Background: Hydromyelia is a common magnetic resonance imaging (MRI) finding associated with compressive myelopathy caused by intervertebral disc extrusion (IVDE).
Objectives: To describe the MRI features of hydromyelia and explore its relationship to clinical history, neurological severity, and the duration of cord compression.
Animals: Ninety-one client-owned dogs with a focal compressive myelopathy secondary to thoracolumbar IVDE.
Methods: A retrospective observational study was conducted in which MRIs were blindly evaluated to grade and localize hydromyelia and measure the degree of spinal cord compression. Duration and severity of clinical signs were recorded. Differences between hydromyelia grades in these variables were statistically assessed using a Wilcoxon and Kruskal Wallis test. Receiver operator curve analysis was used to determine the sensitivity and specificity for duration of clinical signs to predict the presence of hydromyelia.
Results: Hydromyelia was identified at sites of IVDE in 84 of 91 dogs. An absence of hydromyelia was associated a with statistically longer duration of clinical signs (mean 73.1, IQR 76 days) when compared to cases with mild (mean 17.7, IQR 7.25 days, P = .006) or severe (mean 17.9, IQR 10.25 days, P = .006) hydromyelia. Duration of clinical signs <14 days was 78.6% sensitive and 85.7% specific for predicting the presence of hydromyelia.
Conclusions And Clinical Importance: The MRI finding of hydromyelia might be a predictor of lesion chronicity in focal IVDE, helping to guide planning of hemilaminectomy surgery.
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http://dx.doi.org/10.1111/jvim.16433 | DOI Listing |
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 PDFJ Assist Reprod Genet
January 2025
Medical Genetics & Genomics Unit, AULSS8 Berica, Vicenza, Italy.
This document aims to provide good practice recommendations in order to support maternal-foetal medicine specialists, clinical geneticists and clinical laboratory geneticists in the management of pregnancies obtained after the transfer of an embryo tested with preimplantation genetic testing (PGT). It was drafted by geneticists expert in preimplantation genetics and prenatal genetic diagnosis belonging to the "Working Group in Cytogenomics, Prenatal and Reproductive Genetics" of the "Italian Society of Human Genetics" (SIGU). In particular, the paper addresses the diagnostic algorithm to be applied in prenatal follow-up depending on the type of PGT performed, the results obtained and the related diagnostic value based on the most recent literature data and Italian and international recommendations.
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Animals: Twenty-two dogs with non-associative IMHA and 14 healthy dogs.
Brain Imaging Behav
January 2025
Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS.
View Article and Find Full Text PDFJ 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.
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