The consolidation of newly acquired memories on a cellular level is thought to take place in the first few hours following learning. This process is dependent on de novo protein synthesis during this time, which ultimately leads to long-term structural and functional neuronal changes and the stabilisation of a memory trace. Immediate early genes (IEGs) are rapidly expressed in neurons following learning, and previous research has suggested more than one wave of IEG expression facilitates consolidation in the hours following learning. We analysed the expression of Zif268, c-Fos and Arc protein in a number of brain regions involved in spatial learning either 90min, 4h or 8h following training in the Morris water maze task. Consistent with the role of IEGs in the earliest stages of consolidation, a single wave of expression was observed in most brain regions at 90min, however a subsequent wave of expression was not observed at 8h. In fact, Zif268 expression was observed to fall below the levels of naïve controls at this time-point in the medial prefrontal and perirhinal cortices. This may be indicative of synaptic downscaling in these regions in the hours following learning, and an important marker of the consolidation of spatial memory.
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http://dx.doi.org/10.1016/j.bbr.2017.03.019 | DOI Listing |
BMC Med Educ
January 2025
Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
Background: There exists no standardized longitudinal curriculum for teaching bedside ultrasonography (US) in Pulmonary and Critical Care Medicine (PCCM) fellowship programs. Given the importance of mastering bedside US in clinical practice, we developed an integrated year-long US curriculum for first-year PCCM fellows.
Methods: 11 first-year PCCM fellows completed the entire seven-step Blended Learning Curriculum.
CNS Neurosci Ther
January 2025
Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
Aims: To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-energy CT (SECT) for real-time detection of intracranial hemorrhage (ICH) after endovascular thrombectomy.
Materials: We retrospectively collected data from two hospitals, consisting of 237 dual-energy CT (DECT) scans, including matched iodine overlay maps, virtual noncontrast, and simulated SECT images. These scans were randomly divided into a training set (n = 190) and an internal validation set (n = 47) in a 4:1 ratio based on the proportion of ICH.
Intensive Care Med Exp
January 2025
Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.
Background: The discharge practices from the intensive care unit exhibit heterogeneity and the recognition of eligible patients for discharge is often delayed. Recognizing the importance of safe discharge, which aims to minimize readmission and mortality, we developed a dynamic machine-learning model. The model aims to accurately identify patients ready for discharge, offering a comparison of its effectiveness with physician decisions in terms of safety and discrepancies in discharge readiness assessment.
View Article and Find Full Text PDFClin Transl Gastroenterol
January 2025
Division of Gastroenterology, Department of Medicine.
Objectives: Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our aim was to assess whether adherence to ADAPT fluid recommendations versus standard management impacted clinical outcomes in a large prospective cohort.
Method: We analyzed patients consecutively admitted to the Los Angeles General Medical Center between June 2015 to November 2022 whose course was richly characterized by capturing more than 100 clinical variables.
JMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
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