Overview on Juvenile Primary Fibromyalgia Syndrome

JPFS (juvenile primary fibromyalgia syndrome) is a musculoskeletal pain illness that affects children and adolescents. The intricacy of the clinical picture in JPFS has not been adequately characterized in the literature. JFMS symptoms are sometimes difficult to compare to adult fibromyalgia syndrome since many of them are "medically unexplained" and frequently overlap with other medical disorders.  The etiology of the illness is multifaceted, with impaired central pain processing being a significant contributor. Musculoskeletal pain that is severe and pervasive is the defining symptom. Other signs and symptoms include headaches, stiffness, subjective joint swelling, sleep and mood disorders, and headaches. Multiple sensitive spots might be found during a physical examination. The diagnosis has certain criteria and is clinical. Early detection and treatment are crucial. The gold standard of care combines a variety of modalities, but most significantly, exercise and cognitive behavioral therapy. The outlook varies, and symptoms might last well into adulthood. Discussing the epidemiology, etiology, pathophysiology, clinical symptoms, diagnosis, and management of JPFS is the goal of the review

Echo after Transcatheter aortic valve implantation (TAVI) in Medina Cardiac Centre

Introduction: In patients (pts) with aortic valve stenosis (AS), it has been demonstrated that left atrium (LA) mechanics assessed by speckle tracking echocardiography (STE) were reduced compared with controls. LA strain and strain rate parameters are more affected in AS than in pts with hypertension, despite a similar extent of left ventricle (LV) hypertrophy and LA dilatation. Reverse remodeling process of LA post aortic valve replacement was observed in many studies. The aim of this study was to assess LA remodeling and LV diastolic functions after Transcatheter Aortic Valve Implantation (TAVI). Methodology: All patients who had TAVI in the period of 2013 and 2016 at Medina Cardiac Centre were included. LA functions were assessed by STE using TOMTEC software before and 12 ± 3 months after TAVI. In apical 2 chamber view, the average Peak negative strain of left wall of LA, right wall of LA and LA roof were determined. LA volumes and LV diastolic parameters were measured. Results: Eighty patients with severe symptomatic AS were retrospectively enrolled (79.5 ± 4Y, aortic valve area 0.73 ± 0.4cm2 and Logistic Euro score 15.0 ± 13.9. At 12 ± 3 months post TAVI LA diastolic volume index decreased from 36.1 ± 5.3 mL/m2 to 33.9  ± 4.6 mL/m2 (P = 0.2), LA systolic volume index decreased from 24.7 ± 4.5 mL/m2 to 20.7 ± 3.9 mL/m2 (p = 0.3) and LA fraction improved from 20.5 ± 3.1% to 24 ± 3.5% (P = 0.5).The average peak negative LA strain was low at −8.2 ± 7.1% and improved to −13.1 ± 6.8% (P = 0.02). LA strain rate improved from −0.79 ± 0.48 s-1 to −1.12 ± 0.36 s-1 (P = 0.05). The Diastolic parameters including E/A ratio, deceleration time and E/ e- are significantly improved. E/A ratio improved from 1.68 ± 0.5 to 1.2 ± 0.4 (P < 0.05). Deceleration time increased from 215 ± 21 ms to 226 ± 23 ms (P < 0.05). E/e- decreased from 17.8 ± 2.1 to 14.6 ± 1.9 (P < 0.05). Conclusion: Significant improvement of LA mechanical parameters assessed by STE reflects improvement of LA remodeling and recovery of LV diastolic function post TAVI

Metformin use among obese patients with prediabetes in Qassim, Saudi Arabia: An observational study

Background and aims: The high prevalence of prediabetes and diabetes mellitus and its secondary complications in Saudi Arabia is a major healthcare concern. Evidence suggests that despite evidence-based efficacy and safety, metformin is underutilized in prediabetic obese patients. Thus, the aim of this study was to investigate the use of metformin in prediabetic obese patients in the Qassim region of Saudi Arabia. Methods: Prediabetic patients' electronic health records were accessed and screened from 2017 to 2021. The inclusion criteria were patients with obesity (BMI ≥ 35) diagnosed with prediabetes, and who received metformin. Patients with chronic kidney disease and those using metformin for other diseases were excluded. The first major endpoint of this study was the rate of metformin use among obese, prediabetic individuals. The second major endpoint was the factors associated with metformin prescribing in our cohort. Descriptive statistics were used to report the primary and secondary outcomes. Data are presented as percentages, means, standard deviations (SDs), medians, and interquartile ranges, as appropriate. All analyses were conducted using Stata version 16.1. Results: A total of 304 prediabetic patients were included in this study after screening the records of 1,789 patients. The average age was found to be 40, and the majority were female (72%). The average BMI was found to be 39.4 kg/m2, while the average HbA1c was 5.8%. In the entire sample, only 25 (8.22%) obese patients received metformin for diabetes prevention. Among obese patients with a BMI ≥ 30, 19 patients (8.7%) received metformin. Metformin users had higher odds of being on statins (OR 2.72, 95% CI 1.01 to 7.36; p = 0.049). Conclusion: According to the study, metformin is not frequently prescribed to prediabetic obese individuals in the Qassim region of Saudi Arabia. This prevention strategy is a missed opportunity in the management of prediabetes in high-risk patients. Future studies are needed to investigate the root causes of the underuse of metformin and potential interventions to promote evidence-based practice in Saudi Arabia

Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network

Technology evaluation in the electronics field leads to the great development of Wireless Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in hazardous environments, and they are operated by isolated battery sources. Network connectivity is purely based on power availability, which impacts the network lifetime. Hence, power must be used wisely to prolong the network lifetime. The sensor nodes that fail due to power have to detect quickly to maintain the network. In a WSN, classifiers are used to detect the faults for checking the data generated by the sensor nodes. In this paper, six classifiers such as Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, Stochastic Gradient Descent, Random Forest and Probabilistic Neural Network have been taken for analysis. Six different faults (Offset fault, Gain fault, Stuck-at fault, Out of Bounds, Spike fault and Data loss) are injected in the data generated by the sensor nodes. The faulty data are checked by the classifiers. The simulation results show that the Random Forest detected more faults and it also outperformed all other classifiers in that category

Hybrid 2D-CMOS microchips for memristive applications

: Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry1,2. However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm2) devices on unfunctional SiO2-Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm2) interconnection3 and as a channel of large transistors (roughly 16.5 µm2)&nbsp;(refs.&nbsp;4,5), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D-CMOS hybrid microchips for memristive applications-CMOS stands for complementary metal-oxide-semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm2. We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications

Hardware implementation of memristor-based artificial neural networks

Abstract Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach