Prescribing Pattern of Anti-epileptic drugs in Pediatric Patients at a Tertiary Care Teaching Hospital: Pattern of Anti-epileptic drugs in Pediatric Patients

 Introduction: Epilepsy is one of the common neurological conditions worldwide. Material& Methods: The observational, continuous, prospective, single center study was carried out to evaluate prescribing pattern of anti-epileptic drugs (AEDs) in pediatric patients at a tertiary care teaching hospital of Gujarat for a total duration of 18 months.       Patients were followed up monthly for the period of three months to evaluate seizure freedom, breakthrough seizure, and change in the AEDs, add-on therapy, treatment adherence and ADRs. Rationality was assessed for selection of drug and selection of right dose according to recent guidelines. Results: Majority of patients in the age group of 7 to 9 years with mean age of 6.83 ± 3.09 years.  Male to female ratio was 1.3:1. Total 73 AEDs were prescribed to pediatric epilepsy patients after diagnosis. 37 (69.80 %) patients were prescribed AED on visit as monotherapy and 16 (30.20 %) patients were prescribed polytherapy. Sodium valproate (77.36 %) was most commonly prescribed AED followed by levetiracetam and carbamazepine .Most of AEDs were prescribed according to NICE guideline and by generic name. Conclusion: Conventional AEDs are still used as first line of treatment for pediatric epilepsy patients, although newer AEDs also frequently prescribed as add on or primary drug. Low birth weight, NICU admission and non compliance to treatment are associated with breakthrough seizures.                                                                                                 &nbsp

An unusual cause of unilateral facial injuries caused by horseshoe headrest during prone positional craniovertebral junction surgery

Pressure injuries are an accepted complication of prone positioning during the neurosurgical procedures. Horseshoe headrest are intended to reduce the incidence and severity of such injuries by allowing limited areas of contact between the skin of dependent areas of contact and the supporting surfaces. We report a case where a patient positioned prone over a horseshoe headrest developed inadvertent unilateral facial pressure injuries following a 6-h long craniovertebral junction (CVJ) surgery. We attempt to highlight this complication, analyze its causation, and briefly review the existing literature related to similar reported injuries

Improvements in essential newborn care and newborn resuscitation services following a capacity building and quality improvement program in three districts of Uttar Pradesh, India

Background: Neonatal death remains a global challenge contributing to 45% of underfive deaths. With rising institutional delivery, to accelerate decline in neonatal mortality rate (NMR) improvement in the quality of perinatal care requires attention. Objectives: This implementation research targeted improving service delivery readiness for quality of newborn care at public health facilities in three districts of Uttar Pradesh, India, with high NMR. Materials and Methods: This before-after study assessed the facility readiness and quality of newborn services at 42 health facilities. The changes in 26 signal functions for routine and emergency obstetric and newborn care were tracked. Results: There was marked improvement in newborn service availability: skilled birth attendants (51%), resuscitation (30%), and kangaroo mother care (27%) at these facilities. A multifold rise in newborn resuscitation efforts and documentation (n = 4431 vs. n = 144 in preintervention period) with high success rate (98.6%) was observed. There was also improvement in obstetric care services including partograph use (31%) and active management of third stage of labor (46%). However, several infrastructural indicators (electricity, water supply, toilets, and sanitation) remained unchanged. Conclusion: Overall improvements were observed in the majority of the signal functions for perinatal care and newborn resuscitation efforts. There was a limited impact on the infrastructural and supervision components

Monkeypox Diagnosis With Interpretable Deep Learning

As the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeypox disease has raised concerns about another potential pandemic, highlighting the urgency of early detection and intervention to curb its transmission. Deep Learning (DL)-based disease prediction presents a promising solution, offering affordable and accessible diagnostic services. In this study, we harnessed Transfer Learning (TL) techniques to tweak and assess the performance of an array of six different DL models, encompassing VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, VGG19, and Vision Transformer (ViT). Among this diverse collection, it was the modified versions of the VGG19 and MobileNetV2 models that outshone the others, boasting striking accuracy rates ranging from an impressive 93% to an astounding 99%. Our results echo the findings of recent research endeavors that similarly showcase enhanced performance when developing disease diagnostic models armed with the power of TL. To add to this, we used Local Interpretable Model Agnostic Explanations (LIME) to lend a sense of transparency to our model’s predictions and identify the crucial features correlating with the onset of Monkeypox disease. These findings offer significant implications for disease prevention and control efforts, particularly in remote and resource-limited areas

Cloud Based Multi-Robot Task Scheduling Using PMW Algorithm

Scheduling of robots is one of the imperative assignment in a multi robot system. Scheduling is prerequisite when there is a multiple task need to be assigned to multi robot in an arranged manner. There is a growing need for robots to perform complex tasks autonomously. Multi-robot environment becomes complex as there are multiple factors need to be addressed simultaneously which require fast computation and more space. Using cloud computing platform could be one of the optimal solution for this problem. This paper presents the use of cloud computing platform for implementing the proposed Periodic Min-Max Algorithm (PMW) for multi robot task scheduling. Amazon web service (AWS) platform is utilized for deploying the algorithm for multi robot task scheduling. The task performed by the robots is considered as a single service in context with cloud platform and it withdraw an advantage when the number of services increases with time. Time requirement to complete the task and the load balancing parameter are analysed using the proposed approach and is compared with other relevant work. The results presented in the paper clearly shows the performance improvement in both the parameters. There is an improvement of about 3-7% in both the parameters and are reported in the paper. The paper also emphasize on the deployment of cloud computing platform for the service robots. Time completion factor is analysed and reported in the paper to proof the advantage of using cloud platform for the service robots. The novel way of using the algorithm with cloud server seeks many advantage are also observed, analysed and presented in the paper