There is growing interest in accelerated rTMS dosing regimens, wherein multiple sessions of rTMS are applied per day. This Phase IV study evaluated the safety, efficacy, and durability of various accelerated Deep TMS protocols used in clinical practice. Data were aggregated from 111 patients with major depressive disorder (MDD) at 4 sites. Patients received one of several accelerated Deep TMS protocols (2x/day, 3x/day, 5x/day, 10x/day). Self-assessment questionnaires (PHQ-9, BDI-II) and clinician-based rating scales (HDRS-21, MADRS) were collected. On average, accelerated TMS led to an 80.2% response and 50.5% remission rate in the first month based on the most rated scale for each patient. There was no significant difference between protocols (Response: 2x/day:89.6%; 3x/day:75%; 5x/day:81%; 10x/day:67.6%). Response occurred after 10 (3x/day), 20 (5x/day), and 31 sessions (10x/day) on average- all of which occur on day 3-4 of treatment. Of patients with longer term follow up, durability was found in 86.7% (n = 30; 60 days) and 92.9% (n = 14; 180 days). The protocols were well-tolerated with no reported serious adverse events. Accelerated Deep TMS protocols are found to be safe, effective therapeutic options for MDD. They offer treatment resistant patients a treatment option with a rapid onset of action and with long durability.
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http://dx.doi.org/10.1016/j.psychres.2023.115482 | DOI Listing |
World J Orthop
December 2024
School of Health and Nursing, Zhengzhou University, Zhengzhou 450000, Henan Province, China.
Background: Achilles tendon rupture is a common orthopedic injury, with an annual incidence of 11-37 per 100000 people, significantly impacting daily life. Minimally invasive surgery, increasingly favored for its reduced risks and comparable fixation strength to open surgery, addresses these challenges. Despite advantages like accelerated recovery, perioperative care poses emotional support, pain management, and rehabilitation challenges, impacting treatment efficacy and patient experience.
View Article and Find Full Text PDFPlant Dis
January 2025
Biotechnology, plant protection, Nongsheng Group C735, Zijin Campus, Zhejiang University, Hangzhou, Zhejiang, China, 310058;
To meet the need of crop leaf disease detection in complex scenarios, this study designs a method based on the computing power of mobile devices that ensures both detection accuracy and real-time efficiency, offering significant practical application value. Based on a comparison with existing mainstream detection models, this paper proposes a target detection and recognition algorithm, TG_YOLOv5, which utilizes multi-dimensional data fusion on the YOLOv5 model. The triplet attention mechanism and C3CBAM module are incorporated into the network structure to capture connections between spatial and channel dimensions of input feature maps, thereby enhancing the model's feature extraction capabilities without significantly increasing the parameter count.
View Article and Find Full Text PDFJ Psychiatr Res
December 2024
Pamukkale University, Department of Psychiatry, Kınıklı, Denizli, Turkey. Electronic address:
J Environ Manage
December 2024
Changjiang Water Resources Protection Institute, Wuhan, 430051, China.
Selective withdrawal is an effective measure to mitigate the adverse effects caused by reservoir construction. The main types of selective withdrawal include multi-level withdrawal and internal weir withdrawal, each with distinct characteristics. It is urgent to elucidate the thermal response differences between these two types of selective withdrawal to improve scheduling accuracy.
View Article and Find Full Text PDFNeural Netw
December 2024
Institute of Deep Perception Technology, JITRI, 214000, Wuxi, China; XJTLU-JITRI Academy of Technology, Xi'an Jiaotong-Liverpool University, 215123, Suzhou, China; Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511400, China. Electronic address:
Over the past decade, the size of neural network models has gradually increased in both breadth and depth, leading to a growing interest in the application of neural network pruning. Unstructured pruning provides fine-grained sparsity and achieves better inference acceleration under specific hardware support. Unstructured Pruning at Initialization (PaI) optimizes the iterative pruning pipeline, but sparse weights increase the risk of underfitting during training.
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