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Background: Literature on the frequency, response to treatment, and outcomes of acute ischemic stroke (AIS) due to intracranial atherostenosis (ICAS)-related intracranial large artery occlusion (ILAO) from Saudi Arabia is scarce. The aim of this study was to identify the percentage, describe the characteristics, and observe the treatment response in patients with AIS attributed to ICAS-related ILAO., Materials and Methods: This cross-sectional study included all adult patients from 2017-2021 who fulfilled the inclusion criteria for the diagnosis of ICAS-related AIS. Patients were dichotomized based on ILAO. Mortality and functional outcomes (FOCs) based on 90 days' dependence level were compared between the two groups. The association between ILAO and other variables was assessed using the Chi-squared test, odds ratios (OR), and 95% confidence interval (CI)., Results: ILAO was found in 38.7% of patients with ICAS-related AIS. Men comprised three-fourths of the cohort and were more frequent in the ILAO group. Smoking was associated with increased ( P = 0.04) likelihood of ILAO. Patients with ILAO had more severe strokes ( P ≤ 0.001) than patients without. Middle cerebral artery was the most common occluded vessel (52%). Functional dependence ( P = 0.003, OR = 2.87, CI = 1.42-5.77), malignant transformation ( P = 0.001, OR = 8.0, CI = 1.82-35.9), and mortality ( P ≤ 0.001, OR = 7.67, CI = 2.40-24.5) were significantly higher among ILAO group. Patients with ILAO with unfavorable FOC were older than those who achieved better FOC ( P ≤ 0.001). Thrombolysis ( P = 0.02, OR = 2.50, CI = 1.15-5.41) and mechanical thrombectomy (MT) improved FOC in patients with ILAO ( P = 0.04, OR = 2.33, CI = 1.10-4.92)., Conclusion: ILAO is common in patients with ICAS-related AIS. Timely hyperacute stroke treatment can help improve the FOC of otherwise disabling stroke due to ILAO. Raising awareness of the community about stroke is needed, so that a higher number of patients can arrive at hospital within the golden hours. Further data from the region are required to recognize the efficacy of MT in ICAS-related ILAO., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Family and Community Medicine.)

Background: The occurrence of seizures following a stroke is a well-recognized complication associated with a significant increase in morbidity and mortality. Despite the numerous studies examining outcomes and risk factors related to post-stroke seizures (PSS), there remains a lack of clarity regarding the clinical characteristics, treatment, and PSS recurrence (PSSR) rates in patients experiencing their initial episode of PSS., Purpose: This study aimed to determine the risk factors for developing recurrent seizures after first PSS and their effects on functional outcomes and mortality., Methods: All patients underwent an electroencephalography (EEG) and were monitored for a minimum of 24 months following the first PSS. The primary endpoint was the recurrence of seizures. Predictive factors for PSSR were determined by using the Cox-proportional hazards model, and the cumulative latency of recurrence at 90, 180, 360, and 720 days was estimated using Kaplan-Meier analysis., Results: Seizure recurred in 36.8% (39/106). Significant association of PSSR was noted with female gender, use of older anti-seizure medications (ASMs) (p<0.001), EEG findings as focal slow wave activity (p<0.001), Ictal epileptiform abnormalities (p=0.015), status epilepticus (p=0.015), and with severe disability (p=0.008). However, multivariate cox-proportional hazards model showed significant association of female gender (HR=3.28; 95% CI: 1.42-7.58; p=0.006). Hazard ratio (HR) was increased with older ASMs use, focal aware seizure types, Ictal EAs, and periodic discharges on EEG; though, statistically significant., Conclusion: Factors such as the type of ASMs, EEG findings, and seizure type were significantly linked to PSSR. Female gender was the only independent predictor established. Additionally, significant functional decline was reported with recurrence., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: This study aimed to investigate the differences in ASMs prescription, seizure characteristics and predictors of polypharmacy in patients with epilepsy and Intellectual disabilities (IDs) residing in group homes versus family homes., Methods: This nine-year retrospective study analyzed patients with epilepsy and IDs who were admitted to the EMU, epilepsy clinics at LHSC and rehabilitation clinics for patients with IDs at Parkwood Institution. The study included individuals aged 16 years and older residing in either group homes or family homes. Data on demographics, epilepsy characteristics, and ASMs use were collected and analyzed using the Statistical Package for Social Sciences. The study utilized binary logistic regression to identify predictors of polypharmacy in patients with epilepsy and IDs., Results: The study enrolled a total of 81 patients, of which 59.3 % resided in family homes. Group home residents were significantly older (41 vs. 24.5 years; p = 0.0001) and were prescribed more ASMs (3 vs. 2; p = 0.002). Specific ASMs were more common in group homes, including valproic acid (54.5 % vs. 25.0 %), lacosamide (54.5 % vs. 22.9 %), topiramate (33.3 % vs. 14.6 %), and phenytoin (30.3 % vs. 6.2 %). Admission to the EMU was more prevalent in group homes (93.9 % vs. 52.1 %; p = 0.0001). Living in a group home increased the risk of polypharmacy (OR = 10.293, p = 0.005), as did older epilepsy onset age (OR = 1.135, p = 0.031) and generalized or focal & generalized epilepsy (OR = 7.153, p = 0.032 and OR = 10.442, p = 0.025, respectively)., Significance: Our study identified notable differences in the demographic and clinical characteristics of patients with epilepsy and IDs living in group homes versus family homes. Age of epilepsy onset, EMU admissions, epilepsy types, and residency setting were significant predictors of polypharmacy. These findings highlight the need for personalized care strategies and increased awareness of the potential risks associated with polypharmacy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Post-stroke seizures (PSS) are one of the major stroke-related complications. Early therapeutic interventions are critical therefore using electroencephalography (EEG) as a predictive tool for future recurrence may be helpful. We aimed to assess frequencies of different EEG patterns in patients with PSS and their association with seizure recurrence and functional outcomes. Methods: All patients admitted with PSS were included and underwent interictal EEG recording during their admission and monitored for seizure recurrence for 24 months. Results: PSS was reported in 106 patients. Generalized slow wave activity (GSWA) was the most frequent EEG pattern observed (n  =  62, 58.5%), followed by Focal sharp wave discharges (FSWDs) (n  =  57, 55.8%), focal slow wave activity (FSWA) (n  =  56, 52.8%), periodic discharges (PDs) (n  =  13, 12.3%), and ictal epileptiform abnormalities (n  =  6, 5.7%). FSWA and ictal EAs were positively associated with seizure recurrence ( p  < .001 and p   =  .015 respectively) and it remained significant even after adjusting for age, sex, stroke severity, stroke subtype, or use of anti-seizure medications (ASMs). Other positive associations were status epilepticus (SE) ( p   =  .015), and use of older ASM ( p  < .001). FSWA and GSWA in EEG were positively associated with severe functional disability ( p   =  .055, p   =  .015 respectively). Other associations were; Diabetes Mellitus ( p   =  .034), Chronic Kidney Disease ( p   =  .002), use of older ASMs ( p   =  .037), presence of late PSS ( p   =  .021), and those with Ischemic stroke ( p   =  .010). Conclusions: Recognition and documentation of PSS-related EEG characteristics are important, as certain EEG patterns may help to identify the patients who are at risk of developing recurrence or worse functional outcomes., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background and Objectives: Psychogenic non-epileptic seizures (PNES) are commonly associated with co-existing psychiatric disorders. The relationship between psychiatric factors and PNES episodes with and without epilepsy remains understudied. We reviewed co-existing psychiatric disorders in PNES-only, PNES with epilepsy aiming to examine whether these co-existing disorders associated with PNES clinical presentation and long-term outcomes., Methods: We conducted a retrospective, longitudinal cohort study of patients with PNES diagnosed at our EMU from May 2000 to April 2008, with follow-up clinical data until September 2015. We categorized patients into three groups: PNES-only, PNES+ definite epilepsy, and PNES+ possible/probable epilepsy., Results: In total, 271 patients with PNES were identified: 194 had PNES-only, 30 had PNES+ possible or probable epilepsy, and 47 had PNES+ definite epilepsy. No significant differences were observed in the prevalence of depression, anxiety, post-traumatic stress disorder (PTSD), substance abuse, or suicidal thoughts among the three groups. Similarly, no differences in co-existing psychiatric disorders characteristics were discovered among patients grouped by various durations and frequencies of PNES episodes. At EMU admission, for PNES-only patients total of 130/194 patients (67%) were on ASMs, and 64/194 (32.9%) were not. PNES-only not on ASM were the most likely to report at least two of the three main psychiatric disorders (depression, anxiety, and PTSD; p = 0.01). At the final follow-up, 68/130 (52.3%) and 92/130 (70.8%) patients were able to discontinue or reduce their ASM intake, respectively, with no significant differences in co-existing psychiatric disorders among them (p < 0.001). Overall, 51.6% or 31.3% of patients reported reduced or resolved PNES episodes, respectively. Further, this reduction and resolution of PNES episode were not affected by any psychological variable., Conclusions: Co-existing psychiatric disorders prevalence did not differ between patients with PNES-only and those with coexisting epilepsy. Further, co-existing psychiatric disorders characteristics did not reliably predict PNES episode duration, frequency, reduction, or resolution. For patients with PNES-only, the presence of co-existing psychiatric disorders did not predict the rate at which ASMs could be reduced or discontinued., Competing Interests: Declaration of Competing Interest S. M. Mirsattari serves on the advisory boards and speaker bureaus for UCB Canada Inc., Eisai Limited, and Sunovion Pharmaceuticals Canada, Inc. He has been involved in multiple multicenter clinical trials unrelated to PNESs. The other authors have no conflicts of interest or disclosures to report., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer's disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer's prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

Drug-resistant epilepsy, defined as the failure of 2 or more antiseizure medications to achieve seizure freedom, is responsible for 2/3 of epilepsy cases. Tumors are responsible for up to 15% of all adult onset and up to 6% of childhood onset epilepsies. Among these tumors, commonly known subtypes DNET, ganglioglioma, and low-grade astrocytoma are often suspected. New advances in tumor classification have been made, with genetics playing a key role in tumor classification. Polymorphic low-grade neuroepithelial tumor of the young (PLNTY) is a highly epileptogenic subtype of tumors that may mimic low-grade gliomas but offer pathologic and genetic clues: oligodendroglioma-like cellular components and infiltration patterns and strong CD34-immunopositive stain. In addition, a key finding is radiologic: a unifocal abnormality best seen on MRI brain in FLAIR sequence as the "salt and pepper sign" and calcifications appreciated on CT head., (© 2022 American Academy of Neurology.)

Drug-resistant epilepsy is present in nearly 30% of patients. Resection of the epileptogenic zone has been found to be the most effective in achieving seizure freedom. The study of temporal lobe epilepsy for surgical treatment is extensive and complex. It involves a multidisciplinary team in decision-making with initial non-invasive studies (Phase I), providing 70% of the required information to elaborate a hypothesis and treatment plans. Select cases present more complexity involving bilateral clinical or electrographic manifestations, have contradicting information, or may involve deeper structures as a part of the epileptogenic zone. These cases are discussed by a multidisciplinary team of experts with a hypothesis for invasive methods of study. Subdural electrodes were once the mainstay of invasive presurgical evaluation and in later years most Comprehensive Epilepsy Centers have shifted to intracranial recordings. The intracranial recording follows original concepts since its development by Bancaud and Talairach, but great advances have been made in the field. Stereo-electroencephalography is a growing field of study, treatment, and establishment of seizure pattern complexities. In this comprehensive review, we explore the indications, usefulness, discoveries in interictal and ictal findings, pitfalls, and advances in the science of presurgical stereo-encephalography for temporal lobe epilepsy., Competing Interests: SM has received honorarium for speaking engagements for UCB Canada Inc., Eisai Co. Ltd. (Canada), and Sunovion Pharmaceuticals Canada Inc. He has been member of the Epilepsy National Advisory Board for UCB Canada Inc., Eisai Co. Ltd. (Canada) and Sunovion Pharmaceuticals Canada Inc. He has been involved in multiple multi-center clinical trials unrelated to the subjects addressed in this paper. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Paredes-Aragon, AlKhaldi, Ballesteros-Herrera and Mirsattari.)

The COVID-19 pandemic has been a disastrous event that has elevated several psychological issues such as depression given abrupt social changes and lack of employment. At the same time, social scientists and psychologists have gained significant interest in understanding the way people express emotions and sentiments at the time of pandemics. During the rise in COVID-19 cases with stricter lockdowns, people expressed their sentiments on social media. This offers a deep understanding of human psychology during catastrophic events. By exploiting user-generated content on social media such as Twitter, people's thoughts and sentiments can be examined, which aids in introducing health intervention policies and awareness campaigns. The recent developments of natural language processing (NLP) and deep learning (DL) models have exposed noteworthy performance in sentiment analysis. With this in mind, this paper presents a new sunflower optimization with deep-learning-driven sentiment analysis and classification (SFODLD-SAC) on COVID-19 tweets. The presented SFODLD-SAC model focuses on the identification of people's sentiments during the COVID-19 pandemic. To accomplish this, the SFODLD-SAC model initially preprocesses the tweets in distinct ways such as stemming, removal of stopwords, usernames, link punctuations, and numerals. In addition, the TF-IDF model is applied for the useful extraction of features from the preprocessed data. Moreover, the cascaded recurrent neural network (CRNN) model is employed to analyze and classify sentiments. Finally, the SFO algorithm is utilized to optimally adjust the hyperparameters involved in the CRNN model. The design of the SFODLD-SAC technique with the inclusion of an SFO algorithm-based hyperparameter optimizer for analyzing people's sentiments on COVID-19 shows the novelty of this study. The simulation analysis of the SFODLD-SAC model is performed using a benchmark dataset from the Kaggle repository. Extensive, comparative results report the promising performance of the SFODLD-SAC model over recent state-of-the-art models with maximum accuracy of 99.65%.