Unraveling the pathophysiology of schizophrenia: insights from structural magnetic resonance imaging studies

BackgroundSchizophrenia affects about 1% of the global population. In addition to the complex etiology, linking this illness to genetic, environmental, and neurobiological factors, the dynamic experiences associated with this disease, such as experiences of delusions, hallucinations, disorganized thinking, and abnormal behaviors, limit neurological consensuses regarding mechanisms underlying this disease.MethodsIn this study, we recruited 72 patients with schizophrenia and 74 healthy individuals matched by age and sex to investigate the structural brain changes that may serve as prognostic biomarkers, indicating evidence of neural dysfunction underlying schizophrenia and subsequent cognitive and behavioral deficits. We used voxel-based morphometry (VBM) to determine these changes in the three tissue structures: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). For both image processing and statistical analysis, we used statistical parametric mapping (SPM).ResultsOur results show that patients with schizophrenia exhibited a significant volume reduction in both GM and WM. In particular, GM volume reductions were more evident in the frontal, temporal, limbic, and parietal lobe, similarly the WM volume reductions were predominantly in the frontal, temporal, and limbic lobe. In addition, patients with schizophrenia demonstrated a significant increase in the CSF volume in the left third and lateral ventricle regions.ConclusionThis VBM study supports existing research showing that schizophrenia is associated with alterations in brain structure, including gray and white matter, and cerebrospinal fluid volume. These findings provide insights into the neurobiology of schizophrenia and may inform the development of more effective diagnostic and therapeutic approaches

Pixel-Coordinate-Induced Human Pose High-Precision Estimation Method

Accurately estimating human pose is crucial for providing feedback during exercises or musical performances, but the complex and flexible nature of human joints makes it challenging. Additionally, traditional methods often neglect pixel coordinates, which are naturally present in high-resolution images of the human body. To address this issue, we propose a novel human pose estimation method that directly incorporates pixel coordinates. Our method adds a coordinate channel to the convolution process and embeds pixel coordinates into the feature map, while also using coordinate attention to capture position- and structure-sensitive features. We further reduce the network parameters and computational cost by using small-scale convolution kernels and a smooth activation function in residual blocks. We evaluate our model on the MPII Human Pose and COCO Keypoint Detection datasets and demonstrate improved accuracy, highlighting the importance of directly incorporating coordinate location information in position-sensitive tasks

Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches

A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work

Altered Dynamic Functional Connectivity of Cuneus in Schizophrenia Patients: A Resting-State fMRI Study

Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients’ social lives. As a result, accurate diagnosis of SZ has attracted researchers’ interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients’ and 74 healthy controls’ (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease

HMNet: Hierarchical Multi-Scale Brain Tumor Segmentation Network

An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we propose a novel hierarchical multi-scale segmentation network (HMNet), which contains a high-resolution branch and parallel multi-resolution branches. The high-resolution branch can keep track of the brain tumor’s spatial details, and the multi-resolution feature exchange and fusion allow the network’s receptive fields to adapt to brain tumors of different shapes and sizes. In particular, to overcome the large computational overhead caused by expensive 3D convolution, we propose a lightweight conditional channel weighting block to reduce GPU memory and improve the efficiency of HMNet. We also propose a lightweight multi-resolution feature fusion (LMRF) module to further reduce model complexity and reduce the redundancy of the feature maps. We run tests on the BraTS 2020 dataset to determine how well the proposed network would work. The dice similarity coefficients of HMNet for ET, WT, and TC are 0.781, 0.901, and 0.823, respectively. Many comparative experiments on the BraTS 2020 dataset and other two datasets show that our proposed HMNet has achieved satisfactory performance compared with the SOTA approaches

Reduction of Mutual Coupling in UWB/MIMO Antenna Using Stub Loading Technique

The research presents mutual coupling reduction between UWB-MIMO antenna elements using stub loading technique. The proposed 2 × 2 UWB antenna geometry consists of two circular-shaped monopole radiators with a partial ground for perfect impedance matching. Stubs of 20 mm × 0.2 mm are inserted between the two antenna elements in the ground plane to improve the isolation. The decoupling stub leads to a mutual coupling reduction of less than 20 dB. The farfield measurement at a selected frequency of 10 GHz confirms an omnidirectional radiation pattern. Different MIMO antenna metric such as channel capacity loss (CCL), mean effective gain (MEG), total active reflection coefficient (TARC), envelope correlation coefficient (ECC), and surface current are presented. Details of the design considerations and the simulation and measurement results are presented and discussed. The proposed MIMO antenna array can be well suited for UWB applications

Lumbar spinal mobility changes among adults with advancing age

Background: Limitations in spinal mobility can interfere with the attainment of important functional skills and activities of daily living and restrictions in spinal mobility are usually the earliest and reliable indicator of diseases. Objective : The aim of this study was to determine the differences of lumbar spinal mobility among healthy adults with advancing age. Materials and Methods : The modified Schober′s method was used to measure anterior flexion. The guideline of the American Academy of Orthopaedic Surgeons was adapted to measure lateral flexion and extension. Results : The results of this study indicate that spinal mobility decreases with advancing age. The most significant (P < 0.05) differences occurred between the two youngest and the two oldest age categories. Conclusion : Using these data, we developed normative values of spinal mobility for each sex and age group. This study helps the clinicians to understand and correlate the restrictions of lumbar spinal mobility due to age and differentiate the limitations due to disease

Prevalence of coccidiosis among village and exotic breed of chickens in Maiduguri, Nigeria

Aim: Coccidiosis is an important enteric parasitic disease of poultry associated with significant economic losses to poultry farmers worldwide. This survey was conducted from June 2014 through July 2015 with the main goal of investigating the prevalence and associated risk factors of coccidiosis among village and exotic breeds of chickens in Maiduguri, Northeastern Nigeria. Materials and Methods: A total of 600 fecal samples from live and slaughtered birds comprising 284 young, 141, growers and 175 adult birds; 379 male and 221 female birds; 450 exotic and 150 local breeds of birds were randomly collected either as bird’s fresh droppings or cutting open an eviscerated intestine of slaughtered birds, while noting their age, sex, and breeds. Samples were analyzed using standard parasitological methods and techniques. Results: An overall prevalence rate of 31.8% (95% confidence interval: 28.07-35.52) was obtained. Higher prevalence rates were recorded in growing birds 58.9% (50.78-67.02), female birds 35.3% (29.00-41.60), exotic birds 42.4% (37.83- 46.97), and broiler birds 68.7% (61.28-76.12). Similarly, higher infection rates were also observed among birds sampled from Mairi ward 66.7% (56.03-77.37), intensive management system 46.5% (41.61-51.39), and constructed local cages 54.0% (46.02-61.98). The difference in prevalence of coccidiosis among age groups, breeds, among exotic breeds, sampling locations, husbandry management systems, and litter management systems was statistically significant (0.05) of infection rates was observed in sex. Conclusion: Coccidiosis is endemic in both commercial and backyard poultry farms in Maiduguri due to poor management practices encouraging Eimeria oocysts build-up. It is therefore, recommended that poultry farmers should practice strict biosecurity measures on their farms, creating awareness on the prevalence of coccidiosis, routine vaccination against coccidiosis and educating poultry farmers on the need for maintaining good hygienic standards and good flock health management