A frequent co-morbidity of cerebrovascular pathology and Alzheimer's disease pathology has been observed over past decades. Accordingly, much evidence has been reported which indicates that microvascular endothelial dysfunction, due to cerebrovascular risk factors (e.g., atherosclerosis, obesity, diabetes, smoking, hypertension, aging), precedes cognitive decline in Alzheimer's disease and contributes to its pathogenesis. By incorporating appropriate drug(s) into biomimetic (lipid cubic phase) nanocarriers, one obtains a multitasking combination therapeutic which targets certain cell-surface scavenger receptors, mainly class B type I (i.e., SR-BI), and crosses the blood-brain barrier (BBB). Such targeting allows for various Alzheimer's-related cell types to be simultaneously searched out, in vivo, for localized drug treatment. This in vivo targeting advantage may be particularly important for repurposing an FDA-approved drug, especially one which has shown the added ability to restore some cognitive functions in certain animal models of Alzheimer's disease (e.g., the anticancer drug bexarotene); this (candidate repurposing) drug up to now, by itself (i.e, without nanocarrier), displayed poor CNS penetration in human subjects.

Visualization of volumetric data holds great importance, particularly in medical and biomedical applications. Various imaging techniques, such as FMRI and 3D microscopy, are employed to generate volumetric data, used in medical practice, research, and teaching. Commonly utilized tools like 3D Slicer, Fiji, and MATLAB ® aid in rendering and analyzing 3D images. However, these tools may lack comprehensive rendering functionality and face challenges in handling computational demands as data sizes grow. To address these limitations, this work introduces a GPU-supported renderer with a MATLAB ® interface. This solution gives the user flexible control over rendering parameters and optimizes data transfer through a memory management system. By leveraging the computational power of NVIDIA GPUs, the renderer enables complex and high-quality renderings, enhancing speed and efficiency. It therefore facilitates the analysis and visualization of volumetric data within an integrated environment, namely MATLAB ®, streamlining their workflows. This advancement provides valuable opportunities for researchers and medical professionals to explore and comprehend volumetric data effectively.

The human mandible's cancellous bone, which is characterized by its unique porosity and directional sensitivity to external forces, is crucial for sustaining biting stress. Traditional CAD models fail to fully represent bone's anisotropic structure and thus depend on simple isotropic assumptions. For our research, we use the latest versions of nTopology and Creo Parametric software to make biomimetic Voronoi lattice models that accurately reflect the complex geometry and mechanical properties of trabecular bone. The porosity of human cancellous bone is accurately modeled in this work using biomimetic Voronoi lattice models. The porosities range from 70% to 95%, which can be achieved by changing the pore sizes to 1.0 mm, 1.5 mm, 2.0 mm, and 2.5 mm. Finite element analysis (FEA) was used to examine the displacements, stresses, and strains acting on dental implants with a buttress thread, abutment, retaining screw, and biting load surface. The results show that the Voronoi model accurately depicts the complex anatomy of the trabecular bone in the human jaw, compared to standard solid block models. The ideal pore size for biomimetic Voronoi lattice trabecular bone models is 2 mm, taking into account both the von Mises stress distribution over the dental implant, screw retention, cortical bone, cancellous bone, and micromotions. This pore size displayed balanced performance by successfully matching natural bone's mechanical characteristics. Advanced finite element analysis (FEA) improves biomechanical understanding of how bones and implants interact by creating more accurate models of biological problems and dynamic loading situations. This makes biomechanical engineering better.

How crucial is big data in contemporary molecular design? In this publication we elucidate fundamental concepts and terminology in this field, critically addressing overlooked issues. We thoroughly examine the size, accessibility, quality, and structural aspects of big data alongside the primary methodologies employed for their analysis. Within chemical compounds, properties and descriptors represent two distinct data types, forming the basis for categorizing molecular big data. The primary objective of chemistry is property production, which means we are searching for novel drugs or materials rather than chemical compounds, and big data is the central issue of this philosophy. The increasing availability of data in computer-aided technology propels advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL). Accordingly, a broad chemical audience must comprehend these methods to understand data-centered chemistry. Thus, we aim to systemize big data issues through a simple illustrative framework with fundamental descriptor categories: coding, computer-generated descriptors, and property correlates. Although we employ computer-generated descriptors as big data for predictions, the measured data are irreplaceable for achieving high-quality and reliable outcomes and controlling molecular effects. The scarcity of property data remains a significant hurdle limiting comprehensive studies on the structure-property relationships within big data. Accordingly, guided by pragmatics not-so-big data is an option for drug design. We presented also a brief review of the recent big data literature.

In the dynamic landscape of drug discovery, Computer-rAided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADD's historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADD's predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. The paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Thanks to the continued development of experimental structural biology and the half-a-century old Protein Data Bank, 2021 saw a big step forward in the development of protein structure prediction with deep learning algorithms. Recently, DeepMinds AlphaFold has determined the structures of ∼ 200 million proteins from 1 million species. The speed of this progress raise the question of what becomes possible for computational drug discovery and design when we have a systems-wide account of the structures and motions of most proteins. Therefore, this article puts forward the concept of a general intermolecular binding affinity calculator (GIBAC): Kd = f(molA, molB, envPara), towards the acceleration of traditional computer-aided drug design (CADD) and artificial intelligence-integrated drug discovery (AIDD), for both small molecules and biologics such as therapeutic proteins.

Pure orbital blowout fractures occur within the confines of the internal orbital wall. Restoration of orbital form and volume is paramount to prevent functional and esthetic impairment. The anatomical peculiarity of the orbit has encouraged surgeons to develop implants with customized features to restore its architecture. This has resulted in worldwide clinical demand for patient-specific implants (PSIs) designed to fit precisely in the patient's unique anatomy. Fused filament fabrication (FFF) three-dimensional (3D) printing technology has enabled the fabrication of implant-grade polymers such as Polyetheretherketone (PEEK), paving the way for a more sophisticated generation of biomaterials. This study evaluates the FFF 3D printed PEEK orbital mesh customized implants with a metric considering the relevant design, biomechanical, and morphological parameters. The performance of the implants is studied as a function of varying thicknesses and porous design constructs through a finite element (FE) based computational model and a decision matrix based statistical approach. The maximum stress values achieved in our results predict the high durability of the implants, and the maximum deformation values were under one-tenth of a millimeter (mm) domain in all the implant profile configurations. The circular patterned implant (0.9 mm) had the best performance score. The study demonstrates that compounding multi-design computational analysis with 3D printing can be beneficial for the optimal restoration of the orbital floor.

Traumatic brain injury (TBI) is an important global clinical issue, requiring not only prevention but also effective treatment. Following TBI, diverse parallel and intertwined pathological mechanisms affecting biochemical, neurochemical, and inflammatory pathways can have severe impact on the patient’s quality of life. The current review summarizes evidence for utility of amantadine in TBI in connection to its mechanism of action. Amantadine combining multiple mechanisms of action may offer both neuroprotective and neuroactivating effects in TBI patients. Indeed, use of amantadine in TBI has been encouraged by several clinical practice guidelines/recommendations. Amantadine is also available as infusion which may be of particular benefit in unconscious patients with TBI, due to immediate delivery to the central nervous system and the possibility of precise dosing. In other situations, orally administered amantadine may be used. There are several questions that remain to be addressed: Can amantadine be effective in disorders of consciousness requiring long-term treatment and in combination with drugs approved for treatment of TBI? Do the observed beneficial effects of amantadine extend to disorders of consciousness due to factors other than TBI? Well controlled clinical studies are warranted to ultimately confirm its utility in the TBI and provide answers these questions.

Alzheimer’s disease (AD) is a progressive irreversible neurodegenerative disorder that represents a major global public health concern. Traditionally, AD is diagnosed using cerebrospinal fluid biomarker analysis or brain imaging modalities. Recently, less burdensome, more widely available blood biomarker (BBM) assays for amyloid-beta (Aβ42/40) and phosphorylated-tau concentrations have been found to accurately identify the presence/absence of brain amyloid plaques and tau tangles and have helped to streamline AD diagnosis. However, few BBMs have been rigorously analytically validated. Herein, we report the analytical validation of a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantifying plasma phosphorylated-tau217 (p-tau217) and non-phosphorylated-tau217 (np-tau217) peptide concentrations. We combined the p-tau217/np-tau217 concentrations ratio (%p-tau217) and the previously validated LC-MS/MS assay for plasma Ab42/40 into a new multiple analyte blood test and algorithm (PrecivityAD2™) that identifies brain amyloid status based on brain amyloid positron emission tomography. We found: (a) the %p-tau217 assay is precise, accurate, sensitive, linear over a wide analytical measurement range, and free from carryover and interference; (b) the pre-analytical specimen collection, processing, storage and shipping conditions that maintain plasma tau peptide stability; and (c) using the measured analytical imprecision for plasma Aβ42/40 and p-tau217/np-tau217 levels in a worst-case scenario model, the PrecivityAD2 test algorithm for amyloid pathology classification changed for only 3.5% of participants from brain amyloid positive to negative, or negative to positive. The plasma sample preparation and LC-MS/MS methods underlying the PrecivityAD2 test are suitable for use in the clinical laboratory and valid for the test’s intended purpose: to aid in the diagnostic evaluation of individuals aged 55 and older with signs or symptoms of mild cognitive impairment or dementia.

One of the hallmarks of Alzheimer’s disease (AD) is the accumulation of beta-amyloid peptide (Aβ) leading to formation of soluble neurotoxic Aβ oligomers and insoluble amyloid plaques in various parts of the brain. Aβ undergoes post-translational modifications that alter its pathogenic properties. Aβ is produced not only in brain, but also in the peripheral tissues. Such Aβ, including its post-translationally modified forms, can enter the brain from circulation by binding to RAGE and contribute to the pathology of AD. However, the transport of modified forms of Aβ across the blood–brain barrier (BBB) has not been investigated. Here, we used a transwell BBB model as a controlled environment for permeability studies. We found that Aβ42 containing isomerized Asp7 residue (iso-Aβ42) and Aβ42 containing phosphorylated Ser8 residue crossed the BBB better than unmodified Aβ42, which correlated with different contribution of endocytosis mechanisms to the transport of these isoforms. Using microscale thermophoresis, we observed that RAGE binds to iso-Aβ42 an order of magnitude weaker than to Aβ42. Thus, post-translational modifications of Aβ increase the rate of its transport across the BBB and modify the mechanisms of the transport, which may be important for AD pathology and treatment.

: The primary objective of this study was to investigate the association of certain genetic alterations and intraoperative fluorescent activity of 5-aminolevulinic acid (ALA) in brain metastasis (BM) of lung adenocarcinoma. A retrospective cohort study was conducted among 72 patients who underwent surgical resection of BM from lung adenocarcinoma at our institute for 5 years. Cancer cell infiltration was estimated by the intraoperative fluorescent activity of 5-ALA, and genetic alterations were analyzed by next-generation sequencing (NGS). The sensitivity and specificity for detecting cancer cell infiltration using 5-ALA were 87.5% and 96.4%, respectively. Genes associated with cell cycle regulation (p = 0.003) and cell proliferation (p = 0.044) were significantly associated with positive fluorescence activity of 5-ALA in the adjacent brain tissue. Genetic alterations in cell cycle regulation and cell proliferation were also associated with shorter recurrence-free survival (p = 0.013 and p = 0.042, respectively) and overall survival (p = 0.026 and p = 0.042, respectively) in the multivariate analysis. The results suggest that genetic alterations in cell cycle regulation and cell proliferation are associated with positive fluorescence activity of 5-ALA in the adjacent infiltrative brain tissue and influence the clinical outcome of BM of lung adenocarcinoma.

The introduction of anti-amyloid monoclonal antibodies against Alzheimer’s disease (AD) is of high importance. However, even though treated patients show very little amyloid pathology, there is only a modest effect on the rate of cognitive decline. Although this effect possibly can increase over time, there is still a need for alternative treatments that will improve the cognitive function in patients with AD. Therefore, the purpose of this study was to characterize the triazinetrione ACD856, a novel pan-Trk positive allosteric modulator, in multiple models to address its neuroprotective and potential disease modifying effects. The pharmacological effect of ACD856 was tested in recombinant cell lines, primary cortical neurons or animals. We demonstrate that ACD856 enhanced NGF-induced neurite outgrowth, increased the levels of the pre-synaptic protein SNAP25 in PC12 cells and increased the degree of phosphorylated TrkB in SH-SY5Y cells. In primary cortical neurons, ACD856 led to increased levels of phospho-ERK1/2, showed neuroprotective effect against amyloid-beta or energy-deprivation induced neurotoxicity, and increased the levels of brain derived neurotrophic factor (BDNF). Consequently, administration of ACD856 resulted in a significant increase of BDNF in the brains of 21-months old mice. Furthermore, repeated administration of ACD856 resulted in a sustained anti-depressant effect which lasted up to seven days, suggesting effects that go beyond merely symptomatic effects. In conclusion, the results confirm ACD856 as a cognitive enhancer, but more importantly, they provide substantial in vitro and in vivo evidence of neuroprotective and long-term effects that contribute to neurotrophic support and increased neuroplasticity. Presumably, the described effects of ACD856 may improve cognition, increase resilience, and promote neurorestorative processes, thereby leading to a healthier brain in patients with AD.

Diabetes mellitus is one of the most common metabolic diseases worldwide, and its long-term complications include neuropathy, referring both to the peripheral and to the central nervous system. Detrimental effects of dysglycemia, especially hyperglycemia, on the structure and function of blood-brain barrier (BBB) seem to be a significant backgrounds of diabetic neuropathy pertaining to the central nervous system (CNS). Effects of hyperglycemia, including excessive glucose influx to insulin independent cells, may induce oxidative stress and secondary innate immunity dependent inflammatory response which can damage cells within the CNS, thus promoting neurodegeneration and dementia. Advanced glycation end products (AGE) may exert similar, pro-inflammatory effects through activating receptors for advanced glycation end products (RAGE), as well as some pattern-recognition receptors (PRR). Moreover, long-term hyperglycemia can promote brain insulin resistance, which may in turn promote Aβ aggregate accumulation and tau hyperphosphorylation. This review is focused on a detailed analysis of the effects mentioned above towards the CNS, with special regard to mechanisms taking part in the pathogenesis of central long-term complications of diabetes mellitus initiated by the loss of BBB integrity.

Keywords: Hypertensive emergency; diagnosis; lactate dehydrogenase; high sensitivity cardiac troponin T; brain natriuretic peptide; area under the curve.

Unwanted proteins and metabolic waste in cerebral spinal fluid are cleared from the brain by meningeal and nasal lymphatics, and perineural sheath of cranial nerves; however, the distribution and clearance of CSF along the subarachnoid space of the entire spinal cord is not fully understood. It was hypothesized that the anatomical resolution of Cryo-Fluorescence Tomography (CFT) could provide the visual evidence of clearance across the spinal cord at each level of the vertebral column. To that end, isoflurane anesthetized mice were infused into the lateral cerebroventricle with 5.0 µl of quantum dots [QdotR 605 ITKTM amino (PEG)] over two mins. Mice were allowed to recover (ca 2-3 min) and remained awake and ambulatory for 5, 15, 30, 60 and 120 minutes after which they were euthanized, and the entire intact body frozen at -800. The entire mouse was sectioned and imaged to produce an isotropic voxel resolution of 35 µm. Both white light and fluorescent images were captured after each slice to produce high resolution three-dimensional volumes for each mouse. CFT highlighted the circulation of tracer throughout the ventricular system and central canal of the spinal cord and the entire subarachnoid space of the CNS. Signal could be visualized in the nasal cavity, deep cervical lymph nodes, thoracic lymph nodes and more superficial submandibular lymph nodes as early as 15 min post infusion. Fluorescent signal could be visualized along the dorsal root ganglia and down the proximal extension of the spinal nerves of the thoracic and lumbar segments at 30 min. There was significant accumulation of tracer in the lumbar and sacral lymph nodes between 15- 60 min. The dense fluorescent signal in the thoracic vertebrae noted at 5- and 15-mins post infusion was significantly reduced by 30 min. Indeed, all signal in the spinal cord was ostensibly absent by 120 min except for trace amounts in the coccyx. The brain still had some residual signal at 120 min. These data show that Qdots with a hydrodynamic diameter of 16-20 nm rapidly clear from the brain of awake mice. These data also clearly demonstrate the rapid distribution and efflux of trace along a major length of the vertebral column and the potential contribution of the spinal cord in the clearance of brain waste.

Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear because abnormality is often absent in structural imaging findings. The purpose of the study was to determine whether machine learning-based models using functional magnetic resonance imaging (fMRI) biomarkers and demographic or neuropsychological measures at baseline could effectively predict 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically-matched healthy controls enrolled between September 2015 to August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to the significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after concussion. Approximately half of the patients experienced prolonged cognitive impairment at 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy=87.5%), 6 months (accuracy=83.3%), 1 year (accuracy=83.3%), and performed worse at 1-year follow-up compared to baseline assessment (accuracy=83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.

We constructed a novel design integrating the administration of a clinical self-assessment scale with simultaneous acquisition of functional Magnetic Resonance Imaging (fMRI), aiming at cross-validation between psychopathology evaluation and neuroimaging techniques. We hypothesized that areas demonstrating differential activation in two groups of patients (paranoid and depressive) will have distinct connectivity patterns and structural differences.

Neuroscience has overwhelmingly and understandably focused on the synaptic modality of signal transmission. There is evidence, however, that from an evolutionary perspective, non-synaptic transmission (NST) preceded synaptic signaling. Moreover, in modern nervous systems, NST coexists and extensively interacts with synaptic transmission modifying neuronal dynamics. In fact, NST remains widespread in complex animals, especially within the interoceptive system where the dearth of insulating barriers such as myelin sheaths and the blood-brain barrier enhances the communication between neural and non-neural tissues mediated by NST. We suggest that this physiological arrangement makes a fundamental contribution to interoception¾the process of sensing visceral states¾which is an essential underpinning of the capacity to feel and the foundation of affective processing.

Tspo is a receptor involved in the regulation of cellular proliferation, apoptosis and mitochondrial functions. Previous studies showed the expression of TSPO protein correlated positively with tumour malignancy and negatively with patient survival. The aim of this study was to determine the transcription of Tspo mRNA in various types of normal and cancer tissues. In situ hybridization was performed to localise the Tspo mRNA in various human normal and cancer tissues. The relative level of Tspo mRNA was quantified using fluorescent intensity and visual estimation of colorimetric staining. RT-PCR was used to confirm these mRNA levels in normal lung, lung cancer, liver cancer and cervical cancer cell lines. There was a significant increase in the level of transcription in liver, prostate, kidney and brain cancers while a significant decrease was observed in cancers of the colon and lung. Quantitative RT-PCR confirmed that the mRNA levels of Tspo are higher in a normal lung cell line than in a lung cancer cell line. An increase in the expression levels of TSPO makes it a good diagnostic biomarker and TSPO could serve as a target for anticancer drug development.

In this paper, we present a novel approach for managing the file system in Linux using a voice assistant. Our system allows users to perform file system operations such as creating directories, renaming files, and deleting files by issuing voice commands. We develop a voice assistant using Python libraries and integrate it with the file system in Linux. The voice assistant is capable of understanding natural language and executing commands based on the user's voice inputs. We conduct experiments to evaluate the performance of the system and demonstrate that our approach is effective and efficient in managing the file system using voice commands. Our system can enhance the accessibility and usability of the file system in Linux for individuals with disabilities or those who prefer a hands-free approach to file management.

This paper presents a methodology for the integration of Building Information Modeling (BIM) and Life Cycle Cost Analysis (LCCA) to assess the economic implications of designing aging-in-place (AIP) homes at the conceptual stage. With the global increase in the aging population, there is an increased demand for housing solutions tailored to the needs of elderly individuals. Focusing on the importance of the early phase of design, this study aims to improve the process of making efficient decisions by providing a comprehensive assessment of the life cycle costs associated with AIP homes. The study introduces a semi-automated model for the economic evaluation of AIP homes, enabling the estimation of costs throughout the houses’ entire life cycle, from design and construction to operation, maintenance, and eventual renovation or disposal. The said model facilitates the exploration of the long-term economic feasibility of design’s related decisions with an emphasis on the importance of considering the life cycle costs early during the design process to optimize the functionality and economic viability. By investing in accessible and universal design features upfront, the initial costs for modifications can lead to long-term savings by reducing the need for extensive retrofits. The model can easily do comparison between different design alternatives in terms of their lifecycle costs, allowing designers to assess the financial impact of using important features in their design such as wider doorways, accessible bathrooms, and elevators. Overall, this study provides valuable insights for designers and homeowners about the economic aspects of designing AIP homes as a support for efficient decision-making during the early stages of the design process.

In this paper, we critically examine the burgeoning role of advanced computational methodologies in deciphering the complex tapestry of farm animal behaviors and emotions. Leveraging digital imaging and artificial intelligence, we unearth nuanced behavioral patterns and micro-expressions, offering predictive insights into animal emotional states. Sound vocalization analysis, often overlooked, emerges as a pivotal tool, decoding intricate communicative nuances and emotional undertones. Cognitive tests, including mirror and bias assessments, challenge long-standing perceptions, revealing surprising depths of animal self-awareness and cognitive sophistication. However, the paper also underscores the imperative of integrating these tools with a profound understanding of animal psyche, ensuring technology serves as an enhancer, not a replacement, of traditional observational methods. This research not only highlights the transformative potential of cognitive computing in animal welfare but also calls for a judicious application, ensuring technology augments, not undermines, the intrinsic value of human-animal interactions and understanding.

Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation

Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of object detection in challenging environments. However, there is no consolidated reference to cover state-of-the-art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present the quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

The Release of government dataset for public use can potentially strengthen the relationship between the government and its constituents. However, research shows that there are several challenges for open data effectiveness. This paper reviews current determinants and issues associated with the open government data (OGD) procedures. The review concentrates on two ends of the spectrum: First, from the perspective of the preparation by the government, focusing on the organization of traditional governmental datasets and how the recording of the data is administered. Second, from the perspective of the users, focusing on the way in which the data is released to the general public and on human-computer interaction (HCI) issues between end-user and data-consumption interfaces. Following a thorough analysis of these two opposing challenges, the paper proposes approaches to mitigate them. This review and subsequent recommendations contribute and expand current understanding of open government data effectiveness and can lead to public policy changes, development of new procedures and strategies, and ultimately improvements at both ends of the federal open data endeavor.

The neurologic effects of long-COVID are a therapeutic challenge. This article discusses the immunologic manifestations of long-COVID and proposes a non-androgenic, synthetic analog of dehydroepiandrosterone (DHEA), bromo-epi-androsterone (BEA), as a candidate for clinical trials for this exigent collection of neurologic conditions. Moreover, because there is an association between long-COVID and Alzheimer’s disease, the evaluation of BEA in long-COVID may serve as a proxy for its effect on neuroinflammatory biomarkers of Alzheimer’s disease that are shared with long-COVID.

Thyrotropin releasing hormone (TRH; pGlu-His-Pro-NH2) is an intercellular signal produced mainly by neurons. Among the multiple pharmacological effects of TRH, that on food-intake is not well understood. We review data that show that peripheral injection of TRH generally produces a transient anorexic effect, discuss the pathways that might initiate this effect, and explain its short half-life. In addition, central administration of TRH can produce anorexic or orexigenic effects, depending on the site of injection, that are likely due to interaction with TRH receptor 1. Anorexic effects are most notable when TRH is injected into the hypothalamus and the nucleus accumbens, while the orexigenic effect has only been detected by injection into the brain stem. Functional evidence suggests that TRH neurons that are prime candidate vectors for TRH action on food-intake include the caudal raphe nuclei projecting to the dorsal motor nucleus of the vagus, and possibly TRH neurons from the tuberal lateral hypothalamus projecting to the tuberomammillary nuclei. For other TRH neurons, the anatomical or physiological context and impact of TRH in each synaptic domain are still poorly understood. The manipulation of TRH expression in well-defined neuron types will facilitate the discovery of its role in food-intake control in each anatomical scene.

At the end of 2023 the Whole Mouse Brain Atlas was announced, revealing that there are about 5,300 molecularly defined neuronal types in the mouse brain. We ask whether brain models exist that contemplate how this is possible. The conventional columnar model, implicitly used by the authors of the Atlas, is incapable of doing so with only 20 brain columns (5 brain vesicles with 4 columns each). We argue that definition of some 1,250 distinct progenitor microzones, each producing over time minimally 4-5 neuronal types, may be sufficient. This is nearly achieved presently by the prosomeric model amplified by secondary dorsoventral and anteroposterior microzonation of progenitor areas, plus clonal variation of cell types produced on average by each of them.

The responses of inflammatory cytokines and brain-derived neurotrophic factor (BDNF) to a high-intensity interval training (HIIT) session were compared between 7 postmenopausal (57±2 y) and 8 premenopausal females (44±3 y) during the luteal phase of their menstrual cycle, when estrogen and progesterone are highest, and. HIIT consisted of 10 x 1 min running intervals (90% maximum heart rate) separated by 1 min recovery intervals. Blood samples were collected at baseline (fasted), pre-exercise (postprandial), and 0, 30 and 90 min post-HIIT and analyzed for interleukin (IL-6) and 10 (IL-10), tumour necrosis factor-alpha (TNF-α) and BDNF. IL-6 significantly increased from pre-exercise to 0 min post-HIIT in postmenopausal (+40%, p=0.01) but 30 min post-HIIT in premenopausal females (+60%, p=0.02). IL-6 remained elevated at 90 min post-HIIT in premenopausal (+104%, p=0.05) and to a higher degree in postmenopausal (+385%, p<0.001). IL-10 showed no response. TNF-α increased from pre- to 0 min post-HIIT (+10%, p=0.05), then decreased to below pre-exercise at 30 min (-10%, p=0.02) and 90 min (-5%, p=0.04) in both groups. BDNF increased immediate post-HIIT in premenopausal (+60%, p<0.001) but not postmenopausal females. The differences in IL-6 and BDNF response to HIIT between pre and postmenopausal females provide evidence of the role of female reproductive hormones in the regulation of these exercise-induced responses.

Background. Transferrin receptor 1 (TfR1), glucose transporter 1 (GLUT1), and CD98hc are candidates for targeted therapy at the blood-brain barrier (BBB). Our objective was to challenge the expression of TfR1, GLUT1, and CD98hc in brain capillaries by the histone deacetylase inhibitor (HDACi) valproic acid (VPA). Methods. Primary mouse brain capillary endothelial cells (BCECs) and brain capillaries isolated from mice injected intraperitoneally with VPA were examined using RT-qPCR and ELISA. Targeting to the BBB was performed by injecting monoclonal anti-TfR1 (Ri7217)-conjugated gold nanoparticles measured using ICP-MS. Results. In BCECs co-cultured with glial cells, Tfrc mRNA expression was significantly higher after 6 h VPA returning to baseline after 24 h. In vivo Glut1 mRNA expression was significantly higher in males, but not females', receiving VPA, whereas Cd98hc mRNA expression was unaffected by VPA. TfR1 increased significantly in vivo after VPA, whereas GLUT1 and CD98hc were unchanged. Uptake of anti-TfR1-conjugated nanoparticles was unaltered by VPA in spite upregulated TfR expression. Conclusions. VPA upregulates TfR1 in brain endothelium in vivo and in vitro. VPA does not increase GLUT1 and CD98hc proteins. The increase in TfR1 does not result in higher anti-TfR1 antibody targetability, suggesting targeting sufficiently occurs with available transferrin receptors without further contribution from accessory VPA-induced TfR1.

HIV-associated neurocognitive disorders (HAND) persist under antiretroviral therapy as a complex pathology that has been difficult to study in cellular and animal models. Therefore, we generated an ex vivo human brain slice model of HIV-1 infection from surgically resected adult brain tissue. Brain slice cultures processed for flow cytometry showed >90% viability of dissoci-ated cells within the first three weeks in vitro, with parallel detection of astrocyte, myeloid, and neuronal populations. Neurons within brain slices showed stable dendritic spine density and mature spine morphologies in the first weeks in culture, and they generated detectable activity in multi-electrode arrays. We infected cultured brain slices using patient-matched CD4+ T-cells or monocyte-derived macrophages (MDMs) that were exposed to a GFP-expressing R5-tropic HIV-1 in vitro. Infected slice cultures expressed viral RNA and developed a spreading infection up to 9-days post-infection, which were significantly decreased by antiretrovirals. We also detected infected myeloid cells and astrocytes within slices and observed minimal effect on cellular via-bility over time. Overall, this human-centered model offers a promising resource to study the cel-lular mechanisms contributing to HAND (including antiretroviral toxicity, substance use, and aging), infection of resident brain cells, and new neuroprotective therapeutics.

Background: Infections of Coronavirus Disease-2019 (COVID-19) can cause long-term effects known as long COVID. This pilot study aimed to evaluate the feasibility of a clinical study as well as the efficacy and safety of traditional East Asian herbal medicines in alleviating fatigue and cognitive dysfunction “brain fog” in patients with long COVID. Methods: This prospective pilot study investigated the use of three types of herbal medicines, Bojungikki-tang (BIT), Kyungok-go (KOG), and Cheonwangbosim-dan (CBD), for a 12-week period as potential treatments for fatigue and cognitive dysfunction in patients with long COVID. Forty-five patients with long COVID were recruited, and one of three drugs was given based on the patient’s symptoms and pattern identification. The effect of herbal medications on fatigue and cognitive function outcomes was assessed over a 36-week period, with patient adherence closely monitored. Results: After 12 weeks of herbal drug administration, fatigue symptoms improved significantly across all groups, with treatment success rates of 80%, 53.33%, and 46.67% in the BIT, KOG, and CBD groups, respectively. However, “brain fog” symptoms showed less improvement, with treatment success rates of 40%, 46.67%, and 13.33% in the BIT, KOG, and CBD groups, respectively. All adverse events reported were mild and unrelated to the medication. The study design was found to be feasible with high medication adherence. Conclusions: This study demonstrated the feasibility of conducting a clinical trial with three herbal medicines to treat long COVID symptoms like fatigue and “brain fog.”

This review explores the application of the conservative management model for pain to sports-related concussions (SRCs), framing concussions as a distinct form of pain syndrome with a pathophysiological foundation in central sensitization. Drawing parallels with proven pain management models, we underscore the significance of a proactive approach to concussion management. Recognizing concussions as a pain syndrome allows for the tailoring of interventions in alignment with conservative principles. This review first covers the epidemiology and controversies surrounding prolonged concussion recovery and persistent post-concussion symptoms (PPCS). Next, the pathophysiology of concussions is presented within the central sensitization framework, emphasizing the need for early intervention to mitigate the neuroplastic changes that lead to heightened pain sensitivity. Five components of the central sensitization process specific to concussion injuries are highlighted as targets for conservative interventions in the acute period: peripheral sensitization, cerebral metabolic dysfunction, neuroinflammation, glymphatic system dysfunction, and pain catastrophizing. These proactive interventions are emphasized as pivotal in accelerating concussion recovery and reducing the risk of prolonged symptoms and PPCS, in line with the philosophy of conservative management.

Central Nervous System (CNS) cancers pose a formidable challenge in the medical world, characterized by aggressive behaviors and limited therapeutic options. The emergence of nanomedicine has offered new hope in CNS cancer treatment. The COVID-19 pandemic has further accelerated the need for innovative therapeutic strategies, emphasizing the significance of nanomedicine in this era. This mini review explores the current status of nanomedicine in CNS cancer therapy, with a particular focus on the post-COVID-19 landscape. We discuss the unique challenges presented by CNS cancers, the potential of nanomedicine in overcoming these challenges, and the recent developments in the field. Additionally, we highlight the lessons learned from the pandemic and how they can be applied to improve CNS cancer therapeutics. We also shed light on the evolving regulatory landscape and the prospects of personalized nanomedicine in CNS cancer treatment. The integration of nanomedicine in CNS cancer therapy is becoming increasingly promising, and its role in the post-COVID-19 era is poised to shape the future of neuro-oncology.

Non-pharmacologic options like the ketogenic diet (KD) and intermittent fasting (IF) are practical nutritional interventions with minor reported side effects like gastrointestinal symptoms, dyslipidemia, and hypomagnesemia for various medical ailments. In conjunction with IF, KD shows promise in weight loss, diabetes management, cardiovascular disorders, polycystic ovarian syndrome, cancer, and chronic neurological disorders. Based on prior research, we have examined the mechanism of action of KD and IF and their effect on neurological diseases, cancer, and obesity. We have also suggested evidence-based recommendations for the safer practice of KD and IF. Despite potential benefits, long-term adherence to KD poses challenges. Periodic KD implementation may thus benefit newly diagnosed overweight or obese patients with type 2 diabetes mellitus, aiding blood glucose and lipid management while promoting weight loss. KD is a high-fat and low-carbohydrate diet with a ratio of fat to carbohydrates and protein being 4:1 or 3:1, and thus, for peripheral tissues and the brain, fatty acids become the mandatory source of cellular energy. Ketone bodies have been used as the primary energy source during fasting. KD has been utilized as an effective treatment for refractory epilepsy since the 1920s. Evidence of the neuroprotective role of KD in diseases like epilepsy, stroke, traumatic brain injury, Alzheimer’s disease, and other neurological diseases has been noted. Since the 1960s, KD has become a popular method for obesity treatment. In addition, KD has been suggested as a potent anticancer therapy when used alone or as an adjuvant. KD may increase tumor cell sensitivity when combined with classic chemotherapy and radiotherapy. Thus, the probability that modifying the diet can help manage obesity, cancer, and chronic neurological disease without depending on pharmacological treatment and their serious side effects for a lifetime is promising and requires further investigation. KD holds promise as a potential adjunctive therapy in various neurological disorders, offering new avenues for treatment and neuroprotection. IF has shown potential in slowing the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's by promoting antioxidant defense and suppressing inflammation. KD and IF show promise in cancer therapy by targeting altered cancer cell metabolism. Additionally, KD may enhance the effects of standard treatments like chemotherapy and radiotherapy. Some of the most robust reports of keto's possible benefits have come from glioblastoma, a very aggressive brain cancer. KD has also shown strong evidence for its effectiveness in weight loss, mainly attributed to its appetite-suppressing action in ketosis. However, long-term adherence to KD can be challenging, and periodic KDs may help manage blood glucose and lipid levels in overweight or obese patients with type 2 diabetes mellitus. Likewise, IF may be more effective than regular calorie restriction for achieving weight loss goals when combined with exercise programs. More prospective human studies are warranted to evaluate both KD and IF's potential therapeutic effectiveness and safety.

Chronic gastroduodenal symptoms are prevalent worldwide, and there is a need for new diagnostic and treatment approaches. Several overlapping processes may contribute to these symptoms, including gastric dysmotility, hypersensitivity, gut-brain axis disorders, gastric outflow resistance, and duodenal inflammation. Gastric Alimetry® (Alimetry, New Zealand) is a non-invasive test for evaluating gastric function that combines body surface gastric mapping (high-resolution electrophysiology) with validated symptom profiling. Together, these complementary data streams enable important new clinical insights into gastric disorders and their symptom correlations, with emerging therapeutic implications. A comprehensive database has been established, currently comprising >2,000 Gastric Alimetry tests, including both controls and patients with various gastroduodenal disorders. From studies employing this database, this pa-per presents a systematic methodology for Gastric Alimetry test interpretation, together with an extensive supporting literature review. Reporting is grouped into four sections: Test Quality, Spectral Analysis, Symptoms, and Conclusions. This review compiles, assesses, and evaluates each of these aspects of test assessment, with discussion of relevant evidence, example cases, limitations, and areas for future work. The resultant interpretation methodology is recommended for use in clinical practice and research to assist clinicians in their use of Gastric Alimetry as a diagnostic aid, and is expected to continue to evolve with further development.

There is increasing interest in identifying biological and imaging markers for the early detection of neurocognitive decline. In addition, non-pharmacological strategies including physical exercise and cognitive interventions may be beneficial for those developing cognitive impairment. The Feuerstein Instrumental Enrichment (FIE) Program is a cognitive intervention based on Structural Cognitive Modifiability and the Mediated Learning Experience (MLE) and aims to promote problem-solving strategies and metacognitive abilities. The FIE program uses a variety of instruments to enhance the cognitive capacity of the individual as a result of mediation. A specific version of the FIE program was developed for the cognitive enhancement of older adults, focusing on strengthening orientation skills, categorization skills, deductive reasoning and memory. We performed a prospective interventional pilot observational study on older subjects with MCI who participated in 30 mediated FIE sessions (two sessions weekly for 15 weeks). Of the 21 subjects who completed the study, there was a significant improvement in memory on the Neurotrax battery comparing pre- and post-intervention scores (pre: M=95.3, SD=12.2, post: M=101.2, SD=7.9, p<.05). Complete sets of anatomical MRI data for voxel-based morphometry, taken at the beginning and the end of the study, were obtained from 16 participants (mean age 83.5 years). Voxel-based morphometry showed an unexpected increase in grey matter (GM) in the anterolateral occipital border and the middle cingulate cortex. These initial findings of our pilot study support the design of randomized trials to evaluate the effect of cognitive training using the FIE Program on brain volumes and cognitive function.

Recent research has identified the gut-brain axis as a key mechanistic pathway and potential therapeutic target in depression. In this paper, the potential role of gut hormones as potential treatments or predictors of response in depression is examined, with specific reference to the peptide hormone motilin. This possibility is explored through two methods: (a) a conceptual review of the possible links between motilin and depression, including evidence from animal and human research as well as clinical trials, and (b) an analysis of the relationship between a functional polymorphism (rs2281820) of the motilin (MLN) gene and cross-national variations in the prevalence of depression. It was observed that (a) there are several plausible mechanisms, including interactions with diet, monoamine, and neuroendocrine pathways, to suggest that motilin may be relevant to the pathophysiology and treatment of depression, and (b) there was a significant correlation between rs2281820 allele frequencies and the prevalence of depression after correcting for multiple confounding factors. These results suggest that further evaluation of the utility of motilin and related gut peptides as markers of antidepressant response is required, and that these molecular pathways represent potential future mechanisms for antidepressant drug development.

The noradrenergic theory of Cognitive Reserve (Robertson, 2013-2014) postulates that the upregulation of the Locus Coeruleus - Noradrenergic System (LC-NA) originating in the Brainstem might facilitate cortical networks involved in attention, and protracted activation of this system throughout the lifespan may enhance cognitive stimulation contributing to Reserve. To test the above-mentioned theory, a study was conducted on a sample of 686 participants (395 controls, 156 Mild Cognitive Impairment, 135 Alzheimer’s Disease) investigating the relationship between LC volume, attentional performance and a biological index of brain maintenance (BrainPAD – an objective measure which compares an individual’s structural brain health, reflected by their voxel-wise grey matter density, to the state typically expected at that individual’s age). Further analyses were carried out on Reserve indices including education and occupational attainment. Volumetric variation across groups was also explored along with gender differences. Control analyses on the Serotoninergic (5-HT), Dopaminergic (DA) and Cholinergic (Ach) systems were contrasted with the Noradrenergic (NA) hypothesis. The antithetic relationships were also tested across the neuromodulatory subcortical systems.Results supported by bayesian modelling showed that LC volume disproportionately predicted higher attentional performance as well as biological brain maintenance across the three groups. These findings lend support to the role of the noradrenergic system as a key mediator underpinning the neuropsychology of Reserve, and they suggest that early prevention strategies focused on the noradrenergic system (e.g. cognitive-attentive training, physical exercise, pharmacological and dietary interventions) may yield important clinical benefits to mitigate cognitive impairment with age and disease.

During the last trimester of gestation and for the first 18 months after birth, both docosahexaenoic acid,22:6n-3 (DHA) and arachidonic acid,20:4n-6 (ARA) are preferentially deposited within the cerebral cortex at a rapid rate. Although, the structural and functional roles of DHA in brain development are well investigated, similar roles of ARA are not well documented. The mode of action of these two fatty acids and their derivatives at different structural-functional roles and their levels in the gene expression and signaling pathways of the brain have been continuously emanating. In addition to DHA, importance of ARA has been much discussed in recent years for fetal and postnatal brain development and the maternal supply of ARA and DHA. These fatty acids are also involved in various brain developmental processes; however, their mechanistic cross talks are not clearly known yet. This review describes the importance of ARA, in addition to DHA to support the optimal brain development and growth and functional roles in the brain.

Ischemic stroke is one of the most disabling diseases and a leading cause of death globally. Despite advances in medical care, the global burden of stroke continues to grow, as no effective treatments to limit or reverse ischemic injury to the brain are available. However, recent preclinical findings have revealed the potential role of transient receptor potential cation 6 (TRPC6) channels as endogenous protectors of neuronal tissue. Activating TRPC6 in various cerebral ischemia models has been found to prevent neuronal death, whereas blocking TRPC6 enhances sensitivity to ischemia. Evidence has shown that Ca2+ influx through TRPC6 activates cAMP response element-binding protein (CREB), an important transcription factor linked to neuronal survival. Additionally, TRPC6 activation may counter excitotoxic damage resulting from glutamate release by attenuating the activity of NMDA receptors of neurons by posttranslational means. Unresolved though, are the roles of TRPC6 channels in non-neuronal cells such as astrocytes and endothelial cells. Moreover, TRPC6 channels may have detrimental effects on the blood-brain barrier, although their exact role in neurovascular coupling requires further investigation. This review discusses evidence-based cell-specific aspects of TRPC6 in the brain to assess the potential targets for ischemic stroke management.

There is a huge interest in developing novel hollow fiber (HF) membranes able to modulate neural differentiation to produce in vitro blood-brain barrier (BBB) models for biomedical and pharmaceutical research, due to the low cell-inductive properties of the polymer HFs used in current BBB models. In this work, poly(ε-caprolactone) (PCL) and composite PCL/graphene (PCL/G) HF membranes were prepared by phase inversion and were characterized in terms of mechanical, electrical, morphological, chemical, and mass transport properties. The presence of graphene in PCL/G membranes enlarged the pore size and the water flux and presented significantly higher electrical conductivity than PCL HFs. Biocompatibility assay showed that PCL/G HFs significantly increased C6 cells adhesion and differentiation towards astrocytes, may be attributed to their higher electrical conductivity in comparison to PCL HFs. On the other hand, PCL/G membranes produced a cytotoxic effect on the endothelial cell line HUVEC presumably related with a higher production of intracellular reactive oxygen species induced by the nanomaterial in this particular cell line. These results prove the potential of PCL HF membranes to grow endothelial cells and PCL/G HF membranes to differentiate astrocytes, the two characteristic cell types that could develop in vitro BBB models in future 3D co-culture systems.

Over past decades, a frequent co-morbidity of cerebrovascular pathology and Alzheimer's disease pathology has been observed. Numerous published studies indicate that preservation of healthy cerebrovascular endothelium can be an important therapeutic target. By incorporating appropriate drug(s) into biomimetic (lipid cubic-phase) nanocarriers, one obtains a multitasking combination therapeutic which targets certain cell-surface scavenger receptors, mainly class B type 1 (i.e., SR-BI), and crosses the blood-brain barrier. This targeting allows for various Alzheimer’s-related cell types to be simultaneously searched out for localized drug treatment in vivo.

Music has the innate potential to reach all parts of the brain, stimulates certain brain areas which are not achievable through other modalities. Music Therapy (MT) is being used for more than a century to treat individuals who needs personalized care. MT optimizes motor, speech and language responsibilities of the brain and improves cognitive performance. Pervasive developmentdisorder (PDD) is a multifaceted, neuro developmental disorder and autism spectrum disorder (ASD) comes under PDD, which is defined by deficiencies in three principal spheres: social connection with others, communicative and normal movement skills. The conventional imaging studies illustrate reduced brain area connectivity in people with ASD, involving selected parts of the brain cortex. People with ASD express much interest in musical activities which engages the brain network areas and improves communication and social skills.The main objective of this review is to analyze the potential role of MT in treating the neurological conditions, particularly ASD. Evidence based studies have reported the extensive therapeutic application of music on various part of the brain in a nonverbal child with autism through hearing or making music.Hence we hypothesized that MT intervention can improve the communication capacity in people with ASD, than customary neurorestoration therapy alone.

The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

Dams are a critical infrastructure system for many communities, but they are also one of the most challenging to inspect. Dams are typically very large and complex structures, and the result is that inspections are often time-intensive and require expensive, specialized equipment and training to provide inspectors with comprehensive access to the structure. The scale and nature of dam inspections also introduces additional safety risks to the inspectors. Unmanned aerial vehicles (UAV) have the potential to address many of these challenges, particularly when used as a data acquisition platform for photogrammetric three-dimensional (3D) reconstruction and analysis, though the nature of both UAV and modern photogrammetric methods necessitates careful planning and coordination for integration. This paper presents a case study on one such integration at the Brighton Dam, a large-scale concrete gravity dam in Maryland, USA. A combination of multiple UAV platforms and multi-scale photogrammetry was used to create two comprehensive and high-resolution 3D point clouds of the dam and surrounding environment at intervals. These models were then assessed for their overall quality, as well as their ability to resolve flaws and defects that were artificially applied to the structure between inspection intervals. The results indicate that the integrated process is capable of generating models that accurately render a variety of defect types with sub-millimeter accuracy. Recommendations for mission planning and imaging specifications are provided as well.

Myalgic Encephalomyelitis, also known as Chronic Fatigue Syndrome (ME/CFS) and Long COVID are characterized by debilitating post-exertional malaise and other core symptoms related to immune dysregulation resultant from post-viral infection, including mitochondrial dysfunction, chronic neuroinflammation and gut dysbiosis. The reported associations between altered microbiota composition and cardinal symptoms of ME/CFS and Long COVID, suggesting that use of microbial preparations, such as probiotics, by restoring the homeostasis of the brain-immune-gut axis may help in the management of symptoms in both conditions. Therefore, this review aims to investigate the implications of alerted gut microbiome and assess the evidence supporting use of microbial-based preparations, including probiotics, synbiotics, postbiotics alone and/or in combination with other nutraceuticals in the management of fatigue, inflammation, as well as neuropsychiatric and gastrointestinal symptoms among patients with ME/CFS and Long COVID.

Chronic traumatic encephalopathy (CTE) is a unique neurodegenerative disease that is associated with repetitive head impacts (RHI) in both civilian and military settings. In 2014, the research criteria for the clinical manifestation of CTE, traumatic encephalopathy syndrome (TES) were proposed to improve the clinical identification and understanding of the complex neuropathological phenomena underlying CTE. This review provides a comprehensive overview of the current understanding of the neuropathological and clinical features of CTE, proposed biomarkers of traumatic brain injury (TBI) in both research and clinical settings, and a range of treatments based on previous preclinical and clinical research studies. Due to the heterogeneity of TBI, there is no universally agreed-upon serum, CSF, or neuroimaging marker for its diagnosis. However, as our understanding of this complex disease continues to evolve, it is likely that there will be more robust, early diagnostic methods and effective clinical treatments. This is especially important given the increasing evidence of a correlation between TBI and neurodegenerative conditions, such as Alzheimer's disease and CTE¹. As public awareness of these conditions grows, it is imperative to prioritize both basic and clinical research, as well as the implementation of necessary safe and preventative measures.

Keywords: bariatric surgery, sleeve gastrectomy, Roux-en-y gastric bypass, obesity, microbiota, gut-brain axis, beck scale, depression, diet, probiotics

Background: Glioblastoma is the most common primary brain neoplasm in adults, with still a poor prognosis despite a constant effort to improve patients’ survival. Some neuroradiological volumetric parameters seem to play a predictive role on Overall Survival (OS) and Progression Free Survival (PFS). The aim of this study is to analyze the impact that the volumetric areas of contrast-enhancing tumor and perineoplastic edema have on survival of patients treated for glioblastoma; Methods: A series of 87 patients who underwent surgery was retrospectively analyzed; OS and PFS were considered as the end points of the study. For each patient a multidisciplinary revision was conducted in collaboration with the Neuroradiology and Neuro-Oncology board. A manual and semi-automatic measurement were adopted to perform the radiological evaluation: contrast Enhancement Preoperative (CE-PTV) and Postoperative Tumor Volume (CE-RTV), Edema/Infiltration Preoperative (T2/FLAIR-PV) and Postoperative Volume (T2/FLAIR-RV); necrosis volume inside the tumor (NV); total tumor volume, including necrosis (TV); Results: The median OS value was 9 months and the median PFS value was 4 m; the mean values were respectively 12,3 m and 6,9 m. Multivariate analysis showed that the OS related factors were: adjuvant chemo-radiotherapy (p &lt; 0,0001), CE-PTV &lt; 15 cm³ (p=0,03), surgical resection &gt; 95% (p=0,004) and the presence of a “pseudo-capsulated” radiological morphology (p=0,04); Conclusions: maximal safe resection is one of the most relevant predictive factors for patients’ survival. The semi-automatic pre-operative MRI evaluation could play a key role in prognostically categorizing these tumors.

The ubiquitin proteasome system (UPS) utilizes an orchestrated enzymatic cascade of E1, E2, and E3 ligases to add single or multiple ubiquitin-like molecules as post-translational modification (PTM) to proteins. Ubiquitination can alter protein functions and/ or marks ubiquitinated proteins for proteasomal degradation but deubiquitinases (DUBs) can reverse protein ubiquitination. While the importance of DUBs as regulatory factors in the UPS is undisputed, many questions remain on DUB selectivity for protein targeting, their mechanism of action, and the impact of DUBs on the regulation of diverse biological processes. Furthermore, little is known about the expression and role of DUBs in tumors of the human central nervous system (CNS). In this comprehensive review, we have used publicly available transcriptional datasets to determine the gene expression profiles of 99 deubiquitinases (DUBs) from five major DUB families in seven primary pediatric and adult CNS tumor entities. Our analysis identified selected DUBs as potential new functional players and biomarkers with prognostic value in specific subtypes of primary CNS tumors. Collectively, our analysis highlights an emerging role for DUBs in regulating CNS tumor cell biology and offers a rationale for future therapeutic targeting of DUBs in CNS tumors.

Brain-derived neurotrophic factor (BDNF) is a key molecule in promoting neurogenesis, dendritic and synaptic health, and neuronal survival, plasticity, and excitability, all of which are disrupted in neurological and cognitive disorders such as Alzheimer’s disease (AD). Extracellular aggregates of amyloid-β (Aβ) in the form of plaques and intracellular aggregates of hyperphosphorylated tau protein have been identified as major pathological insults in AD brain, along with immune dysfunction, oxidative stress, and other toxic stressors. Although aggregated Aβ and tau lead to decreased brain BDNF expression, early losses in BDNF prior to plaque and tangle formation may be due to other insults such as oxidative stress and contribute to early synaptic dysfunction. Physical exercise, on the other hand, protects synaptic and neuronal structure and function, with increased BDNF as a major mediator of exercise-induced enhancements in cognitive function. Here, we review recent literature on mechanisms behind exercise induced BDNF upregulation and its effects on improving learning and memory and on Alzheimer’s disease pathology. Mechanisms include elevations in peripheral BDNF-inducing hormones such as osteocalcin, FNDC5/irisin, and lactate. The fundamental mechanisms of how exercise impacts BDNF and cognition are unclear but are a prerequisite to developing new biomarkers and therapies to delay or prevent cognitive decline.

Abstract: The brain endothelial cell (BEC) glycocalyx (ecGCx) is a BEC surface coating consisting of a complex interwoven polysaccharide (sweet husk) mesh-like network of membrane-bound proteoglycans, glycoproteins, and glycosaminoglycans (GAGs) covering the apical luminal layer of the brain endothelial cells. The ecGCx may considered as the 1st barrier of a tripartite blood-brain barrier (BBB) consisting of 1) ecGCx; 2) BECs; (3) extravascular compartment of pericytes, extracellular matrix, and perivascular astrocytes, Perturbations of this barrier allow for increased permeability in the postcapillary venule that will be permissive to both fluids, solutes, and proinflammatory peripherally-derived leukocytes into the perivascular spaces (PVS) to result in enlargement as well as increased neuroinflammation. The ecGCx is known to have multiple functions, which include its physical and charge barrier, mechanical transduction, regulation of vascular permeability, modulation of inflammatory response, and anticoagulation functions. This review discusses each of the listed functions in detail and utilizes multiple transmission electron micrographs and illustrations to allow for a better understanding of the ecGCx structural and functional roles as it relates to enlarged perivascular spaces (EPVS). This is the 5th review of a series quintet that discuss the importance of EPVS from the perspective of the cells of brain barriers. Attenuation and/or loss of the ecGCx results in brain barrier disruption with increased permeability to proinflammatory leukocytes, fluids and solutes, which accumulate in the postcapillary venule perivascular spaces. This accumulation results in obstruction and results in EPVS with impaired waste removal of the recently recognized glymphatic system. Importantly, EPVS are progressively being regarded as a marker of cerebrovascular and neurodegenerative pathology.

Advanced MRI methods and PET using radiolabeled amino acids provide valuable information in addition to conventional MR imaging for brain tumor diagnostics. These methods are particularly helpful in challenging situations such as the differentiation of malignant processes from benign lesions, the identification of non-enhancing glioma subregions, the differentiation of tumor progression from treatment-related changes, and the early assessment of response to anticancer therapy. The debate over which of the methods is preferable in which situation is ongoing and has been addressed in numerous studies. Currently, most radiology and nuclear medicine departments perform these examinations independently of each other leading to multiple examinations for the patient. The advent of hybrid PET/MRI allowed a convergence of the methods but to date simultaneous imaging has reached little relevance in clinical neuro-oncology. This is partly due to the limited availability of hybrid PET/MRI scanners, but is also due to the fact that PET is a second-line examination in brain tumors. PET is only required in equivocal situations, and spatial co-registration of PET examinations of the brain to previous MRI is possible without disadvantage. A key factor for the benefit of PET/MRI in neuro-oncology is a multimodal approach that provides decisive improvements in the diagnostics of brain tumors compared with a single modality. This systematic review focuses on studies that were able to demonstrate the additive value of amino acid PET and ‘advanced’ MRI in the diagnosis of brain tumors. Available studies suggest that the combination of amino acid PET and advanced MRI improves grading and the histomolecular characterization of newly diagnosed tumors. However, data concerning the delineation of tumor extent and biopsy guidance are of limited value. A clear additive diagnostic value of amino acid PET and advanced MRI can be achieved regarding the differentiation of tumor recurrence from treatment-related changes. Here, PET-guided evaluation of advanced MR methods seems to be helpful. In summary, there is growing evidence that a multimodal approach can achieve decisive improvements in the diagnostics of brain tumors, for which hybrid PET/MRI offers optimal conditions.

Pericytes are increasingly recognized as being important in the control of blood-brain barrier permeability and vascular flow. Research on this important cell type has been hindered by widespread confusion regarding the phenotypic identity and nomenclature of pericytes and other perivascular cell types. In addition, pericyte heterogeneity and mouse-human species differences have contributed to confusion. Herein we summarize our present knowledge on the identification of pericytes and pericyte subsets in humans, primarily focusing on recent findings in humans and nonhuman primates. Precise identification and definition of pericytes and pericyte subsets in humans may help us to better understand pericyte biology and develop new therapeutic approaches specifically targeting disease-associated pericyte subsets.

KLHL genes and the related KBTBD genes encode components of the Cullin-E3 ubiquitin ligase complex and typically target tissue-specific proteins for degradation, thereby affecting differentiation, homeostasis, metabolism, cell signaling, and the oxidative stress response. Despite their importance in normal cell function and in disease (KLHL40, KLHL41, KBTBD13, KEAP1, and ENC1), previous studies that examined epigenetic factors affecting transcription were predominantly limited to promoter DNA methylation. Using diverse tissue and cell culture whole-genome profiles, we examined 17 KLHL or KBTBD genes preferentially expressed in skeletal muscle or brain to identify tissue-specific enhancer and promoter chromatin, open chromatin (DNaseI hypersensitivity), and DNA hypomethylation. Sixteen of the 17 genes displayed muscle- or brain-specific enhancer chromatin in their gene bodies, and most exhibited specific intergenic enhancer chromatin as well. Seven genes were embedded in a super-enhancer. The enhancer chromatin regions typically displayed foci of DNA hypomethylation at peaks of open chromatin. In addition, we found evidence that gene neighbors (HHATL and FBXO32) of KLHL40 and KLHL38 harbor enhancer chromatin that likely upregulates the adjacent KLHL gene. Many KLHL/KBTBD genes had tissue-specific promoter chromatin at their 5’ ends, but surprisingly, two (KBTBD11 and KLHL31) had constitutively unmethylated promoter chromatin in their 3’ exons that overlaps a retrotransposed KLHL gene. Our findings demonstrate the importance of expanding epigenetic analyses beyond the 5’ ends of genes in studies of normal and abnormal gene regulation.

How can neuroscience help everyone to live and care for our confined brains? Understanding and studying human behaviour allows us to measure the impact of such confinement on each of us and to identify those who need help. The challenges are to understand the psychological repercussions following confinement and to understand how neuroscience and neurotechnology can be very interesting tools for dealing with the health crisis. Neuroscience is changing our traditional philosophical and ethical views by providing information about the biological basis of our moral behaviour. The exception of the brain is at the heart of neuroethical thinking and discourse. Neuroethics is an interdisciplinary discipline placed at the intersection between the human sciences and the neurosciences, and aims to help understand how knowledge and research in neurosciences and neurotechnologies will affect the future of society, their impact on humans, relationships, daily life, the labour market ... but also how they can help and provide solutions and answers to the questions of citizens in a health crisis. But it's a double-edged sword, collecting and analyzing brain data in real time seems to be increasingly simple and within everyone's reach, without having the hindsight of the real meaning of these data, allowing one day to read people's thoughts, control them and manipulate them. It is becoming clear that the boundaries between medical and non-medical uses of neuroscience and neurotechnology are becoming very porous, inviting us to reflect on neuroethical issues in order to put safeguards on these uses.

Multidisciplinary neurotechnology holds the promise of understanding and non-invasively treating neurodegenerative diseases. In this preclinical trial on Parkinson's disease (PD), we combined neuroscience together with the nascent field of medical virtual reality and generated several important observations. First, we established the Oculus Rift virtual reality system as a potent measurement device for parkinsonian involuntary hand tremors (IHT). Interestingly, we determined changes in rotation were the most sensitive marker of PD IHT. Secondly, we determined parkinsonian tremors can be abolished in VR with algorithms that remove tremors from patients' digital hands. We also found that PD patients were interested in and were readily able to use VR hardware and software. Together these data suggest PD patients can enter VR and be asymptotic of PD IHT. Importantly, VR is an open-medium where patients can perform actions, activities, and functions that positively impact their real lives - for instance, one can sign tax return documents in VR and have them printed on real paper or directly e-sign via internet to government tax agencies. Lastly, we generated a technical framework wherein movements in the real world can be measured side-by-side with those in virtual reality. With this framework, we observed anecdotal evidence of parkinsonian tremors being reduced in real life when our algorithms abolished digital hand tremors in VR.

Victor Vasarely’s (1906-1997) important legacy to the study of human perception is brought to the forefront and discussed. A large part of his impressive work conveys the appearance of striking three-dimensional shapes and structures in a large-scale pictorial plane. Current perception science explains such effects by invoking brain mechanisms for the processing of monocular (2D) depth cues. Here in this study, we illustrate and explain the local effects of 2D color and contrast cues on the perceptual organization in terms of figure-ground assignments, i.e. which local surfaces are likely to be seen as “nearer” or “bigger” in the image plane. Paired configurations are embedded in a larger, structurally ambivalent pictorial context inspired by some of Vasarely’s creations. The figure-ground effects these configurations produce reveal a significant correlation between perceptual solutions for “nearer” and “bigger” when no other monocular depth cues are given in the image. In consistency with previous findings on similar, albeit simpler visual displays, a specific color may compete with luminance contrast in resolving the planar ambiguity of a complex pattern context. Vasarely intuitively understood, and successfully exploited, this kind of subtle context effect in his art, well before empirical investigations had set out to study and explain their genesis in terms of information processing by the visual brain.

Due to Motor Neurone Diseases, a large population remains disabled worldwide, negatively impacting their independence and quality of life. This typically involves a weakness in the hand and forearm muscles, making it difficult to perform fine motor tasks such as writing, buttoning a shirt, or gripping objects. This project presents a vision-enabled rehabilitation hand exoskeleton to assist disabled persons in their hand movements. The design goal was to create an accessible tool to help with a simple interface requiring no training. This prototype is built on a commercially available glove where a camera and embedded processor were integrated to help open and close the hand, using air pressure, thus grabbing an object. An accelerometer is also implemented to detect the characteristic hand gesture to release the object when desired. This passive vision-based control differs from active EMG-based designs as it does not require individualized training. Continuing the research will reduce the cost, weight, and power consumption to facilitate mass implementation.

Some of the barriers preventing Virtual Reality (VR) from being widely adopted are the cost and unfamiliarity of VR systems. Here we propose that in many cases the specialized controllers shipped with most VR head-mounted displays can be replaced by a regular smartphone, cutting the cost of the system, and allowing users to interact in VR using a device they are already familiar with. To achieve this, we developed SmartVR Pointer, an approach that uses smartphones as a replacement for the specialized controllers for two essential operations in VR: selection and navigation by teleporting. In SmartVR Pointer a camera mounted on the head-mounted display (HMD) is tilted downwards so that it points to where the user will naturally be holding their phone in front of them. SmartVR Pointer supports three selection modalities: tracker-based,gaze-based, and combined/hybrid. In the tracker-based SmartVR pointer selection we use image-based tracking to track a QR code displayed on the phone screen and them map the phone’s position to a pointer shown within the field of view of the camera in the Virtual Environment. In the gaze-based selection modality the user places the pointer using their gaze and taps on the phone for selection. The combined technique is a hybrid between head-gaze-based interaction in VR and smartphone-based Augmented Reality. It allows the user to control a VR pointer that behaves like a mouse pointer by moving their smartphone to select objects within the virtual environment, and to interact with the selected objects using the smartphone’s touch screen. The touchscreen is used for selection and dragging. SmartVR Pointer is simple and requires no calibration and no complex hardware assembly or disassembly. We demonstrate successful interactive applications of SmartVR Pointer in a VR environment with a demo where the user navigates in the virtual environment using teleportation points on the floor and then solves a Tetris-style key-and-lock challenge.

The paper presents a proposal for an in-house mathematical model of a liquid flat-plate solar collector. The proposed model is a one-dimensional distributed parameter model enabling simulations of the collector operation under arbitrarily variable boundary conditions. The model is based on the solution of energy balance equations for all components of the collector. The formulated differential equations are solved iteratively using an implicit difference scheme. In order to obtain a stable numerical solution, it is necessary to use appropriate steps of the time and spatial division. These were found by comparing the results obtained from the model with the results of the analytical solution available in the literature for the transient state, which constitutes the novelty of the present study. The accuracy of the results obtained from the model was verified experimentally by comparing the measured and the calculated history of the fluid temperature at the outlet of the collector. The calculation of the collector time constant is proposed in the paper as an example of the model practical application. The results of the time constant calculation were compared with the values obtained experimentally on the test stand. This is another novelty of the presented research. The analysed collector instantaneous efficiency was then calculated for selected outdoor conditions. The presented mathematical model can also be used to verify the correctness of the collector operation. Comparing, on an ongoing basis, the measured and the calculated values of the fluid temperature at the collector outlet, conclusions can be drawn about the process of solar glass fouling or glycol gelling. The model's simplicity and the low computational demands enable such comparisons in an online mode.

This paper presents a methodology to detect the erosion and progradation effects in the Atrato river delta, located in the Gulf of Urabá in Colombia using SAR (Synthetic Aperture Radar) data. Other authors have studied this area by using optical images, encountering difficulties given the persistent presence of clouds in this region. SAR imagery is rescaled, despeckled, composed, classified, registered and processed to obtain a heatmap that shows the overall changes during eight years (2016–2023) in one single image. Since this technology uses microwave active signals, the presence of clouds and other environmental phenomena was not a problem, and all the images from the selected dates could be processed and analyzed. Also, detection of the dynamic processes was obtained for every period considered in this study with respect to the previous one in time, calculating areas and detecting the regions subject to the erosion and progradation processes. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. A comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons.

Integrating voice assistant technology can potentially enhance accessibility and user interaction in web-based platforms for geospatial data visualisation and processing. This paper presents an open-source development incorporating a voice virtual assistant into the BStreams web application platform, designed specifically for visualising geospatial data. The study aimed to mitigate the observed limitations of virtual assistants in understanding the semantics and context of user commands in the geospatial domain. A discourse framework specialised for geospatial data was developed using a structured prototype methodology. In addition, a survey assisted in identifying commonly used English terminologies for geospatial data interaction. The questionnaire results were subsequently assessed using the ChatGPT natural language processing model. The application used open-source frameworks, including Web Speech API, Leaflet, Mapbox geocoding, and JavaScript. This investigation indicates that voice virtual assistants can potentially enhance human-computer interaction for geospatial data visualisation and processing. However, challenges related to natural language understanding, accents, and domain knowledge were raised. The paper advocates for further research to refine the integration of voice technology in this domain.

Assessment of health-related quality of life (HRQoL) after pediatric traumatic brain injury (TBI) has been limited in children and adolescents due to a lack of disease-specific instruments. To fill this gap, the Quality of Life after Traumatic Brain Injury for Children and Adolescents (QOLIBRI-KID/ADO) was developed for the German-speaking population. Reference values from a comparable general population are essential for comprehending the impact of TBI on health and well-being. This study examines the validity of the German QOLIBRI-KID/ADO in a general pediatric population in Germany and provides reference values for use in clinical practice. Overall, 1997 children and adolescents aged 8-17 years from the general population and 300 from the TBI population participated in the study. The questionnaire was tested for reliability and validity. A measurement invariance (MI) approach was used to assess the comparability of the HRQoL construct between both samples. Reference values were determined by percentile-based stratification according to factors that significantly influenced HRQoL in regression analyses. The QOLIBRI-KID/ADO demonstrated strong psychometric properties. The HRQoL construct was measured largely equivalently in both samples, and reference values could be provided. The QOLIBRI-KID/ADO was considered reliable and valid for assessing HRQoL in a general German-speaking pediatric population, allowing for clinically meaningful comparisons between general and TBI populations.

The pentose phosphate pathway (PPP) is one of three major pathways involved in glucose metabolism, regulated by glucose-6-phosphate dehydrogenase (G6PD), controlling NADPH formation. NADPH, in turn, regulates the balance of oxidative stress and reactive oxygen species (ROS) levels. G6PD dysfunction, affecting PPP, is implicated in neurological disorders, including epilepsy. However, PPP's role in epileptogenesis and ROS production during epileptic activity remains unclear. To clarify these points, we conducted electrophysiological and imaging analyses on mouse hippocampal brain slices. Using the specific G6PD inhibitor G6PDi-1, we assessed its effects on mouse hippocampal slices, examining intracellular ROS, glucose/oxygen consumption, and ROS production during synaptic stimulation and in the 4AP epilepsy model. G6PDi-1 increased basal intracellular ROS levels and reduced synaptically induced glucose consumption but had no impact on ROS production from synaptic stimulation. In the 4AP model, G6PDi-1 didn't significantly alter spontaneous seizure frequency or H2O2 release amplitude but increased the frequency and peak amplitude of interictal events. These findings suggest that short-term PPP inhibition has a minimal impact on synaptic circuit activity.

Background: Glioblastoma remains a challenging brain tumor to treat due to its infiltrative nature. Accurately identifying tumor boundaries during surgery is crucial for successful tumor resection. This study introduces an innovative intraoperative visualization method utilizing a surgical fluorescence microscope to precisely locate tumor cells. The focus of the study is on IR-Glint, a novel preparation comprising Cy7.5-labeled aptamers Gli-233nt and Gli-55_3L specific to human glioblastoma. Methods: The aptamers were modified using molecular modeling and quantum chemical techniques. The effectiveness of the preparation was assessed using flow cytometry and microscopy on primary cultures of human glioblastoma. In vivo studies were conducted on mouse and rabbit models, employing orthotopic xenotransplantation of human brain glioblastoma with various imaging techniques, including PET/CT, in vivo fluorescence visualization, confocal laser scanning, and surgical microscopy. Results: The experiments validated the potential of IR-Glint for intraoperative visualization of glioblastoma using infrared imaging. Surface application of the aptamer-based formulation on the brain allowed clear visualization of the tumor, aiding surgeons in tumor resection. This approach also reduces the dosage required and mitigates potential toxic effects on the patient. Conclusions: This study demonstrates the promising potential of using in-frared dye-labeled aptamers for intraoperative visualization of glioblastoma. By improving surgical treatment outcomes in neurosurgery, this novel approach may pave the way for future advancements in the field.

Neurological disorders have been reported in a large number of coronavirus disease 2019 (COVID-19) patients, suggesting that this disease may have long-term adverse neurological consequences. COVID-19 occurs from the infection by a positive-sense single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The membrane fusion protein of SARS-CoV-2, the spike protein, binds to its human host receptor, angiotensin-converting enzyme 2 (ACE2), to initiate membrane fusion between the virus and host cell. The spike protein of SARS-CoV-2 contains the furin protease recognition site and its cleavage enhances the infectivity of this virus. The binding of SARS-CoV-2 to the ACE2 receptor has been shown to downregulate ACE2, thereby increasing the levels of pathogenic angiotensin II (Ang II). The furin protease cleaves between the S1 subunit of the spike protein with the binding domain toward ACE2 and the S2 subunit with the transmembrane domain that anchors to the viral membrane, and this activity releases the S1 subunit into the blood circulation. The released S1 subunit of the spike protein also binds to and downregulates ACE2, in turn, increasing the level of Ang II. Considering that a viral particle contains many spike protein molecules, furin-dependent cleavage would release many free S1 protein molecules each of which can downregulate ACE2, while the infection with a viral particle only affects one ACE2 molecule. Therefore, the furin-dependent release of S1 protein would dramatically amplify the ability to downregulate ACE2 and produce Ang II. We hypothesize that this amplification mechanism that the virus possesses, but not the infection per se, is the major driving force behind COVID-19 associated neurological disorders.

The microbiota–gut–brain axis has garnered increasing attention in recent years for its role in various health conditions, including neuroinflammatory disorders like complex regional pain syndrome (CRPS). CRPS is a debilitating condition characterized by chronic neuropathic pain, and its etiology and pathophysiology remain elusive. Emerging research suggests that alterations in the gut microbiota composition and function could play a significant role in CRPS development and progression. Our paper explores the implications of microbiota in CRPS and the potential therapeutic role of boron (B). Studies have demonstrated that individuals with CRPS often exhibit dysbiosis, with imbalances in beneficial and pathogenic gut bacteria. Dysbiosis can lead to increased gut permeability and systemic inflammation, contributing to the chronic pain experienced in CRPS. B, an essential trace element, has shown promise in modulating the gut microbiome positively and exerting anti-inflammatory effects. Recent preclinical and clinical studies suggest that B supplementation may alleviate neuropathic pain and improve CRPS symptoms by restoring microbiota balance and reducing inflammation. Our review highlights the complex interplay between microbiota, inflammation, and neuropathic pain in CRPS and underscores the potential of B as a novel therapeutic approach to target the microbiota–gut–brain axis, offering hope for improved management of this challenging condition.

Anterior-tooth (ANT) contacts induce short latency reflex inhibition of human jaw-closing muscles. The jaw is a rigid class 1 lever for pinpoint-targeting muscle force into a single bite-point, the pivoting food particle. See-saw reflex movements around the food particle fulcrum multiply the food-crushing force. Unpredictable jolts of reaction force caused by food-crushing are subjected to the rostral ANT and caudally to the two articulate ends of the jaw triangle. Compression/distraction strains of food-crushing must be monitored and inhibited by withdrawal reflexes. The mesencephalic ganglion (Vmes), neural myelin sheath, and muscle stretch receptors evolved subsequently to the advent of jaws to improve the velocity of proprioceptive and withdrawal reflexes. In mammalians, the spindles of the taut motor units, stretched by the food-fulcrum send excitatory monosynaptic feedback for the efferent neurons of the respective ipsilateral muscle units, via Vmes. In the Vmes, the spindle-input-mediating afferent neurons are coupled with another source of afferent feedback, which is also excitatory, from the back-tooth (BAT) mechanoreceptors. The two sources of excitatory pulses are summated and targeted for the efferent neurons to boost the stretched and taut motor units. Likewise, the afferent feedback from ANT mechanoreceptors is also coupled in the Vmes with concomitant feedback from spindles. The ANT output, however, is inhibitory to negate the excitatory feedback from the stretched jaw-muscle units. The inhibitory feed from ANT temporarily blocks the excitatory potential of masticatory motor efferent neurons to protect the ANT and jaw-joints from inadvertent strains. The inhibitory inputs from ANT alternate with the excitatory inputs from BAT to determine which jaw-closing muscle units are activated or inhibited at any given instant of food-crushing. The Vmes exists in all jawed vertebrates, and its’ evolution was probably motivated by demands for the control of bite force. The monosynaptic unilateral food-crushing excitatory and inhibitory reflexes (UFCR) override the coexisting bilaterally executed feed for jaw muscles from the central nervous system. Hypothesis: The Vmes-mediated UFCR combine neural inputs from tooth contacts with concomitant feedback from muscle stretch receptors for the control of mammalian food-crushing bite force.

Astrocytes are essential for normal brain development and functioning. They respond to brain injury and disease through a process referred to as reactive astrogliosis, where the reactivity is highly heterogenous and context dependent. Reactive astrocytes are active contributors to brain pathology and can exert beneficial, detrimental, or mixed effects following brain insults. Transforming growth factor-β (TGF-β) has been identified as one of the key factors regulating astrocyte reactivity. Genetic and pharmacological manipulation of TGF-β signaling pathway in animal models of CNS injury and disease alters pathological and functional outcomes. This review aims to provide recent understanding regarding astrocyte reactivity and TGF-β signaling in brain injury, aging, and neurodegeneration. Further, it explores how TGF-β signaling modulates astrocyte reactivity and function.

Background: Continuous subcutaneous apomorphine infusion (CSAI), levodopa-carbidopa intestinal gel infusion (LCIG), and deep brain stimulation of the subthalamic nucleus (STN-DBS) have markedly changed the treatment landscape of advanced Parkinson’s disease (aPD). Despite a similar outcome of all device-aided therapies (DATs), some patients switch or combine DATs. The aim of this retrospective study was to explore the frequency and reasons for switching between or combining DATs in two movement disorders centres in Slovenia and Israel. Methods: We collected and analysed demographic and clinical data from aPD patients who switched between or combined DATs. Motor and non-motor reasons and their frequency for switching/combining were examined, as was the effect of DAT using the Global Improvement subscale of the Clinical Global Impression Scale. Non-parametric tests were used to analyse the data. Results: Of 505 aPD patients treated with DATs at both centres between January 2009 and June 2021, we identified in total 30 patients (6%), who either switched DAT (N=24: 7 LCIG-to-STN-DBS, 1 LCIG-to-CSAI, 5 CSAI-to STN-DBS, 8 CSAI-to-LCIG, 1 STN-DBS-to-LCIG, 1 LCIG-to-CSAI-to-STN-DBS, and 1 STN-DBS-to-CSAI-to-LCIG) or combined DATs (N=6: 5 STN-DBS+LCIG and 1 STN-DBS+CSAI-to-STN-DBS+LCIG). In most of these patients, inadequate control of motor symptoms was the main reason for switching or combining DATs, but non-motor reasons (related to the disease and/or DAT) were also identified. Conclusions: Switching between and combining DATs is uncommon, but in some patients brings substantial clinical improvement and should be considered in those who have either inadequate symptom control on DAT treatment or have developed DAT related complications.

This theoretical article provides a brief description of the model of living systems' functioning by defining them as resource flows patterns. It reviews the living systems growth limitation between their development cycles by the Fibonacci sequence. Besides, there are presented systems resource base criteria, necessary for accumulating the resources and their investment. The article also considers the conditions for the formation of various systems strategies. Then we reviewed the principles of elemental analysis of information by a person as a living system according to the considered model. The study also shows the possibility of forming priorities in analyzing information for 16 combinations as maximum. At that, it remains crucial to divide a human's information analysis between the two hemispheres of the brain. The described combinations of priorities in a person's information analysis are compared with the existing differential personality models, such as the big five personality traits, the Myers-Briggs type indicator, temperaments model and Honey and Mumford Learning styles.

In the field of nasal delivery, one of the most fascinating applications is the delivery of drugs directly to the central nervous system bypassing the blood brain barrier. This approach would provide a series of benefits, such as dose lowering and direct targeting of potent drugs, ultimately reducing their systemic side effects. Recently, clinical trials have explored the nasal administration of insulin for the treatment of Alzheimer’s disease, with promising results. The use of nanomedicines could provide further options for making nose-to-brain delivery reality. In particular, apart from the selection of devices able to deposit the formulation in the upper part of the nose, surface modification of these nanomedicines appears the key strategy to optimize the delivery of drugs from the nasal cavity to the brain. In this review, nanomedicines delivery approaches based on surface electrostatic charges, mucoadhesive polymers, as well as chemical moieties targeting nasal epithelium, will be discussed and critically evaluated for nose-to-brain delivery.

Tables in the document image are one of the most important entities since they contain crucial information. Therefore, accurate table detection can significantly improve information extraction from tables. In this work, we present a novel end-to-end trainable pipeline, HybridTabNet, for table detection in scanned document images. Our two-stage table detector uses the ResNeXt-101 backbone for feature extraction and Hybrid Task Cascade (HTC) to localize the tables in scanned document images. Moreover, we replace conventional convolutions with deformable convolutions in the backbone network. This enables our network to detect tables of arbitrary layouts precisely. We evaluate our approach comprehensively on ICDAR-13, ICDAR-17 POD, ICDAR-19, TableBank, Marmot, and UNLV. Apart from the ICDAR-17 POD dataset, our proposed HybridTabNet outperforms earlier state-of-the-art results without depending on pre and post-processing steps. Furthermore, to investigate how the proposed method generalizes unseen data, we conduct an exhaustive leave-one-out-evaluation. In comparison to prior state-of-the-art results, our method reduces the relative error by 27.57% on ICDAR-2019-TrackA-Modern, 42.64% on TableBank (Latex), 41.33% on TableBank (Word), 55.73% on TableBank (Latex + Word), 10% on Marmot, and 9.67% on UNLV dataset. The achieved results reflect the superior performance of the proposed method.

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

General–purpose automatic speech recognition (ASR) systems have improved their quality and are being used for pronunciation assessment. However, the assessment of isolated short utterances, as words in minimal pairs for segmental approaches, remains an important challenge, even more for non-native speakers. In this work, we compare the performance of our own tailored ASR system (kASR) with the one of Google ASR (gASR) for the assessment of Spanish minimal pair words produced by 33 native Japanese speakers in a computer-assisted pronunciation training (CAPT) scenario. Participants of a pre/post-test training experiment spanning four weeks were split into three groups: experimental, in-classroom, and placebo. Experimental group used the CAPT tool described in the paper, which we specially designed for autonomous pronunciation training. Statistically significant improvement for experimental and in-classroom groups is revealed, and moderate correlation values between gASR and kASR results were obtained, beside strong correlations between the post-test scores of both ASR systems with the CAPT application scores found at the final stages of application use. These results suggest that both ASR alternatives are valid for assessing minimal pairs in CAPT tools, in the current configuration. Discussion on possible ways to improve our system and possibilities for future research are included.

As people grow a custom to effortless outdoor navigation there is a rising demand for similar possibility indoors as well. Unfortunately, indoor localization, being one of the necessary requirements for navigation, continues to be problem without a clear solution. In this article we are proposing a method for an indoor positioning system using a single image. This is made possible using small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with SIFT algorithm we can look through the database and find image which is the most similar to the image taken by user. Pair of images is then used to find coordinates of database image using PnP problem. Furthermore, projection and essential matrices are determined allowing for the user image localization ~ determining the position of the user in indoor environment. Benefits of this approach lies in the single image being the only input from user and no requirements for new onsite infrastructure and thus enables a simpler realization for the building management.

Without protective and/or therapeutic agents the SARS-CoV-2 infection known as coronavirus disease 2019 (COVID-19) is quickly spreading worldwide. It has surprising transmissibility potential, since it could infect all ages, gender, and human sectors. It attacks respiratory, gastrointestinal, urinary, hepatic, and endovascular systems and can reach the peripheral nervous system (PNS) and central nervous system (CNS) through known and unknown mechanisms. The reports on the neurological manifestations and complications of the SARS-CoV-2 infection are increasing exponentially. Herein, we enumerate seven candidate routes, which the mature or immature SARS-CoV-2 components could use to reach the CNS and PNS, utilizing the within-body crosstalk between organs. The majority of SARS-CoV-2 infected patients suffer from some neurological manifestations (e.g., confusion, anosmia, and ageusia). It seems that although the mature virus did not reach the CNS or PNS of the majority of patients, its unassembled components and/or the accompanying immune-mediated responses may be responsible for the observed neurological symptoms. The viral particles and/or its components have been specifically documented in endothelial cells of lung, kidney, skin, and CNS. This means that the blood-endothelial-barrier may be considered as the main route for SARS-CoV-2 entry into the nervous system, with the barrier disruption being more logical than barrier permeability, as evidenced by postmortem analyses.

Artificial Intelligence (AI) is revolutionizing decision-making procedures in healthcare information systems (HIS), increasing the efficacy, accuracy, and personalization of healthcare. Notwithstanding remarkable progresses, yet there remain gaps in the holistic comprehension and implication of AI-driven technologies across various HIS scenarios. These gaps, consisting limited integration of AI across a variety of HIS parts and inadequate emphasize on interpretability and privacy, motivate our study to conduct a structured evaluation of recent AI applications in this domain. In this study, we present a novel taxonomy for the AI implication in medical decision-making. We analyzed 30 articles and categorized them into six groups including: Clinical Decision Support Systems (CDSS), Predictive Analytics, Natural Language Processing (NLP), Computer-Aided Diagnostics (CAD), Robotic-Assisted Surgery, and Virtual Health Assistants (VHAs/chatbots). Our study aims to propose a holistic perspective of the fast evolving perspective of AI in HIS. The majority of the investigated studies were published from 2023 and 2022, with Springer as the leading publisher (33%). Python and MATLAB were the most well-known simulation languages, applied in 48% and 20% of the papers, respectively, reflecting the technical trends in the area. By emphasizing on pivotal criteria like interpretability, accuracy, and privacy, this research aims to elucidate the crucial issues and development driving AI-driven decision-making in HIS.

Abstract: Although experimental attempts to measure a single molecule/particle at room temperature or under physiological conditions without immobilization, for example on a surface, or without hydrodynamic flow have so far failed, this failure has given impetus to the underlying theory of Brownian motion towards its stochastic due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion).

Surgical navigation has evolved as a vital tool in maxillofacial surgery, offering precise and patient-specific data. This study explores the clinical applications and accuracy of intraoperative tool tracking in maxillofacial surgery. The research includes 37 patients with various pathologies who underwent surgery assisted by a surgical navigation system using tracked instruments. The study showcases two representative cases: one involving coronoid hyperplasia with mouth opening deficit and another addressing nose-orbital-ethmoidal-frontal ossifying fibroma resection. The results indicate that surgical navigation with tracked instruments provides high precision (<1.5 mm error), reduced surgical time, and less invasive approaches. The study highlights the potential for reproducible outcomes and increased safety, especially in complex cases. Despite some limitations, the synergy between surgical navigation and tracked instruments offers a promising approach in maxillofacial surgery, expanding its applications beyond current practices.

The psychedelic renaissance has reignited interest in the therapeutic potential of psychedelics for mental health and well-being. An emerging area of interest is the potential modulation of psychedelic effects by the gut microbiome - the ecosystem of microorganisms residing in our digestive tract. This review explores the intersection of the gut microbiome and psychedelic therapy, underlining potential implications for personalized medicine and mental health. We delve into the current understanding of the gut-brain axis, its influence on mood, cognition, and behavior, and how the microbiome may affect the metabolism and bioavailability of psychedelic substances. We also discuss the role of microbiome variations in shaping individual responses to psychedelics, along with potential risks and benefits. Moreover, we consider the prospect of microbiome-targeted interventions as a fresh approach to boost or modulate psychedelic therapy's effectiveness. By synthesizing insights from the fields of psychopharmacology, microbiology, and neuroscience, our objective is to advance knowledge about the intricate relationship between the microbiome and psychedelic substances, thereby paving the way for novel strategies to optimize mental health outcomes amid the ongoing psychedelic renaissance.

Magnetic resonance elastography (MRE) has revealed sexual dimorphism in brain stiffness in healthy individuals and multiple sclerosis (MS) patients. In the animal model of MS, experimental autoimmune encephalomyelitis (EAE), we showed previously that inflammation-induced brain softening was associated with alterations of the extracellular matrix (ECM). However, it remained unclear whether the brain ECM presents sex-specific properties that can be visualized by MRE. Therefore, we aimed here at quantifying sexual dimorphism in brain viscoelasticity in association with ECM changes in healthy and inflamed brains. Multifrequency MRE was applied to the midbrain of healthy and EAE mice of both sexes to quantitatively map regional stiffness. To define differences in brain ECM composition, gene expression of the key basement membrane components laminin (Lama4, Lama5), collagen (Col4a1, Col1a1) and fibronectin (Fn1) was investigated by RT-qPCR. We showed that the healthy male cortex expressed less Lama4, Lama5, Col4a1 but more Fn1 (all p < 0.05) than the healthy female cortex, which was associated with 9% softer properties (p = 0.044) in that region. At peak EAE, cortical softening was similar in both sexes compared to healthy tissue, with an 8% difference remaining between males and females during EAE (p < 0.001). Cortical Lama4, Lama5 and Col4a1 expression increased 2 to 3-fold in EAE in both sexes while Fn1 decreased only in males (all p < 0.05). No significant sex differences in stiffness were detected in other brain regions. In conclusion, sexual dimorphism in the ECM composition of cortical tissue in the mouse brain is reflected by in vivo stiffness measured with MRE and should be considered in future studies by sex-specific reference values.

Animal models of Alzheimer’s disease amyloidosis that recapitulate cerebral amyloid-beta pathology have been widely used in preclinical research, and have greatly enabled the mechanistic understanding of Alzheimer’s disease and the development of therapeutics. Comprehensive deep phenotyping of the pathophysiological and biochemical features in these animal models are essential. Recent advances in positron emission tomography have allowed the non-invasive visualization of the alterations in the brain of animal models as well as in patients with Alzheimer’s disease, These tools have facilitated our understanding of disease mechanisms, and provided longitudinal monitoring of treatment effect in animal models of Alzheimer’s disease amyloidosis. In this review, we focus on recent positron emission tomography studies of cerebral amyloid-beta accumulation, hypoglucose metabolism, synaptic and neurotransmitter receptor deficits (cholinergic and glutamatergic system), blood-brain barrier impairment and neuroinflammation (microgliosis and astrocytosis) in animal models of Alzheimer’s disease amyloidosis. We further propose the emerging targets and tracers for reflecting the pathophysiological changes, and discuss outstanding challenges in disease animal models and future outlook in on-chip characterization of imaging biomarkers towards clinical translation.

We discuss what we believe could be an improvement in future discussions of the ever-changing brain. We do so by distinguishing different types of brain variability and outlining methods suitable to analyse them. We argue that, when studying brain and behaviour data, classical methods such as regression analysis and more advanced approaches both aim to decompose the total variance into sensible variance components. In parallel, we argue that a distinction needs to be made between innate and acquired brain variability. For varying high-dimensional brain data, we present methods useful to extract their low-dimensional representations. Finally, to trace potential causes and predict plausible consequences of brain variability, we discuss how to combine statistical principles and neurobiological insights to make associative, explanatory, predictive, and causal enquires; but cautions are needed to raise association- or prediction-based neurobiological findings to causal claims.

Enzyme replacement therapy (ERT) improves the somatic manifestations in mucopolysaccharidoses (MPS))．However, because intravenously administered enzymes cannot cross the blood brain barrier (BBB), ERT is ineffective against the progressive neurodegeneration and resultant severe central nervous system (CNS) symptoms observed in patients with neuronopathic MPS. Attempts to surmount this problem have been made with intrathecal and intracerebroventricular ERT intend to achieve CNS effects, but the burdens on patients are inimical to long-term multiple administrations. However, after pabinafusp alfa, a human iduronate-2-sulfatase fused with a BBB-crossing anti-transferrin receptor antibody, showed both central and peripheral efficacy in mice model, subsequent clinical trials in a total of 62 patients with MPS-II (Hunter syndrome) in Japan and Brazil substantiated this dual efficacy and provided an acceptable safety profile. To date, pabinafusp alfa is the only approved intravenous ERT effective against both the somatic and CNS symptoms of patients with MPS-II. This article summarizes the hitherto obtained preclinical and clinical evidence associated with this drug, and discusses the preclinical, translational and clinical challenges of evaluating, ameliorating and preventing the neurodegeneration in patients with MPS-II.

Transcranial direct current stimulation (tDCS) over the left Inferior Frontal Gyrus (IFG) was found to improve apraxia of speech (AOS) in post-stroke aphasia, speech fluency in adults who stutter, naming and spelling in primary progressive (PPA). This paper aims to determine whether tDCS over the left IFG coupled with AOS therapy improves speech fluency in patients with PPA more than sham. Eight patients with non-fluent PPA with AOS symptoms received either active or sham tDCS, along with speech therapy for 15 weekday sessions. Speech therapy consisted of repetition of increasing syllable-length words. Evaluations took place before, immediately after, and two months post-intervention. Words were segmented into vowels and consonants and the duration of each vowel and consonant was measured. Segmental duration was significantly shorter after tDCS than sham for both consonants and vowels. tDCS gains generalized to untrained words. The effects of tDCS sustained over two months post-treatment in trained words. Taken together, these results demonstrate that the tDCS over the left IFG facilitates speech production by reducing segmental duration. The results provide preliminary evidence that tDCS can maximize efficacy of speech therapy in non-fluent PPA with AOS.

As the world endures the coronavirus disease 2019 (COVID-19) pandemic, conditions of 35 million vulnerable individuals struggling with substance use disorders (SUDs) worldwide have not received sufficient attention for their special health and medical needs. Many of these individuals are complicated by underlying health conditions, such as cardiovascular and lung diseases and undermined immune systems. During the pandemic, access to the healthcare systems and support groups is greatly diminished. Current research on COVID-19 has not addressed the unique challenges facing individuals with SUDs, including the heightened vulnerability and susceptibility to the disease. In this systematic review, we will discuss the pathogenesis and pathology of COVID-19, and highlight potential risk factors and complications to these individuals. We will also provide insights and considerations for COVID-19 treatment and prevention in patients with SUDs.

Stroke remains a major cause of death and disability in the United States and around the world. Solid safety and efficacy profiles of novel stroke therapeutics have been generated in the laboratory, but most failed in clinical trials. Investigations into the pathology and treatment of the disease remain a key research endeavor in advancing scientific understanding and clinical applications. In particular, cell-based regenerative medicine, specifically stem cells transplantation, may hold promise as stroke therapy because grafted cells and their components may recapitulate the growth and function of the neurovascular unit, which arguably represents the alpha and omega of stroke brain pathology and recovery. Recent evidence has implicated mitochondria, organelles with a central role in energy metabolism and stress response, in stroke progression. Recognizing that stem cells offer a source of healthy mitochondria, potentially transferrable into ischemic cells, may provide a new therapeutic tool. To this end, deciphering cellular and molecular processes underlying dysfunctional mitochondria may reveal innovative strategies for stroke therapy. Here, we review recent studies capturing the intimate participation of mitochondrial impairment in stroke pathology, and showcase promising methods of healthy mitochondria transfer into ischemic cells, to critically evaluate the potential of mitochondria-based stem cell therapy for stroke.

Autism spectrum disorder (ASD) is a neuropsychiatric condition characterized by impaired social interactions and repetitive stereotyped behaviors. Growing evidence highlights an important role of the gut-brain-microbiome axis in the pathogenesis of ASD. Research indicates an abnormal composition of the gut microbiome and the potential involvement of bacterial molecules in neuroinflammation and brain development disruptions. Concurrently, attention is directed towards the role of short-chain fatty acids (SCFAs) and impaired intestinal tightness. This comprehensive review emphasizes the potential impact of maternal gut microbiota changes on the development of autism in children, especially considering maternal immune activation (MIA). The following paper evaluates the impact of the birth route on the colonization of the child with bacteria in the first weeks of life. Furthermore, it explores the role of pro-inflammatory cytokines, such as IL-6 and IL-17a and mother’s obesity as potentially environmental factors of ASD. The purpose of the present analysis is to advance our understanding of ASD pathogenesis, while also searching for the positive implications of the latest therapies, such as probiotics, prebiotics or fecal microbiota transplantation, targeting the gut microbiota and reducing inflammation. This review aims to provide valuable insights that could instruct future studies and treatments for individuals affected by ASD.

Different nutraceuticals are often considered by parents of infants and children with abdominal pain and disorders of the gut-brain interaction. Herb extracts and natural compounds have long been used in traditional medicine, but clinical pediatric trials are very limited. This narrative review based on relevant studies identified through Pubmed-Medline literature search updated to October 2023, focused on the effect of nutraceuticals in infantile colic, functional abdominal pain and irritable bowel syndrome in children and adolescents. Significant reduction of colic episodes and crying time was reported in two studies on fennel (seeds oil or tea), in three studies on different multiple herbal extracts (all including fennel), in one study on Mentha piperita, and in at least two double-blind randomized controlled studies on Lactobacillus reuteri DSM 17938 and Bifidobacterium lactis BB-12 (108 CFU/day for at least 21 days) in breast-fed infants. Compared to placebo, in children with functional abdominal pain or irritable bowel syndrome a significant reduction of pain was reported in two studies supplementing peppermint oil capsules or psyllium fibers, in one study on corn fiber cookies or partial hydrolysed guar gum or a specific multiple herbal extract (STW-5) or vitamin D supplementation. To date, there is a moderate certainty of evidence with a weak grade of recommendation for Lactobacillus reuteri DSM 17938 (108 CFU/day) for reducing pain intensity in children with functional abdominal pain and for Lactobacillus rhamnosus GG (1-3 x109 CFU twice daily) for reducing pain frequency and intensity in children with IBS. Further large and well-designed pediatric studies are needed to prove efficacy and safety of different herbal extracts and prolonged use of studied products in infants and children with pain disorders of the gut-brain interaction.

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

Experiments to measure a single molecule/particle, i.e. an individual molecule/particle, at room temperature or under physiological conditions without immobilization, for example on a surface, or without significant hydrodynamic flow have so far failed. This failure has given impetus to the underlying theory of Brownian molecular motion towards its stochastic due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion).

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of fifteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Three-dimensional (3D) printing technologies are transforming the design and manufacture of components and products across a variety of disciplines, however their application in the construction industry is still limited. Material deposition processes can achieve infinite geometries and have advanced from rapid prototyping and model-scale markets to their application in fabricating functional products, large objects and the construction of full-scale buildings. Many international projects have recently been realized and the construction industry is beginning to utilise these dynamic technologies. The potential advantages for integrating 3D printing into house construction are significant, these include the capacity for mass customization of designs and parameters for functional and aesthetic purposes, reduction in construction waste from highly precise material placement, and the use of recycled waste products in layer deposition materials. With the ultimate goal of improving construction efficiency and decreasing building costs, applying Strand7 Finite Element Analysis software, a numerical model was designed specifically for 3D printing in a cement mix incorporated with recycled waste product High Density Polyethylene (HDPE) and found that construction of an arched truss-like roof was structurally feasible without the need for steel reinforcements. The lab sizes prototypes were manufactured based on the destined numerical model by using a 3D printing technology. Currently available 3D printing technologies can be adopted for building construction and this paper discusses the applications, advantages, limitations and future directions of 3D printing as an innovative and viable solution for affordable house construction.

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D Cluster Variation Method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy and the interaction enthalpy. Initial investigations with two different representative topographies (``scale-free-like'' and ``rich club-like'') produce interesting results when brought to a CVM free energy minimum. Additional phase space investigations, where one of these two parameters has been set to zero, identify useful parameter ranges. Careful comparison of the analytically-predicted configuration variables versus those obtained when performing computational free energy minimization on a 2-D grid show that the computational results differ significantly from the analytic solution. The 2-D CVM can potentially function as a secondary free energy minimization within the hidden layer of a neural network, providing a basis for extending node activations over time and allowing temporal correlation of patterns.

This paper presents a comprehensive comparative analysis of four different computational vision algorithms for implementing an unpalleting system in a factory environment, under typical edge computing constraints. The primary objective is to automate the process of detecting and locating specific variable-shaped objects on a pallet, enabling a robotic system to accurately unstack them. The four algorithms evaluated are Pattern Matching, Scale-Invariant Feature Transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifiers. Each technique is described in detail in this paper, and their implementations are outlined. Experimental results are thoroughly analyzed, assessing the algorithms’ performance based on accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The findings reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of the unpalleting system. This research contributes to the advancement of automated object detection and localization in industrial environments, paving the way for more efficient and reliable unpalleting systems.