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• Telemedicine offers the advantage of bridging the gap between patients in resource-limited areas and specialized care. • Various machine learning models have the potential to identify interictal biomarkers and localize seizure onset zones in patients with epilepsy. • Data bias, access to data, and integration into clinical workflows are some of the challenges facing artificial intelligence application in epilepsy care. The emergence of telemedicine and artificial intelligence (AI) has set the stage for a possible revolution in the future of medicine and neurology including the diagnosis and management of epilepsy. Telemedicine, with its proven efficacy during the COVID-19 pandemic, offers the advantage of bridging the gap between patients in resource-limited areas and specialized care, where in one study telemedicine reduced the epilepsy treatment gap from 43 % to 9 %. AI innovations promise a transformation in epilepsy care by possibly enhancing the accuracy of electroencephalogram (EEG) interpretation and seizure prediction through machine and deep learning. In one study, abnormal EEG recordings were classified into different categories using a convolutional neural networks (CNN) model showing a specificity of 90 % and an accuracy of 88.3 %. Other models constructed to predict seizures have also achieved a sensitivity of 96.8 % and specificity of 95.5 %. Various machine learning (ML) models highlight the potential AI holds in identifying interictal biomarkers and localizing seizure onset zones aiding in epilepsy treatment decision and outcome prediction. An ML model highlighted in this review localized seizure onset zone with an accuracy reaching 73 % and predicted surgical outcomes with an accuracy reaching 79 % compared to the 43 % accuracy of clinicians. However, limitations and challenges hinder the application of such technologies to reach their full potential in epilepsy care. Limitations include access to compatible devices, integration into clinical workflows, data bias, and availability of sufficient data. Extensive validated research is needed to guide future clinical practice with the implementation of technology-enhanced epilepsy care. This narrative review article will explore the use of AI and telemedicine in EEG and epilepsy care, examining their individual and combined impacts in shaping the future of epilepsy care and discussing the challenges and limitations faced in their usage. [ABSTRACT FROM AUTHOR]

Royal jelly (RJ) is a multifunctional bee product with a unique composition and wide-ranging biological properties, including antioxidant, anti-inflammatory and antiproliferative activities. Still, little is known about the possible myocardial protective properties of RJ. Considering that sonication could enhance RJ bioactivity, this study aimed to assess the effects of non-sonicated (NS) and sonicated (S) RJ on fibrotic signaling, cell proliferation, and collagen production in cardiac fibroblasts. S-RJ was produced by ultrasonication at 20 kHz. Ventricular fibroblasts isolated from neonatal rats were cultured and treated with different concentrations of NS-RJ or S-RJ (0, 50, 100, 150, 200, and 250 µg/well). S-RJ significantly depressed the expression levels of transglutaminase 2 (TG2) mRNA across all the concentrations tested and was inversely associated with the expression of this profibrotic marker. S-RJ and NS-RJ displayed distinct dose-dependent effects on mRNA expression of several other profibrotic, proliferation, and apoptotic markers. Unlike NS-RJ, S-RJ elicited strong negative dose-dependent relationships with the expression of profibrotic markers (TG2, COL1A1, COL3A1, FN1, CTGF, MMP-2, α-SMA, TGF-β1, CX43, periostin), as well as proliferation (CCND1) and apoptotic (BAX, BAX/BCL-2) markers, indicating that RJ dose-response effects were significantly modified by sonification. NS-RJ and S-RJ increased the content of soluble collagen, while decreasing collagen cross-linking. Collectively, these findings show that S-RJ has a greater range of action than NS-RJ for downregulating the expression of biomarkers associated with cardiac fibrosis. Reduced biomarker expression and collagen cross-linkages upon cardiac fibroblast treatment with specific concentrations of S-RJ or NS-RJ suggests putative roles and mechanisms by which RJ may confer some protection against cardiac fibrosis. • Royal jelly (RJ) displayed antifibrogenic activity in cultured ventricular fibroblasts. • RJ administration reduced collagen cross-linking in fibroblasts. • RJ altered the expression of key profibrotic, proliferation, and apoptotic markers. • Sonication enhanced RJ bioactivity in downregulating cardiac fibrosis signaling. • Sonicated RJ also showed superior potential for reducing fibroblast proliferation. [ABSTRACT FROM AUTHOR]