The neurovascular niche is crucial for constant blood supply and blood-brain barrier (BBB) function and is altered in a number of different neurological conditions, making this an intensely active field of research. Brain vasculature is unique for its tight association of endothelial cells with astrocytic endfeet processes. Separation of the vascular compartment by centrifugation-based methods confirmed enrichment of astrocytic endfeet processes, making it possible to study the entire vascular niche with such methods. Several centrifugation-based separation protocols are found in the literature; however, with some constraints which limit their applicability and the scope of the studies. Here, we describe and validate a protocol for physically separating the neurovascular niche from the parenchyma, which is optimized for smaller tissue quantities. Using endothelial, neuronal and astrocyte markers, we show that quantitative Western blot-based target detection can be performed of both the vascular and parenchymal fractions using as little as a single mouse brain hemisphere. Validation of our protocol in rodent stroke models by detecting changes in serum albumin signals and astrocyte activation, i.e. increased glial fibrillary acidic protein expression, between the ipsilateral and the lesion-free contralateral hemisphere demonstrates this protocol as a new way of detecting BBB breakdown and astrogliosis, respectively.

Dietary components are important for the structural and functional development of the brain. Among these, docosahexaenoic acid,22:6n-3 (DHA) is critically required for the structure and development of the growing fetal brain in utero. DHA is the major n-3 long-chain fatty acid in brain gray matter representing about 15% of all fatty acids in the human frontal cortex. DHA affects neurogenesis, neurotransmitter, synaptic plasticity & transmission, and signal transduction in the brain. Studies in animals and humans show that adequate levels of DHA in neural membranes are important for cortical astrocyte maturation and vascular coupling, and helps cortical glucose uptake and metabolism. In addition, specific metabolites of DHA are bioactive molecules that protect tissues from oxidative injury and stress in the brain. A low DHA level in the brain results in behavior changes and is associated with learning problems and memory deficits. In humans, the third trimester-placental supply of maternal DHA to the growing fetus is critically important as the growing brain obligatory requires DHA during this window period. Besides, DHA is also involved in the early placentation process, essential for placental development. This underscores the critical importance of maternal DHA intake for the structural and functional development of the brain. This review describes DHA's multiple roles during gestation, lactation, and the consequences of its lower intake during pregnancy and postnatally on the children's brain development and function.

Brain Tumor is one of the severe diseases and occurrence of this disease threats human life. Detection of brain tumor in advance can secure patient’s life from unwanted loss. Well-timed and swift disease detection and treatment strategy can lead to improved quality of life in these patients. This paper attempts to use Machine Learning based ensemble approaches for recognising patients with brain tumor. Ensemble technique based AdaBoost classifier and 10-fold stratified cross-validation method are assembled in single platform is proposed in this paper for prediction of brain tumor. This prediction is compared against three baseline classifiers such as Gradient Boost, Random Forest and Extra Trees classifier. Experimental result implies the superiority of this model with an accuracy of 98.97%, f1-score of 0.99, kappa statistics score of 0.95 and MSE of 0.0103.

Purpose: To explore the influence of education and other factors on an athlete’s decision to return to sport post-concussion injury, and whether general risk-taking tendencies are related to return to sport post-concussion decisions in these athletes. Participants and Methods: A self-administered electronic survey was designed to examine their decision-making process when faced with scenario-based questions regarding returning to sport post-concussion injury. Students from the Health Sciences and Medicine Faculty at Bond University were invited to participate. Participants were allocated to a concussion education or non-education group prior to commencement of questionnaire via the random generator on Qualtrics software function. The risk propensity scale was used to assess the risk aversion of each participant. Results: Sixteen respondents were randomized evenly to education and non-education groups. Seven (43.8%) had previously received concussion education training prior to completing the questionnaire, with one (14%) choosing to return to sport in the scenario-based questions. The education group reported two (25%) respondents return to sport, while three (75%) respondents out of four returned to sport with no education or previous concussion training. Influential factors that impacted the decision whether to return to sport or not included: game importance, concussion severity and symptoms, and various internal and external factors. Finally, there was a divergence in results from the risk propensity scale when deciding to return to sport and general risk-taking propensities. Conclusion: This study identified several influential factors including game importance, concussion severity and symptoms which play a significant role in the return to sport decisions post-concussion injury.

The brain, rather than being homogeneous, displays an almost infinite topological genus, because is punctured with a very high number of topological vortexes, i.e., .e., nesting, non-concentric brain signal cycles resulting from inhibitory neurons devoid of excitatory oscillations. Starting from this observation, we show that the occurrence of topological vortexes is constrained by random walks taking place during self-organized brain activity. We introduce a visual model, based on the Pascal’s triangle and linear and nonlinear arithmetic octahedrons, that describes three-dimensional random walks of excitatory spike activity propagating throughout the brain tissue. In case of nonlinear 3D paths, the trajectories in brains crossed by spiking oscillations can be depicted as the operation of filling the numbers of the octahedrons in the form of “islands of numbers”: this leads to excitatory neuronal assemblies, spaced out by empty area of inhibitory neuronal assemblies. These procedures allow us to describe the topology of a brain of infinite genus, to assess inhibitory neurons in terms of Betti numbers, and to highlight how non-linear random walks cause spike diffusion in neural tissues when tiny groups of excitatory neurons start to fire.

Mammalian genomes encode tens of thousands of long-noncoding RNAs (lncRNAs), which are capable of interactions with DNA, RNA and protein molecules, thereby enabling a variety of transcriptional and post-transcriptional regulatory activities. Strikingly, about 40% of lncRNAs are expressed specifically in the brain in precisely regulated temporal and spatial expression patterns. In stark contrast to the highly conserved repertoire of protein-coding genes, thousands of new lncRNAs have appeared during primate nervous system evolution with hundreds of human-specific lncRNAs. Their evolvable nature and the myriad of potential functions make lncRNAs ideal candidates for drivers of human brain evolution. The human brain displays the largest relative volume of any animal species and the most remarkable cognitive abilities. In addition to brain size, structural reorganization and adaptive changes represent crucial hallmarks of human brain evolution. LncRNAs are increasingly reported to be involved in neurodevelopmental processes including proliferation, neurite outgrowth and synaptogenesis, as well as in neuroplasticity, suggested to underlie human brain evolution. Hence, evolutionary human brain adaptations are proposed to be essentially driven by lncRNAs, which will be discussed in this review.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

Drug delivery to the brain represents a challenge especially in the therapy of central nervous system malignancies. Simvastatin (SVT), as other statins, has shown potential anticancer properties that are difficult to exploit in the CNS. In the present work the physico-chemical, mucoadhesive and permeability enhancing properties of simvastatin-loaded poly-ε-caprolactone nanocapsules coated with chitosan for nose-to-brain administration were investigated. Lipid-core nanocapsules coated with different molecular weight (MW) chitosans (LNCchit) prepared by a novel one-pot technique were characterized for particle size, surface charge, particle number density, morphology, drug encapsulation efficiency, interaction between surface nanocapsules with mucin, drug release and permeability across two nasal mucosa models. Results show that all formulations present adequate particle size (below 220 nm), positive surface charge, narrow droplet size distribution (PDI<0.2) and high encapsulation efficiency. Nanocapsules presented controlled drug release and mucoadhesive properties dependent on the MW of the coating chitosan. The results of permeation across RPMI 2650 human nasal cell line evidenced that LNCchit increased the permeation of SVT. In particular, the amount of SVT permeated after 4h for nanocapsules coated with low MW chitosan, high MW chitosan and control SVT was 13.91 ± 0.78 µg, 9.15 ± 1.23 µg and 1.42 ± 0.21 µg respectively. These results were confirmed by the SVT ex vivo permeation across rabbit nasal mucosa. This study highlighted the suitability of LNCchit as promising strategy for the administration of simvastatin for a nose-to-brain approach for the therapy of brain tumors.

Social plasticity, defined as the ability to adaptively change the expression of social behavior according to previous experience and to social context, is a key ecological performance trait that should be viewed as crucial for Darwinian fitness. The neural mechanisms for social plasticity are poorly understood, in part due to skewed reliance on rodent models. Fish model organisms are relevant in the field of social plasticity for at least two reasons: first, the diversity of social organization among fish species is staggering, increasing the breadth of evolutionary relevant questions that can be asked. Second, that diversity also suggests translational relevance, since it is more likely that “core” mechanisms of social plasticity are discovered by analyzing a wider variety of social arrangements than relying on a single species. We analyze examples of social plasticity across fish species with different social organizations, concluding that a “core” mechanism is the initiation of behavioral shifts through the modulation of a conserved “social decision-making network”, along with other relevant brain regions, by monoamines, neuropeptides, and steroid hormones. The consolidation of these shifts may be mediated via neurogenomic adjustments and regulation of the expression of plasticity-related molecules (transcription factors, cell cycle regulators, and plasticity products).

The peripheral lymphatic system plays a crucial role in the recovery mechanisms after many pathological changes, such as infection, trauma, vascular, or metabolic diseases. The lymphatic clearance of different tissues from waste products, viruses, bacteria and toxic proteins significantly contributes to the correspondent recovery processes. However, understanding of the meningeal lymphatics functions is a challenging problem. The exploration of mechanisms of lymphatic communication with brain fluids as well as the role of the lymphatic system in the brain drainage, clearance and recovery are still in its infancy. Here we review novel concepts on the anatomy and physiology of the lymphatics in the brain, which warrant a substantial revision of our knowledge about the role of lymphatics in the rehabilitation of the brain functions after neural pathologies. We discuss a new vision on how to recruit the meningeal lymphatics by the opening of blood-brain barrier as a trigger mechanism of activation of the meningeal lymphatic drainage. This leads to innovative strategies in neurorehabilitation therapy.

Children with central nervous system (CNS) malignancies often suffer from high symptom burden and risk of death. Pediatric palliative care is a medical specialty, provided by an interdisciplinary team, which focuses on enhancing quality of life and minimizing suffering for children with life-threatening or life-limiting disease, and their families. Primary palliative care skills which include basic symptom management, facilitation of goals-of-care discussions, and transition to hospice can and should be developed by all providers of neuro-oncology care. This chapter will review the fundamentals of providing primary palliative care

The tumor microenvironment is known to have increased levels of cytokines and metabolites, such as glutamate, due to their release from the surrounding cells. A normal cell around the tumor that responds to the inflammatory environment is likely to be subsequently altered. We will discuss how these abnormalities will support tumor survival via the actions of gap junctions (GJs) and hemichannels (HCs) which are composed of hexamer of connexin 43 (Cx43) protein. In particular, we will discuss how GJ intercellular communication (GJIC) in glioma cells, the primary brain tumor, is a regulatory factor and its attenuation leads to tumor invasion. In contrast, the astrocytes, which are normal cells around the glioma, are "hijacked" by tumor cells, either by receiving the transmission of malignant substances from the cancer cells via GJIC, or perhaps via astrocytic HC activity through the paracrine signaling which enable the delivery of these substances to the distal astrocytes. This astrocytic signaling would promote tumor expansion in the brain. In addition, brain metastasis from peripheral tissues has also been known to be facilitated by GJs formed between cerebral vascular endothelial cells and cancer cells. Astrocytes and microglia are generally thought to eliminate cancer cells at the blood brain barrier. In contrast, some reports suggest they facilitate tumor progression as tumor cells take advantage of the normal functions of astrocytes that support the survival of the neurons by exchanging nutrients and metabolites. In summary, GJIC is essential for the normal physiological function of growth and allowing the diffusion of physiological substances. Therefore, whether GJIC is cancer promoting or suppressing may be dependent by what permeates through GJs, when it is active, and to which cells. The nature of GJs, which has been ambiguous in brain tumor progression, will need to be re-visited and understood together with new findings on Cx proteins and HC activities.

Background: Post-traumatic stress disorder (PTSD) is a common debilitating psychiatric condition for which pharmacological therapy is not always solvable. Various treatments have been suggested for these patients. Deep brain stimulation (DBS) is currently under investigation for patients affected by PTSD. 2) Methods: We review the neurocircuitry and up to date clinical concepts that may be of relevance for the implementation of DBS in posttraumatic stress disorder (PTSD). 3) Results: The role of DBS in treatment-refractory PTSD patients has been investigated relying on both preclinical and clinical studies. 4) Conclusions: DBS for PTSD is in its preliminary phases and likely to provide hope to patients with medical refractory PTSD following the results of randomized controlled studies.

The nature of the interaction between Th17 cells and the blood-brain barrier (BBB) is critical for the development of autoimmune inflammation in the central nervous system (CNS). TNF-a or IL-17 stimulation is known to enhance the adherence of Th17 cells to the brain endothelium. The brain endothelial cells (bEnd.3) express VCAM-1, the receptor responsible for inflammatory cell adhesion, which binds VLA-4 on migrating effector lymphocytes at the early stage of brain inflammation. The present study examines the effect of the pro-inflammatory cytokines TNF-a and IL-17 on the adherence of Th17 cells to bEnd.3 The bEnd.3 cells were found to increase production of CCL2 and CXCL1 after stimulation by pro-inflammatory cytokines, while CCL2, CCL5, CCL20 and IL17 induced Th17 cell migration through a bEnd.3 monolayer. This interaction between Th17 cells and the brain endothelium appears to be mediated by VCAM-1 and some chemotactic cytokines. This observation may suggest potential therapeutic targets for the prevention of autoimmune neuroinflammation development in the CNS.

Brain metastases are highly evolved manifestations of breast cancer arising in a unique microenvironment, giving them exceptional adaptability in the face of new extrinsic pressures. The incidence is rising in line with population ageing, and use of newer therapies that stabilise metastatic disease burden with variable efficacy throughout the body. Historically, there has been a widely held view that brain metastases do not respond to circulating therapeutics because the blood-brain-barrier (BBB) restricts their uptake. However, emerging data are beginning to paint a far more complex picture where the brain acts as a sanctuary for dormant, subclinical proliferations that are initially protected by the BBB, but then exposed to dynamic selection pressures as tumours mature and vascular permeability increases. Here, we review key experimental approaches and landmark studies that have charted the genomic landscape of breast cancer brain metastases. These findings are contextualised with the factors impacting on clonal outgrowth in the brain: intrinsic breast tumour cell capabilities required for brain metastatic fitness, and the neural niche, which is initially hostile to invading cells but then engineered into a tumour-support vehicle by the successful minority. We also discuss how late detection, abnormal vascular perfusion and interstitial fluid dynamics underpin the recalcitrant clinical behaviour of brain metastases, and outline active clinical trials in the context of precision management.

The integration of sign language recognition systems into digital platforms has the potential to bridge communication gaps between the deaf community and the broader population. This paper introduces an advanced Israeli Sign Language (ISL) recognition system designed to interpret dynamic motion gestures, addressing a critical need for more sophisticated and fluid communication tools. Unlike conventional systems that focus solely on static signs, our approach incorporates both Deep Learning and Computer Vision techniques to analyze and translate dynamic gestures captured in real-time video. We provide a comprehensive account of our preprocessing pipeline, detailing every stage from video collection to the extraction of landmarks using MediaPipe, including the mathematical equations used for preprocessing these landmarks and the final recognition process. The dataset utilized for training our model is unique in its comprehensiveness and is publicly accessible, enhancing the reproducibility and expansion of future research. The deployment of our model on a publicly accessible website allows users to engage with ISL interactively, facilitating both learning and practice. We discuss the development process, the challenges overcome, and the anticipated societal impact of our system in promoting greater inclusivity and understanding.

In the extended malfatti problem, the numbers of circles in the triangle are triangle numbers, because the tangency properties among the internal circles and 3 sides of the triangle have special type of structure. That is, the corner circle tangent to two sides of the triangle and two boundary circles, the boundary circles tangent to one side of the triangle and 4 other circles, and the inner circles always tangent to 6 other circles. The circles we find in extended general Malfatti’s problem has the property that the smallest radius and the largest radius for the circle has big difference. In this paper, we proposed algorithms to solve the problem that the tangency properties among circles and sides of triangle is not fixed, so that the number of circles in the triangle is not necessary a triangle numbers. The purpose of this change is try to find the radii of the circles in the triangle is within a small range.

Right heart masses are usually suspected during a routine examination by transthoracic echocardiography or due to cardiac symptoms. Although very important, sometimes it may be a challenge to differentiate between normal structures (variants) and aberrant or pathologic masses regarding the right heart cardiac tumors. In the last entity, the majority of primary cardiac tumors in adults and children are benign; however, they may have a malignant counterpart. Right heart tumors might involve not only the walls (myocardium, endocardium and pericardium), but also valves and cardiac chambers. Cardiac tumors exhibit altered signal intensity and enhancement characteristics, requiring the use of several imaging methods following a multimodality-imaging algorithm that culminates in the excision or biopsy of the tumor formation. Therefore, various imaging modalities, including echocardiography, computer tomography, positron emission tomography, cardiac magnetic resonance imaging, scintigraphy or, even, invasive angiography, can be useful during their diagnosis. Echocardiography is the first-line non-invasive imaging modality used in the evaluation of the right heart tumors. Before the other diagnosis imaging modalities it is essential for defining morphology, assessing size, observing dynamic appearance, and evaluating functional abnormalities and hemodynamic consequences,. However, all imaging methods have frequently a complementary role; therefore, an integrated imaging approach is recommended.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

This Article demonstrates how an eigendecomposition problem is inputted into a quantum circuit, how gates are applied in the quantum circuit, and how the output measurements are the correct eigenvalues. This process is known as quantum phase estimation (QPE). A quantum harmonic oscillator example, a foundational quantum physical chemistry problem, is demonstrated within the context of QPE. A particle in a box example, another quantum physical chemistry problem, may be solved by QPE with a caveat. These examples are of the limiting cases of diagonal matrices. Future advances in taking matrix inverses for solving linear sets of equations or finding ground state energies in the Schrödinger equation will use the principles implemented in this Article.

The prevalence of digital devices in modern society has raised concerns about the potential neg-ative effects of blue light emissions on eye health and biological rhythms. Research into blue light emissions from digital devices and their potential impact on eye health emphasizes the importance of understanding and quantifying the extent and scope of blue light emissions produced by commonly used screens (smartphones, tablets and computers). This comparative study specifically evaluates three popular Apple devices: iPhone 12 mini®, iPad Pro 12.9® and MacBook Pro®. The devices' spectral power was measured using a spectroradiometer while displaying text and a game at different brightness levels. The study also examines the effectiveness of blue-blocking lenses from well-known brands. The research highlights the importance of quantifying blue light emissions and understanding their potential impact on eye health. By doing so, appropriate measures can be developed to mitigate adverse effects on ocular structures. Above all, this research provides valuable information about the risks associated with exposure to blue light, showing that all tested devices have blue wavelength peaks between 445 nm and 455 nm. By recognizing and addressing the potential negative impacts of blue light, the risks of eye health problems can be reduced and ensure more responsible and conscious use of digital devices.

A maxillary totally edentulism was rehabilitated with one-piece implants starting from pre-existing totally removable denture. A 3D digital scan of the removable denture and upper and lower arches was performed. On this basis, a prototype with optimal esthetic and functional outcomes was re-alized and turned into a custom-made radiological stent with markers. The superimposition of STL and DICOM files allowed the virtual planning of one-piece implants ideal position. The stackable templates composed of a fixed component, namely base template, and removable components, were realized. The fixed component, once secured with anchor pins to the bone, was no longer removed. The removable components, screwed to the fixed component, were used to perform the implant surgery and the immediate prosthetic loading. A base template allowed for maintaining a fixed reference during the entire workflow, improving the transition between the digital project, the surgical procedure, and the prosthetic rehabilitation.

The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Pre-operative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared localization accuracies between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. Patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

Virtual reality experiences are frequently created using game engines, yet they are not simple for novices and unskilled professionals who don’t have programming and 3D modeling skills. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. This work compiles design guidelines derived from a systematic literature review to contribute in the development of more intuitive virtual reality authoring tools. We searched the Scopus and Web of Science databases for works published between 2018 and 2021 and discovered 14 papers. We compiled 14 requirement and feature design guidelines, such as Visual Programming, Immersive Authoring, Reutilization, Sharing and Collaboration, Metaphors, and Movement Freedom, among others. The collected guidelines have the potential to either guide the development of new authoring tools or to evaluate the intuitiveness of existing tools. Furthermore, they can also support the development of the metaverse, since virtual content creation is one of its bases.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease. Technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which include RT-PCR, antigen-antibody and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include Deep Learning and Transfer learning approach. The review also provide comparison between these 2 emerging technologies and open research issues for the development of smart-IoMT-enable platform for the detection of COVID-19.

18F-fluorodeoxyglucose positron emission tomography and computed tomography (18F-FDG PET-CT) is increasingly used in the diagnosis, prognosis, staging, and treatment monitoring of many tumor types. However, only a small number of studies have reported the use of 18F-FDG PET-CT in intrahepatic cholangiocarcinoma (ICC). This study aimed to examine the accuracy of tumor staging in ICC by using 18F-FDG PET-CT. Between January 2009 and December 2020, patients with suspected ICC were retrospectively enrolled in the study and underwent imaging. The sensitivity and specificity of 18F-FDG PET-CT, CT, and magnetic resonance imaging (MRI) in detecting tumors, satellite focus, vascular invasion, and lymph node metastases were analyzed. The efficacy of 18F-FDG PET-CT for tumor staging was evaluated. Of the 110 patients who were enrolled in the study, 52 underwent surgical treatment and 45 were histologically diagnosed with ICC. When compared with CT or MRI, 18F-FDG PET-CT had similar sensitivity and specificity values for diagnosing satellite focus and vascular and bile duct invasion; however, PET-CT showed higher accuracy in diagnosing regional lymph node metastases. The accuracy of tumor staging by 18F-FDG PET-CT was higher than that by CT/MRI. Thus, 18FDG PET-CT may support tumor staging in ICC.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying breast cancer from unregistered multi-view mammograms. This motivates us to leverage the architecture of Vision Transformers to capture long-range relationships of multiple mammograms from the same patient. For this purpose, we employ local Transformer blocks to separately learn patch relationships within a specific view (CC/MLO) of one side (right/left) of mammogram. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of CC and MLO view mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our two-view Transformer-based model achieves lesion classification accuracy of 77.0% and area under ROC curve (AUC = 0.814), outperforming state-of-the-art multi-view CNNs by 3.1% and 3%, respectively. Meanwhile, the new two-view model improves mammographic case classification accuracies of two single-view models by 7.4% (CC) and 4.5% (MLO), respectively. The promising results unveil the great potential of using Transformers to develop high-performing computer-aided diagnosis (CADx) schemes of mammography.

Melanoma is considered to be the most aggressive form of skin cancer. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. At present, the evaluation of malignancy is performed primarily by invasive histological examination of the suspicious lesion. Developing an accurate classifier for early and efficient detection can minimize and monitor the harmful effects of skin cancer and increase patient survival rates. This paper proposes a multi-class classification task using the CoAtNet architecture, a hybrid model that combines the depthwise convolution matrix operation of traditional convolutional neural networks with the strengths of Transformer models and self-attention mechanics to achieve better generalization and capacity. The proposed multi-class classifier achieves an overall precision of 0.901, recall 0.895, and AP 0.923, indicating high performance compared to other state-of-the-art networks.

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improve the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared: Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps, however FCNNs are an emerging set of algorithms within Deep Learning with sparse application towards seagrass mapping. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiscale segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with standard OBIA method used by ecologists, while also noting an increase in performance for mapping ecological features that are sparsely labelled across the study site.

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have got interest following the recent publication of their pharmacologically beneficial effects. Recently it comes out that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other metabolic diseases, and some type of cancers. In the past years, hundreds of effective FABP4 inhibitors have been synthesized and discovered but, unfortunately, none of them is in the clinical research phase. The field of computer-aided drug design seems to be promising and useful for the identification of FABP4 inhibitors; hence, different structure- and ligand-based computational approaches were already performed for their identification. In this paper, we searched for new potentially active FABP4 ligands in the Marine Natural Products (MNP) database. 14,492 compounds were retrieved from this database and filtered through a statistical and computational filter. Seven compounds were suggested by our methodology to possess a potential inhibitory activity upon FABP4 in the range of 79–245 nM. ADMET properties prediction were performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives; from these analyses, three molecules resulted as excellent candidates for becoming new drugs.

Autonomous aerial refueling technology is an effective solution to extend flight duration of unmanned aerial vehicles, and also a great challenge due to its high risk. For autonomous probe-and-drogue refueling tasks, relative navigation to provide relative position between the receiver aircraft and the refueling drogue is the first and essential step, and vision-based method is the most frequently used. A new monocular vision navigation sensor with image processing strategy consisting of the drogue detection method and the tracking method is developed for autonomous aerial refueling in this paper. In the drogue detection method, thresholding and mathematical morphology method are adopted to eliminate image interference, and contours extraction method is applied to obtain all contours, which are then subsequently checked to achieve target contour of drogue. In the tracking method, a rectangle of interest (ROI) of current frame image is determined by positioning results of last frame, and then processed by the previous drogue detection method. Finally, the proposed image processing strategy in monocular vision navigation sensor is validated using real flight images, which are captured from an autonomous aerial refueling testbed using a micro six-rotor aircraft as receiver aircraft.

This research explores the transformative impact of artificial intelligence (AI) on personalizing marketing campaigns, highlighting how AI technologies are reshaping modern marketing practices. AI tools such as machine learning, natural language processing (NLP), predictive analytics, deep learning, and computer vision are integral to enhancing customer engagement and optimizing marketing strategies. Machine learning algorithms enable precise customer segmentation and personalized recommendations, leading to more targeted and effective marketing messages. NLP facilitates the analysis of customer sentiment and enhances communication through advanced language processing and AI-powered chatbots. Predictive analytics and deep learning provide businesses with the capability to forecast customer behaviors and recognize complex data patterns, further refining marketing efforts. Computer vision technology contributes to personalization through innovations like visual search and augmented reality, creating immersive customer experiences. Despite the numerous benefits, the adoption of AI in marketing presents several challenges. Data privacy and security concerns, integration complexities, and the potential for algorithmic bias are critical issues that need to be addressed. Ethical considerations, including transparency and fairness in data usage, are also paramount to maintaining customer trust and ensuring responsible AI application. The future of AI in marketing promises continued advancements, with trends such as hyper-personalization and enhanced integration with technologies like the Internet of Things and voice search expected to drive further innovation. Overall, AI significantly enhances marketing effectiveness by offering tailored solutions and strategic insights while necessitating a thoughtful approach to ethical and practical challenges.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.

Virtual reality software might be challenging to utilize for beginners and unskilled professionals who do not have a programming or 3D modeling background. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. These tools are frequently insufficient due to a lack of support and standard operating procedures. Adopting the Design Science Research paradigm, this study aims to evaluate the validity of fourteen design guidelines for the development of intuitive virtual reality authoring tools as an artifact. While a previous study completed the first steps of the Design Science Research, by identifying problems, defining solution objectives, and developing and demonstrating the design guidelines, this work seeks to qualitatively evaluate their application in a practical experiment. A group of engineering students with no prior experience in creating virtual worlds were tasked with examining the design guidelines while using the NVIDIA Omniverse Enterprise as an exemplary use case and responding to a questionnaire and a focus group interview about how they perceived these guidelines. A correlation analysis confirmed that most guidelines scores behaved as expected and were ranked according to the use-case functionality. The participants understood the guidelines' definition and could decide if they agreed or disagreed with their presence during the experiment. We conclude that, in accordance with the Design Science Research, the proposed artifact is useful, i.e., the design guidelines for virtual reality authoring tools perform what they are designed to do and are operationally reliable in accomplishing their goals.

This paper presents an original method of additive manufacturing of cylindrical parts with variable circumference thickness, which allows the control of the deposition of molten material using an algorithm for decomposing the part geometry into volumetric elements with known dimensional configuration. In the absence of a post-processor capable of controlling additive manufacturing on a 5-axis numerical control machine, control of the deposition of molten material is done using parameterized programs, which can control both the feed speeds of the machine tool axes and the specific functions of the printing equipment. Additive manufacturing can make a positive contribution to sustainable development compared to traditional manufacturing technologies, thus making a positive contribution for a sustainable future. The aim of the work is to make it possible to 3D print parts with variable wall thickness using a CNC machining centre. To obtain the variable thickness layer we have implemented an original method of deposition with molten material (FDM), the coordination of the system composed of physical elements respectively programmable elements, is realized through control functions materialized in parameterized part programs, the generated outputs being the variable speed of the machine axes on a circular trajectory, the angular positioning, the filament advance.

People often make mistakes, so we try to automate every aspect of our lives. Sports is no exception. While just over a decade ago humans were analyzing games, today this is being done by artificial intelligence. Due to rapid development over the past decade, neural networks are now faster, more accurate, and in some areas even better than their human counterparts. In this paper, we present an algorithm that can detect player statistics during an NBA broadcast. It also helps users better understand the game and the use of augmented reality. The algorithm detects players on the court, tracks their movements, and assigns them to their respective teams. Using homography estimation, we transform the players’ positions from a three-dimensional space in the video to a two-dimensional space on the playing field plane. We define a new algorithm that predicts the players’ actions and their statistics. The results show that the proposed method can effectively identify the players, their respective teams, and their positions. It can also analyze their actions with high accuracy.

There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.

Quantification of bone loss for follow-up comparisons has not been routinely performed in patients with parietal thinning so far. This study compares different methodological approaches including both 2- and 3-dimensional methods in computerized tomographies of a single patient. The 2-dimensional method provides accurate measurements of bone thickness, while the 3-dimensional method allows for a more comprehensive view of the bone's structure, including parameters like shape, size, and thickness. The techniques provided valuable data for the treating physician and suggest a possible effect of corticosteroids in this individual patient. The study concludes that 3-dimensional reconstruction of computerized tomographies with subsequent 2-dimensional analyses appears justified for routine radiological practice to increase clinical awareness of the size of the parietal bone defects and allow for quantification of bone loss during long-term follow-ups. Further validation of the proposed analytical approaches is warranted in larger studies to investigate patterns of bone thinning across the skull and changes in bone volumes over time, with specialized software to be approved for such analyses.

In recent years, due to the advancement of machine learning, object detection has become a mainstream task in the computer vision domain. The first phase of object detection is to find the regions where objects can exist. With the improvement of deep learning, traditional approaches such as sliding windows and manual feature selection techniques have been replaced with deep learning techniques. However, object detection algorithms face a problem when performing in low light, challenging weather, and crowded scenes like any other task. Such an environment is termed a challenging environment. This paper exploits pixel-level information to improve detection under challenging situations. To this end, we exploit the recently proposed hybrid task cascade network. This network works collaboratively with detection and segmentation heads at different cascade levels. We evaluate the proposed methods on three complex datasets of ExDark, CURE-TSD, and RESIDE and achieve an mAP of 0.71, 0.52, and 0.43, respectively. Our experimental results assert the efficacy of the proposed approach.

In all areas of medicine, especially in radiology, computers are increasing year by year. Filmless radiology, speech recognition software, electronic application forms, and teleradiology are recent developments that have greatly improved radiologists' performance. This research explores radiology software trends, predictions, and the challenges posed by informatics and historical trend analysis. The rationale behind this research is that information technology (IT) is overgrowing almost every day. We must continuously seek new ways to apply IT to make more use of resources. Consequently, IT becomes increasingly crucial to radiology organizations' innovative thinking, workflow, and business models. This study aimed to analyze all radiology software publications in the Science Citation Index (SCI). From 1991 to July 2021, SCI was used to search for publications systematically. We have also widely used this historical method in radiology software research. The findings and discussions are base on an assessment of trends, predictions, contributions, and challenges in radiology software, and we are exploring radiology software with six evolutionary stages. The gift of this research is that radiology managers realize that the use of new information technologies is closely related to survival in a competitive environment. Radiology companies can review these new technologies to develop more innovative business models and services to improve operational deficiencies.

Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

Based on a local causal model of the dynamics of curved discrete spacetime, a causal model of quantum field theory in curved discrete spacetime is described. At the elementary level, space(-time) is assumed to consists of interconnected space points. Each space point is connected to a small discrete set of neighbor space points. Density distribution of the space points and the lengths of the space point connections depend on the distance from the gravitational sources. This leads to curved spacetime in accordance with general relativity. Dynamics of spacetime (i.e., the emergence of space and the propagation of space changes) dynamically assigns "in-connections" and "out-connections" to the affected space points. Emergence and propagation of quantum fields (including particles) are mapped to the emergence and propagation of space changes by utilizing identical paths of in/out-connections. Compatibility with standard quantum field theory (QFT) requests the adjustment of the QFT techniques (e.g., Feynman diagrams, Feynman rules, creation/annihilation operators), which typically apply to three in/out connections, to n > 3 in/out connections. In addition, QFT computation in position space has to be adapted to a curved discrete space-time.

Based on a local causal model of the dynamics of curved discrete spacetime, a causal model of quantum field theory in curved discrete spacetime is described. At the elementary level, space(-time) is assumed to consists of interconnected space points. Each space point is connected to a small discrete set of neighbor space points. Density distribution of the space points and the lengths of the space point connections depend on the distance from the gravitational sources. This leads to curved spacetime in accordance with general relativity. Dynamics of spacetime (i.e., the emergence of space and the propagation of space changes) dynamically assigns "in-connections" and "out-connections" to the affected space points. Emergence and propagation of quantum fields (including particles) are mapped to the emergence and propagation of space changes by utilizing identical paths of in/out-connections. Compatibility with standard quantum field theory (QFT) requests the adjustment of the QFT techniques (e.g., Feynman diagrams, Feynman rules, creation/annihilation operators), which typically apply to three in/out connections, to n > 3 in/out connections. In addition, QFT computation in position space has to be adapted to a curved discrete space-time.

A unique nucleus, the cerebrospinal fluid-contacting nucleus (CsfR), has been identified in the brain parenchyma. This nucleus features neurons with somas located within the parenchyma and processes extending into the cerebrospinal fluid (CSF). This anatomical configuration suggests that the CsfR may serve as a crucial interface between the nervous and body fluid regulatory systems, potentially playing a significant role in overall physiological modulation. Despite its importance, the precise biological significance of the CsfR remains to be fully elucidated. Previous research has characterized the CsfR in rats, detailing its position, adjacency, neuron distribution, size reconstruction, and stereotaxic coordinates in rats and non-human primates. Given the relevance of mice as a model organism, especially the C57BL/6J strain, this study aims to explore the existence and morphology of the CsfR in mice. Our findings confirm the presence of the CsfR, consistently located in the ventral gray area of the lower part of the aqueduct and the upper part of the fourth ventricle floor. It is bilaterally symmetrical and heart-shaped, which is slightly different from the Y-shape observed in rats. This study provides significant references for researchers investigating this specialized nucleus.

Genetic factors influence medication response (pharmacogenetics), affecting the pharmacody-namics and pharmacokinetics of many medicaments used in clinical care. The ability of medica-tions to cross the blood-brain barrier (BBB) represents a critical putative factor in the effectiveness and tolerability of various central nervous system (CNS) medications at a pharmacokinetic (dosing) level. Pharmacogenetics has the potential to personalise medicine to a greater extent than has been possible, with the potential to help reduce heuristic delays to effective tolerable pharmacotherapy. Here, we critically examine and summarise the evidence, particularly for ABCB1 polymorphisms associated with various CNS medications. These transporters appear to have a role in BBB pharmacogenetics. We identify some of the most promising variants for clinical translation while spotlighting the complexities of the involved systems and limitations of the current empirical literature.

Cooperation is essential in social life, involving collaborative efforts for mutual benefit. Individual differences in the cooperativeness trait are pivotal in these interactions. Previous research suggested that Duchenne smiles signal cooperative intent, and human-like eyes are theorized to enhance cooperation evolutionarily. However, the relationships between cooperative behavior, cooperativeness and possible behavioral cues signaling cooperative behavior are yet to be studied. In the current study, we explored the relationships among cooperativeness, Duchenne smiling with gaze, IBS during conversation, and their impacts on cooperative behavior. We hypothesized Duchenne smiling with gaze would mediate cooperativeness’ impact on cooperative behavior. Additionally, using functional near-infrared spectroscopy (fNIRS), we evaluated inter-brain synchrony (IBS) in the left prefrontal region, expecting it to predict cooperative behavior. The results demonstrated that cooperativeness significantly predicted Duchenne smiling with gaze and cooperative behavior, however, Duchenne smiling with gaze did not mediate the relationship between them. We further conducted a path analysis to examine the role of IBS during conversation in successive cooperative behavior. The path analysis result showed that cooperativeness directly affected cooperative behavior. Cooperativeness significantly predicted Duchenne smiling with gaze, however, neither Duchenne smiling with gaze nor IBS during conversation predicted successive cooperative behavior. These results suggest dispositional factors like cooperativeness may play a more decisive role than momentary expressional cues or neural synchrony in naturalistic unstructured communication in shaping cooperative behavioral outcomes after the communication. The study highlights how personality traits like cooperativeness shape nonverbal communication and social interactions.

Background: Alzheimer's Disease (AD) is a progressive and incurable condition that prompts the exploration of interventions to slow its advancement and enhance the quality of life. The cortical brain waves could have a role in the process of executive function deterioration, increasing dementia symptoms. Objective: To interrogate the effects of physical exercise, and non-invasive brain stimulation, both in electrophysiological behavior of the brain, the executive functions, and dementia symptoms in older adults with AD. Methods: One hundred and sixteen older adults were grouped (n=22 each): GAE (AD, PMED+NIBS), GCP (AD, PMED), GCN (AD, NIBS), GCS (control, no intervention). Binaural beats were used as a non-invasive brain stimulation, physical exercise, or both, were applied during 16 weeks. Pre/post measurements included Alpha Band, and SMR Rhythms, executive functions, and dementia symptoms. Results: GCS showed no significant changes (p>0.05). GCN and GCS slightly enhanced inhibitory control (p>0.05) and working memory (p>0.05). In contrast, GCP and GAE improved executive function (p

The gut microbiota (GM) communicates with the brain via biochemical signaling constituting the gut-brain axis, which significantly regulates the body's physiological processes. The GM dysbiosis can impact the digestive system and the functioning of the central nervous system (CNS) linked to the onset of neurodegenerative diseases. In this review, the scientific data compiled from diverse sources primarily emphasizes the neuropathological characteristics linked to the accumulation of modified insoluble proteins (such as β-Amyloid peptides and hyperphosphorylated tau proteins) in Alzheimer’s Disease (AD) and the potential impact of gut microbiota (GM) on AD susceptibility or resilience. The specific GM profile of human beings may serve as an essential tool for preventing or progressing neurodegenerative diseases like AD. This review focuses mainly on the effect of gut microfauna on the gut-brain axis in the onset and progression of AD. The GM produces various bioactive molecules that may serve as proinflammatory or anti-inflammatory signaling, contributing directly or indirectly to the repression or progression of neurodegenerative disorders by modulating the response of the brain axis. Human studies must focus on further understanding the gut-brain axis and venture to clarify microbiota-based therapeutic strategies for AD.

Preterm birth accounts for about 10% of births worldwide. Studying risk factors for perinatal brain damage is essential, as findings suggest that almost 20% of disabilities are linked to risks in the early stages of development. This research aimed to study longitudinal changes in intelligence from 6 to 8 years of age in a sample of 39 preterm children with a history of risk of brain damage and a control group of 35 children born at term. The Wechsler Intelligence Scale (WISC-IV) was used for cognitive measurement. The results showed that the preterm group obtained significantly lower scores than the control group. The working memory indicator significantly affected the interaction between age and prematurity. We consider that it is crucial to expand the knowledge we have about the neurocognitive development of premature infants both in specific cognitive domains and in age ranges so that the information obtained helps to predict the probability of presenting cognitive alterations from early stages and, therefore, to implement intervention strategies and programs based on scientific evidence and complemented in their design by clinical experience, empirical and theoretical knowledge of the different professionals involved in infant cognitive intervention.

Consumers often cite cognitive improvements as reasons for making dietary changes or using dietary supplements, a motivation that if leveraged could greatly enhance public health. However, rarely is it considered whether standardized cognitive tests that are used in nutrition research are aligned to outcomes of interest to the consumer. This knowledge gap presents a challenge to the scientific substantiation of nutrition-based cognitive health benefits. Here we combined qualitative consumer research and expert group methodology to (1) understand how consumers comprehend the potential benefits of nutrition for brain health, and (2) determine the alignment between consumers' articulated brain benefits and validated tests and tools. We derived a ‘Consumer Taxonomy of Cognitive and Affective Health in Nutrition Research’ which describes the cognitive and affective structure from the consumers' perspective. Experts agreed that validated tests exist for some consumer benefits including focused attention, sustained attention, episodic memory, energy levels, and anxiety. Prospective memory, flow, and presence represented novel benefits that require the development and validation of new tests and tools. Closing the gap between science and consumers, and fostering co-creative approaches to nutrition research, are critical to the development of products and dietary recommendations that support realizable cognitive benefits that benefit public health.

This study investigates the effects of subclinical hypothyroidism and BDNF on telomere length in T1DM mothers and their neonates. Methods: In this prospective cohort study, 70 pregnant women with T1DM were enrolled. Subclinical hypothyroidism during the first trimester was characterized by elevated thyroid-stimulating hormone (TSH) levels ranging from 2.5 to 5.0mIU/L alongside normal free thyroxine (FT4) concentrations. Participants were stratified into two groups based on TSH levels: a maternal subclinical hypothyroid group (SHG, n=35) and a maternal euthyroid group (ETG, n=35). Leukocyte telomere length (LTL) measurements were conducted using whole blood samples from the mothers and umbilical veins. Results: The maternal LTL mean was 1.7 (SD=1.1), which was significantly shorter than the neonatal telomeres, with a mean of 2.3 SD (1.2). A significant correlation was observed between maternal and neonatal LTL (r=0.750, p&lt;0.001). A positive association was found between neonatal LTL and BDNF levels in maternal circulation [β=0.421 (95% CI 0.536; 1.873; p=0.001)]. Neonatal LTL was negatively associated with gestational weight gain [β=-0.266 (95% CI-0.138; -0.009)], and with TSH in the first trimester [β=-0.237; (95% CI -0.493; - 0.010)]. Notably, neonates in the SHG exhibited shorter LTL, with a mean of 2.1 (1.0), compared to those in the ETG, who had a mean LTL of 2.7 (1.4). A positive association was found between maternal LTL and BDNF levels in maternal circulation [β=0.523 (95% CI 0.790; 1.950; P&lt;0.001)]. Maternal LTL was negatively associated with gestational weight gain [β=-0.259 (95% CI -0.122; -0.010)], and with TSH in the first trimester [β=-0.231; (95% CI -0.422; - 0.015)]. Conclusions: Genetic factors affect neonatal LTL. Maternal subclinical hypothyroidism reduces neonatal LTL. The link between maternal and neonatal LTL and BDNF reveals the complex endocrine-neurotrophic interaction in T1DM telomere dynamics.

Fever control is essential in patients with severe traumatic brain injury (TBI). The efficacy of therapeutic hypothermia (TH) in severe TBI has been investigated over the last few decades; however, in contrast to experimental studies showing benefits, no evidence of efficacy has been demonstrated in clinical practice. In this review, the mechanisms and history of hypothermia were briefly outlined, while the results of major randomized controlled trials (RCTs) and meta-analyses investigating TH for adult TBI are introduced and discussed. The retrieved meta-analyses showed conflicting results, with a limited number of studies indicating benefits of TH. Some studies have shown benefits of long-term TH compared with short-term TH. Although TH is effective at lowering elevated intracranial pressure (ICP), reduced ICP does not lead to favorable outcomes. Low-quality RCTs overestimated the benefits of TH, while high-quality RCTs showed no difference or worse outcomes with TH. RCTs assessing standardized TH quality demonstrated the benefits of TH. As TBI has heterogeneous and complicated pathologies, applying a uniform treatment may not be ideal. A meta-analysis of young patients who underwent early cooling and hematoma removal showed better TH results. TH should not be abandoned, and its optimal should be advocated on an individual basis.

IBD, encompassing CD and UC, are characterized by chronic gastrointestinal inflammation due to abnormal immune responses to gut microflora. The bidirectional communication between the gut and the brain, known as the GBA, is disrupted in IBD, leading to neurobiological imbalances and affective symptoms. Systemic inflammation in IBD affects the brain's inflammatory response system, hormonal axis, and BBB integrity, influencing gut microbiota and clinical responses to probiotics. This review aims to explore the links between dysregulations in the GBA, serum biomarkers, and the development of MCI and NDDs. The objective is to identify potential correlations and propose future research directions to understand the impact of altered microbiomes and intestinal barrier functions on NDDs. Studies suggest a potential link between IBD and extraintestinal complications such as MCI and NDDs. Mechanisms include systemic inflammation, BBB dysfunction, autoimmune responses, GBA dysfunction, medication effects, and comorbidities. Serum biomarkers, including vitamin D3, C-reactive protein, high-sensitivity C-reactive protein, vitamin B12, homocysteine, serum amyloid, neuron-specific enolase, neurofilament light chain, S100 proteins, brain-derived neurotrophic factor, IL-6, LP-2 associated phospholipase, prostaglandin E2, IL-1β, and TNF-α, have been investigated for their potential to predict MCI but current results are lacking clarity.

Background and Objectives: In recent years Electronic scooters (E-Scooters) have gained popularity, whether for private use or as a publicly available transportation method. With introduction of these vehicles, reports of E-Scooter related accidents have surged, sparking a public debate and concern. Aim of this study was to analyse epidemiological data, characteristics and severity of traumatic brain injury (TBI) related to E-Scooter accidents. Materials and Methods: This retrospective case series evaluated 28 patients who were admitted to three largest neurosurgery clinics in Riga, Latvia between time period from April to October in two separate years – 2022 and 2023 after an E-Scooter related accident. Data were collected on patient demographics, time of the accident, alcohol consumption, helmet use, type of TBI, other related injuries, treatment and assessment at discharge. Results: A total of 28 patients were admitted with TBI related to E-Scooter use, with median age 30 years (Q1-Q3, 20.25-37.25), four individuals under age of 18, majority (64%) were male. In 23 cases the injury mechanism was fall, in 5 cases collision. None were wearing a helmet at time of injury. Alcohol intoxication was evident in over half of the patients (51.5%), with severe intoxication (&gt;1.2ppm) in 75% cases among them. Neurological symptoms upon admission were noted in 50% of cases. All patients had intracranial trauma - 50% brain contusions, 43% traumatic subdural hematoma, almost 30% - epidural hematoma. Craniofacial fractures were evident in 71% of cases, fractures in other parts of body in 3 patients. 6 patients required emergency neurosurgical intervention. Neurological complications were noted in 2 patients, 1 patient died. Conclusions: E-Scooter related accidents result in a significant number of brain and other associated injuries, with notable frequency linked to alcohol influence and lack of helmet use. Prevention campaigns to raise awareness of potential risks and implementation of more strict regulations should be done.

Appropriate diagnosis, treatment and care contributes to better service engagement, improvements to wellbeing, cost savings, reductions in morbidity and mortality for people with Alcohol Related Brain Damage. In the North-East of England large amounts of alcohol are consumed, reflected in the number of alcohol-related deaths in the region. However, the pathway for people with Alcohol Related Brain Damage to receipt of diagnosis, treatment and care is unknown and could be unwittingly influenced by stigma. Qualitative in-depth semi-structured interviews were completed with 25 health and social care professionals from organisations involved with people with Alcohol Related Brain Damage recruited via snowball-sampling. Interviews were recorded, transcribed verbatim, coded, and analysed. People with Alcohol Related Brain Damage were stigmatised both by society and professionals which inhibited entry into services therefore Alcohol Related Brain Damage remained underdiagnosed and misdiagnosed. There was no dedicated service but silos with revolving doors and underfunded generic care with long waiting lists typically excluding those with alcohol or neurological problems. Professionals need to be challenged on the assumption that Alcohol Related Brain Damage is self-inflicted via training aimed at challenging unconscious bias. Reducing stigmatising processes associated with Alcohol Related Brain Damage could counteract professionals’ reluctance to provide care.

The MMP-9-1562C/T polymorphism influence incidence and course of many diseases of the central nervous system. We found, using luciferase assays and Q-RT-PCR technique, allele-specific in-fluence of MMP-9-1562C/T polymorphism on the MMP-9 promoter activity and on the expression of MMP-9 mRNA in human neurons derived from SH-SY5Y cells. Then, we have elucidated the mechanism responsible for the allele-specific action of the MMP-9-1562C/T polymorphism on transcriptional regulation of the MMP-9 gene using pull-down assay combined with mass spec-trometry analysis, EMSA and EMSA supershift techniques, as well as DsiRNA-dependent gene silencing. We have found that MMP-9 promoter activity and MMP-9 mRNA expression are reg-ulated in human neurons in the MMP-9-1562C/T allele-specific manner with stronger upregulation conferred by the C allele. Moreover, we have revealed that the allele-specific action of the MMP-9-1562C/T polymorphism on the neuronal MMP-9 expression is related to HDAC1 and ZNF384 transcriptional regulators. We show that HDAC1 and ZNF384 bind differentially to the C and the T alleles forming in vitro different regulatory complexes. Moreover, our data demonstrate that HDAC1 and ZNF384 downregulate differentially the MMP-9 gene promoter activity and MMP-9 mRNA expression in vivo in human neurons acting mostly via the T allele.

The present review aimed to identify through what means neurologic injury can predispose individuals to Post-Traumatic Stress Disorder (PTSD). In recent years, comprehensive studies have helped to clarify which structures in the central nervous system (CNS) that, when damaged, lead to distinct PTSD symptoms–namely dissociative reactions or flashbacks. Our review narrowed its focus to three common neurologic injuries including traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), and stroke. Beyond discussing the potential mechanisms by which neurotrauma may lead to PTSD, we summarized our current understanding of the pathophysiology of the disorder and discussed predicted associations between the limbic system and PTSD. In particular, the role of noradrenergic neuromodulatory signaling on the HPA axis as it pertains to fear memory recall needs to be further explored to better understand its effects on limbic structures in PTSD patients. This paper also described how damage to the CNS can alter the function of the limbic system and result in characteristic symptoms of PTSD like intrusive memories and acute psychological distress. Further, our review intended to draw attention to a lack of PTSD prognosis in TBI, SAH, and stroke patients who could benefit from early treatment. PTSD symptoms often compound with pre-existing issues further deteriorating health outcomes for these patients. It is ultimately our goal to clarify the relationship between neurotrauma and PTSD such that earlier diagnoses and appropriate treatment are observed in clinic.

Abstract: Autism spectrum disorders (ASDs) are complex, lifelong, neurodevelopmental conditions of largely unknown cause. The global prevalence of autism has increased twentyfold to thirtyfold since the earliest epidemiologic studies were conducted in the late 1960s and early 1970s. Recent reports agree on the association of ASD with the alteration of the microbiome (dysbiosis), which raises the possible role of external factors. Our study aimed at identifying antibiotic classes that might be associated with the development of ASD-related dysbiosis either promoting or inhibiting the process. Statistical comparison was made between the average yearly consumption of different antibiotic classes (1997-2020) and the number of individuals living with ASD estimated for 2023/100000 population in 30 European countries and the results were statistically analyzed. Tetracycline (J01A) showed significant positive (promoting) association with the prevalence of ASD (Pearson r: 0.373, p: 0.043. OR: 1.312, CI95%: 0.995-1.791, p: 0.065) and narrow-spectrum, beta-lactamase resistant penicillin (J01CF) (Pearson r: 0.524, p: 0.003, OR: 3.240, CI95%: 1.710-8.853, p: 0.004, Kruskal-Wallis p: 0.032). Mild, negative (inhibitory) association was observed with broad-spectrum, beta-lactamase sensitive penicillin (J01CA) (Pearson r: -0.278, p: 0.157, OR: 0.808, CI95%: 0649-0957, p: 0.028) and narrow-spectrum, beta-lactamase-sensitive penicillin (J01CE) (Pearson p: -0.032, r: 0.865, OR: 0.725, CI95%: 0.543-0.885, p: 0,009). Our findings strongly support the animal experiments when penicillin-exposed newborn mice developed "autism-like" behavior.

Background: Abdominal migraine (AM) is a clinical diagnosis specified by Rome IV and ICHD III as a functional gastrointestinal disease (FGID) and a migraine associated syndrome, respectively. AM is undiagnosed and undertreated, and thus far the FDA has not approved any drug for AM treatment. We and others showed that changes in the kynurenine (KYN) pathway of tryptophan (TRP) metabolism played an important role in the pathogenesis and treatment of FIGDs. Changes in the KYN pathway were shown in migraine. Findings: Abdominal migraine reflects impairments in communication within the gut-brain axis. Treatment approaches have not been sufficiently documented and are based on experience of physicians, presenting personal rather than evidence-based practice and including efficacy of some drugs used in adult migraine. Non-pharmacological treatment of AM is aimed preventing or ameliorating AM triggers. Modulations within the KYN pathway of TRP metabolism induced by changes in TRP content in the diet, may ameliorate FGIDs and support their pharmacological treatment. Pharmacological manipulations of brain KYNs in animals have brought promising results for clinical applications. Conclusions: In conclusion, controlled placebo-based clinical trials with dietary manipulation to adjust the amount of the product of the KYN pathway of TRP metabolism are justified in children and adolescents with AM. Further preclinical studies are needed to establish details of these trials.

Background: This review aims to explore the regulatory mechanism of microglial exosomes, particularly focusing on the role of miR-124-3p, in modulating neuroinflammation and promoting neuronal repair following traumatic brain injury (TBI). Methods: Multiple studies investigating the impact of microglial exosomal miRNAs, specifically miR-124-3p, on injured neurons and brain microvascular endothelial cells (BMVECs) in the context of TBI were reviewed. Results: Animal models of TBI, in vitro cell culture experiments, RNA sequencing analysis, and functional assays were employed to elucidate the mechanisms underlying the effects of miR-124-3p-loaded exosomes on neuroinflammation and neuronal repair. The increased expression of miR-124-3p in microglial exosomes after TBI inhibited neuronal inflammation and promoted neurite outgrowth. This effect was achieved by promoting the anti-inflammatory M2 polarization of microglia, suppressing mTOR signaling through downregulation of PDE4B, and inducing autophagy in BMVECs. These actions collectively contributed to neuroprotection, reduced blood-brain barrier leakage, and improved neurologic outcomes in animal models of TBI. Conclusion: Microglial exosomes, particularly those carrying miR-124-3p, have emerged as promising candidates for therapeutic interventions in TBI. These exosomes exhibit neuroprotective effects, attenuate neuroinflammation, and promote neuronal repair and plasticity. Targeting miR-124-3p and utilizing microglial exosomes as therapeutic agents hold great potential for future clinical translation in TBI management. However, further research is required to fully elucidate the underlying mechanisms and optimize their delivery strategies for effective treatment in human TBI cases.

Fisetin has been shown to be beneficial for brain injury and age-related brain disease via different mechanisms. The purpose of this study was to determine the presence of senescent cells and the effects of fisetin on cellular senescence in the brain and other organs in old sheep, a more translational model. Approximately 6-7 years old female sheep (N=6) were treated with 100mg/kg fisetin or vehicle two consecutive days a week for 8 weeks. All organs were harvested at the time of sacrifice. Histology, immunofluorescent staining, as well as Q-PCR was performed on different regions of brain tissues and organs. Our results indicated that Fisetin treatment at the current regimen did not affect general morphology of the brain. The presence of senescent cells in both cerebral brain cortex and cerebellum was detected by SA-β-Gal staining. More senescent cells were observed in the gray matter when compared to the white matter of the cerebral brain cortex. These senescent cells are mainly neurons in both gray and white matter of either cerebral brain cortex or cerebellum. Fisetin treatment showed a trend of decrease SA-β-Gal cells in gray matter of both cerebral brain cortex and cerebellum and significantly decreased in brain cortex white matter. Furthermore, fisetin treatment significantly decreased P16+ cells in brain cortex NEUN+ neurons, GFAP+ astrocytes, IBA+ microglia cells in both gray and white matter of cerebral brain cortex. Fisetin treatment also significantly decreased P16+ cells in microglia cells and a trend of decrease of P16+ cells in astrocytes in the non- (Cornu Ammonis) CA area of hippocampus. However, fisetin treatment did not change P16+ cells in the NEUN+ neurons in the CA1-4 area of the hippocampus. At the mRNA level, fisetin showed a decreased in GLB1 in heart ventricle muscle tissue and spleen tissues but not in other organs tested. Fisetin treatment also showed a decreased antioxidant gene SOD1 and increased CAT in spleen and bone marrow. Fisetin treatment showed variable effects in SASP and inflammasome genes in different organs. In conclusion, we found senescent cells are widely present in the cerebral brain cortex and cerebellum of old sheep. In addition, fisetin treatment decreased senescent cells as well as P16+ cells in the neurons in both gray and white matter of cerebral brain cortex and in astrocytes and microglia cells of cerebral brain cortex and non-CA area of hippocampus. Fisetin treatment represents a promising therapeutic strategy for age-related brain disease.

Embryonic genetic mechanisms are present in the brain and ready to be placed into action upon cellular injury termed the response to injury wound healing (RTIWH) mechanism. When injured, regional brain endothelial cells initially undergo activation and dysfunction with initiation of hemostasis, inflammation (peripheral leukocytes and innate microglia and perivascular macrophage cells), proliferation (astrogliosis), remodeling, repair, and resolution phases if the injurious stimuli are removed. In conditions wherein the injurious stimuli are chronic as occurs in obesity, metabolic syndrome, and type 2 diabetes mellitus this process does not undergo resolution and there is persistent RTIWH with remodeling. Indeed, the brain is unique, in that it utilizes its neuroglia: the microglia cell along with peripheral inflammatory cells and its astroglia instead of peripheral scar-forming fibrocytes/fibroblasts. The brain undergoes astrogliosis to form a gliosis scar instead of a fibrosis scar to protect the surrounding neuropil from regional parenchymal injury. One of the unique and evolving remodeling changes in the brain is the development of enlarged perivascular spaces (EPVS), which is the focus of this brief review. EPVS are important since they serve as a biomarker for cerebral small vessel disease and also represent an impairment of the effluxing glymphatic system that is important for the clearance of metabolic waste from the interstitial fluid to the cerebrospinal fluid and disposal. Therefore, it is important to better understand how the RTIWH mechanism is involved in the development of EPVS that are closely associated with and important to the development of premature and age-related cerebrovascular and neurodegenerative diseases with impaired cognition.

Extracorporeal membrane oxygenation (ECMO), in conjunction with its life-saving benefits, carries a significant risk of acute brain injury (ABI). Hypoxic-ischemic brain injury (HIBI) is one of the most common types of ABI in ECMO patients. Various risk factors such as history of hypertension, high day 1 lactate level, low pH, cannulation technique, large peri-cannulation PaCO2 drop (∆PaCO2), and early low pulse pressure, have been associated with the development of HIBI in ECMO patients. The pathogenic mechanisms of HIBI in ECMO are complex and multifactorial, attributing to the underlying pathology requiring initiation of ECMO and the risk of HIBI associated with ECMO itself. HIBI is likely to occur in the peri-cannulation or peri-decannulation time secondary to underlying refractory cardiopulmonary failure before or after ECMO. Current therapeutics target pathological mechanisms, cerebral hypoxia and is-chemia, by employing targeted temperature management in the case of extracorporeal cardiopulmonary resuscitation (eCPR), and optimizing cerebral O2 saturations and cerebral perfusion. This review describes the pathophysiology, neuromonitoring, and therapeutic techniques to improve neurological outcomes in ECMO patients so as to prevent and minimize the morbidity of HIBI. Further studies aimed at stand-ardizing the most relevant neuromonitoring techniques, optimizing cerebral perfusion, and minimizing the severity of HIBI once it occurs will improve long-term neurological outcomes in ECMO patients.

Traumatic brain injury (TBI) affects millions of people annually, however, our knowledge of the diffuse pathologies associated with TBI is limited. As diffuse pathologies, including axonal injury and neuroinflammatory changes, are difficult to visualize in the clinical population, animal models are used. In the current study we used the central fluid percussion injury (CFPI) model in a micro pig to study the potential scalability of these diffuse pathologies in a gyrencephalic brain of a species with inflammatory systems very similar to humans. We found that both axonal injury and microglia activation within the thalamus and corpus callosum are positively correlated to the weight-normalized pressure pulse, while subtle changes in blood gas and mean arterial blood pressure are not. We also found that the majority of tissue generated up to 10 years previously is viable for immunofluorescent labeling after long-term refrigeration storage. This study indicates that a micro pig CFPI model could allow for specific investigations of various degree of diffuse pathological burdens following TBI.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterised by poor social interaction and communication, as well as restricted and stereotyped interests. Due of the high prevalence of gastrointestinal disorders in individuals with ASD, researchers have investigated the gut microbiota as a potential contributor to its aetiology. The relationship between the microbiome, gut, and brain (microbiome-gut-brain axis) has been acknowledged as a key factor in modulating brain function and social behaviour, but its connection to the aetiology of ASD is not well understood. Recently, there has been increasing attention on the relationship between the immune system, gastrointestinal disorders, and neurological issues in ASD, particularly in relation to the loss of specific species or a decrease in microbial diversity. It focuses on how gut microbiota dysbiosis can affect gut permeability, immune function, and microbiota metabolites in ASD. Although a very complete study suggests that dysbiosis is a consequence of the disease and that it has practically no effect on autistic manifestations. This is a review of the relationship between the immune system, microbial diversity, and the microbiome-gut-brain axis in the development of autistic symptoms severity and, a proposal of a novel role of gut microbiome in ASD, where dysbiosis is a consequence of ASD-related behaviour and where dysbiosis in turn accentuates the autistic manifestations of the patients via microbiome-gut-brain axis in a feedback circuit.

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. Despite extensive research efforts, the majority of trialed monotherapies to date have failed to demonstrate significant benefit. It has been suggested that this is due to the complex pathophysiology of TBI, which may possibly be addressed by a combination of therapeutic interventions. In this article, we have reviewed combinations of different pharmacologic treatments, combinations of non-pharmacologic interventions, and combined pharmacologic and non-pharmacologic interventions for TBI. Both preclinical and clinical studies have been included. While promising results have been found in animal models, clinical trials of combination therapies have not yet shown clear benefit. This may possibly be due to their application without consideration of the evolving pathophysiology of TBI. Improvements of this paradigm may come from novel interventions guided by multimodal neuromonitoring and multimodal imaging techniques, as well as the application of multi-targeted non-pharmacologic and endogenous therapies. There also needs to be a greater representation of female subjects in preclinical and clinical studies.

Nowadays, fostered by technological progress and contextual circumstances such as economic crisis and pandemic restrictions, remote education is living a growing deployment. However, this growth generated widespread doubts about the actual effectiveness of remote/online compared to face-to-face education. The present study aimed at comparing face-to-face and remote education through a multimodal neurophysiological approach. It involved forty students at a driving school, during a real classroom, experiencing both the modalities. Wearable devices to measure brain, ocular, heart and sweating activities were employed in order to analyse the students’ neurophysiological signals to obtain insights about their cognitive dimension. In particular, four parameters were considered, the Eye Blink Rate, the Heart Rate and its Variability and the Skin Conductance Level. Also, the students filled a questionnaire at the end to obtain an explicit measure of their learning performance. Data analysis showed a higher cognitive activity, in terms of attention and mental engagement, in presence with respect to remote modality. On the other hand, students by remote felt more stressed, in particular during the first part of the lesson. Analysis of questionnaires demonstrated worst performance by remote, thus suggesting a common “disengaging” behaviour when attending remote courses, thus undermining their effectiveness. In conclusion, neuroscientific tools could help to obtain insights about mental concerns, often «blind», such as attentional decreasing and stress increasing, as well as their dynamics during the lesson itself, so allowing to define proper countermeasures to emerging issues when introducing new practices into daily life.

Background: Hypereosinophilic syndromes (HES) are a group of relatively rare disorders in which neurological manifestations are not uncommon including ischemic stroke. The hypothesized pathophysiological mechanisms are hypercoagulability, cardioembolism (mainly mediated by myocardial involvement) and damage to the endothelium. A variable ischemic pattern has been described, including an association of territorial and border zone ischemic stroke. Methods: Three patients who presented to our department with acute stroke were selected aiming to show these three different mechanisms inferred from the stroke pattern on brain Magnetic Resonance Imaging (MRI) and to simultaneously illustrate the three main causes of HES. Results and Discussion: The first patient is a 55-year-old man with an abrupt onset of aphasia due to an acute ischemic stroke involving the left parietal lobule and the angular gyrus; recent lab test had shown hypereosinophilia. An extensive workup excluded primary and secondary causes of hy-pereosinophilia so a diagnosis of idiopathic hypereosinophilia was formulated and he was treated with high doses of steroids. The second patient had severe hypereosinophilia and developed multiple small scattered ischemic lesions, mainly in watershed zones. The history of severe asthma and recurrent sinusitis supported the diagnosis of EGPA (Eosinophilic Granulomatosis with Polyangiitis); considering the severe clinical conditions and the presumptive role of hypereo-sinophilia in determining her symptoms, steroid treatment was promptly started, with good clinical response. The third patient also presented with multiple metachronous ischemic lesions, both in cortical and watershed distribution and marked eosinophilia; the diagnostic work-up found an ovarian cancer. She was treated with steroids and then underwent surgery and adjuvant chemotherapy. Conclusions: HES should be considered in stroke etiological evaluation, although it is a rare disorder, and border zones pattern on neuroimaging is quite suggestive. A thorough research of the sources of hypereosinophilia should be performed to select the appropriate therapy.

The effects of transcranial magnetic stimulations (TMS) show that the human brain is impacted by some magnetic fields (EMFs). Moreover, after a delay, it produces potentials that reveal a subsequent processing of this impact. The human brain might also be sensitive to very weak magnetic fields of extremely low frequencies (vwEMFelf). Namely, to the vwEMelf produced by the brain of other persons when they process visual stimuli. In effect, two studies report that the event-related brain potentials (ERPs) that are evoked by presenting a picture to a participant can be modulated by simultaneously presenting a picture to a partner. To confirm it here, we followed most of the methods of these studies. We recorded the ERPs evoked by presenting, at each trial, the photograph of a face. Simultaneously and, most importantly, privately, we presented a partner with the same or with a different face photograph. ERPs of participants were found to depend on that sameness (p0.001), unbeknownst to them. These joint processing effects (JPEs), confirm a sensitivity of the human brain to the vwEMFelf produced by other brains.

Glioblastoma (GBM) is the most common primary brain tumor. Due to high resistance to treatment, local invasion, and high risk of recurrence, GBM patient prognoses are often dismal, with median survival around 15 months. The current standard of care is threefold: surgery, radiation therapy and chemotherapy with temozolomide (TMZ). However, patient survival has only marginally improved. Radioimmunotherapy (RIT) is a fourth modality under clinical trials and aims at combining immunotherapeutic agents with radiotherapy. Here, we develop in vitro assays for rapid evaluation of RIT strategies. Using a standard cell irradiator and an Electric Cell Impedance Sensor, we quantify cell migration following the combination of radiotherapy and chemotherapy with TMZ and RIT with durvalumab, a PD-L1 immune checkpoint inhibitor. We measure cell survival using a cloud-based clonogenic assay. Irradiated T98G and U87 GBM cells migrate significantly (p < 0.05) more than untreated cells in the first 20-40 hours post-treatment. Addition of TMZ increased migration rates for T98G at the 20 Gy (p < 0.01). Neither TMZ nor durvalumab significantly changed cell survival in 21 days post-treatment. Interestingly, durvalumab abolished the enhanced migration effect, indicating possible potency against local invasion. These results provide parameters for rapid supplementary evaluation of RIT against brain tumors.

Functional Data Analysis (FDA) is a relatively new field of statistics dealing with data expressed in the form of functions. FDA methodologies can be easily extended to the study of imaging data, an application proposed in Wang et al. (2020), where the authors settle the mathematical groundwork and properties of the proposed estimators. This methodology allows for the estimation of mean functions and simultaneous confidence corridors (SCC), also known as simultaneous confidence bands, for imaging data and for the difference between two groups of images. This is especially relevant for the field of medical imaging, as one of the most extended research setups consists on the comparison between two groups of images, a pathological set against a control set. FDA applied to medical imaging presents at least two advantages compared to previous methodologies: it avoids loss of information in complex data structures and avoids the multiple comparison problem arising from traditional pixel-to-pixel comparisons. Nonetheless, computing times for this technique have only been explored in reduced and simulated setups (Arias-López et al., 2021). In the present article, we apply this procedure to a practical case with data extracted from open neuroimaging databases and then measure computing times for the construction of Delaunay triangulations, and for the computation of mean function and SCC for one-group and two-group approaches. The results suggest that previous researcher has been too conservative in its parameter selection and that computing times for this methodology are reasonable, confirming that this method should be further studied and applied to the field of medical imaging.

Insulin receptors are internalized by endothelial cells; however, the impact of hyperinsulinemia on this process is not known. Thus, the aim of this study is to determine the role of hyperinsulinemia on insulin receptor function and internalization, as well as the potential impact of protein tyrosine phosphatase 1B (PTP1B). To this end, hippocampal microvessels were isolated from male C57Bl/6J mice on either a control or high-fat diet and assessed for insulin receptor signaling. Cell surface insulin receptors in brain microvascular endothelial cells were labelled with biotin to assess the role hyperinsulinemia plays on receptor internalization in response to stimulation, with and without Claramine treatment, a potent PTP1B antagonist. Our results indicated that insulin receptor levels increased in tandem with insulin receptor dysfunction in the high-fat diet mouse hippocampal microvessels. Hyperinsulinemic cell-receptors demonstrate a shift in splice variation towards decreased IR-A/IR-B ratios and demonstrate a higher membrane-localized proportion. This corresponded with decreased autophosphorylation at sites critical for receptor internalization and signaling, however, Claramine restored signaling and receptor internalization in hyperinsulinemic cells. In conclusion, hyperinsulinemia negatively impacts brain microvascular endothelial cell insulin receptor function and internalization, likely through both alternative splicing and increased negative feedback from PTP1B.

Blood-brain-barrier is a major obstacle in treating brain-related disorders as it does not allow to deliver drugs in the brain. In order to facilitate delivery of drugs in brain, we developed a method for predicting blood-brain-barrier penetrating peptides. These blood-brain barriers penetrating peptides (B3PPs) can act as therapeutic as well as drug delivery agents. We trained, tested, and evaluated our models on blood-brain-barrier peptides obtained from the B3Pdb database. First, we compute a wide range of peptide features then we select relevant peptide features. Finally, we developed numerous machine learning-based models for predicting blood-brain-barrier peptides using selected features. Our model based on random forest performed best on the top 80 selected features and achieved a maximum 85.08% accuracy with 0.93 AUROC. We also developed a web server, B3pred that implements our best models. It has three major modules that allow users to; i) predict B3PPs, ii) scanning B3PPs in a protein sequence, and iii) designing B3PPs using analogs. Our web server and standalone software is freely available at https://webs.iiitd.edu.in/raghava/b3pred/.

Key features of glycogen metabolism in excitable tissues are not well-explained by current concepts. Glycogen stores in brain and skeletal muscle are generally considered to function as local glucose reserves, to be utilized during transient mismatches between glucose supply and demand; however, quantitative measures show that blood glucose supply is likely never rate-limiting for energy metabolism in either brain or muscle under physiological conditions. These tissues nevertheless do normally utilize glycogen during intervals of increased energy demand, despite the availability of free glucose, and despite the ATP cost of cycling glucose through glycogen polymer. This seemingly wasteful shunt can be explained by considering the effect of glycogenolysis on the amount of energy derived from ATP (ΔG’ATP). ΔG’ATP is diminished by elevations in Pi, such as occur at sites of rapid ATP hydrolysis and net phosphocreatine consumption. Glycogen utilization counters this effect by sequestering Pi in glycolytic metabolites (glycogen_n + Pi → glycogen_n-1 + glucose-1-phosphate → phosphorylated glycolytic intermediates), and thereby maintains the amount of energy obtained from ATP at sites of rapid ATP consumption. This thermodynamic effect may be particularly important in the narrow, spatially constricted astrocyte processes that ensheath neuronal synapses. This effect can also explain the co-localization of glycogen and cytosolic phosphocreatine in brain astrocytes, glycolytic super-compensation in brain when glycogen is not available, and aspects of exercise physiology in muscle glycogen phosphorylase deficiency (McArdle’s disease).

Previously we demonstrated, in rats, that the treatment with growth hormone (GH) and rehabilitation, carried out immediately after a motor cortical ablation, significantly improved the motor affectation produced by the lesion and induced the re-expression of nestin in the contralateral motor cortex. Here we analyze cortical proliferation after ablation of the frontal motor cortex and investigate the re-expression of nestin in the contralateral motor cortex and the role of the striatum and thalamus in motor recovery. The rats were subjected to ablation of the frontal motor cortex in the dominant hemisphere or sham-operated and immediately treated with GH or vehicle (V), for five days. At 1 dpi (days after injury), 5 rats received daily injections (4 days) of bromodeoxyuridine and were sacrificed. The other 15 rats (n = 5 / group) underwent treatment and rehabilitation and were sacrificed at 25 dpi. GH induced the greatest number of proliferating cells in the perilesional cortex. GH and rehabilitation produced the functional recovery of the motor lesion and increased the expression of nestin in the striatum. In the thalamic ventral nucleus ipsilateral to the lesion, cells positive for nestin and actin were detected, but this was independent of GH. Our data suggest that GH-induced striatal nestin is involved in motor recovery.

Hereditary Parkinson’s disease (PD) can be triggered by an autosomal dominant overdose of alpha-Synuclein (SNCA) as stressor or the autosomal recessive deficiency of PINK1 Serine/Threonine-phosphorylation activity as stress-response. We demonstrated the combination of PINK1-knockout with overexpression of SNCAA53T in double mutant (DM) mice to exacerbate locomotor deficits and to reduce lifespan. To survey posttranslational modifications of proteins underlying the pathology, brain hemispheres of old DM mice underwent quantitative label-free global proteomic mass spectrometry, focused on Ser/Thr-phosphorylations. As exceptionally strong effect, we detected >300-fold reductions of phosphoThr1928 in MAP1B, a microtubule-associated protein, and a similar reduction of phosphoSer3781 in ANK2, an interactor of microtubules. MAP1B depletion is known to trigger perturbations of microtubular mitochondria trafficking, neurite extension and synaptic function, so it was noteworthy that relevantly decreased phosphorylation was detected also for other microtubule and microfilament factors, namely MAP2S1801, MARK1S394, MAP1AT1794, KIF1AS1537, 4.1NS541, 4.1GS86 and ADD2S528. While the MAP1B heavy chain supports regeneration and growth cones, its light-chain assists DAPK1-mediated autophagy. Interestingly, relevant phosphorylation decreases of DAPK2S299, VPS13DS2429 and VPS13CS2480 in the DM brain affected regulators of autophagy, which are implicated in PD. Overall, significant downregulations were enriched for PFAM C2 domains, other kinases, and synaptic transmission factors upon automated bioinformatics, while upregulations were not enriched for selective motifs or pathways. Validation experiments confirmed the change of LC3 processing as reflection of excessive autophagy in DM brain, and dependence of ANK2/MAP1B expression on PINK1 levels. Our new data provide independent confirmation in a mouse model with combined PARK1/PARK4/PARK6 pathology that MAP1B/ANK2 phosphorylation events are implicated in Parkinsonian neurodegeneration. These findings expand on previous observations in D. melanogaster that the MAP1B ortholog futsch in the presynapse is a primary target of the PARK8 protein LRRK2, and on a report that MAP1B is a component of the pathological Lewy body aggregates in PD patient brains. Similarly, ANK2 gene locus variants are associated with the risk of PD, ANK2 interacts with PINK1/Parkin-target proteins such as MIRO1 or ATP1A2, and ANK2-derived peptides are potent inhibitors of autophagy.

Mind and brain/matter interaction is one of the important and controversial issues in Islamic philosophy. In fact, in the resources of Islamic philosophy, one of the basic parts of philosophical discussions is related to mind’s nature and its interaction with the brain. Especially, in Avicenna’s philosophy, there are many articles and books which have addressed the topic of mind and brain and the relation between them. Avicenna was a profound philosopher, an expert physicist and a proficient physician of his time. Because of his experimental proficiency in medicine and surgery and his deep philosophical analysis, his discussion about mind and brain is very interesting for our time, due to recent advances in neuroscience. In this article, we have explained one of Avicenna’s arguments (in his famous opus “al-Isharat”) about the incorporeity of mind (self), which is very close to modern neuroscience and physics literature. In addition, we explain his model of mind and brain interaction. Avicenna described the mechanism of the causal effect of mind on the brain via a third identity, which works as an interface between them (in his main book “al-Shifa”). We try to illustrate his model by the use of some examples, inspired from modern physics. Also, we explore the philosophical constraints which must be considered in any model of mind-matter interaction, within the Islamic philosophy framework. In fact, we propose a new understanding of Avicenna’s philosophy which is in agreement with modern physics and neuroscience.

A risk of ischemic stroke increases exponentially after menopause. Even a mild-ischemic stroke can result in increased frailty. Frailty is a state of increased vulnerability to adverse outcomes, which subsequently increases risk of cerebrovascular events and severe cognitive decline, particularly after menopause. Several interventions to reduce frailty and subsequent risk of stroke and cognitive decline have been proposed in laboratory animals and patients. One of them is whole body vibration (WBV). WBV recuperates cerebral function and cognitive ability that deteriorates with increased frailty. The goal of the current study is to test the efficacy of WBV in reducing post-ischemic stroke frailty and brain damage in reproductively senescent female rats. Reproductively senescent Sprague–Dawley female rats were exposed to transient middle cerebral artery occlusion (tMCAO) and randomly assigned to either WBV or control groups. Animals placed in the WBV group underwent 30 days of WBV (40 Hz) treatment performed twice daily for 15 min each session, 5 days each week. The motor functions of animals belonging to both groups were tested intermittently and at the end of treatment period. Brains were then harvested for inflammatory markers and histopathological analysis. The results demonstrate a significant reduction in inflammatory markers and infarct volume with significant increases in brain-derived neurotrophic factor and improvement in functional activity after tMCAO in middle-aged female rats that were treated with WBV as compared to the control group. Our results may facilitate a faster translation of the WBV intervention for improved outcome after stroke, particularly among frail women.

Recent studies have indicated that several anti-hypertensive drugs may delay the development and progression of Alzheimer’s disease (AD). However, the relationships among AD, hypertension, and oxidative stress remain to be elucidated. In the present study, we aimed to determine whether treatment with resveratrol reduces reactive oxygen species (ROS) generation in the brain, thereby reducing cognitive impairment in rats with angiotensin II (Ang-II)-induced early AD. Male WKY rats with Ang-II-induced AD were treated with losartan or resveratrol for 2 weeks. Our results revealed that treatment with resveratrol (10 mg/kg/day) decreased blood pressure, increased levels of brain-derived neurotrophic factor (BDNF) in the hippocampus, and decreased ROS production in the nucleus tractus solitarius (NTS) in the Ang-II groups. In addition, inhibition of Tau^T231 phosphorylation in the hippocampus using resveratrol significantly abolished Ang-II-induced expression of Ab precursors, active caspase 3, and glycogen synthase kinase 3b (GSK-3b)^Y216 while increasing Akt^S473 phosphorylation. Notably, resveratrol reversed impairments in hippocampal-dependent contextual memory induced by deleting NADPH oxidase and NOX2. Overall, our results suggest that resveratrol exerts neuroprotective effects against memory impairment and hippocampal damage in a rat model of early stage AD by reducing oxidative stress. These novel findings indicate that resveratrol may represent a pharmacological option for patients with hypertension at a risk of AD during old age.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It sheds light on the journey of radiology, from the pioneering discovery of X-rays to today’s intricate imaging technologies, infused with machine learning and deep learning in medical image analysis. At the crux of this review lies an in-depth study of AI applications in radiology, elucidating its seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology — data quality, the ’black box’ enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. It concludes by firmly cementing the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.

Geometrical assessments of human skull have been done based on the anatomical landmarks. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also anthropologically. An automated system with multi-phased deep learning networks to predict three-dimensional coordinate values of cranio-facial landmarks, was developed. From a publicly available database, computed tomography images of craniofacial area were obtained. They were digitally reconstructed into three-dimensional models. Sixteen anatomical landmarks were plotted on each of the models and coordinate values of them were recorded. Three-phased regression deep learning networks were trained respectively with 90 training datasets. For evaluation, 30 testing datasets were employed. Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.88. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

Sustainability is the core concern of every business; The exploration of avenues to maintain sustainability while staying competitive with high level of productivity remains a vital endeavor. Production flexibility is a key area that can enhance the sustainability of manufacturing industries as it can ensure product availability, scalability, agility/fault tolerance as well as disaster recovery potentials. Technological advancements have provided avenues where companies can enhance virtually all aspects of their operations for efficiency, effectiveness, and productivity. This paper uses both quantitative and qualitative research approach to identify the capability requirements for smart and effective production management and subsequent analysis is done using Multi-Criteria Decision-Making methodology to identify and rank various industry 4.0 technologies and concepts that can provide these smart capabilities in in manufacturing industries to aid the businesses to achieve sustainability with production flexibility. The paper identifies over 12 smart capabilities and 9 Industry 4.0 Technologies which are applicable to production management. It also compares results from the analytics of historical I4.0 implementation as discussed in literatures with the current state as deduces from survey feedbacks from various manufacturing industries.

In this paper, we investigate the relationship people have with their smart devices. We use the concept of agency to capture aspects of users’ sense of mastery as they relate to their device. This study gives preliminary evidence of the existence of two independent dimensions of agency for modeling the interaction between humans and smart devices: (i) user agency and (ii) device agency. These constructs emerged from an exploratory factorial analysis conducted on a survey data collected from 587 participants. In addition, we investigate the correlation between user agency and device agency with background variables of the respondents. Finally, we argue that mapping the users’ dynamics with their device into user agency and device agency fosters a better understanding of the needs of the users and helps in designing interfaces tailored for the specific capabilities and expectations of the users.

Bell type inequalities are proven using oversimplified probabilistic models and/or counterfactual definiteness (CFD). If setting-dependent variables describing measuring instruments are correctly introduced none of these inequalities may be proven. In spite of this a belief in a mysterious quantum nonlocality is not fading. Computer simulations of Bell tests allow studying different scenarios how the experimental data might have been created. They allow also to generate outcomes of various counterfactual experiments such as repeated or simultaneous measurements performed in different settings on the same ‘’ photon-pair” etc. They allow reinforcing or relaxing CFD- compliance and /or to study the impact of various “photon identification procedures” mimicking those used in real experiments. Using a specific setting- dependent identification procedure data samples consistent with quantum predictions may be generated. It reflects an active role of instruments during the measurement process. Each setting dependent data samples are consistent with specific setting –dependent probabilistic models which may not be deduced using non-contextual local realistic or stochastic hidden variables. In this paper we discuss the results of these simulations. Since the data samples are generated in a locally causal way, these simulations provide additional strong arguments for closing the door on quantum nonlocality

Traumatic brain injury (TBI) affects millions of people each year. Previous studies using the central fluid percussion injury (CFPI) model in adult male rats indicated that elevated intracranial pressure (ICP) was associated with long-term effects, including neuronal cell loss and increased sensory sensitivity post-injury and secondary ICP elevation, that were not seen following injury alone. Investigations also indicated that Cathepsin B (Cath B), a lysosomal cysteine protease, may play a role in the pathological progression of neuronal membrane disruption; however, the specific impact of Cath B inhibition following CFPI remains unknown. Thus, the focus of this study was to evaluate the effects of Cath B inhibition via intracerebroventricular infusion of CA-074Me 2w following injury with or without secondary elevation of ICP. This was accomplished using adult male rats continuously infused with CA-074Me or 10% DMSO as a vehicle control for 2w following either sham injury, a CFPI only, or a CFPI with subsequent ICP elevation to 20mmHg. We assessed Cath B activity in the lateral neocortices and liver, evaluated protein levels of Cath B and Cath B binding partners AIF, Bcl-XL, and Bak. We also conducted histological analyses of total cell counts to assess for cell loss, membrane disruption, and Cath B localization. Finally, we investigated somatosensory changes with the whisker nuisance task. Overall, this study demonstrated that Cath B is not a direct driver of membrane disruption, however, administration of CA-074Me alters Cath B localization and reduced hypersensitivity, emphasizing Cath B being an important component in late secondary pathologies.

Evidence is emerging on the role of maternal diet, gut microbiota, and other lifestyle factors in establishing lifelong health and disease, which are determined by trans-generationally inherited epigenetic modifications. Understanding epigenetic mechanisms may help identify novel biomarkers for pregnancy-related exposure, burden, or disease risk. Such biomarkers are essential for developing tools for the early detection of risk factors and exposure levels. It is necessary to establish the exposure threshold to the fetus due to nutrient deficiencies or other environmental factors that can lead to clinically relevant epigenetic changes that modulate disease risks. This narrative review summarizes the latest updates on the roles of maternal nutrients (n-3 fatty acids, polyphenols, vitamins) and gut microbiota on the placental epigenome and its impacts on fetal brain development.

The intestinal microbiota, comprised of bacteria, archaea, and phages, inhabits the gastrointestinal tract of the organism. Male reproductive sterility is currently a prominent topic in medical research. Increasing research suggests that gut microbiota dysbiosis can result in various reproductive health problems. This article specifically investigates the impact of gut microbiota dysbiosis on male reproductive infertility development. Gut microbiota imbalances can disrupt the immune system and immune cell metabolism, affecting testicular growth and sperm production. This dysfunction can compromise the levels of hormones produced and secreted by the endocrine glands, affecting male reproductive health. Furthermore, imbalance of the gut microbiota can disrupt the gut-brain-reproductive axis, resulting in male reproductive infertility. This article will explore how the imbalance of the gut microbiota impacts male reproductive infertility through immune regulation, endocrine regulation, and interactions of the gut-brain-reproductive axis, concluding with recommendations for prevention and treatment.

Background: Catheter ablation (CA) benefits atrial fibrillation (AF) patients with heart failure (HF). Brain natriuretic peptide (BNP), a marker of left-ventricular pressure load, may serve as a potential surrogate for predicting quality of life (QOL) in broader range of patients. Methods: Within the multicenter KiCS-AF registry, 491 AF patients underwent CA without clinical HF (e.g. documented history of HF, left ventricular ejection fraction ≤40%, or BNP levels ≥100 pg/mL). Participants, aged 61±10 years, were categorized by baseline BNP quartiles. Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) questionnaire assessments were assessed at baseline and 1 year. Results: Lower baseline BNP correlated with reduced AFEQT scores. Post-CA, all groups showed significant AFEQT score improvements. Lower BNP group displayed notable enhancements (18.2±1.2, 15.0±1.1, 12.6±1.2, 13.6±1.2, p<0.005), especially in symptom and treatment concern areas. Even those with normal BNP levels (≤18.4 pg/ml) exhibited significant QOL improvements. Comparing PAF and non-PAF groups, the PAF group, especially with higher BNP levels, showed greater AFEQT score improvements. Conclusion: This study establishes BNP as a predictive marker for QOL enhancement in non-HF patients undergoing CA for AF. BNP levels represent AF stages, with individuals in earlier stages, especially within normal BNP levels, experiencing greater QOL improvement.

Perinatal brain injury is a major public health problem burdened with high morbidity, mortality and severe neurological sequelae. Despite advances in neonatal life-saving technologies, neonatal encephalopathy lacks specific and effective treatment. Therefore, mechanisms of central nervous system changes associated with perinatal brain injury have been a hot topic of neonatology research. In recent years, researchers have shifted more focus from neurons themselves to perineuronal cells and intercellular interactions. As a member of the nervous system network, astrocytes are one of the dominant glial cells that regulate ion homeostasis, blood flow, and antioxidant function in the brain. Their changes in molecular mechanisms (e.g., alterations in RNA regulatory networks) during brain injury storms deserve further attention. Non-coding RNAs participate in and promote reactive pathophysiologic changes in astrocytes. The review summarizes and discusses the role of non-coding RNAs in astrocytes in recent years in studies of neonatal brain injury. It focuses on the most studied disease-related ncRNAs: miRNAs, lncRNAs and circRNAs, and hopes to provide possible directions for pathophysiological studies and therapeutic approaches of neonatal brain injury.

Multiple sclerosis is a chronic, autoimmune disease that mainly affects the central nervous system, including the brain, spinal cord, and optic nerve. This disease can cause clinical symptoms such as cognitive decline, muscle weakness, spasms, and fatigue in patients, and the onset tends to be younger. Current medication can only prevent or alleviate symptoms, so early diagnosis of this disease can increase patients' chances of treatment. Although the use of nuclear magnetic resonance detection can improve the efficiency of early auxiliary diagnosis, it still requires experienced doctors to spend too much time and energy on comprehensive judgment. To reduce the time cost and improve the efficiency of multiple sclerosis diagnosis, this article proposes a detection and recognition algorithm for multiple sclerosis based on wavelet entropy and self-adaptive particle swarm optimization. Firstly, a triple discrete wavelet transform is performed on the brain image of multiple sclerosis, and then 10 wavelet entropies are extracted from the decomposed wavelet subbands, which can reduce the feature dimensions of the image; Then, the algorithm of self-adaptive particle swarm optimization is used to optimize the feedforward neural network, in order to obtain the optimal connection weights and thresholds during the training process. The result of the model is with an average sensitivity of 92.29±1.89, specificity of 92.54±0.67, precision of 92.48±0.59, accuracy of 92.42±0.88, and F1 score of 84.85±1.74, Matthews correlation coefficient of 92.37±0.96, and Fowlkes223 Mallows Index of 92.38±0.96. The experimental results indicate that this algorithm has a very important data support role in detecting multiple sclerosis.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

Preterm infants often experience frequent intermittent hypoxia (IH) episodes which is associated with neuroinflammation. We tested the hypotheses that early caffeine and/or non-steroidal in-flammatory drugs (NSAIDs) confer superior therapeutic benefits for protection against IH-induced neuroinflammation than late treatment. Newborn rats were exposed to IH or hy-peroxia (50% O2) from birth (P0) to P14. For early treatment, the pups were administered: 1) dai-ly caffeine citrate (Cafcit, 20 mg/kg IP loading on P0, followed by 5 mg/kg from P1-P14); 2) ke-torolac topical ocular solution in both eyes from P0 to P14; 3) ibuprofen (Neoprofen, 10 mg/kg loading dose on P0 followed by 5 mg/kg/day on P1 and P2); 4) caffeine+ketorolac co-treatment; 5) caffeine+ibuprofen co-treatment; or 6) equivalent volume saline. On P14, animals were placed in room air (RA) with no further treatment. For late treatment, pups received similar treatments from P15-P21 (caffeine and/or ketorolac), or P15-P17 (ibuprofen). RA control were similarly treated. At P21, whole brains were assessed for histopathology, apoptosis, mye-lination, and biomarkers of inflammation. IH caused significant inflammation, reduced mye-lination, and apoptosis in preterm rat brains, an effect that was improved predominantly with combined caffeine/NSAID early treatment. Early caffeine/NSAID co-treatment confers syner-gistic neuroprotection against IH-induced damage.

Non-small-cell lung cancer (NSCLC) is the second most common cancer worldwide, resulting in 1.8 million deaths/year. Most of the patients are diagnosed with a metastatic disease. Central Nervous System is one of the major metastatic sites. Brain metastases are associated with severe neurological symptoms, shorter survival and worst clinical outcomes. Brain radiotherapy and systemic oncological therapies are currently used for controlling both cancer progression and neurological symptoms. Brain radiotherapy includes stereotactic brain ablative radiotherapy (SBRT) or whole brain radiotherapy (WBRT). SBRT is applied for single or multiple (≤ 4) small lesions (&lt; 3 cm), while WBRT represents the best treatment choice in case of multiple and large brain metastases. In both cases radiotherapy application can represent an overtreatment causing severe toxicities without achieving a significant clinical benefit. So far, some scores have been proposed to define the potential clinical benefits derived from brain radiotherapy. However, most of them are not well validated into clinical practice. In this article, by presenting a clinical case of a patient with advanced NSCLC carrying a BRAFV600E mutation and brain metastases, we review the variables as well as the potential applicable scores to be considered in order to predict clinical outcomes and benefits from brain radiotherapy in patients with NSCLC and brain metastases.

Non-small-cell lung cancer (NSCLC) is the second most common cancer worldwide, resulting in 1.8 million deaths/year. Most of the patients are diagnosed with a metastatic disease. Central Nervous System is one of the major metastatic sites. Brain metastases are associated with severe neurological symptoms, shorter survival and worst clinical outcomes. Brain radiotherapy and systemic oncological therapies are currently used for controlling both cancer progression and neurological symptoms. Brain radiotherapy includes stereotactic brain ablative radiotherapy (SBRT) or whole brain radiotherapy (WBRT). SBRT is applied for single or multiple (≤ 4) small lesions (&lt; 3 cm), while WBRT represents the best treatment choice in case of multiple and large brain metastases. In both cases radiotherapy application can represent an overtreatment causing severe toxicities without achieving a significant clinical benefit. So far, some scores have been proposed to define the potential clinical benefits derived from brain radiotherapy. However, most of them are not well validated into clinical practice. In this article, by presenting a clinical case of a patient with advanced NSCLC carrying a BRAFV600E mutation and brain metastases, we review the variables as well as the potential applicable scores to be considered in order to predict clinical outcomes and benefits from brain radiotherapy in patients with NSCLC and brain metastases.

Background: Liquid biopsies are revolutionary tools to detect tumor-specific genetic alterations in body fluids, and cell-free DNA (cfDNA) can be used for molecular diagnosis in cancer patients. In brain tumors cerebrospinal fluid (CSF) cfDNA might be more informative than plasma cfDNA. Here, we assess the use of CSF cfDNA in pediatric embryonal brain tumors (EBT) for molecular diagnosis. Methods: CSF cfDNA of pediatric patients with medulloblastoma (n=18), ATRT (n=3), ETMR (n=1), CNS NB FOXR2 (n=2) and pediatric EBT NOS (n=1) (mean cfDNA concentration 48 ng/mL; range 4-442 ng/mL) and matched tumor genomic DNA were sequenced by WES, and/or a targeted sequencing approach, to determine single nucleotide variations (SNVs)/copy number alterations (CNA). A specific capture covering transcription start sites (TSS) of genes of interest was used for nucleosome footprinting in CSF cfDNA. Results: 15/25 CSF cfDNA samples yielded informative results, with informative CNA and SNVs in 11 and 15 cases, respectively. For cases with paired tumor and CSF cfDNA WES (n=15), a mean of 83 (range 1-160) shared SNVs was observed, including SNVs in classical medulloblastoma genes such as SMO and KMT2D. Interestingly, tumor-specific SNVs (mean 18; range 1-62) or CSF-specific SNVs (mean 5; range 0-25) were observed suggesting clonal heterogeneity. The TSS panel resulted in differential coverage profiles across all 112 studied genes in 7 cases, indicating distinct promoter accessibility. Conclusion: CSF cfDNA sequencing yielded informative results in 60% of cases (15/25), with WES feasible in 83% (15/18). These results pave the way for implementation of these novel approaches for molecular diagnosis and minimal residual disease monitoring.

Obstructive sleep apnea (OSA) is a chronic condition characterized by intermittent hypoxia (IH) and sleep fragmentation (SF). In murine models, chronic SF can impair endothelial function and induce cognitive declines. These deficits are likely mediated, at least in part, by alterations in Blood-Brain Barrier (BBB) integrity. Male C57Bl/6J mice were randomly assigned to SF or sleep control (SC) conditions for 4 or 9 weeks, and in a subset 2 or 6 weeks of normal sleep recovery. Presence of inflammation and microglia activation were evaluated. Explicit memory function was assessed with the novel object recognition (NOR) test, while BBB permeability was determined by systemic dextran-4kDA-FITC injection and Claudin 5 expression. SF exposures resulted in decreased NOR performance and in increased inflammatory markers and microglial activation as well as enhanced BBB permeability. Explicit memory and BBB permeability were significantly associated. BBB permeability remained elevated after 2 weeks of sleep recovery (p<0.01) and returned to baseline values only after 6 weeks. Chronic SF exposures mimicking the fragmentation of sleep that characterizes patient with OSA elicits evidence of inflammation in brain regions and explicit memory impairments in mice. Similarly, SF is also associated with increased BBB permeability, the magnitude of which is closely associated with cognitive functional losses. Despite normalization of sleep patterns, BBB functional recovery is a protracted process that merits further investigation.

Glioblastoma is the most common and malignant primary brain tumor. Despite a century of research efforts, the survival of patients has not significantly improved. Currently, diagnosis is based on neuroimaging techniques followed by histopathological and molecular analysis of resected or biopsied tissue. A recent paradigm shift in diagnostics ranks the molecular analysis of tissue samples as the new gold standard over classical histopathology, thus correlating better with the biological behavior of glioblastoma and clinical prediction, especially when a tumor lacks the typical hallmarks for glioblastoma. Liquid biopsy aims to detect and quantify tumor-derived content, such as nucleic acids (DNA/RNA), circulating tumor cells (CTC), or extracellular vesicles (EV) in biofluids, mainly blood, cerebrospinal fluid (CSF), or urine. Liquid biopsy has the potential to overcome the limitations of both neuroimaging and tissue-based methods to identify early recurrence and to differentiate tumor progression from pseudoprogression, without the risks of repeated surgical biopsies. This review highlights the origins and time-frame of liquid biopsy in glioblastoma and points to recent developments, limitations and challenges of adding liquid biopsy to support the clinical management of glioblastoma patients.