Both the primary motor cortex (M1) and the cerebellum are crucial for postural stability and deemed as potential targets for non-invasive brain stimulation (NIBS) to enhance balance performance. However, the optimal target remains unknown. The purpose of this study was to compare the role of M1 and the cerebellum in modulating balance performance in young healthy adults using facilitatory 5 Hz repetitive transcranial magnetic stimulation (rTMS). Twenty-one healthy young adults (mean age = 27.95 ± 1.15 years) received a single session of 5 Hz rTMS on M1 and the cerebellum in a cross-over order with a 7-day washout period between the two sessions. Three balance assessments were performed on the Biodex Balance system SD: Limits of Stability (LOS), modified Clinical Test of Sensory Interaction on Balance (mCTSIB), and Balance Error Scoring System (BESS). No significant effect of rTMS was found on the LOS. The effect of rTMS on the mCTSIB was mediated by stimulation target, proprioception, and vision (p = .003, η = 0.37). Cerebellar rTMS improved the mCTSIB sway index under eyes closed-foam surface condition (p = .02), whereas M1 rTMS did not result in improvement on the mCTSIB. The effect of rTMS on the BESS was mediated by stimulation target, posture, and proprioception (p = .049, η = 0.14). Cerebellar rTMS enhanced reactive balance performance during most sensory deprived conditions.
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http://dx.doi.org/10.1111/ejn.16386 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
Otol Neurotol
February 2025
Department of Otolaryngology-Head and Neck Surgery.
Objective: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.
Study Design: Prospective, double-blinded, observational study of fall risk scores between trained observers and those of IMU-HAs.
Setting: Tertiary referral center.
Adv Sci (Weinh)
January 2025
The Department of Medical Imaging, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Xingangzhong Road 466, Guangzhou, 518037, P. R. China.
Photodynamic therapy (PDT) and photothermal therapy (PTT) have emerged as promising treatment options, showcasing immense potential in addressing both oncologic and nononcologic diseases. Single-component organic phototherapeutic agents (SCOPAs) offer advantages compared to inorganic or multicomponent nanomedicine, including better biosafety, lower toxicity, simpler synthesis, and enhanced reproducibility. Nonetheless, how to further improve the therapeutic effectiveness of SCOPAs remains a challenging research area.
View Article and Find Full Text PDFPLoS One
January 2025
School of Humanities, Ningbo University of Finance and Economics, Ningbo, Zhejiang, China.
Lightweight container technology has emerged as a fundamental component of cloud-native computing, with the deployment of containers and the balancing of loads on virtual machines representing significant challenges. This paper presents an optimization strategy for container deployment that consists of two stages: coarse-grained and fine-grained load balancing. In the initial stage, a greedy algorithm is employed for coarse-grained deployment, facilitating the distribution of container services across virtual machines in a balanced manner based on resource requests.
View Article and Find Full Text PDFJ Occup Health
January 2025
Panasonic Corporation, Department Electric Works Company/Engineering Division, Osaka, Japan.
Background: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers.
Methods: Train data (n=190, age 54.
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