Continuous Spikes and Waves during Sleep: Electroclinical Presentation and Suggestions for Management

Continuous spikes and waves during sleep (CSWS) is an epileptic encephalopathy characterized in most patients by (1) difficult to control seizures, (2) interictal epileptiform activity that becomes prominent during sleep leading to an electroencephalogram (EEG) pattern of electrical status epilepticus in sleep (ESES), and (3) neurocognitive regression. In this paper, we will summarize current epidemiological, clinical, and EEG knowledge on CSWS and will provide suggestions for treatment. CSWS typically presents with seizures around 2–4 years of age. Neurocognitive regression occurs around 5-6 years of age, and it is accompanied by subacute worsening of EEG abnormalities and seizures. At approximately 6–9 years of age, there is a gradual resolution of seizures and EEG abnormalities, but the neurocognitive deficits persist in most patients. The cause of CSWS is unknown, but early developmental lesions play a major role in approximately half of the patients, and genetic associations have recently been described. High-dose benzodiazepines and corticosteroids have been successfully used to treat clinical and electroencephalographic features. Corticosteroids are often reserved for refractory disease because of adverse events. Valproate, ethosuximide, levetiracetam, sulthiame, and lamotrigine have been also used with some success. Epilepsy surgery may be considered in a few selected patients

Localization of the Epileptogenic Foci in Tuberous Sclerosis Complex: A Pediatric Case Report

Tuberous sclerosis complex (TSC) is a rare disorder of tissue growth and differentiation, characterized by benign hamartomas in the brain and other organs. Up to 90% of TSC patients develop epilepsy and 50% become medically intractable requiring resective surgery. The surgical outcome of TSC patients depends on the accurate identification of the epileptogenic zone consisting of tubers and the surrounding epileptogenic tissue. There is conflicting evidence whether the epileptogenic zone is in the tuber itself or in abnormally developed surrounding cortex. Here, we report the localization of the epileptiform activity among the many cortical tubers in a 4-year-old patient with TSC-related refractory epilepsy undergoing magnetoencephalography (MEG), electroencephalography (EEG), and diffusion tensor imaging (DTI). For MEG, we used a prototype system that offers higher spatial resolution and sensitivity compared to the conventional adult systems. The generators of interictal activity were localized using both EEG and MEG with equivalent current dipole (ECD) and minimum norm estimation (MNE) methods according to the current clinical standards. For DTI, we calculated four diffusion scalar parameters for the fibers passing through four ROIs defined: (i) at a large cortical tuber identified at the right quadrant, (ii) at the normal appearing tissue contralateral to the tuber, (iii) at the cluster formed by ECDs fitted at the peak of interictal spikes, and (iv) at the normal appearing tissue contralateral to the cluster. ECDs were consistently clustered at the vicinity of the large calcified cortical tuber. MNE and ECDs indicated epileptiform activity in the same areas. DTI analysis showed differences between the scalar values of the tracks passing through the tuber and the ECD cluster. In this illustrative case, we provide evidence from different neuroimaging modalities, which support the view that epileptiform activity may derive from abnormally developed tissue surrounding the tuber rather than the tuber itself

A Study to evaluate the Effectiveness of nutrition ball on haemoglobin level among adolescent girls with iron deficiency anaemia at selected industry Hostel in Madurai

A Study to evaluate the Effectiveness of nutrition ball on haemoglobin level among adolescent girls with iron deficiency anaemia at selected industry Hostel in Madurai. Health is a fundamental human right and health is central to the concept of quality life. Adolescent is a period of second decade of life. Eating right food right time will prevent the nutritional deficiencies especially iron deficiency. Iron deficiency anemia is a public problem that is increasing throughout the world especially in developing countries. The study was aimed to assessing the haemoglobin level and improves the haemoglobin level through nutrition ball intervention. METHODOLOGY: A quantitative approach Quasi experimental – one group pre test and post test design was used in this study. A sample size of 60 adolescent girls with iron deficiency anemia selected by Non probability Purposive sampling technique was used to collect the samples. The modified Abdellah‘s Typology of Nursing Problems model (1960) was adopted for this study. The stool used for this study was demographic variables of adolescent girls, Clinical assessment of symptoms of anemia with observation checklist, Clinical assessment of hemoglobin estimation among adolescent girls before and after nutritional intervention (Sahli‘s method of haemoglobin testing). FINDING OF THE STUDY: It reveals that the ‘t' value 18.48 was much higher than the table value at 0.001 (pre set level of significance was 0.05). The mean post test score of haemoglobin level will be significantly higher than their mean test score of haemoglobin level. The symptoms are reduced after nutrition ball intervention. CONCLUSION: Deworming and Nutrition ball intervention provided to the adolescent girls improved their heamoglobin level and reduced the symptoms of anemia there by incidence of complications of anemia was prevented

A novel DHT Routing Protocol for MANETs

The central challenge in Mobile Ad hoc Networks (MANETs) is to provide a stable routing strategy without depending on any central administration. This work presents and examines the working of Radio Ring Routing Protocol (RRRP), a DHT based routing protocol for MANETs inspired from structured overlays in the internet. This design joins effort in answering the fundamental question of efficiency of a DHT substrate compared to conventional routing in ad hoc networks

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to PPG signals can predict BGLs with high accuracy. However, training DNN models requires large and diverse datasets, which are typically distributed across multiple healthcare institutions. Privacy concerns and regulatory restrictions further limit data sharing, making conventional centralized machine learning (ML) approaches less effective. To address these challenges, this study proposes a federated learning (FL)-based solution that enables multiple healthcare organizations to collaboratively train a global model without sharing raw patient data, thereby enhancing model performance while ensuring data privacy and security. In the data preprocessing stage, continuous wavelet transform (CWT) is applied to smooth PPG signals and remove baseline drift. Adaptive cycle-based segmentation (ACBS) is then used for signal segmentation, followed by particle swarm optimization (PSO) for feature selection, optimizing classification accuracy. The proposed system was evaluated on diverse datasets, including VitalDB and MUST, under various conditions with data collected during surgery and anesthesia. The model achieved a root mean square error (RMSE) of 19.1 mg/dL, demonstrating superior predictive accuracy. Clarke error grid analysis (CEGA) confirmed the model’s clinical reliability, with 99.31% of predictions falling within clinically acceptable limits. The FL-based approach outperformed conventional deep learning models, making it a promising method for non-invasive, privacy-preserving glucose monitoring

Epileptic Spasms in CDKL5 Deficiency Disorder: Delayed Treatment and Poor Response to First-Line Therapies

OBJECTIVE: We aimed to assess the treatment response of infantile-onset epileptic spasms (ES) in CDKL5 deficiency disorder (CDD) vs other etiologies. METHODS: We evaluated patients with ES from the CDKL5 Centers of Excellence and the National Infantile Spasms Consortium (NISC), with onset from 2 months to 2 years, treated with adrenocorticotropic hormone (ACTH), oral corticosteroids, vigabatrin, and/or the ketogenic diet. We excluded children with tuberous sclerosis complex, trisomy 21, or unknown etiology with normal development because of known differential treatment responses. We compared the two cohorts for time to treatment and ES remission at 14 days and 3 months. RESULTS: We evaluated 59 individuals with CDD (79% female, median ES onset 6 months) and 232 individuals from the NISC database (46% female, median onset 7 months). In the CDD cohort, seizures prior to ES were common (88%), and hypsarrhythmia and its variants were present at ES onset in 34%. Initial treatment with ACTH, oral corticosteroids, or vigabatrin started within 1 month of ES onset in 27 of 59 (46%) of the CDD cohort and 182 of 232 (78%) of the NISC cohort (p \u3c .0001). Fourteen-day clinical remission of ES was lower for the CDD group (26%, 7/27) than for the NISC cohort (58%, 106/182, p = .0002). Sustained ES remission at 3 months occurred in 1 of 27 (4%) of CDD patients vs 96 of 182 (53%) of the NISC cohort (p \u3c .0001). Comparable results were observed with longer lead time (≥1 month) or prior treatment. Ketogenic diet, used within 3 months of ES onset, resulted in ES remission at 1 month, sustained at 3 months, in at least 2 of 13 (15%) individuals with CDD. SIGNIFICANCE: Compared to the broad group of infants with ES, children with ES in the setting of CDD often experience longer lead time to treatment and respond poorly to standard treatments. Development of alternative treatments for ES in CDD is needed

A Deep Sparse Capsule Network for Non-Invasive Blood Glucose Level Estimation Using a PPG Sensor

Diabetes, a chronic medical condition, affects millions of people worldwide and requires consistent monitoring of blood glucose levels (BGLs). Traditional invasive methods for BGL monitoring can be challenging and painful for patients. This study introduces a non-invasive, deep learning (DL)-based approach to estimate BGL using photoplethysmography (PPG) signals. Specifically, a Deep Sparse Capsule Network (DSCNet) model is proposed to provide accurate and robust BGL monitoring. The proposed model’s workflow includes data collection, preprocessing, feature extraction, and predictions. A hardware module was designed using a PPG sensor and Raspberry Pi to collect patient data. In preprocessing, a Savitzky–Golay filter and moving average filter were applied to remove noise and preserve pulse form and high-frequency components. The DSCNet model was then applied to predict the sugar level. Two models were developed for prediction: a baseline model, DSCNet, and an enhanced model, DSCNet with self-attention. DSCNet’s performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Relative Difference (MARD), and coefficient of determination (R2), yielding values of 3.022, 0.05, 0.058, 0.062, 10.81, and 0.98, respectively