17 research outputs found
Feasibility of Using Convalescent Plasma Immunotherapy for MERS-CoV Infection, Saudi Arabia
We explored the feasibility of collecting convalescent plasma for passive immunotherapy of Middle East respiratory syndrome coronavirus (MERS-CoV) infection by using ELISA to screen serum samples from 443 potential plasma donors: 196 patients with suspected or laboratory-confirmed MERS-CoV infection, 230 healthcare workers, and 17 household contacts exposed to MERS-CoV. ELISA-reactive samples were further tested by indirect fluorescent antibody and microneutralization assays. Of the 443 tested samples, 12 (2.7%) had a reactive ELISA result, and 9 of the 12 had reactive indirect fluorescent antibody and microneutralization assay titers. Undertaking clinical trials of convalescent plasma for passive immunotherapy of MERS-CoV infection may be feasible, but such trials would be challenging because of the small pool of potential donors with sufficiently high antibody titers. Alternative strategies to identify convalescent plasma donors with adequate antibody titers should be explored, including the sampling of serum from patients with more severe disease and sampling at earlier points during illness
Next-generation DNA sequencing of medically relevant biofilms
Thesis by publication.Bibliography: pages 210-230.Introduction -- Chapter 1. Literature review -- Chapter 2. Materials and methods -- Chapter 3. Microbial biofilms associated with healthcare infections -- Chapter 4. Microbial biofilms and their role in implantable device-associated infections -- Chapter 5. Microbial biofilms in diabetic foot infections -- Chapter 6. Discussion and conclusions -- References -- Appendix.Background - A better understanding of the microbial communities in medical environments is crucial for improving human health, given the increasing problem of biofilm-related infections. With the rapidly expanding molecular methods opening new horizons in the study of the presence of microbial biofilm from samples collected within medical settings. The aims of this project were therefore to better understand the microbiome and the role of biofilm in (i) providing a protected source of pathogens that can cause healthcare-associated infections (HAIs), (ii) causing granulomatous reactions and its possible role in potentiating cancer, and (iii) chronic wound infections.Methods - We employed advanced molecular methods, including next-generation DNA sequencing, fluorescence in situ hybridisation (FISH) and real-time quantitative polymerase chain reaction (qPCR), along with confocal laser scanning and scanning electron microscopy, to investigate the project aims.Results(i) The majority of ICU surfaces sampled from three hospitals were contaminated with polymicrobial biofilms, which contained MDR strains, including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE) and extended spectrum beta-lactamase (ESBL) Sphingomonas paucimobilis. Similarly, a high number of patient-ready endoscopes were contaminated with biofilms composed of potentially pathogenic Gram-negative bacteria, including Shigella dysenteriae, Escherichia coli and Klebsiella pneumonia. (ii) Soft-tissue fillers support the growth of Staphylococcus epidermidis biofilm in vitro. While soft-tissue filler clinical samples all demonstrated multi-species biofilm with a predominance of Pseudomonas, Staphylococcus and Propionibacterium. In breast implant-associated anaplastic large-cell lymphoma (BIA-ALCL) clinical samples, a high bacterial load, present as a biofilm was identified. Moreover, a distinct microbiome in BIA-ALCL specimens was identified, with a significantly greater proportion of Ralstonia spp. compared to non-tumour contracted capsules. (iii) Diabetic foot ulcer (DFU) clinical samples all contained biofilm, with multi-species communities comprising of both strict anaerobes and aerobic species. In addition, chronic DFUs were found to be associated with a highly polymicrobial microbiome with greater species richness and diversity. In the treatment of chronic non-healing DFUs complicated by biofilm, cadexomer iodine was found to significantly reduce the microbial load. However, short exposure times to topical antimicrobial solutions (commonly utilised by clinicians) were found to be ineffective against microbial biofilms in vivo.Conclusions - Using next-generation DNA sequencing it was possible to investigate the complex arrays of bacterial species residing in hospital surfaces, medical and implantable devices, and DFU tissue specimens. This provides significant new insights into the study of the microbiome in medical practice, which is crucial for improving human health.Mode of access: World wide web1 online resource (x, 238 pages) colour illustration
Assessment of the Dental Students Awareness about Bleeding Disorders in Buraidah and Riyadh city, Saudi Arabia
Aim: To assess dental students (6th year and Interns) knowledge and attitude regarding management of bleeding disorders in Buraidah and Riyadh City.
Materials and method: A cross sectional survey was conducted among dental students enrolled in 6th year and internship program in Buraidah and Riyadh City. A Questionnaire containing 28 questions assessing awareness of dental students about bleeding disorders was mailed to all the students. A total of 158 students responded to the questionnaire. Data obtained was analyzed using SPSS Version 11.5.
Results: A majority of students in internship and final year had moderate (48.1%) to poor (36.1%) knowledge about bleeding disorders.
Conclusion: In this study it can be concluded that the knowledge of the dental students is not optimal about the bleeding disorders and additional specific training is required to improve their skills in the management of patients with bleeding disorders
A Countrywide Survey in Saudi Arabia Regarding the Knowledge and Attitude of Health Care Professionals about Coronavirus Disease (COVID-19)
Coronavirus disease (COVID-19) has emerged as a pandemic. The updated knowledge and a positive attitude of health care professionals (HCPs) towards fighting any pandemic is the key to success. Thus, the present study aims to assess the knowledge and attitude of HCPs towards COVID-19 in the Kingdom of Saudi Arabia (KSA). A cross-sectional study was conducted across the KSA, covering its five geographical regions with a non-probability quota sample. Twenty-nine, close-ended questions evaluating the knowledge and attitude domain were included in the questionnaire. It was developed with the help of Qualtrics software and circulated among the HCPs through the electronic mode. We analyzed data from about 1040 HCPs using the statistical package of social sciences (SPSS) v.21. All variables were presented in number and percentages. Univariate and multivariate logistic regression was performed to explore the odds ratio (OR) and adjusted odds ratio (aOR) of independent variables for inadequate knowledge and attitude. Considering the “good” level of the respective domain, the HCPs have displayed better knowledge (48.2%) over attitude (33.8%). Female (aOR: 1.55; 95% CI: 1.15–2.09; p = 0.004), Diploma degree (aOR: 2.51; 95% CI: 1.64–3.83; p < 0.001), 7–10 years’ experience (aOR: 1.47; 95% CI: 1.01–2.15; p = 0.045) were at higher risk of having inadequate knowledge compared to their contemporaries. Among the sources, the Ministry of Health (MOH) website was the most popular source of information (76%). The knowledge and attitude of HCPs regarding COVID-19 was similar across all the regions of KSA. However, the continuing education program is warranted to fill the potential gap in knowledge for HCPs in higher-risk groups
Molecular epidemiology of carbapenem-resistant acinetobacter baumannii isolates in the Gulf cooperation council states: dominance of OXA-23-type producers
The molecular epidemiology and mechanisms of resistance of carbapenem-resistant Acinetobacter baumannii (CRAB) were determined in hospitals in the states of the Cooperation Council for the Arab States of the Gulf (Gulf Cooperation Council [GCC]), namely, Saudi Arabia, United Arab Emirates, Oman, Qatar, Bahrain, and Kuwait. Isolates were subjected to PCR-based detection of antibiotic resistance genes and repetitive sequence-based PCR (rep-PCR) assessments of clonality. Selected isolates were subjected to multilocus sequence typing (MLST). We investigated 117 isolates resistant to carbapenem antibiotics (either imipenem or meropenem). All isolates were positive for OXA-51. The most common carbapenemases were the OXA-23-type, found in 107 isolates, followed by OXA-40-type (OXA-24-type), found in 5 isolates; 3 isolates carried the ISAba1 element upstream of blaOXA-51-type. No OXA-58-type, NDM-type, VIM-type, or IMP-type producers were detected. Multiple clones were detected with 16 clusters of clonally related CRAB. Some clusters involved hospitals in different states. MLST analysis of 15 representative isolates from different clusters identified seven different sequence types (ST195, ST208, ST229, ST436, ST450, ST452, and ST499), as well as three novel STs. The vast majority (84%) of the isolates in this study were associated with health care exposure. Awareness of multidrug-resistant organisms in GCC states has important implications for optimizing infection control practices; establishing antimicrobial stewardship programs within hospital, community, and agricultural settings; and emphasizing the need for establishing regional active surveillance systems. This will help to control the spread of CRAB in the Middle East and in hospitals accommodating transferred patients from this region
BactericidalActivity of Crevicular Polymorphonuclear Neutrophils in Chronic Periodontitis Patients and Healthy Subjects under the Influence of Areca Nut Extract: An In Vitro Study
Arecanutchewing is an established risk factor for oral submucous fibrosis (OSMF), but its role in periodontal disease has not yet been defined. Thisstudy aimed to assess the effect of areca nut extracts (ANE) on the bactericidal activity of crevicular polymorphonuclear neutrophils (cPMNs) in healthy subjects and chronic periodontitis (CP) patients. An in vitro study was designed with an equal number of (n = 30) gingival crevicular fluid (GCF) samples collected from CP patients and healthy subjects. Bactericidal activity and hydrogen peroxide (H2O2) assays were performed with the GCF samples pre-treated with extracts of two varieties of areca nut: ripe and tender. Simultaneously, controls were also carried out with Hank’s balanced salt solution (HBSS) and catechin. Independent t-test and one-way analysis of variance (ANOVA), along with post-hoc analysis, were employed for statistical analysis. In both study groups, a significant reduction (p < 0.01)in the bactericidal activity was noted when the samples treated with the ripe areca nut (rANE) were compared with the tender variant (tANE). Similarly, H2O2 levels were significantly reduced (p < 0.001) in the rANE in contrast to tANE for both study groups. The above results were significant within the group but were found to be non-significant between the study groups, except when it was treated with HBSS (p < 0.001). In the present study, it was found that there was a reduction in the bactericidal activity and H2O2 production of cPMNs in both healthy subjects and CP patients in the presence of areca nut extract. Moreover, the effect of rANE on cPMNs was more detrimental than tANE
Novel Approach to Dental Biofilm Management through Guided Biofilm Therapy (GBT): A Review
Dental biofilm plays a very crucial role in the etiopathogenesis of periodontal andperi-implant diseases. Over the past decade, tremendous research has been carried outto know the structure of biofilm and the mechanism by which it causes the destruction of supporting tissues of tooth or implant. Periodontal or peri-implant therapy usually begins with primarily removing thebiofilm and is considered as non-surgical mechanical debridement. Although scaling and root planing (SRP) is regarded as a gold standard for mechanical plaque debridement, various other means of biofilm removal have constantly been evolving. These may vary from different scaling systems such as vector systems to decontamination of pockets with LASER therapy. Nowadays, a new concept has emerged known as "guided biofilm therapy" (GBT). It is beneficial in removing the biofilm around the tooth and implant structures, resulting in better or comparable clinical outcomes than SRP. These results were substantiated with the reduction in the microbial load as well as the reduction in the inflammatory cytokines. This review will highlight the various aspects of GBT used in periodontal and peri-implant disease.Sin financiación4.128 JCR (2020) Q2, 52/136 Microbiology0.858 SJR (2020) Q2, 75/152 MicrobiologyNo data IDR 2020UE
Comparative Analysis of Electric Field Strength, Magnetic Field Strength and Power Density around the Cell Phone Towers of Varying Characteristics with a Proposed Classification Facilitating Research on Human Population
The continuous exposure of electromagnetic field (EMF) radiation from cell phone towers may possibly have an influence on public health. Each cell phone tower is unique in terms of number of antennas and its associated attributes; thus, the radiation exposure varies from one tower to another. Hence, a standardized method for quantifying the exposure is beneficial while studying the effects of radiation on the human population residing around the cell phone towers. A mere collection of data or human samples without understanding the cell phone tower differences may show study results such as an increase or decrease in biological parameters. Those changes may not be due to the effects of EMF radiation from cell phone towers but could be due to any other cause. Therefore, a comparative study was designed with the aim of quantifying and comparing the electric field strength (EF), magnetic field strength (MF) and power density (PD) on four sides of cell phone towers with varying numbers of antennas at 50 m and 100 m. Further, an attempt was made to develop a PD-based classification for facilitating research involving human biological samples. Through convenience sampling, sixteen cell phone towers were selected. With the use of coordinates, the geographic mapping of selected towers was performed to measure the distance between the towers. Based on the number of antennas, the cell phone towers were categorized into four groups which are described as group I with 1–5 antennas, group II comprising of 6–10 antennas, group III consisting of 11–15 antennas and group IV comprised of towers clustered with more than 15 antennas. The study parameters, namely the EF, MF and PD, were recorded on all four sides of the cell phone towers at 50 m and 100 m. One-way ANOVA was performed to compare the study parameters among study groups and different sides using the Statistical Package for the Social Sciences (SPSS) version 25.0. The mean MF in Group IV was 2221.288 ± 884.885 μA/m and 1616.913 ± 745.039 μA/m at 50 m and 100 m respectively. The mean PD in Group IV at 50 m was 0.129 ± 0.094 μW/cm2 and 0.072 ± 0.061 μW/cm2 at 100 m. There was a statistically significant (p < 0.05) increase in the MF and PD at 50 m compared to 100 m among cell phone tower clusters with more than 15 antennas (Group IV). On the other hand, a non-significant increase in EF was observed at 50 m compared to 100 m in Group II and IV. The EF, MF and PD on all four sides around cell phone towers are not consistent with distance at 50 m and 100 m due to variation in the number of antennas. Accordingly, a PD-based classification was developed as low, medium and high for conducting research involving any biological sample based on quantile. The low PD corresponds to 0.001–0.029, medium to 0.03–0.099 and high to 0.1–0.355 (μW/cm2). The PD-based classification is a preferred method over the sole criteria of distance for conducting human research as it measures the true effects of EMF radiation from the cell phone towers
A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks
The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%)
A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks
The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%)