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Validating the Accuracy of Parkinson's Disease Clinical Diagnosis: A UK Brain Bank Case-Control Study.
Ann Neurol
January 2025
Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
Objective: Despite diagnostic criteria refinements, Parkinson's disease (PD) clinical diagnosis still suffers from a not satisfying accuracy, with the post-mortem examination as the gold standard for diagnosis. Seminal clinicopathological series highlighted that a relevant number of patients alive-diagnosed with idiopathic PD have an alternative post-mortem diagnosis. We evaluated the diagnostic accuracy of PD comparing the in-vivo clinical diagnosis with the post-mortem diagnosis performed through the pathological examination in 2 groups.
View Article and Find Full Text PDFStratified Impact of Therapies on Anaplastic Thyroid Cancer Outcomes in the Pre-Gene-Targeted Therapy Era.
Ann Surg Oncol
January 2025
Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Anaplastic thyroid cancer (ATC) is a highly lethal disease, often diagnosed with advanced locoregional and distant metastases, resulting in a median survival of just 3-5 months. This study determines the stratified effectiveness of baseline treatments in all combinations, enabling precise prognoses prediction and establishing benchmarks for advanced therapeutic options.
Methods: The study extracted a cohort of pathologically confirmed ATC patients from the Surveillance, Epidemiology, and End Results program.
Background: Urine neutrophil gelatinase-associated lipocalin (uNGAL) is a biomarker for the early diagnosis of AKI.
Objectives: To evaluate uNGAL in dogs with non-associative immune mediated hemolytic anemia (IMHA) and to evaluate whether uNGAL correlates with disease severity markers, negative prognostic indicators and outcome.
Animals: Twenty-two dogs with non-associative IMHA and 14 healthy dogs.
In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.
J Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFBrain Midline Approximation to Improve Symmetry Analysis of Brain CT Scans.
J Imaging Inform Med
January 2025
Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
Analysis of the symmetry of the brain hemispheres at the level of individual structures and dominant tissue features has been the subject of research for many years in the context of improving the effectiveness of imaging methods for the diagnosis of brain tumor, stroke, and Alzheimer's disease, among others. One useful approach is to reliably determine the midline of the brain, which allows comparative analysis of the hemispheres and uncovers information on symmetry/asymmetry in the relevant planes of, for example, CT scans. Therefore, an effective method that is robust to various geometric deformations, artifacts, varying noise characteristics, and natural anatomical variability is sought.
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