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Twenty-four weeks of combined training in different environments, aquatic and land, in the type 2 diabetes management (Aquatic and Land Exercise for Diabetes -ALED): protocol of a randomized clinical trial.
Trials
January 2025
Department of Physical Education, Sports Center, Federal University of Santa Catarina, University Campus Trindade, Florianópolis, Santa Catarina, 88040-900, Brazil.
Background: Physical exercise is crucial in type 2 diabetes management (T2D), and training in the aquatic environment seems to be a promising alternative due to its physical properties and metabolic, functional, cardiovascular, and neuromuscular benefits. Research on combined training in aquatic and dry-land training environments is scarce, especially in long-term interventions. Thus, this study aims to investigate the effects of combined training in both environments on health outcomes related to the management of T2D patients.
View Article and Find Full Text PDFAcute hypoalgesic and neurophysiological responses to lower-limb ischaemic preconditioning.
Exp Brain Res
January 2025
Faculty of Sport, Technology and Health Sciences, St. Mary's University, Twickenham, Middlesex, UK.
The aim of this study was to assess if ischaemic preconditioning (IPC) can reduce pain perception and enhance corticospinal excitability during voluntary contractions. In a randomised, within-subject design, healthy participants took part in three experimental visits after a familiarisation session. Measures of pressure pain threshold (PPT), maximum voluntary isometric force, voluntary activation, resting twitch force, corticospinal excitability and corticospinal inhibition were performed before and ≥10 min after either, unilateral IPC on the right leg (3 × 5 min); a sham protocol (3 × 1 min); or a control (no occlusion).
View Article and Find Full Text PDFHistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification.
BMC Med Imaging
January 2025
Department of Information, Third Affiliated Hospital of Naval Medical University, No. 225 Changhai Road, Yangpu District, 200438, Shanghai, China.
Purpose: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital pathology workflow.
Methods: We propose a dual-mechanism feature pyramid fusion technique that integrates nuclear segmentation and classification tasks to construct the HistoNeXt network model. HistoNeXt utilizes an encoder-decoder architecture, where the encoder, based on the advanced ConvNeXt convolutional framework, efficiently and accurately extracts multi-level abstract features from tissue images.
A deep learning-based multi-view approach to automatic 3D landmarking and deformity assessment of lower limb.
Sci Rep
January 2025
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Anatomical Landmark detection in CT-Scan images is widely used in the identification of skeletal disorders. However, the traditional process of manually detecting anatomical landmarks, especially in three dimensions, is both time-consuming and prone to human errors. We propose a novel, deep-learning-based approach to automatic detection of 3D landmarks in CT images of the lower limb.
View Article and Find Full Text PDFElectrocatalytic HER Performance of [FeFe]-Hydrogenase Mimics Bearing M-salen Moieties (M=Zn, Ni, Fe, Mn).
Chemistry
December 2024
Departamento de Química Orgánica, Facultad de Química, Universidad Complutense, 28040-, Madrid, Spain.
The synthesis and characterization of novel compounds (5-8) as mimetics of [FeFe]-hydrogenase, combining two distinct systems capable of participating in hydrogen evolution reactions (HER): the [(μ-adt)Fe(CO)] fragment and M-salen complexes (salen=N,N'-bis(salicylidene)ethylenediamine) (M=Zn, Ni, Fe, Mn), is reported. These complexes were synthesized in high yields via a three-step procedure from N,N'-bis(4-R-salicylidene)ethanediamine) 4 [R=Fe(CO)(μ-SCH)NCOCHO]. Structural analysis through spectroscopic, spectrometric, and computational (DFT) methods confirmed distorted tetrahedral and square-planar geometries for Zn-salen and Ni-salen complexes (5 and 6) respectively, while complexes Fe-salen 7 and Mn-salen 8 exhibit square-based pyramidal structures typical of Fe(III) and Mn(III) high-spin salen-complexes.
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