Background: Percutaneous transforaminal endoscopic discectomy (PTED) has steep learning curves and a high incidence of complications, but currently, efficient and economical training methods are lacking. This study aimed to validate a novel simulator for PTED.
Methods: The simulated PTED included puncturing and establishing the working channel (PEWC) and endoscopic discectomy, with the PEWC being the tested module. Eleven experts and 21 novices were included and introduced to the simulator and tasks; all participants completed the PEWC. Outcomes included: total operation time, number of fluoroscopy for positioning the working sheath, number of spinal risk region invasion, Global Rating Scale (GRS) and a modified GRS, etc. The Mann-Whitney U test was used to compare 2 groups. Spearman's correlation coefficient analyzed continuous variables.
Results: Experts outperformed novices in total operation time (P = 0.001), requiring fewer number of fluoroscopies for positioning the working sheath (P = 0.003). Additionally, experts had a lower number of spinal risk region invasions (P = 0.016) and higher scores on both the GRS (P < 0.001) and modified GRS (P < 0.001). PTED experience correlated with GRS scores (P = 0.001) and modified GRS (P < 0.001). The overall realism scored a median of 4 (3.75-5), and educational value had a median of 4 (range 3-5).
Conclusions: This study demonstrates the validity of the novel simulator, revealing significant associations between PTED experience and performance metrics in a simulated PEWC setting. Furthermore, the PEWC module also offers a good realistic design and high education value according to experts.
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http://dx.doi.org/10.1016/j.wneu.2024.04.070 | DOI Listing |
Clin Infect Dis
January 2025
IQVIA Inc., Falls Church, VA.
Background: Efficient and cost-effective testing strategies are needed to reduce HIV transmission. The aim of this study was to compare the costs of using the current CDC 3-step HIV algorithm - antigen/antibody screening, differentiation immunoassay (Geenius) and NAT against an alternative algorithm using a cobas® HIV-1/HIV-2 qualitative NAT.
Methods: A one-year cost calculator was developed from a US payer perspective using a decision-tree approach to simulate testing in a population of 1 million individuals.
Brief Bioinform
November 2024
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Xi'an Jiaotong University, School of Microelectronics & State Key Laboratory for Mechanical Behavior of Materials, Xi'an 710049, China.
The bismuth monolayer has recently been experimentally identified as a novel platform for the investigation of two-dimensional single-element ferroelectric system. Here, we model the potential energy surface of a bismuth monolayer by employing a message-passing neural network and achieve an error smaller than 1.2 meV per atom.
View Article and Find Full Text PDFNaunyn Schmiedebergs Arch Pharmacol
January 2025
School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang, China.
Microglia-mediated neuroinflammation plays a crucial role in Alzheimer's disease (AD). Tinosinenside A (Tis A) is a novel sesquiterpene glycoside isolated from the dried rattan stem of Tinospora sinensis (Lour.) Merr.
View Article and Find Full Text PDFPhysiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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