 2-part article, we present further perspectives regarding the pathogenesis of NMO/SD and how this disease might be amenable to emerging technologies aimed at restoring immune tolerance to disease-implicated self-antigens. NMO/SD appears to be particularly well-suited for these strategies since aquaporin-4 has already been identified as the dominant autoantigen. The recent technical advances in reintroducing immune tolerance in experimental models of disease as well as in humans should encourage quantum leaps in this area that may prove productive for novel therapy. In this part of the article series, the potential for regulatory T and B cells is brought into focus, as are new approaches to oral tolerization. Finally, a roadmap is provided to help identify potential issues in clinical development and guide applications in tolerization therapy to solving NMO/SD through the use of emerging technologies. Each of these perspectives is intended to shine new light on potential cures for NMO/SD and other autoimmune diseases, while sparing normal host defense mechanisms.

ObjectiveTo develop consensus recommendations for reporting of quantitative optical coherence tomography (OCT) study results.MethodsA panel of experienced OCT researchers (including 11 neurologists, 2 ophthalmologists, and 2 neuroscientists) discussed requirements for performing and reporting quantitative analyses of retinal morphology and developed a list of initial recommendations based on experience and previous studies. The list of recommendations was subsequently revised during several meetings of the coordinating group.ResultsWe provide a 9-point checklist encompassing aspects deemed relevant when reporting quantitative OCT studies. The areas covered are study protocol, acquisition device, acquisition settings, scanning protocol, funduscopic imaging, postacquisition data selection, postacquisition data analysis, recommended nomenclature, and statistical analysis.ConclusionsThe Advised Protocol for OCT Study Terminology and Elements recommendations include core items to standardize and improve quality of reporting in quantitative OCT studies. The recommendations will make reporting of quantitative OCT studies more consistent and in line with existing standards for reporting research in other biomedical areas. The recommendations originated from expert consensus and thus represent Class IV evidence. They will need to be regularly adjusted according to new insights and practices.

Medical call centres can evaluate and refer patients to an emergency department (ED), a physician or provide guidance for self-care. Our aim was (1) to determine parental adherence to an ED orientation after being referred by the nurses of a call centre, (2) to observe how adherence varies according to children’s characteristics and (3) to assess parents’ reasons for non-adherence. This was a prospective cohort study set in the Lausanne agglomeration, Switzerland. From 1 February to 5 March 2022, paediatric calls (

Modulation of Alzheimer′s disease (AD) risk begins early in life. During embryo development and postnatal maturation, the brain receives maternal physiological influences and establishes epigenetic patterns that build its level of resilience to late-life diseases. The soluble epoxide hydrolase inhibitor N-[1-(1-oxopropyl)-4-piperidinyl]-N′-[4-(trifluoromethoxy)phenyl] urea (TPPU), reported as ant-inflammatory and neuroprotective against AD pathology in the adult 5XFAD mouse model of AD, was administered to wild-type (WT) female mice mated to heterozygous 5XFAD males during gestation and lactation. Two-month-old 5XFAD male and female offspring of vehicle-treated dams showed memory loss as expected. Remarkably, maternal treatment with TPPU fully prevented memory loss in 5XFAD. TPPU-induced brain epigenetic changes in both WT and 5XFAD mice, modulating global DNA methylation (5-mC) and hydroxymethylation (5-hmC) and reducing the gene expression of some histone deacetylase enzymes (Hdac1 and Hdac2), might be on the basis of the long-term neuroprotection against cognitive impairment and neurodegeneration. In the neuropathological analysis, both WT and 5XFAD offspring of TPPU-treated dams showed lower levels of AD biomarkers of tau hyperphosphorylation and microglia activation (Trem2) than the offspring of vehicle-treated dams. Regarding sex differences, males and females were similarly protected by maternal TPPU, but females showed higher levels of AD risk markers of gliosis and neurodegeneration. Taken together, our results reveal that maternal treatment with TPPU impacts in preventing or delaying memory loss and AD pathology by inducing long-term modifications in the epigenetic machinery and its marks.

Brain parenchymal lesions are frequently observed on conventional magnetic resonance imaging (MRI) scans of patients with neuromyelitis optica (NMO) spectrum disorder, but the specific morphological and temporal patterns distinguishing them unequivocally from lesions caused by other disorders have not been identified. This literature review summarizes the literature on advanced quantitative imaging measures reported for patients with NMO spectrum disorder, including proton MR spectroscopy, diffusion tensor imaging, magnetization transfer imaging, quantitative MR volumetry, and ultrahigh-field strength MRI. It was undertaken to consider the advanced MRI techniques used for patients with NMO by different specialists in the field. Although quantitative measures such as proton MR spectroscopy or magnetization transfer imaging have not reproducibly revealed diffuse brain injury, preliminary data from diffusion-weighted imaging and brain tissue volumetry indicate greater white matter than gray matter degradation. These findings could be confirmed by ultrahigh-field MRI. The use of nonconventional MRI techniques may further our understanding of the pathogenic processes in NMO spectrum disorders and may help us identify the distinct radiographic features corresponding to specific phenotypic manifestations of this disease.