Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning.

BACKGROUND: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges. METHODS: The study employed a three-stage model to gain a better understanding of the neurobiology underlying schizophrenia and techniques that could be applied for diagnosis. At first, we constructed four-level hierarchical sets from each fMRI volume of 34 schizophrenia patients (SZ) and healthy controls (HC) individually in terms of hemisphere, gyrus, lobes, and Brodmann areas. Second, we employed statistical methods, namely, t-tests and Pearson's correlation, to assess the group differences in cortical activation. Finally, we assessed the predictive power of the brain regions for machine learning algorithms using K-nearest Neighbor (KNN), Naive Bayes, Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Learning Machine (ELM). RESULTS: Our investigation depicts promising results, obtaining an accuracy of up to 84% when applying Pearson's correlation-selected features at lobes and Brodmann region level (81% for Gyrus), as well as Hemispheres involving different stages. Thus, the results of our study were consistent with previous studies that have revealed some functional abnormalities in several brain regions. We also discovered the involvement of other brain regions which were never sufficiently studied in previous literature, such as the posterior lobe (posterior cerebellum), Pyramis, and Brodmann Area 34. CONCLUSIONS: We present a unique and comprehensive approach to investigating the neurological basis of schizophrenia in this study. By bridging the gap between neuroimaging and computable analysis, we aim to improve diagnostic accuracy in patients with schizophrenia and identify potential prognostic markers for disease progression.

Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning.

Introduction: Chronic pain is a multifaceted condition that has yet to be fully comprehended. It is frequently linked with a range of disorders, particularly osteoarthritis (OA), which arises from the progressive deterioration of the protective cartilage that cushions the bone endings over time. Methods: In this paper, we examine the impact of chronic pain on the brain using advanced deep learning (DL) algorithms that leverage resting-state functional magnetic resonance imaging (fMRI) data from both OA pain patients and healthy controls. Our study encompasses fMRI data from 51 pain patients and 20 healthy subjects. To differentiate chronic pain-affected OA patients from healthy controls, we introduce a DL-based computer-aided diagnosis framework that incorporates Multi-Layer Perceptron and Convolutional Neural Networks (CNN), separately. Results: Among the examined algorithms, we discovered that CNN outperformed the others and achieved a notable accuracy rate of nearly 85%. In addition, our investigation scrutinized the brain regions affected by chronic pain and successfully identified several regions that have not been mentioned in previous literature, including the occipital lobe, the superior frontal gyrus, the cuneus, the middle occipital gyrus, and the culmen. Discussion: This pioneering study explores the applicability of DL algorithms in pinpointing the differentiating brain regions in OA patients who experience chronic pain. The outcomes of our research could make a significant contribution to medical research on OA pain patients and facilitate fMRI-based pain recognition, ultimately leading to enhanced clinical intervention for chronic pain patients.

Preclinical characterization of the Toll-like receptor 7/8 antagonist MHV370 for lupus therapy.

Genetic and in vivo evidence suggests that aberrant recognition of RNA-containing autoantigens by Toll-like receptors (TLRs) 7 and 8 drives autoimmune diseases. Here we report on the preclinical characterization of MHV370, a selective oral TLR7/8 inhibitor. In vitro, MHV370 inhibits TLR7/8-dependent production of cytokines in human and mouse cells, notably interferon-α, a clinically validated driver of autoimmune diseases. Moreover, MHV370 abrogates B cell, plasmacytoid dendritic cell, monocyte, and neutrophil responses downstream of TLR7/8. In vivo, prophylactic or therapeutic administration of MHV370 blocks secretion of TLR7 responses, including cytokine secretion, B cell activation, and gene expression of, e.g., interferon-stimulated genes. In the NZB/W F1 mouse model of lupus, MHV370 halts disease. Unlike hydroxychloroquine, MHV370 potently blocks interferon responses triggered by specific immune complexes from systemic lupus erythematosus patient sera, suggesting differentiation from clinical standard of care. These data support advancement of MHV370 to an ongoing phase 2 clinical trial.

A Non-Traumatic Avulsion Fracture of the Lesser Trochanter: Other Diagnosis Not to Be Missed.

In adults, an isolated non-traumatic fracture of the lesser trochanter should arouse strong suspicion of an underlying malignant pathology. In this article, we present the case of a 55-year-old male patient who presented with a non-traumatic isolated fracture of the lesser trochanter secondary to a delayed diagnosis of metastases of bronchial carcinoma.