The use of mathematical methods along four interconnected directions in clinical cardiology is analysed: prognosis and diagnosis, elaboration of most accurate clinical concepts and classifications, rules of management of the patient, information-research systems.
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Motor cortex repetitive transcranial magnetic stimulation in fibromyalgia: a multicentre randomised controlled trial.
Br J Anaesth
March 2025
LIM 62 - Pain Center, Department of Neurology, São Paulo, Brazil; Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, University of Aalborg, Gistrup, Denmark. Electronic address:
Background: Despite affecting 2-4% of the population worldwide, fibromyalgia often remains refractory to treatment. Here we report the first international randomised double-blind, sham-controlled trial developed to assess the efficacy of repetitive transcranial magnetic stimulation (rTMS) as an add-on therapy for fibromyalgia.
Methods: Women aged ≥18 yr with fibromyalgia refractory to best available treatment were enrolled in Brazil, France, and Japan, and randomised to 10 Hz motor cortex (M1) rTMS, 3000 pulses day, or sham stimulation.
Enhancing the washing of harvested quinoa seeds with ultrasound-assisted hydration.
Ultrason Sonochem
March 2025
College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
To improve the efficiency of harvested quinoa seed wash processing, this study comparatively evaluated the effects of ultrasound-assisted hydration (UH) and conventional hydration (CH) on hydration dynamics, saponin mass transfer kinetics, and pericarp structural changes in quinoa seeds. Moisture uptake was monitored using a gravimetric method, saponin content was determined through enzyme-linked immunosorbent assay, and pericarp structural changes were observed via scanning electron microscopy. The results showed that UH significantly enhanced the water absorption rate of quinoa seeds, with the Peleg model effectively fitting all hydration stages (R > 0.
View Article and Find Full Text PDFApplication of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach.
Comput Biol Chem
March 2025
MM College of Pharmacy, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana 133207, India; Ibne Seena College of Pharmacy, Azmi Vidya Nagri, Anjhi Shahabad, Hardoi - Uttar Pradesh (U.P.) 241124 India. Electronic address:
Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have been employed to represent molecular structures and predict effective anti-leishmanial molecules. However, each method faces challenges and limitations that must be addressed to optimize the drug discovery and design process.
View Article and Find Full Text PDFEffect of a Multicomponent Exercise Program with Virtual Reality (MEP-VR) versus standard approaches on functional and cognitive domains in hospitalised geriatric patients: Study protocol for a randomized controlled trial.
Rev Esp Geriatr Gerontol
March 2025
Navarrabiomed, Department of Geriatric Medicine - Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain; CIBER of Frailty and Healthy Aging (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain; School of Medicine - University of Navarra, Pamplona, Spain. Electronic address:
Background: Conventional care models for older adults often disregard negative effects of hospitalization and neglect potential benefits of technology. This trial aims to investigate effects of Multicomponent Exercise Program with Virtual Reality (MEP-VR) on functional and cognitive outcomes in hospitalized older adults, compared to MEP-only or usual care approaches.
Methods: This three-arm, parallel-group, randomized controlled trial will include 255 participants aged 75 or older, with a Barthel Index score of at least 60, able to walk and cooperate, have an estimated hospital stay of at least four days, and provide informed consent.
A deep Bi-CapsNet for analysing ECG signals to classify cardiac arrhythmia.
Comput Biol Med
March 2025
University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, Belgrade, 11010, Serbia; Yuan Ze University, College of Engineering, Department of Industrial Engineering and Management, Taoyuan City 320315, Taiwan. Electronic address:
- In recent times, the electrocardiogram (ECG) has been considered as a significant and effective screening mode in clinical practice to assess cardiac arrhythmias. Precise feature extraction and classification are considered as essential concerns in the automated prediction of heart disease. A deep bi-directional capsule network (Bi-CapsNet) uses a new method based on an intelligent deep learning (DL) classifier model to make the classification process very accurate.
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