Search Results (16)

Background and Aims: Vitamin D is currently well regarded for its pleiotropic effects on the immune system, stimulating an anti-inflammatory response and enhancing immune tolerance. Vitamin D deficiency is an established risk factor for multiple sclerosis (MS). Additionally, lower vitamin D serum levels are associated with worse disease outcomes. However, current randomized clinical trials provide conflicting evidence about the beneficial role of vitamin D on disease progression. Most studies have evaluated the effect of vitamin D supplementation on clinical and radiological activity, yet very few have examined the impact on brain atrophy. Methods: A 4-year observational, non-interventional study design was applied to evaluate the association between vitamin D supplementation and disease progression. Altogether, 132 relapsing–remitting multiple sclerosis patients were enrolled in the study (97 subjects in the group with vitamin D supplementation and 35 subjects in the group without supplementation). The analyzed groups were similar in terms of age, body mass index, sun exposure, comorbidities, nicotinism, duration of the disease, and current treatment. The number of relapses, Expanded Disability Status Scale assessments, and the number of new/enlarged T2-weighted lesions and gadolinium-enhancing lesions in magnetic resonance imagining analyses, as well as 25-hydroxyvitamin D serum levels, were assessed every 12 months of a 4-year follow-up, whereas brain atrophy was assessed at the baseline and after 36 months using two-dimensional measurements. Results: After 36 months, a significant increase in atrophy was observed in both groups; however, patients without vitamin D supplementation had a significantly higher increase in intercaudate distance, third ventricle width, and bicaudate ratio after 36 months of observation (p < 0.05). Vitamin D supplementation among the studied group did not affect other disease activity outcomes. Conclusions: Our study revealed an observed association between vitamin D supplementation and reduced brain atrophy in patients with MS. Randomized controlled trials are required to establish the impact of vitamin D supplementation on brain atrophy progression. Full article

Brain tumors significantly impact global health and pose serious challenges for accurate diagnosis due to their diverse nature and complex characteristics. Effective diagnosis and classification are essential for selecting the best treatment strategies and forecasting patient outcomes. Currently, histopathological examination of biopsy samples is the standard method for brain tumor identification and classification. However, this method is invasive, time-consuming, and prone to human error. To address these limitations, a fully automated approach is proposed for brain tumor classification. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown promise in improving the accuracy and efficiency of tumor detection from magnetic resonance imaging (MRI) scans. In response, a model was developed that integrates machine learning (ML) and deep learning (DL) techniques. The process began by splitting the data into training, testing, and validation sets. Images were then resized and cropped to enhance model quality and efficiency. Relevant texture features were extracted using a modified Visual Geometry Group (VGG) architecture. These features were fed into various supervised ML models, including support vector machine (SVM), k-nearest neighbors (KNN), logistic regression (LR), stochastic gradient descent (SGD), random forest (RF), and AdaBoost, with GridSearchCV used for hyperparameter tuning. The model’s performance was evaluated using key metrics such as accuracy, precision, recall, F1-score, and specificity. Experimental results demonstrate that the proposed approach offers a robust and automated solution for brain tumor classification, achieving the highest accuracy of 94.02% with VGG19 and 96.30% with VGG16. This model can significantly assist healthcare professionals in early tumor detection and in improving diagnostic accuracy. Full article

Background: Artificial intelligence (AI) has recently made unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into the healthcare system still seems far-fetched. Diagnostic radiology is no exception. Imagining techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans have been widely and very effectively employed by radiologists and neurologists for the differential diagnoses of neurological disorders for decades, yet no AI-powered systems to analyze such scans have been incorporated into the standard operating procedures of healthcare systems. Why? It is absolutely understandable that in diagnostic medicine, precious human lives are on the line, and hence there is no room even for the tiniest of mistakes. Nevertheless, with the advent of explainable artificial intelligence (XAI), the old-school black boxes of deep learning (DL) systems have been unraveled. Would XAI be the turning point for medical experts to finally embrace AI in diagnostic radiology? This review is a humble endeavor to find the answers to these questions. Methods: In this review, we present the journey and contributions of AI in developing systems to recognize, preprocess, and analyze brain MRI scans for differential diagnoses of various neurological disorders, with special emphasis on CAD systems embedded with explainability. A comprehensive review of the literature from 2017 to 2024 was conducted using host databases. We also present medical domain experts’ opinions and summarize the challenges up ahead that need to be addressed in order to fully exploit the tremendous potential of XAI in its application to medical diagnostics and serve humanity. Results: Forty-seven studies were summarized and tabulated with information about the XAI technology and datasets employed, along with performance accuracies. The strengths and weaknesses of the studies have also been discussed. In addition, the opinions of seven medical experts from around the world have been presented to guide engineers and data scientists in developing such CAD tools. Conclusions: Current CAD research was observed to be focused on the enhancement of the performance accuracies of the DL regimens, with less attention being paid to the authenticity and usefulness of explanations. A shortage of ground truth data for explainability was also observed. Visual explanation methods were found to dominate; however, they might not be enough, and more thorough and human professor-like explanations would be required to build the trust of healthcare professionals. Special attention to these factors along with the legal, ethical, safety, and security issues can bridge the current gap between XAI and routine clinical practice. Full article

Penetrating captive bolt (PCB) is widely used for stunning and on-farm dispatch of livestock, yet its efficacy can vary, with the potential for animal welfare compromise. This study investigated the pathophysiology of PCB-induced trauma in horned and polled sheep (Ovis aries), focusing on factors contributing to incomplete concussion. Thirty-seven (n = 18 horned Scottish blackface and n = 19 polled North Country mule) mature ewes (aged 4–10 years) were shot with PCB with varying cartridge power and PCB modifications, followed by clinical assessment and post-mortem analysis using magnetic resonance imagining (MRI) and gross pathology. The results indicated that damage to the reticular activating system, bolt velocity and penetration depth are crucial for inducing irreversible unconsciousness, with depths less than 37 mm often resulting in incomplete concussion. MRI provided detailed insights into brain injuries, aligning well with gross pathological findings. This study highlights the importance of precise bolt placement and appropriate PCB configurations in ensuring humane outcomes, with MRI proving to be a valuable tool for assessing brain trauma in stunned animals. These findings enhance the understanding of effective stunning techniques and support improved welfare practices in livestock management. Full article

Background/Objectives: Cognitive impairment represents a core and prodromal clinical feature of cerebral amyloid angiopathy (CAA). We sought to assess specific cognitive domains which are mainly affected among patients with CAA and to investigate probable associations with neuroimaging markers and Cerebrospinal Fluid (CSF) biomarkers. Methods: Thirty-five patients fulfilling the Boston Criteria v1.5 or v2.0 for the diagnosis of probable/possible CAA were enrolled in this prospective cohort study. Brain Magnetic Resonance Imaging and CSF biomarker data were collected. Every eligible participant underwent a comprehensive neurocognitive assessment. Spearman’s rank correlation tests were used to identify possible relationships between the Addenbrooke’s Cognitive Examination—Revised (ACE-R) sub-scores and other neurocognitive test scores and the CSF biomarker and neuroimaging parameters among CAA patients. Moreover, linear regression analyses were used to investigate the effects of CSF biomarkers on the ACE-R total score and Mini-Mental State Examination (MMSE) score, based on the outcomes of univariate analyses. Results: Cognitive impairment was detected in 80% of patients, and 60% had a coexistent Alzheimer’s disease (AD) pathology based on CSF biomarker profiles. Notable correlations were identified between increased levels of total tau (t-tau) and phosphorylated tau (p-tau) and diminished performance in terms of overall cognitive function, especially memory. In contrast, neuroimaging indicators, including lobar cerebral microbleeds and superficial siderosis, had no significant associations with cognitive scores. Among the CAA patients, those without AD had superior neurocognitive test performance, with significant differences observed in their ACE-R total scores and memory sub-scores. Conclusions: The significance of tauopathy in cognitive impairment associated with CAA may be greater than previously imagined, underscoring the necessity for additional exploration of the non-hemorrhagic facets of the disease and new neuroimaging markers. Full article

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost–benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability. Full article

Haemophilic arthropathy (HA) is one of the most serious complications of haemophilia. It starts with joint bleeding, leading to synovitis which, in turn, can cause damage to the cartilage and subchondral bone, eventually inducing degenerative joint disease. Despite significant improvements in haemophilia treatment over the past two decades and recent guidelines from ISTH and WFH recommending FVIII trough levels of at least 3 IU/dL during prophylaxis, patients with haemophilia still develop joint disease. The pathophysiology of HA is complex, involving both inflammatory and degenerative components. Early diagnosis is key for proper management. Imaging can detect joint subclinical changes and influence prophylaxis. Magnetic resonance imagining (MRI) and ultrasound are the most frequently used methods in comprehensive haemophilia care centres. Biomarkers of joint health have been proposed to determine osteochondral joint deterioration, but none of these biomarkers has been validated or used in clinical practice. Early prophylaxis is key in all severe haemophilia patients to prevent arthropathy. Treatment is essentially based on prophylaxis intensification and chronic joint pain management. However, there remain significant gaps in the knowledge of the mechanisms responsible for HA and prognosis-influencing factors. Better understanding in this area could produce more effective interventions likely to ultimately prevent or attenuate the development of HA. Full article

This article presents a method for extracting neural signal features to identify the imagination of left- and right-hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery (MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI (rt-fMRI) classification system for left- and right-hand MI is developed using the Open-NFT platform. We conducted data acquisition and processing on three subjects, and all of whom were recruited from a local college. As a result, the maximum accuracy of using Support Vector Machine (SVM) classifier on real-time three-class classification (rest, left hand, and right hand) with effective connections is 69.3%. And it is 3% higher than that of traditional multivoxel pattern classification analysis on average. Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies. Full article

Background: Placenta accreta spectrum (PAS) disorders are associated with high mortality and morbidity due to postpartum hemorrhage, hysterectomy, and organ injury, and a multidisciplinary team is required for an individualized case management. In this study, we assessed the diagnostic and prognostic accuracy of the most important ultrasonographic (US) and magnetic resonance imagining (MRI) markers for PAS disorders. Material and Methods: The study included 39 adult pregnant patients with at least one previous cesarean delivery and both US and MRI investigations for placenta previa evaluated at the tertiary maternity hospital ‘Cuza Voda’, Iasi, between 2019 and 2021. The following US signs were evaluated: intra-placental lacunae, loss of the retroplacental hypoechoic zone, myometrial thinning < 1 mm, bladder wall interruption, placental bulging, bridging vessels, and the hypervascularity of the uterovesical or retroplacental space. The MRI signs that were evaluated were intra-placental dark T2 bands, placental bulging, loss of the retroplacental hypointense line on T2 images, myometrial thinning, bladder wall interruption, focal exophytic placental mass, and abnormal vascularization of the placental bed. Results: The US and MRI signs analyzed in our study presented adequate sensitivities and specificities for PAS, but no sign proved to be a useful predictor by itself. The presence of three or more US markers for accretion was associated with a sensitivity of 84.6.6% and a specificity of 92.3% (p < 0.001). The presence of three or more MRI signs supplemented these results and were associated with a sensitivity of 92.3% and a specificity of 61.5% for predicting PAS (p < 0.001). Moreover, US and MRI findings were correlated with FIGO grading and severity of PAS. Conclusions: Even though no US or MRI finding alone can predict PAS with high sensitivity and specificity, our study proves that the presence of three or more imagistic signs could significantly increase the diagnostic accuracy of this condition. Furthermore, US and MRI could be useful tools for evaluating prognostic and perinatal planning. Full article

Background: Campylobacter jejuni (C. jejuni) is a common cause of mostly self-limiting enterocolitis. Although rare, myocarditis has been increasingly documented as a complication following campylobacteriosis. Such cases have occurred predominantly in younger males and involved a single causative species, namely C. jejuni. Case report: We report herein a case of myocarditis complicating gastroenteritis in a 23-year-old immunocompetent patient, caused by this bacterium with a favorable outcome. Cardiac magnetic resonance imagining was useful in establishing an early diagnosis. Conclusions: Myocarditis should be considered in younger patients presenting with chest pain and plasmatic troponin elevations. The occurrence of myocarditis complicating C. jejuni is reviewed. Full article

In thalassemia major, pancreatic iron was demonstrated as a powerful predictor not only for the alterations of glucose metabolism but also for cardiac iron, fibrosis, and complications, supporting a profound link between pancreatic iron and heart disease. We determined for the first time the prevalence of pancreatic iron overload (IO) in thalassemia intermedia (TI) and systematically explored the link between pancreas T2* values and glucose metabolism and cardiac outcomes. We considered 221 beta-TI patients (53.2% females, 42.95 ± 13.74 years) consecutively enrolled in the Extension–Myocardial Iron Overload in Thalassemia project. Magnetic Resonance Imaging was used to quantify IO (T2* technique) and biventricular function and to detect replacement myocardial fibrosis. The glucose metabolism was assessed by the oral glucose tolerance test (OGTT). Pancreatic IO was more frequent in regularly transfused (N = 145) than in nontransfused patients (67.6% vs. 31.6%; p < 0.0001). In the regular transfused group, splenectomy and hepatitis C virus infection were both associated with high pancreatic siderosis. Patients with normal glucose metabolism showed significantly higher global pancreas T2* values than patients with altered OGTT. A pancreas T2* < 17.9 ms predicted an abnormal OGTT. A normal pancreas T2* value showed a 100% negative predictive value for cardiac iron. Pancreas T2* values were not associated to biventricular function, replacement myocardial fibrosis, or cardiac complications. Our findings suggest that in the presence of pancreatic IO, it would be prudent to initiate or intensify iron chelation therapy to prospectively prevent both disturbances of glucose metabolism and cardiac iron accumulation. Full article

The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors. Full article

Organisms are adapted to each other and the environment because there is an inbuilt striving toward security, stability, and equilibrium. A General Theory of Behavior connects imagery, affect, and action with the central executive system we call consciousness, a direct emergent property of cerebral activity. The General Theory is founded on the assumption that the primary motivation of all of consciousness and intentional behavior is psychological homeostasis. Psychological homeostasis is as important to the organization of mind and behavior as physiological homeostasis is to the organization of bodily systems. Consciousness processes quasi-perceptual images independently of the input to the retina and sensorium. Consciousness is the “I am” control center for integration and regulation of (my) thoughts, (my) feelings, and (my) actions with (my) conscious mental imagery as foundation stones. The fundamental, universal conscious desire for psychological homeostasis benefits from the degree of vividness of inner imagery. Imagery vividness, a combination of clarity and liveliness, is beneficial to imagining, remembering, thinking, predicting, planning, and acting. Assessment of vividness using introspective report is validated by objective means such as functional magnetic resonance imaging (fMRI). A significant body of work shows that vividness of visual imagery is determined by the similarity of neural responses in imagery to those occurring in perception of actual objects and performance of activities. I am conscious; therefore, I am. Full article

Background: the evaluation of cartilage thickness has become possible with new techniques such as musculoskeletal ultrasonography (US) and magnetic resonance imagining (MRI), making the evaluation of the treatment response and the progression of the disease more accurate. Objective: to evaluate the efficacy of a Symptomatic Slow Acting Drug for Osteoarthritis using both US and MRI for measuring cartilage thickness at baseline and after 1 year. Methods: The study included the clinical evaluation of 20 patients at baseline, at 6 and 12 months as well as imaging exams (US and MRI) at baseline and after 1 year. Measurements were performed in both knees, in lateral and medial condyles, and in the intercondylar area. After the baseline visit, patients underwent a SYSADOA treatment which included Harpagophytum procumbens (HPc) administered on a daily basis, in a specific regimen. Results and discussions: The US examination permitted the detailed evaluation of the femoral hyaline cartilage thickness, with statistically significant differences before and after treatment at the level of the medial compartment, both in the dominant (1.59 ± 0.49 vs. 1.68 ± 0.49, p = 0.0013) and non-dominant knee (1.73 ± 0.53 vs. 1.79 ± 0.52, p = 0.0106). The US and the MRI correlated well (r = 0.63) and showed no radiographic progression in knee osteoarthritis after one year of treatment with specific SYSADOA. Moreover, the US showed improvement in the cartilage thickness of the medial compartment. Conclusions: The combination with HPc could increase the delay in the radiographic progression of the knee osteoarthritis, with improvement of femoral hyaline cartilage thickness in the medial and lateral compartment. The US might be an important tool in OA evaluation and monitoring. Full article

Conduct disorders are the most frequent psychiatric diagnosis in the pediatric and adolescent population, with different etiology and difficult to treat. Delinquent, aggressive, and impulsive behavior, lack of empathy and inability to predict possible consequences of the behavior lead to significant desadaptation and danger for these patients. In clinical practice, focus is usually given on social and psychological causes of conduct disorders ignoring possible biological factors in etiology and pathophysiology. A clinical case described in this article shows the linkage between frontal brain dysfunction and behavioral symptoms. The first clues of organic brain disorder were multiple and severe symptoms of disinhibition resistant to treatment with dopaminergic drugs and the results of neuropsychological testing. Computed tomography, magnetic resonance imagining, and single-photon emission computed tomography findings were minor and not supported by associated neurological symptoms. However, the location of alterations of brain structure and perfusion significantly correlated with psychopathology. Clarification of the organic cause of the conduct disorder allowed choosing an effective strategy of psychopharmacologic treatment. A positive clinical effect was achieved after switching the treatment from dopaminergic antipsychotic drugs to carbamazepine, which modulates the GABAergic system. Presenting this clinical case, we intended to emphasize the importance of careful attention to the findings of neurovisual and neuropsychological testing diagnosing conduct disorders and individually choosing the most effective psychopharmacologic treatment. Full article