Objective: Temporal lobe epilepsy (TLE) is the most common cause of drug-resistant epilepsy and can be treated surgically to control seizures. In this study, we analyzed the relevant research literature in the field of temporal lobe epilepsy (TLE) treatment to understand the background, hotspots, and trends in TLE treatment research.
Methods: We discussed the trend, frontier, and hotspot of scientific output in TLE treatment research in the world in the last 20 years by searching the core collection of the Web of Science database. Excel and CiteSpace software were used to analyze the basic data of the literature.
Result: We identified a total of 2,051 publications on TLE treatment from 75 countries between 2003 and 2023. We found that the publication rate was generally increasing. The United States was the most publishing country; among the research institutions on TLE treatment, the University of California system published the most relevant literature and collaborated the most with other institutions. The co-citation of literature, keyword co-occurrence, and its clustering analysis showed that the early studies focused on open surgical treatment, mainly by lobectomy. In recent years, the attention given to stereotactic, microsurgery, and other surgical techniques has gradually increased, and the burst analysis indicated that new research hotspots may appear in the future in the areas of improved surgical procedures and mechanism research.
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http://dx.doi.org/10.3389/fneur.2023.1223457 | DOI Listing |
Cureus
January 2025
Dermatology and Dermatologic Surgery, Prince Sultan Military Medical City, Riyadh, SAU.
Tumid lupus erythematosus (TLE) is a rare subtype of cutaneous lupus, which can present diagnostic challenges due to its overlapping features with other skin disorders. Understanding the clinical and histopathological characteristics of TLE is essential for accurate diagnosis and management. In this article, we describe a case of TLE in a 45-year-old man who presented with annular, urticarial, non-scarring plaques on the scalp associated with non-scarring alopecia in the affected area.
View Article and Find Full Text PDFJ Neurol
January 2025
Epilepsy Unit - Sleep Disorders Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
Background: Temporal lobe epilepsy with isolated amygdala enlargement (TLE-AE) still lacks a definite characterization and controversies exist.
Methods: We conducted a retrospective study identifying brain MRI scans with isolated AE between 2015 and 2021. We collected clinical and paraclinical data of patients with TLE-AE and evaluated the outcome.
Eur J Neurol
January 2025
Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Background: Temporal lobe epilepsy (TLE) can lead to structural brain abnormalities, with thalamus atrophy being the most common extratemporal alteration. This study used probabilistic tractography to investigate the structural connectivity between individual thalamic nuclei and the hippocampus in TLE.
Methods: Thirty-six TLE patients who underwent pre-surgical 3 Tesla magnetic resonance imaging (MRI) and 18 healthy controls were enrolled in this study.
J Clin Med
December 2024
Degenerative and Chronic Diseases of the Faculty of Health Sciences (FGW), University Potsdam, 14469 Potsdam, Germany.
: About 65 million people worldwide are affected by epilepsy, with temporal lobe epilepsy being the most common type resistant to drugs and often requiring surgical treatment. Although open surgical approaches, such as temporal lobectomy, have been the method of choice for decades, minimally invasive MRgLITT has demonstrated promising results. However, it remains unknown whether patients who underwent one of these two approaches would show better performance on vestibulo-spatial tasks.
View Article and Find Full Text PDFDigit Health
January 2025
Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
Objective: Breast cancer detection is critical for timely and effective treatment, and automatic detection systems can significantly reduce human error and improve diagnosis speed. This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant categories using a novel machine learning architecture.
Methods: We propose a dense-ResNet attention integration (DRAI) architecture that combines DenseNet and ResNet models with three attention mechanisms to enhance feature extraction from the BreakHis dataset.
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