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Zebrafish models of genetic epilepsy benefit from the ability to assess disease-relevant knock-out alleles with numerous tools, including genetically encoded calcium indicators (GECIs) and hypopigmentation alleles to improve visualization. However, there may be unintended effects of these manipulations on the phenotypes under investigation. There is also debate regarding the use of stable loss-of-function (LoF) alleles in zebrafish, due to genetic compensation (GC).
View Article and Find Full Text PDFFront Immunol
January 2025
School of Nursing, Zunyi Medical University, Zunyi, China.
Background: Most patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models used to predict postoperative recurrence in patients with NMIBC have limitations, such as a limited number of included cases and a lack of validation. Therefore, there is an urgent need to develop new models to compensate for the shortcomings and potentially provide evidence for predicting postoperative recurrence in NMIBC patients.
View Article and Find Full Text PDFBMC Psychol
January 2025
Educational Psychology Department, Faculty of Education, Zagazig University, Sharkia, Egypt.
Background: Recent years have witnessed a revolutionary transformation in information technology, characterized by the proliferation of electronic information platforms, with the Egyptian Knowledge Bank being a notable example. Understanding how to effectively navigate these complex systems requires investigation into key factors, particularly system intelligence.
Objectives: This study aimed to examine the mediating role of research motivation in the relationship between system intelligence, Academic Grit, and Academic Achievement.
Appl Nurs Res
February 2025
Herbert H. Lehman College, City University of New York [CUNY], School of Health Sciences, Human Services and Nursing, The Nursing Education Research and Practice Center [NERPC], Room #: 329, 250 Bedford Park Blvd West, Bronx, NY 10468, USA. Electronic address:
Radiol Med
January 2025
Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Purpose: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.
Methods: On lateral full-spine radiographs of 295 consecutive patients, a two-staged region-based convolutional neural network (R-CNN) was trained to detect anatomical landmarks and calculate thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), and sagittal vertical axis (SVA). Performance was evaluated on 65 radiographs not used for training, which were measured independently by 6 readers (3 radiologists, 3 surgeons), and the median per measurement was set as the reference standard.
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