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Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans.
Curr Eye Res
January 2025
Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, Vagelos College of Physicians and Surgeons, New York, NY, USA.
Purpose: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
Methods: The retinal layer thickness data obtained from C57BL/6 and DBA/2J mice were processed for machine learning after segmenting mouse retinal SD-OCT scans. Twenty-two models were trained to predict the mouse groups.
Management of Systemic Anti-psoriatic Drugs in Psoriasis Patients with Concurrent Paraplegia or Tetraplegia: Insights From a 6-Year Multicenter, Retrospective Observational Study.
Dermatol Ther (Heidelb)
January 2025
Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Section of Dermatology, University of Messina, 98125, Messina, Italy.
Introduction: Patients with psoriasis (PsO) and permanent spinal cord injuries (SCI) resulting in paraplegia and tetraplegia may experience a higher rate of infections compared to patients with PsO without SCI. It can result in further challenges for therapeutic management with immunosuppressants (biological and non-biological treatments). Thus, we aimed to evaluate the rate of infections in patients with PsO and SCI treated with systemic immunosuppressants.
View Article and Find Full Text PDFWound Segmentation with U-Net Using a Dual Attention Mechanism and Transfer Learning.
J Imaging Inform Med
January 2025
Computer Science Department, University of Geneva, Geneva, Switzerland.
Accurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image analysis. In this paper, we introduce a novel dual attention U-Net model designed for precise wound segmentation. Our proposed architecture integrates two widely used deep learning models, VGG16 and U-Net, incorporating dual attention mechanisms to focus on relevant regions within the wound area.
View Article and Find Full Text PDFComprehensive quantification of C4 to C26 free fatty acids using a supercritical fluid chromatography-mass spectrometry method in pharmaceutical-grade egg yolk powders intended for total parenteral nutrition use.
Anal Bioanal Chem
January 2025
Department of Chemistry - Biomedical Center, Analytical Chemistry and Neurochemistry, Uppsala University, Uppsala, Sweden.
Free fatty acids (FFAs) are important energy sources and significant for energy transport in the body. They also play a crucial role in cellular oxidative stress responses, following cell membrane depolarization, making accurate quantification of FFAs essential. This study presents a novel supercritical fluid chromatography-mass spectrometry (SFC-MS) method using selected ion recording in negative electrospray ionization mode, enabling rapid quantification of 31 FFAs within 6 min without derivatization.
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