Case report: Reversible encephalopathy caused by dyskinesia-hyperpyrexia syndrome

Parkinson's disease (PD) is a common neurodegenerative disorder. Some patients with advanced-stage disease are accompanied by emergencies and critical issues such as dyskinesia-hyperpyrexia syndrome (DHS), parkinsonism-hyperpyrexia syndrome (PHS), and serotonin syndrome (SS). In this study, we report a patient with reversible encephalopathy caused by DHS who presented with an acute onset of fidgetiness, dyskinesia, and hyperpyrexia after antiparkinsonian drug abuse. In the present case, brain magnetic resonance imaging (MRI) showed multiple abnormal signals in the cortex and subcortex of the bilateral parietal and occipital lobes that resolved within weeks, which coincided with the characteristic MRI findings in posterior reversible encephalopathy (PRES). Our report expands on the neuroimaging features of DHS and highlights the importance of early identification, diagnosis, and treatment to improve patient prognosis

Outcomes of single- vs two-stage primary joint arthroplasty for septic arthritis: a systematic review and meta-analysis

Purpose: Septic arthritis (SA) is an intra-articular infection caused by purulent bacteria and the only effective method is surgical intervention. Two-stage arthroplasty is considered the gold standard treatment for SA, but recent studies have found that single-stage arthroplasty can achieve the same efficacy as two-stage arthroplasty. This study aimed to compare the efficacy of single- vs two-stage arthroplasty in the treatment of (acute or quiescent) SA. Methods: The review process was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched the PubMed, EMBASE, Medline, and Cochrane Library databases to identify all literature on the treatment of SA using single- and two-stage arthroplasty from the date of database inception to November 10, 2022. Data on reinfection rates were expressed as odds ratios and 95% CIs. Results: Seven retrospective studies with a total of 413 patients were included. Pooled analysis showed no difference in the reinfection rate between single- and two-stage arthroplasty. Subgroup analysis found no difference between the single- and two-stage arthroplasty groups in the incidence of purulent infection of the hip and knee. Cumulative meta-analysis showed gradual stabilization of outcomes. Conclusions: Based on our meta-analysis of available retrospective studies, we found no significant difference in reinfection rates between single- and two-stage arthroplasty for SA. Further prospective cohort studies are needed to confirm our results, although our meta-analysis provides important insights into the current literature on this topic

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

Smart meters generate a massive volume of energy consumption data which can be analyzed to recover some interesting and beneficial information. Non-intrusive load monitoring (NILM) is one important application fostered by the mass deployment of smart meters. This paper presents a supervised event-based NILM approach for non-linear appliance activities identification. Firstly, the additive properties (stating that, when a certain amount of specific appliances’ feature is added to their belonging network, an equal amount of change in the network’s feature can be observed) of three features (harmonic feature, voltage–current trajectory feature, and active–reactive–distortion (PQD) power curve features) were investigated through experiments. The results verify the good additive property for the harmonic features and Voltage–Current (U-I) trajectory features. In contrast, PQD power curve features have a poor additive property. Secondly, based on the verified additive property of harmonic current features and the representation of waveforms, a harmonic current features based approach is proposed for NILM, which includes two main processes: event detection and event classification. For event detection, a novel model is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Compared to other event detectors, the proposed event detector not only can detect both event timestamp and two adjacent steady states but also shows high detection accuracy over public dataset with F1-score up to 98.99%. Multi-layer perceptron (MLP) classifiers are then built for multi-class event classification using the harmonic current features and are trained using the data collected from the laboratory and the public dataset. The results show that the MLP classifiers have a good performance in classifying non-linear loads. Finally, the proposed harmonic current features based approach is tested in the laboratory through experiments, in which multiple on–off events of multiple appliances occur. The research indicates that clustering-based event detection algorithms are promising for future works in event-based NILM. Harmonic current features have perfect additive property, and MLP classifier using harmonic current features can accurately identify typical non-linear and resistive loads, which could be integrated with other approaches in the future