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Demyelinating diseases, including multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and chronic inflammatory demyelinating polyneuropathy (CIDP), are characterized by dysregulated immune responses to self-antigens, leading to myelin loss. Genetic predisposition and environmental factors, such as alterations in the gut microbiome, influence these conditions. We have examined studies on gut microbiome in patients and animal models and reviewed the existing literature on this topic. Germ-free mice showed a reduced susceptibility to demyelinating disorders, highlighting the crucial role of gut bacteria in disease development. Gut dysbiosis promotes the generation of proinflammatory Th17 cells, contributing to blood–brain barrier (BBB) disruption and central nervous system (CNS) autoimmunity. In MS, an increased presence of Th17 cells correlates with disease severity, accompanied by elevated levels of Streptococcus and a higher Firmicutes/Bacteroidetes ratio. Moreover, dysbiosis in MS involves more pathogenic bacteria and fewer beneficial taxa, further influencing disease progression. Similarly, NMOSD exhibits gut microbiota alterations linked to disease severity, with Streptococcus and Clostridium perfringens being significant contributors, suggesting a gut–CNS interaction in pathogenesis. Prebiotic and probiotic interventions can modify gut microbiota, reduce inflammatory markers, and improve patient outcomes, indicating the therapeutic potential of targeting gut microbiota in managing MS. In conclusion, gut microbiome serves as a primary risk factor in developing demyelinating diseases by producing toxins and metabolites that facilitate communication between the gut and brain. Understanding these relationships provides potential therapeutic approaches targeting gut microbiota to more effectively manage and alleviate demyelinating diseases.

The potent pain-relieving properties of opioids come at a steep price. Their addictive nature and side effects raise critical concerns in managing pain after surgical spine procedures. Postoperatively, spinal surgeries often accompany acute intense pain, which presents a significant challenge in optimal recovery. This paper reviews the historical approach to pain management in spine surgeries and expands on the use of alternatives and novel agents with reduced addictive potential. Additionally showcasing individualized multimodal strategies for postoperative pain management beyond pharmacological approaches such as cognitive behavioral therapy (CBT), physical therapy, and transcutaneous electrical nerve stimulation (TENS). Given the global opioid addiction crisis, there is a growing need for a fundamental shift towards safer and effective alternatives. Transitioning beyond opioid-centric practices in spinal surgery can optimize pain relief while improving patient outcomes and minimizing risk.

Despite their relatively low incidence globally, central nervous system (CNS) tumors remain amongst the most lethal cancers, with only a few other malignancies surpassing them in 5-year mortality rates. Treatment decisions for brain tumors heavily rely on histopathological analysis, particularly intraoperatively, to guide surgical interventions and optimize patient outcomes. Frozen sectioning has emerged as a vital intraoperative technique, allowing for highly accurate, rapid analysis of tissue samples, although it poses challenges regarding interpretive errors and tissue distortion. Raman histology, based on Raman spectroscopy, has shown great promise in providing label-free, molecular information for accurate intraoperative diagnosis, aiding in tumor resection and the identification of neurodegenerative disease. Techniques including Stimulated Raman Scattering (SRS), Coherent Anti-Stokes Raman Scattering (CARS), Surface-Enhanced Raman Scattering (SERS), and Tip-Enhanced Raman Scattering (TERS) have profoundly enhanced the speed and resolution of Raman imaging. Similarly, Confocal Laser Endomicroscopy (CLE) allows for real-time imaging and the rapid intraoperative histologic evaluation of specimens. While CLE is primarily utilized in gastrointestinal procedures, its application in neurosurgery is promising, particularly in the context of gliomas and meningiomas. This review focuses on discussing the immense progress in intraoperative histology within neurosurgery and provides insight into the impact of these advancements on enhancing patient outcomes.

Arctic air masses undergo intense transformations when moving southward from closed sea ice to warmer open waters in marine cold-air outbreaks (CAOs). Due to the lack of measurements of diabatic heating and moisture uptake rates along CAO flows, studies often depend on atmospheric reanalysis output. However, the uncertainties connected to those datasets remain unclear. Here, we present height-resolved airborne observations of diabatic heating, moisture uptake, and cloud evolution measured in a quasi-Lagrangian manner. The investigated CAO was observed on 1 April 2022 during the HALO-(AC)3 campaign. Shortly after passing the sea-ice edge, maximum diabatic heating rates over 6 K h−1 and moisture uptake over 0.3 gkg-1h-1 were measured near the surface. Clouds started forming and vertical mixing within the deepening boundary layer intensified. The quasi-Lagrangian observations are compared with the fifth-generation global reanalysis (ERA5) and the Copernicus Arctic Regional Reanalysis (CARRA). Compared to these observations, the mean absolute errors of ERA5 versus CARRA data are 14 % higher for air temperature over sea ice (1.14 K versus 1.00 K) and 62 % higher for specific humidity over ice-free ocean (0.112 g kg−1 versus 0.069 g kg−1). We relate these differences to issues with the representation of the marginal ice zone and corresponding surface fluxes in ERA5, as well as the cloud scheme producing excess liquid-bearing, precipitating clouds, which causes a too-dry marine boundary layer. CARRA's high spatial resolution and demonstrated higher fidelity towards observations make it a promising candidate for further studies on Arctic air mass transformations.

Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. This poses a serious problem when applied to high-stakes applications. To solve this issue, uncertainty quantification (UQ) models have been developed to allow the detection of misclassifications. Meta-model-based UQ methods are promising due to the lack of predictive model re-training and low resource requirement. However, there are still several issues present in the training process. (1) Most current meta-models are trained using hard labels that do not allow quantification of the uncertainty associated with a given data sample; and (2) in most cases, the base model has a high test accuracy. Therefore, the samples used to train the meta-model primarily consist of correctly classified samples. This leads the meta-model to learn a poor approximation of the true decision boundary. To address these problems, we propose a novel soft-label formulation that better differentiates between correct and incorrect classifications, thereby allowing the meta-model to distinguish between correct and incorrect classifications with high uncertainty (i.e., low confidence). In addition, a novel training framework using adversarial samples is proposed to explore the decision boundary of the base model and mitigate issues related to training datasets with label imbalance. To validate the effectiveness of our approach, we use two predictive models trained on SVHN and CIFAR10 and evaluate performance according to sensitivity, specificity, an F1-score-style metric, average precision, and the Area Under the Receiver Operating Characteristic curve. We find the soft-label approach can significantly increase the model’s sensitivity and specificity, while the training with adversarial samples can noticeably improve the balance between sensitivity and specificity. We also compare our method against four state-of-the-art meta-model-based UQ methods, where we achieve significantly better performance than most models.

Huntington’s disease (HD) is an inherited neurodegenerative disease characterized by uncontrolled movements, emotional disturbances, and progressive cognitive impairment. It is estimated to affect 4.3 to 10.6 per 100,000 people worldwide, and the mean prevalence rate among all published studies, reviews, and genetic HD registries is 5.7 per 100,000. A key feature of HD is the loss of striatal neurons and cortical atrophy. Although there is no cure at present, the discovery of the gene causing HD has brought us into a new DNA era and therapeutic advances for several neurological disorders. PubMed was systematically searched using three search strings: ‘“Huntington disease” + “stem cell”’, ‘”Huntington disease” + Mesenchymal stromal cell’, and ‘”Huntington disease” + “induced pluripotent stem cell”’. For each string, the search results were categorized based on cell type, and papers that included a clinical analysis were categorized as well. The data were extracted up to 2024. We did not include other databases in our search to have a comparable and systematic review of the literature on the topic. The collected data were analyzed and used for critical interpretation in the present review. Data are presented chronologically as clinical studies were published. Therapeutic strategies based on stem cells have drawn a lot of interest as possible HD therapies. Recent research indicates that NSCs have been the most often utilized stem cell type for treating HD. NSCs have been generated and extracted from a variety of sources, including HD patients’ somatic cells and the brain itself. There is strong evidence supporting the transplantation of stem cells or their derivatives in HD animal models, even if stem-cell-based preclinical and clinical trials are still in their early stages. Current treatment only aims at relieving the symptoms rather than treating the pathogenesis of the disease. Although preclinical trials in HD models have shown promise in improving cognitive and motor functions, stem cell therapy still faces many challenges and disadvantages including immunosuppression and immunorejection as well as ethical, technical, and safety concerns. Further research is required for a definitive conclusion.

With the funding line for the so-called Nationale Bildungsplattform (National Education Platform), the German federal government attracted a great deal of attention within the national education landscape in 2021. Alongside praise for the ambitious initiative, there was also criticism of individual aspects. Until the release of the closed beta under the new name Mein Bildungsraum (My Education Space) in the fall of 2023, public discussion was quieter. Recently, with the handover of essential middleware components to the Federal Agency for Disruptive Innovation, the initiative is now visibly picking up speed again in the summer of 2024. However, the vision still remains vague, and some doubts persist about the feasibility of the goals set. Against this backdrop, the article explores the question of which target images of a digital ecosystem for education are recognizable and which decisions are required to define a tangible – and therefore also implementable – vision.

Abstract Background A hereditary haemoglobinopathy known as sickle cell disease (SCD) affects over 100 000 people in the United States severely. Cerebrovascular disease is a prominent consequence of SCD. By the age of 30, 53% of patients have silent cerebral infarcts (SCIs) (a stroke that occurs without any obvious symptoms because it damages a small part of the brain that isn't responsible for any essential functions), and by the age of 40, 3.8% have overt strokes. Main body The multidimensional burden of cerebrovascular illness in SCD is reviewed in detail in this article, which includes both clinical strokes and the frequently asymptomatic SCIs. The intricate pathophysiology of SCD and stroke is explored. With SCD, there are currently very few methods for preventing primary and secondary stroke; the most common ones are hydroxyurea and blood transfusion. Nevertheless, not enough research has been done on the possible contributions of anticoagulation and aspirin to strokes linked to SCD. Promising evidence is also highlighted in the study, suggesting that new drugs intended to treat SCD may be able to alleviate leg ulcers and renal impairment in addition to reducing unusually high transcranial Doppler flow velocity – a crucial component of cerebrovascular events. Given that these novel medications specifically target haemolysis and vaso‐occlusion, the two main causes of strokes in this population, more research is desperately needed to determine whether they are effective in avoiding strokes in people with SCD. The literature review also emphasizes how common healthcare inequities are and how they hinder advancements in SCD research and management in the United States. Conclusion To successfully address these inequities, the evaluation recommends more funding for both SCD management and research, as well as for patient and clinician education. This multimodal viewpoint highlights the intricate terrain of cerebrovascular problems associated with SCD and the urgent need for all‐encompassing and fair strategies to improve patient outcomes and advance research.

Asynchronous discussions are a popular feature in online higher education as they enable instructor-student and student-student interactions at the users' own time and pace. AI-driven discussion platforms are designed to relieve instructors of automatable tasks, e.g., low-stakes grading and post moderation. Our study investigated the validity of an AI-generated score compared to human-driven methods of evaluating student effort and the impact of instructor interaction on students' discussion post quality. A series of within-subjects MANOVAs was conducted on 14,599 discussion posts among over 800 students across four classes to measure post 'curiosity score' (i.e., an AI-generated metric of post quality) and word count. After checking assumptions, one MANOVA was run for each type of instructor interaction: private coaching, public praising, and public featuring. Instructor coaching appears to impact curiosity scores and word count, with later posts being an average of 40 words longer and scoring an average of 15 points higher than the original post that received instructor coaching. AI-driven tools appear to free up time for more creative human interventions, particularly among instructors teaching high-enrollment classes, where a traditional discussion forum is less scalable.

Riming is a key precipitation formation process in mixed-phase clouds which efficiently converts cloud liquid to ice water. Here, we present two methods to quantify riming of ice particles from airborne observations with the normalized rime mass, which is the ratio of rime mass to the mass of a size-equivalent spherical graupel particle. We use data obtained during the HALO-(AC)3 aircraft campaign, where two aircraft collected radar and in situ measurements that were closely spatially and temporally collocated over the Fram Strait west of Svalbard in spring 2022. The first method is based on an inverse optimal estimation algorithm for the retrieval of the normalized rime mass from a closure between cloud radar and in situ measurements during these collocated flight segments (combined method). The second method relies on in situ observations only, relating the normalized rime mass to optical particle shape measurements (in situ method). We find good agreement between both methods during collocated flight segments with median normalized rime masses of 0.024 and 0.021 (mean values of 0.035 and 0.033) for the combined and in situ method, respectively. Assuming that particles with a normalized rime mass smaller than 0.01 are unrimed, we obtain average rimed fractions of 88 % and 87 % over all collocated flight segments. Although in situ measurement volumes are in the range of a few cubic centimeters and are therefore much smaller than the radar volume (about 45 m footprint diameter at an altitude of 500 m above ground, with a vertical resolution of 5 m), we assume they are representative of the radar volume. When this assumption is not met due to less homogeneous conditions, discrepancies between the two methods result. We show the performance of the methods in a case study of a collocated segment of cold-air outbreak conditions and compare normalized rime mass results with meteorological and cloud parameters. We find that higher normalized rime masses correlate with streaks of higher radar reflectivity. The methods presented improve our ability to quantify riming from aircraft observations.

Background Frailty is increasingly recognized as a predictor of surgical outcomes; however, its utility in anterior cranial fossa (ACF) surgery remains unclear. We analyzed whether age and frailty are independent predictors of outcomes after ACF surgery using a retrospective cohort study.

This study investigates the distribution, morphology, and properties of these soils, focusing on areas such as littoral plains, high hilly areas, and rift depression valleys. Black soils occur in the eastern Mediterranean with a limited distribution, and some of them meet the requirements for black soils according to the INBS (International Network of Black Soils), while others do not. Black soils can be categorized into three types based on their genesis and evolution: calcareous black soils (mainly raw rocky rendzina), hydromorphic black soils, and black soil on basalt. While black soils were found in various bioclimatic stages and parent materials, their presence was notably limited in certain areas, contrary to prior indications. A soil morphology analysis revealed distinct color variations and depths, influenced by the accumulation of organic matter for hydromorphic and calcareous black soils and basaltic parent material for black soils on basalt. A particle size analysis indicated texture variations from clay to loam, with no clear indication of illuviation. A chemical analysis showed alkaline pH levels, except in basalt-derived soils, which exhibited a slight acidity. Hydromorphic black soil is the most important in terms of expansion and agricultural use and is only found in limestone marl deposits and lakes in depressions emerging from Dead Sea rifts under conditions of saturation or poor drainage. These soils have a thick, dark moly horizon and a high organic matter content.

Dysfunction of the neuroglia can have profound consequences on the blood–brain barrier (BBB). Studies have shown that the disruption of astrocytic–endothelial interaction can compromise the permeability of BBB and its effectiveness in selectively regulating the exchange of substances. Microglia have recently been recognized to have a significant role in the initiation of chronic pain and in its interactions with various nerve blockers and anesthetic agents. Microglia have a role in pain resolution via a pathway that involves Cannabinoid receptor type 2 activation and MAP kinase phosphorylation. Understanding the role of these cells in the context of neuropathic pain and neurological disorders can aid in improving clinical outcomes and the challenging nature of managing pain. Advancing studies have proposed pharmacological and genetic modulation of microglia as a potential treatment option for patients with chronic pain.

In this narrative review, we explore the evolving role of machine learning (ML) in the diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The increasing prevalence of TBI necessitates advanced techniques for timely and accurate diagnosis, and ML offers promising tools to meet this challenge. Current research predominantly focuses on integrating clinical data, patient demographics, lab results, and imaging findings, but there remains a gap in fully harnessing the potential of image features. While advancements have been made in areas such as subdural hematoma segmentation and prognosis prediction, the translation of these techniques into clinical practice is still in its infancy. This is further compounded by challenges related to data privacy, clinician trust, and the interoperability of various health systems. Despite these hurdles, FDA-approved ML applications for TBI and their subsequent promising results underscore the potential of ML in revolutionizing TBI care. This review concludes by emphasizing the importance of bridging the gap between theoretical research and real-world clinical application and the necessity of addressing the ethical and privacy implications of integrating ML into healthcare.