Microbiological Contamination of Mobile Phones of Clinicians in Intensive Care Units and Neonatal Care Units in Public Hospitals in Kuwait

Background: The objective of this study was to explore the prevalence of microbiological contamination of mobile phones that belong to clinicians in intensive care units (ICUs), pediatric intensive care units (PICUs), and neonatal care units (NCUs) in all public secondary care hospitals in Kuwait. The study also aimed to describe mobile phones disinfection practices as well as factors associated with mobile phone contamination. Methods: This is a cross-sectional study that included all clinicians with mobile phones in ICUs, PICUs, and NCUs in all secondary care hospitals in Kuwait. Samples for culture were collected from mobile phones and transported for microbiological identification using standard laboratory methods. Self-administered questionnaire was used to gather data on mobile phones disinfection practices. Results: Out of 213 mobile phones, 157 (73.7 %, 95 % CI [67.2-79.5 %]) were colonized. Coagulase-negative staphylococci followed by Micrococcus were predominantly isolated from the mobile phones; 62.9 % and 28.6 % of all mobile phones, respectively. Methicillin-resistant Staphylococcus aureus (MRSA) and Gram-negative bacteria were identified in 1.4 % and 7.0 % of the mobile phones, respectively. Sixty-eight clinicians (33.5 %) reported that they disinfected their mobile phones, with the majority disinfecting their mobile phones only when they get dirty. The only factor that was significantly associated with mobile phone contamination was whether a clinician has ever disinfected his/her mobile phone; adjusted odds ratio 2.42 (95 % CI [1.08-5.41], p-value = 0.031). Conclusion: The prevalence of mobile phone contamination is high in ICUs, PICUs, and NCUs in public secondary care hospitals in Kuwait. Although some of the isolated organisms can be considered non-pathogenic, various reports described their potential harm particularly among patients in ICU and NCU settings. Isolation of MRSA and Gram-negative bacteria from mobile phones of clinicians treating patients in high-risk healthcare settings is of a major concern, and calls for efforts to consider guidelines for mobile phone disinfection

Improvement in facial aesthetics of orthognathic patients after surgery-first approach

Background: Facial appearance significantly affects psychosocial well-being, and improvement in facial aesthetics is considered an essential outcome of orthognathic treatment. The surgery-first approach (SFA) has emerged as a promising alternative to the conventional orthodontics-first approach (OFA), due to its potential advantages in reducing treatment duration and cost, delivering early aesthetic improvement, and increasing patient satisfaction. However, the impact of the SFA on final facial aesthetics, and how it compares with the OFA, has not yet been investigated. Method: This retrospective study aimed to compare the improvement in facial aesthetics after orthognathic surgery, in an SFA and an OFA group. Pre- and postoperative three-dimensional stereophotogrammetry facial images for 40 patients were evaluated by five professional assessors using the Global Aesthetic Improvement Scale (GAIS). Results: Similar aesthetic improvement outcomes were found for both the SFA and OFA groups. The GAIS score was significantly correlated with the following facial variables: upper lip projection, chin prominence, facial proportions, para-nasal hollowing, lip competence, mandibular projection, and facial profile. No significant correlation was found between the change in aesthetic score and the surgical variables. There was a positive association between the overall GAIS score and the gender and experience level of the individual assessors. Conclusion: This study suggests that the facial aesthetic improvement achieved with the SFA is satisfactory and comparable to that of the OFA

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

EEG-based brain connectivity analysis for identifying neurodevelopmental disorders

This dissertation aims to identify the neurological biomarkers that could assist in providing reliable, automated and objective prediction of neurodevelopmental disorders (NDDs) in early infancy. Quantitative electroencephalography analysis (qEEG), mainly phase synchronisation-based functional brain connectivity estimated using phase locking value (PLV) and weighted phase lag index (WPLI), were investigated to deduce whether it can be used for the early prediction of such disorders. The resulting connectivity network was quantitatively characterised using complex graph-theoretical features, namely transitivity, global efficiency, radius, diameter, and characteristic path length. These features were then fed into the machine learning algorithms such as linear discriminant analysis (LDA), support vector machine (SVM), decision tree and k-nearest neighbour to examine their discriminant capability in classifying /predicting NDDs. The proposed framework has gained initial validation in classifying autism spectrum disorders (ASD) from an experimentally obtained EEG data set of 24 children. Then, the framework was utilised to predict the appearance of cerebral palsy (CP) at two years of age. The EEG data were recorded within the first week after birth from a cohort of infants born with hypoxic-ischaemic encephalopathy (HIE). The exploration results revealed that the proposed analytical methodology successfully predicted the infants that would develop CP with a performance of 84.6% accuracy, 83% sensitivity, 85% specificity, 84% balanced accuracy and 0.85 area under the curve (AUC) in the delta band, with a close result also obtained in the theta and alpha bands. The WPLI and graph parameters were then used to predict the cognitive scores of infants born with HIE by developing the regression framework correlating these EEG features and a cognitive profile completed in a follow-up assessment at two years of age. The regression analysis showed that the radius feature yielded the best performance (root mean square error (RMSE)= 16.78, mean absolute error (MAE)= 12.07 and R-squared= 0.24). Although this study has successfully demonstrated that the qEEG features could be considered potential biomarkers for identifying the brain deficits causing the NDDs, it has a certain limitation due to the size of the data set. It needs to be validated on large trials with a statistically significant population

Classification of Autism Spectrum Disorder From EEG-based functional brain connectivity analysis

Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children

Characterization of the 12S rRNA Gene Sequences of the Harvester Termite <i>Anacanthotermes ochraceus</i> (Blattodea: Hodotermitidae) and Its Role as A Bioindicator of Heavy Metal Accumulation Risks in Saudi Arabia

Termites are social insects of economic importance that have a worldwide distribution. Identifying termite species has traditionally relied on morphometric characters. Recently, several mitochondrial genes have been used as genetic markers to determine the correlation between different species. Heavy metal accumulation causes serious health problems in humans and animals. Being involved in the food chain, insects are used as bioindicators of heavy metals. In the present study, 100 termite individuals of Anacanthotermes ochraceus were collected from two Saudi Arabian localities with different geoclimatic conditions (Riyadh and Taif). These individuals were subjected to morphological identification followed by molecular analysis using mitochondrial 12S rRNA gene sequence, thus confirming the morphological identification of A. ochraceus. Furthermore, a phylogenetic analysis was conducted to determine the genetic relationship between the acquired species and other termite species with sequences previously submitted in the GenBank database. Several heavy metals including Ca, Al, Mg, Zn, Fe, Cu, Mn, Ba, Cr, Co, Be, Ni, V, Pb, Cd, and Mo were measured in both collected termites and soil samples from both study sites. All examined samples (termite and soil) showed high concentrations of metals with different concentrations and ratios. Generally, most measured metals had a significantly high concentration in soil and termites at Taif, except for Ca, Cd, Co, Cr, Cu, Mg, and Ni showing significantly high concentrations at Riyadh. Furthermore, termites accumulated higher amounts of heavy metals than the soil at both locations. The mean concentrations of the measured metals in soil samples were found to be in the descending order Ca ˃ Al ˃ Mg ˃ Zn ˃ Fe ˃ Cu ˃ Mn ˃ Ba ˃ Cr ˃ Co ˃ Be ˃ Ni ˃ V ˃ Pb ˃ Cd ˃ Mo, while it was Ca ˃ Mg ˃ Al ˃ Fe ˃ Zn ˃ Cu ˃ Mn ˃ Be ˃ Ba ˃ Pb ˃ Cr ˃ V ˃ Ni ˃ Cd ˃ Mo ˃ Co in termite specimens. The mean concentrations of the studied metals were determined in the soil and termite specimens at both locations. In addition, the contamination factor, pollution load index (PLI) and degree of contamination were calculated for all studied metals in different samples, indicating that both studied sites were polluted. However, Taif showed a significantly higher degree of pollution. Thus, the accurate identification of economically important insects, such as termites, is of crucial importance to plan for appropriate control strategies. In addition, termites are a good bioindicator to study land pollution

Integrated management of the orthognathic patient

This chapter gives an overview of our approach to the management of dentofacial deformities, which are commonly corrected through a combi nation of orthodontic treatment and orthognathic surgery. The importance of the multidisciplinary team in comprehensively assessing and diagnosing the patient is emphasized, including the role of the psychologist. Even though there have been no profound changes in orthognathic surgical techniques or in the fundamentals of orthodontics, there have been some significant developments in the process of soft tissue prediction and occlusal planning, as well as the treatment pathways. Three-dimensional digital planning is increasingly being adopted and we will describe our state-of-the-art process in detail, along with discussion of recent developments and future aspirations. The conventional, orthodontics-first approach to orthognathic treatment is typically long, with presurgical orthodontics often accentuating the patient’s dentofacial deformity prior to surgical correction. The surgery-first approach has gained increasing attention in recent years, offering a potentially more time-efficient path way, with immediate correction of the skeletal jaw discrepancy. We will give an account of both treatment pathways, including their benefits and limitations, and discuss their indications. Evidence will be cited, where possible, to support our views