Virtual reality in pediatrics, effects on pain and anxiety: A systematic review and meta-analysis update

Introduction: Medical procedures are often accompanied by pain and anxiety in pediatric patients. A relatively new technique to reduce pediatric pain and anxiety is virtualreality. Virtual reality is both applied as a distraction tool and as an exposure tool to prepare patients for medical procedures. Research into the application of virtual reality inmedical settings is rapidly evolving. This meta-analysis is an update of the meta-analysisof Eijlers et al. investigating the effectiveness of virtual reality as an intervention tool onpain and anxiety in pediatric patients undergoing medical procedures.Methods: We searched the databases Embase, Medline, Web of Science CoreCollection, Cochrane Central Register of Controlled Trials and PsycINFO. For eachof these databases, different search strategies were developed. The search periodfrom the meta-analysis from Eijlers et al., reaching until April 2018, was extended toDecember 2020. Pain and anxiety outcomes during medical procedures were compared for virtual reality and standard care conditions for various medical procedures.Results: The search yielded 1824 articles, of which 13 met our inclusion criteria.Combined with 13 articles of Eijlers' review study, this resulted in 26 articles. Virtualreality was applied as distraction (n = 23) during medical procedures or as exposure(n = 4) before medical procedures. The effect of virtual reality distraction was mostlystudied in patients during venous access (n = 10). The overall weighted standardizedmean difference for virtual reality distraction was −0.67 (95% CI, −0.89 to −0.45;p< .001) on patient-reported pain (based on 21 studies) and −0.74 (95% CI, −1.00to −0.48; p< .001) on patient-reported anxiety (based on 10 studies). The effect ofvirtual reality as an exposure tool on patient-reported anxiety was significant too(standardized mean difference = −0.58; 95% CI, −1.15 to −0.01; p< .05).Discussion: The current updated systematic review and meta-analysis indicates thatvirtual reality is a useful tool to reduce pain and anxiety in pediatric patients undergoing a range of medical procedures as it significantly decreases pain and anxietyoutcomes when compared to care as usual

Concomitant granule cell neuronopathy in patients with natalizumab-associated PML

Granule cell neuronopathy (GCN) is a rare JC virus infection of the cerebellar granule cell neurons in immunocompromised patients. On brain imaging, GCN is characterized by cerebellar atrophy which can be accompanied by infratentorial white matter lesions. The objective of this study is to investigate the prevalence of MRI findings suggestive of GCN in a large natalizumab-associated progressive multifocal leukoencephalopathy (PML) cohort. MRI scans from before, at the time of, and during follow-up after diagnosis of PML in 44 natalizumab-treated MS patients, and a control group of 25 natalizumab-treated non-PML MS patients were retrospectively reviewed for imaging findings suggestive of GCN. To assess and quantify the degree of cerebellar atrophy, we used a 4 grade rating scale. Three patients in the PML group showed imaging findings suggestive of GCN and none in the control group. In two of these PML patients, cerebellar atrophy progressed from grade 0 at the time of diagnosis of isolated supratentorial PML to grade 1 and 2 after 2.5 and 3 months, respectively, in the absence of infratentorial white mater lesions. The third patient had grade 1 cerebellar atrophy before diagnosis of infra- and supratentorial PML, and showed progression of cerebellar atrophy to grade 2 in the 3 months following PML diagnosis. None of the other eight patients with infratentorial PML lesions developed cerebellar atrophy suggestive of GCN. Three cases with imaging findings suggestive of GCN were detected among 44 natalizumab-associated PML patients. GCN may, therefore, be more common than previously considered in natalizumab-associated PML patients

Mono/Multi-material Characterization Using Hyperspectral Images and Multi-Block Non-Negative Matrix Factorization

Plastic sorting is a very essential step in waste management, especially due to the presence of multilayer plastics. These monomaterial and multimaterial plastics are widely employed to enhance the functional properties of packaging, combining beneficial properties in thickness, mechanical strength, and heat tolerance. However, materials containing multiple polymer species need to be pretreated before they can be recycled as monomaterials and therefore should not end up in monomaterial streams. Industry 4.0 has significantly improved materials sorting of plastic packaging in speed and accuracy compared to manual sorting, specifically through Near Infrared Hyperspectral Imaging (NIRHSI) that provides an automated, fast, and accurate material characterization, without sample preparation. Identification of multimaterials with HSI however requires novel dedicated approaches for chemical pattern recognition. Non negative Matrix Factorization, NMF, is widely used for the chemical resolution of hyperspectral images. Chemically relevant model constraints may make it specifically valuable to identify multilayer plastics through HSI. Specifically, Multi Block Non Negative Matrix Factorization (MBNMF) with correspondence among different chemical species constraint may be used to evaluate the presence or absence of particular polymer species. To translate the MBNMF model into an evidence based sorting decision, we extended the model with an F test to distinguish between monomaterial and multimaterial objects. The benefits of our new approach, MBNMF, were illustrated by the identification of several plastic waste objects

Systematic reduction of Hyperspectral Images for high-throughput Plastic Characterization

Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information. Recent high-tech developments in chemometrics enable automated and evidence-based data reduction, which can substantially enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a widely used algorithm for chemical resolution of HSI data. By recovering the pure contribution maps and spectral profiles of distributed compounds, NMF can provide evidence-based sorting decisions for efficient waste management. To improve the quality and efficiency of data analysis on hyperspectral imaging (HSI) data, we apply a convex-hull method to select essential pixels and wavelengths and remove uninformative and redundant information. This process minimizes computational strain and effectively eliminates highly mixed pixels. By reducing data redundancy, data investigation and analysis become more straightforward, as demonstrated in both simulated and real HSI data for plastic sorting

Complete genome sequence of the Clostridium difficile laboratory strain 630Δerm reveals differences from strain 630, including translocation of the mobile element CTn5

Molecular Technology and Informatics for Personalised Medicine and Healt

From more testing to smart testing:data-guided SARS-CoV-2 testing choices, the Netherlands, May to September 2020

BACKGROUND: SARS-CoV-2 RT-PCR assays are more sensitive than rapid antigen detection assays (RDT) and can detect viral RNA even after an individual is no longer infectious. RDT can reduce the time to test and the results might better correlate with infectiousness. AIM: We assessed the ability of five RDT to identify infectious COVID-19 cases and systematically recorded the turnaround time of RT-PCR testing. METHODS: Sensitivity of RDT was determined using a serially diluted SARS-CoV-2 stock with known viral RNA concentration. The probability of detecting infectious virus at a given viral load was calculated using logistic regression of viral RNA concentration and matched culture results of 78 specimens from randomly selected non-hospitalised cases. The probability of each RDT to detect infectious cases was calculated as the sum of the projected probabilities for viral isolation success for every viral RNA load found at the time of diagnosis in 1,739 confirmed non-hospitalised COVID-19 cases. RESULTS: The distribution of quantification cycle values and estimated RNA loads for patients reporting to drive-through testing was skewed to high RNA loads. With the most sensitive RDT (Abbott and SD Biosensor), 97.30% (range: 88.65–99.77) of infectious individuals would be detected. This decreased to 92.73% (range: 60.30–99.77) for Coris BioConcept and GenBody, and 75.53% (range: 17.55–99.77) for RapiGEN. Only 32.9% of RT-PCR results were available on the same day as specimen collection. CONCLUSION: The most sensitive RDT detected infectious COVID-19 cases with high sensitivity and may considerably improve containment through more rapid isolation and contact tracing

UNC13A in amyotrophic lateral sclerosis: from genetic association to therapeutic target

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with limited treatment options and an incompletely understood pathophysiology. Although genomewide association studies (GWAS) have advanced our understanding of the disease, the precise manner in which risk polymorphisms contribute to disease pathogenesis remains unclear. Of relevance, GWAS have shown that a polymorphism (rs12608932) in the UNC13A gene is associated with risk for both ALS and frontotemporal dementia (FTD). Homozygosity for the C-allele at rs12608932 modifies the ALS phenotype, as these patients are more likely to have bulbar-onset disease, cognitive impairment and FTD at baseline as well as shorter survival. UNC13A is expressed in neuronal tissue and is involved in maintaining synaptic active zones, by enabling the priming and docking of synaptic vesicles. In the absence of functional TDP-43, risk variants in UNC13A lead to the inclusion of a cryptic exon in UNC13A messenger RNA, subsequently leading to nonsense mediated decay, with loss of functional protein. Depletion of UNC13A leads to impaired neurotransmission. Recent discoveries have identified UNC13A as a potential target for therapy development in ALS, with a confirmatory trial with lithium carbonate in UNC13A cases now underway and future approaches with antisense oligonucleotides currently under consideration. Considering UNC13A is a potent phenotypic modifier, it may also impact clinical trial outcomes. This present review describes the path from the initial discovery of UNC13A as a risk gene in ALS to the current therapeutic options being explored and how knowledge of its distinct phenotype needs to be taken into account in future trials

Activity-based probes for functional interrogation of retaining β-glucuronidases

Humans express at least two distinct β-glucuronidase enzymes that are involved in disease: exo-acting β-glucuronidase (GUSB), whose deficiency gives rise to mucopolysaccharidosis type VII, and endo-acting heparanase (HPSE), whose overexpression is implicated in inflammation and cancers. The medical importance of these enzymes necessitates reliable methods to assay their activities in tissues. Herein, we present a set of β-glucuronidase-specific activity-based probes (ABPs) that allow rapid and quantitative visualization of GUSB and HPSE in biological samples, providing a powerful tool for dissecting their activities in normal and disease states. Unexpectedly, we find that the supposedly inactive HPSE proenzyme proHPSE is also labeled by our ABPs, leading to surprising insights regarding structural relationships between proHPSE, mature HPSE, and their bacterial homologs. Our results demonstrate the application of β-glucuronidase ABPs in tracking pathologically relevant enzymes and provide a case study of how ABP-driven approaches can lead to discovery of unanticipated structural and biochemical functionality