Mycobacterium marinum infection could have various presentations, from superficial skin infection to deep structure destruction. The prognosis is relatively poor when deep structure is involved as it is more destructive. The prognosis is even worse when operation is required. In the retrospective study of 136 patients who suffered this disease with deep structure involvement, their clinical presentations could be classified into benign and aggressive type. It was found that both types of presentation could be treated conservatively by medication alone. Benign presentations could be treated successfully with chemotherapy alone without complications. Patients with aggressive presentation were usually associated with worse prognosis as there were more complications regardless of the management option. Therefore, the clinical presentation not only had prognostic value but could also guide the treatment plan.
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http://dx.doi.org/10.1142/S0218810410004874 | DOI Listing |
Indian J Orthop
January 2025
Dayanand Medical College and Hospital, Tagore Nagar, civil lines, Ludhiana, Punjab 141001 India.
Purpose: There is paucity of guidelines with inadequate data available about the extent and prevention of bone and joint disease in beta-thalassemic patients in Indian population. This study aims to determine bone and joint involvement in beta-thalassemic patients. It evaluates serum biochemical parameters of bone formation and resorption and correlates with the symptomatology in these patients.
View Article and Find Full Text PDFFront Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
Netw Neurosci
December 2024
Retired Professor, The University of Melbourne, Victoria, Australia.
Several recent studies have optimized deep neural networks to learn high-dimensional relationships linking structural and functional connectivity across the human connectome. However, the extent to which these models recapitulate individual-specific characteristics of resting-state functional brain networks remains unclear. A core concern relates to whether current individual predictions outperform simple benchmarks such as group averages and null conditions.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Precision Imaging, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
Low-intensity transcranial ultrasound stimulation (TUS) is a noninvasive technique that safely alters neural activity, reaching deep brain areas with good spatial accuracy. We investigated the effects of TUS in macaques using a recent metric, the synergy minus redundancy rank gradient, which quantifies different kinds of neural information processing. We analyzed this high-order quantity on the fMRI data after TUS in two targets: the supplementary motor area (SMA-TUS) and the frontal polar cortex (FPC-TUS).
View Article and Find Full Text PDFWorld J Gastroenterol
December 2024
School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
Background: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive tract structure, and the diversity of lesion types, results in different sites and types of lesions distinctly appearing in the images, posing a challenge for the accurate identification of digestive tract diseases.
Aim: To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions, thereby improving the diagnostic efficiency of doctors, and creating significant clinical application value.
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