Basal ganglia stroke is often associated with functional deficits in patients, including difficulties to learn and execute new motor skills (procedural learning). To measure procedural learning in a murine model of stroke (30min right MCAO), we submitted C57Bl/6J mice to various sensorimotor tests, then to an operant procedure (Serial Order Learning) specifically assessing the ability to learn a simple motor sequence. Results showed that MCAO affected the performance in some of the sensorimotor tests (accelerated rotating rod and amphetamine rotation test) and the way animals learned a motor sequence. The later finding seems to be caused by difficulties regarding the chunking of operant actions into a coherent motor sequence; the appeal for food rewards and ability to press levers appeared unaffected by MCAO. We conclude that assessment of motor learning in rodent models of stroke might improve the translational value of such models.
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http://dx.doi.org/10.1016/j.bbr.2016.03.032 | DOI Listing |
Int J Med Inform
December 2024
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. Electronic address:
Background: Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Predicting protein-protein interactions (PPIs) is crucial for advancing drug discovery. Despite the proposal of numerous advanced computational methods, these approaches often suffer from poor usability for biologists and lack generalization. In this study, we designed a deep learning model based on a coattention mechanism that was capable of both PPI and site prediction and used this model as the foundation for PPI-CoAttNet, a user-friendly, multifunctional web server for PPI prediction.
View Article and Find Full Text PDFPurpose: The classic paradigm of procedural education in medical training has involved trainees learning and performing invasive bedside procedures and subsequently teaching these procedures to more junior trainees. Many existing resident-as-teacher curricula focus on cognitive domains; there has been a lack of literature examining the transition from learner to teacher in procedural education. This hypothesis-generating instrumental case study explored how expert procedural educators transitioned from novice procedural educators to experts.
View Article and Find Full Text PDFJ Comput Assist Tomogr
November 2024
From the Department of Medical Imaging, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin City, Jiangsu Province, China.
Objectives: The aims of the study are to predict lung function impairment in patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD) through computed tomography (CT) quantitative analysis parameters based on CT deep learning model and density threshold method and to assess the severity of the disease in patients with CTD-ILD.
Methods: We retrospectively collected chest high-resolution CT images and pulmonary function test results from 105 patients with CTD-ILD between January 2021 and December 2023 (patients staged according to the gender-age-physiology [GAP] system), including 46 males and 59 females, with a median age of 64 years. Additionally, we selected 80 healthy controls (HCs) with matched sex and age, who showed no abnormalities in their chest high-resolution CT.
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