Search Results (394)

Background/Objectives: Brain metastasis (BM) is a common and often early manifestation in lung adenocarcinoma (LUAD), yet its tumor microenvironment remains poorly defined at the time of initial diagnosis. This study aims to characterize early immune microenvironmental alterations in synchronous BM using spatial proteomic profiling. Methods: We performed digital spatial proteomic profiling using the NanoString GeoMx platform on formalin-fixed paraffin-embedded tissues from five treatment-naïve LUAD patients in whom BM was the initial presenting lesion. Paired primary lung and brain metastatic samples were analyzed across tumor and stromal compartments using 68 immune- and tumor-related protein markers. Results: Spatial profiling revealed distinct expression patterns between primary tumors and brain metastases. Immune regulatory proteins—including IDO-1, PD-1, PD-L1, STAT3, PTEN, and CD44—were significantly reduced in brain metastases (p < 0.01), whereas pS6, a marker of activation-induced T-cell death, was significantly upregulated (p < 0.01). These alterations were observed in both tumor and stromal regions, suggesting a more immunosuppressive and apoptotic microenvironment in brain lesions. Conclusions: This study provides one of the first spatially resolved proteomic characterizations of synchronous BM at initial LUAD diagnosis. Our findings highlight early immune escape mechanisms and suggest the need for site-specific immunotherapeutic strategies in patients with brain metastasis. Full article

Cell migration assays provide valuable insights into pathological conditions, such as tumor metastasis and immune cell infiltration, and the regenerative capacity of tissues. In vitro tools commonly used for cell migration studies exploit commercial transwell systems, whose functionalities can be improved through engineering of the pore pattern. In this context, we propose the fabrication of a transwell-like device pursued by combining the proton beam writing (PBW) technique with wet etching onto thin layers of polydimethylsiloxane (PDMS). The resulting transwell-like device incorporates a PDMS membrane with finely controllable pore patterning that was used to study the arrangement and migration behavior of HCMEC/D3 cells, a well-established human brain microvascular endothelial cell model widely used to study vascular maturation in the brain. A comparison between commercial polycarbonate membranes and the PBW-holed membranes highlights the impact of the ordering of the pattern and porosity on cellular growth, self-organization, and transmigration by combining fluorescent microscopy and advanced digital processing. Endothelial cells were found to exhibit distinctive clustering, alignment, and migratory behavior close to the pores of the designed PBW-holed membrane. This is indicative of activation patterns associated with cytoskeletal remodeling, a critical element in the angiogenic process. This study stands up as a novel approach toward the development of more biomimetic barrier models (such as organ-on-chips). Full article

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article

Memristors with resistive switching capabilities are vital for information storage and brain-inspired computing, making them a key focus in current research. This study demonstrates non-volatile analog resistive switching behavior in Al/TiO_x/TiN/Si(n⁺⁺)/Al memristive devices. Analog resistive switching offers gradual, controllable conductance changes, which are essential for mimicking brain-like synaptic behavior, unlike digital/abrupt switching. The amorphous titanium oxide (TiO_x) active layer was deposited using the pulsed-DC reactive magnetron sputtering technique. The impact of increasing the oxide thickness on the electrical performance of the memristors was investigated. Electrical characterizations revealed stable, forming-free analog resistive switching, achieving endurance beyond 300 DC cycles. The charge conduction mechanisms underlying the current–voltage (I–V) characteristics are analyzed in detail, revealing the presence of ohmic behavior, Schottky emission, and space-charge-limited conduction (SCLC). Experimental results indicate that increasing the TiO_x film thickness from 31 to 44 nm leads to a notable change in the current conduction mechanism. The results confirm that the memristors have good stability (>1500 s) and are capable of exhibiting excellent long-term potentiation (LTP) and long-term depression (LTD) properties. The analog switching driven by oxygen vacancy-induced barrier modulation in the TiO_x/TiN interface is explained in detail, supported by a proposed model. The remarkable switching characteristics exhibited by the TiO_x-based memristive devices make them highly suitable for artificial synapse applications in neuromorphic computing systems. Full article

In the wide range of human diseases, Parkinson’s disease (PD) has a high incidence, according to a recent survey by the World Health Organization (WHO). According to WHO records, this chronic disease has affected approximately 10 million people worldwide. Patients who do not receive an early diagnosis may develop an incurable neurological disorder. PD is a degenerative disorder of the brain, characterized by the impairment of the nigrostriatal system. A wide range of symptoms of motor and non-motor impairment accompanies this disorder. By using new technology, the PD is detected through speech signals of the PD victims by using the reduced instruction set computing 5th version (RISC-V) processor. The RISC-V microcontroller unit (MCU) was designed for the voice-controlled human-machine interface (HMI). With the help of signal processing and feature extraction methods, the digital signal is impaired by the impairment of the nigrostriatal system. These speech signals can be classified through classifier modules. A wide range of classifier modules are used to classify the speech signals as normal or abnormal to identify PD. We use Matrix Laboratory (MATLAB R2021a_v9.10.0.1602886) to analyze the data, develop algorithms, create modules, and develop the RISC-V processor for embedded implementation. Machine learning (ML) techniques are also used to extract features such as pitch, tremor, and Mel-frequency cepstral coefficients (MFCCs). Full article

Background: Digital technologies offer innovative opportunities for recovering and maintaining intellectual and mental health. The use of a multitask approach that combines motor component with various cognitive tasks in a virtual environment can optimize cognitive and physical functions and improve the quality of life of cardiac surgery patients. This study aimed to localize current sources of theta and alpha power in patients who have undergone virtual multitask training (VMT) and a control group in the early postoperative period of coronary artery bypass grafting (CABG). Methods: A total of 100 male CABG patients (mean age, 62.7 ± 7.62 years) were allocated to the VMT group (n = 50) or to the control group (n = 50). EEG was recorded in the eyes-closed resting state at baseline (2–3 days before CABG) and after VMT course or approximately 11–12 days after CABG (the control group). Power EEG analysis was conducted and frequency-domain standardized low-resolution tomography (sLORETA) was used to assess the effect of VMT on brain activity. Results: After VMT, patients demonstrated a significantly higher density of alpha-rhythm (7–9 Hz) current sources (t > −4.18; p < 0.026) in Brodmann area 30, parahippocampal, and limbic system structures compared to preoperative data. In contrast, the control group had a marked elevation in the density of theta-rhythm (3–5 Hz) current sources (t > −3.98; p < 0.017) in parieto-occipital areas in comparison to preoperative values. Conclusions: Virtual reality-based multitask training stimulated brain regions associated with spatial orientation and memory encoding. The findings of this study highlight the importance of neural mechanisms underlying the effectiveness of multitask interventions and will be useful for designing and conducting future studies involving VR multitask training. Full article

Background: Clinical imaging is an important part of health care providing physicians with great assistance in patients treatment. In fact, segmentation and grading of tumors can help doctors assess the severity of the cancer at an early stage and increase the chances of cure. Despite that Deep Learning for cancer diagnosis has achieved clinically acceptable accuracy, there still remains challenging tasks, especially in the context of insufficient labeled data and the subsequent need for expensive computational ressources. Objective: This paper presents a lightweight classification and segmentation deep learning model to assist in the identification of cancerous tumors with high accuracy despite the scarcity of medical data. Methods: We propose a multi-task architecture for classification and segmentation of cancerous tumors in the Brain, Skin, Prostate and lungs. The model is based on the UNet architecture with different pre-trained deep learning models (VGG 16 and MobileNetv2) as a backbone. The multi-task model is validated on relatively small datasets (slightly exceed 1200 images) that are diverse in terms of modalities (IRM, X-Ray, Dermoscopic and Digital Histopathology), number of classes, shapes, and sizes of cancer pathologies using the accuracy and dice coefficient as statistical metrics. Results: Experiments show that the multi-task approach improve the learning efficiency and the prediction accuracy for the segmentation and classification tasks, compared to training the individual models separately. The multi-task architecture reached a classification accuracy of 86%, 90%, 88%, and 87% respectively for Skin Lesion, Brain Tumor, Prostate Cancer and Pneumothorax. For the segmentation tasks we were able to achieve high precisions respectively 95%, 98% for the Skin Lesion and Brain Tumor segmentation and a 99% precise segmentation for both Prostate cancer and Pneumothorax. Proving that the multi-task solution is more efficient than single-task networks. Full article

In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem. Full article

This study proposes a novel image reconstruction algorithm for nuclear medicine imaging based on the maximum likelihood expectation maximization (MLEM) framework with dynamic ElasticNet regularization. Whereas conventional the L1 and L2 regularization methods involve trade-offs between noise suppression and structural preservation, ElasticNet combines their strengths. Our method further introduces a dynamic weighting scheme that adaptively adjusts the balance between the L1 and L2 terms over iterations while ensuring nonnegativity when using a sufficiently small regularization parameter. We evaluated the proposed algorithm using numerical phantoms (Shepp–Logan and digitized Hoffman) under various noise conditions. Quantitative results based on the peak signal-to-noise ratio and multi-scale structural similarity index measure demonstrated that the proposed dynamic ElasticNet regularized MLEM consistently outperformed not only standard MLEM and L1/L2 regularized MLEM but also the fixed-weight ElasticNet variant. Clinical single-photon emission computed tomography brain image experiments further confirmed improved noise suppression and clearer depiction of fine structures. These findings suggest that our proposed method offers a robust and accurate solution for tomographic image reconstruction in nuclear medicine imaging. Full article

Leveraging the human brain as a paradigm of energy-efficient computation, considerable attention has been paid to photonic neurons and neural networks to achieve higher computing efficiency and lower energy consumption. This study experimentally demonstrates on-chip silicon photonic neurons and neural networks based on thermally tunable microring resonators (MRRs) implement weighting and nonlinear operations. The weight component consists of eight cascaded MRRs thermally tuned within wavelength division multiplexing (WDM) architecture. The nonlinear response depends on the MRR’s nonlinear transmission spectrum, which is analogous to the rectified linear unit (ReLU) function. The matrix multiplication and recognition task of digits 2, 3, and 5 represented by seven-segment digital tube are successfully completed by using the photonic neural networks constructed by the photonic neurons based on the on-chip thermally tunable MRR as the nonlinear units. The power consumption of the nonlinear unit was about 5.65 mW, with an extinction ratio of about 25 dB between different digits. The proposed photonic neural network is CMOS-compatible, which makes it easy to construct scalable and large-scale multilayer neural networks. These findings reveal that there is great potential for highly integrated and scalable neuromorphic photonic chips. Full article

Spinal cord injury (SCI) results in a significant loss of motor, sensory, and autonomic function, imposing substantial biosocial and economic burdens. Traditional approaches, such as stem cell therapy and immune modulation, have faced translational challenges, whereas neuromodulation and digital brain–spinal cord interfaces combining brain–computer interface (BCI) technology and epidural spinal cord stimulation (ESCS) to create brain–spine interfaces (BSIs) offer promising alternatives by leveraging residual neural pathways to restore physiological function. This review examines recent advancements in neuromodulation, focusing on the future translation of clinical trial data to clinical practice. We address key considerations, including scalability, patient selection, surgical techniques, postoperative rehabilitation, and ethical implications. By integrating interdisciplinary collaboration, standardized protocols, and patient-centered design, neuromodulation has the potential to revolutionize SCI rehabilitation, reducing long-term disability and enhancing quality of life globally. Full article

Background: The UK Department for Digital, Culture, Media, and Sport (DCMS) grassroots concussion guidance, May 2023, advised that all community-based sport-related concussions (SRCs) be diagnosed by a healthcare practitioner. This may require that general practitioners (GPs) diagnose and manage SRCs. Diagnosing SRCs in primary care settings in the United Kingdom (UK) presents significant challenges, primarily due to the lack of validated tools specifically designed for general practitioners (GPs). This scoping review aims to identify diagnostic and management tools for SRCs in grassroots sports and primary care settings. Aims: To identify tools that can be used by GPs to diagnose and manage concussions in primary care, both adult and paediatric populations. Design and Methods: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScRs). Five databases (MEDLINE, EMBASE, CINAHL, Cochrane Library, Google Scholar) were searched from 1946 to April 2025. Search terms included “concussion”, “primary care”, and “diagnosis”. Studies that discussed SRCs in community or primary care settings were included. Those that exclusively discussed secondary care and elite sports were excluded, as well as non-English studies. Two reviewers independently screened titles, abstracts, and full texts, with a third resolving any disagreements. Data were extracted into Microsoft Excel. Studies were assessed for quality using the Joanna Briggs critical appraisal tools and AGREE II checklist. Results: Of 727 studies, 12 met the inclusion criteria. Identified tools included Sport Concussion Assessment Tool 6 (SCAT6, 10–15 min, adolescent/adults), Sport Concussion Office Assessment Tool 6 (SCOAT6, 45–60 min, multidisciplinary), the Buffalo Concussion Physical Examination (BCPE, 5–6 min, adolescent-focused), and the Brain Injury Screening Tool (BIST, 6 min, ages 8+). As part of BCPE, a separate Telehealth version was developed for remote consultations. SCAT6 and SCOAT6 are designed for healthcare professionals, including GPs, but require additional training and time beyond typical UK consultation lengths (9.2 min). BIST and BCPE show promise but require UK validation. Conclusions: SCAT6, SCOAT6, BIST, and BCPE could enhance SRC care, but their feasibility in UK primary care requires adaptation (e.g., integration with GP IT systems and alignment with NICE guidelines). Further research is required to validate these tools and assess additional training needs. Full article

Background/Objectives: Child abuse is a pervasive global issue with significant implications for the physical, emotional, and psychological well-being of victims. This review highlights the clinical, molecular, and therapeutic dimensions of child abuse, emphasizing its long-term impact and the need for interdisciplinary approaches. Early exposure to abuse activates the hypothalamic-pituitary-adrenal (HPA) axis, leading to chronic cortisol release and subsequent neuroplastic changes in brain regions such as the hippocampus, amygdala, and prefrontal cortex. These molecular alterations, including epigenetic modifications and inflammatory responses, contribute to the heightened risk of psychiatric disorders and chronic illnesses in survivors. Clinically, child abuse presents with diverse manifestations ranging from physical injuries to psychological and developmental disorders, making timely diagnosis challenging. Methods: A multidisciplinary approach involving thorough clinical evaluation, detailed histories, and collaboration with child protection services is essential for accurate diagnosis and effective intervention. Results: Recent advances in molecular biology have identified biomarkers, such as stress-related hormones and epigenetic changes, which provide novel insights into the physiological impact of abuse and potential targets for therapeutic intervention. Current treatment strategies prioritize the child’s safety, psychological well-being, and prevention of further abuse. Trauma-focused cognitive behavioral therapy and family-centered interventions are pivotal in promoting recovery and resilience. Conclusions: Emerging research focuses on integrating molecular findings with clinical practice, utilizing digital health tools, and leveraging big data to develop predictive models and personalized treatments. Interdisciplinary collaboration remains crucial to translating research into policy and practice, ultimately aiming to mitigate the impact of child abuse and improve outcomes for survivors. Full article

Background: Dementia is a syndrome with a high global prevalence that includes a number of progressive diseases of the brain affecting various cognitive domains such as memory and thinking and the performance of daily activities. It manifests as symptoms which often include significant mood and behaviour changes that are highly varied. Changed moods and behaviours due to dementia may reflect distress and may be stressful for both the person living with dementia and their informal and formal carers. To provide dementia care support specific to mood and behaviour changes, the Behaviours in Dementia Toolkit website (BiDT) was developed using human-centred design principles. The BiDT houses a user-friendly, digital library of over 300 free, practical, and evidence-informed resources to help all care partners better understand and compassionately respond to behaviours in dementia so they can support people with dementia to live well. Objective: (1) To characterize the users that visited the BiDT; and (2) to understand the platform’s early impact on these users. Methods: A multi-method, descriptive study was conducted in the early post-website launch period. Outcomes and measures examined included the following: (1) reach: unique visitors, region, unique visits, return visits, bounce rate; (2) engagement: engaged users, engaged sessions, session duration, pages viewed, engagement rate per webpage, search terms, resources accessed; (3) knowledge change; (4) behaviour change; and (5) website impact: relevance, feasibility, intention to use, improving access and use of dementia guidance, recommend to others. Data was collected using Google Analytics and an electronic survey of website users. Results: From 4 February to 31 March 2024, there were 76,890 unique visitors to the BiDT from 109 countries. Of 76,890 unique visitors to the BiDT during this period, 16,626 were engaged users as defined by Google Analytics (22%) from 80 countries. The highest number of unique engaged users were from Canada (n = 8124) with an engagement rate of 38%. From 5 March 2024 to 31 March 2024, 100 electronic surveys were completed by website users and included in the analysis. Website users indicated that the BiDT validated or increased their dementia care knowledge, beliefs, and activities (82%) and they reported that the website validated their current care approaches or increased their ability to provide care (78%). Further, 77% of respondents indicated that they intend to continue using the BiDT and 81.6% said that they would recommend it to others to review and adopt. Conclusions: The BiDT is a promising tool for sharing practical and evidence-informed information resources to support people experiencing dementia-related mood and behaviour changes. Early evaluation of the website has demonstrated significant reach and engagement with users in Canada and internationally. Survey data also demonstrated high ratings of website relevance, feasibility, intention to use, knowledge change, practice support, and its contribution to dementia guidance. Full article

Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual’s age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice. Full article