Knowledge of mothers about use of fissure sealant therapy and professional fluoride therapy among children in Saudi Arabia

Purpose: To evaluate mothers’ knowledge of the use of fissure sealant (FS) and topical fluoride (TF) therapy among children aged between 7 to 12 years in Saudi Arabia. Methods: In this cross-sectional study, participants (n = 350) were selected based on simple random sampling method from the mothers’ of children aged from 7 to 12 years old, attending outpatient pediatric dental clinics in College of Dentistry King Khalid University, Abha, Saudi Arabia. Results: The age group of mothers included are as follows: 31.7 % in 20 - 30 years age group, 53.1 % in 31 - 40 years age range, and 15.1 % in 41 - 50 years. On FS therapy benefits in the prevention of caries in children, 22 % responded that it was beneficial. When participants were asked regarding FS wearing out easily after application on the tooth, 8 % agreed while 13.4 % disagreed. A majority of mothers (40.9 %) agreed that TF therapy prevents caries, while 47.7 % stated that they brush twice daily with fluoride toothpaste. The mothers that disagreed that fluoride gel is recommended only for children, not for adults were 32.9 %. When the mothers were asked about the benefit of fluoride if its cost is taken into consideration, 46.6 % disagreed that fluoride gel was worth its cost, while 22.3 % took the opposite view. Conclusion: Positive knowledge of FS and TF therapy have been observed among mothers. However, mothers demonstrated greater positive knowledge of TF therapy than FS therapy

Quantifying the Importance of Latent Features in Neural Networks

The susceptibility of deep learning models to adversarial examples raises serious concerns over their application in safety-critical contexts. In particular, the level of understanding of the underlying decision processes often lies far below what can reasonably be accepted for standard safety assurance. In this work, we provide insights into the high-level representations learned by neural network models. We specifically investigate how the distribution of features in their latent space changes in the presence of distortions. To achieve this, we first abstract a given neural network model into a Bayesian Network, where each random variable represents the value of a hidden feature. We then estimate the importance of each feature by analysing the sensitivity of the abstraction to targeted perturbations. An importance value indicates the role of the corresponding feature in underlying decision process. Our empirical results suggest that obtained feature importance measures provide valuable insights for validating and explaining neural network decisions

Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms

In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models.

The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters

A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority

The metabolomic analysis of five Mentha species: cytotoxicity, anti-Helicobacter assessment, and the development of polymeric micelles for enhancing the anti-Helicobacter activity

Mentha species are medicinally used worldwide and remain attractive for research due to the diversity of their phytoconstituents and large therapeutic indices for various ailments. This study used the metabolomics examination of five Mentha species (M. suaveolens, M. sylvestris, M. piperita, M. longifolia, and M. viridis) to justify their cytotoxicity and their anti-Helicobacter effects. The activities of species were correlated with their phytochemical profiles by orthogonal partial least square discriminant analysis (OPLS-DA). Tentatively characterized phytoconstituents using liquid chromatography high-resolution electrospray ionization mass spectrometry (LC-HR-ESI-MS) included 49 compounds: 14 flavonoids, 10 caffeic acid esters, 7 phenolic acids, and other constituents. M. piperita showed the highest cytotoxicity to HepG2 (human hepatoma), MCF-7 (human breast adenocarcinoma), and CACO2 (human colon adenocarcinoma) cells using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays. OPLS-DA and dereplication studies predicted that the cytotoxic activity was related to benzyl glucopyranoside-sulfate, a lignin glycoside. Furthermore, M. viridis was effective in suppressing the growth of Helicobacter pylori at a concentration of 50 mg mL−1. OPLS-DA predicted that this activity was related to a dihydroxytrimethoxyflavone. M. viridis extract was formulated with Pluronic® F127 to develop polymeric micelles as a nanocarrier that enhanced the anti-Helicobacter activity of the extract and provided minimum inhibitory concentrations and minimum bactericidal concentrations of 6.5 and 50 mg mL−1, respectively. This activity was also correlated to tentatively identified constituents, including rosmarinic acid, catechins, carvone, and piperitone oxide

Assessing the probiotic potential, antioxidant, and antibacterial activities of oat and soy milk fermented with Lactiplantibacillus plantarum strains isolated from Tibetan Kefir

Sufficient intake of probiotics has been shown to help in the digestion, protect the body against pathogenic microorganisms and boost the immune system. Recently, due to high prevalence of milk allergies and lactose intolerance in population, the non-dairy based probiotic alternative are becoming increasing popular. In this context, the oat milk and soya milk-based fermented products can be an ideal alternative for the development of Lactic acid bacteria bacteria based probiotics. These bacteria can not only improve the product’s flavor and bioavailability but also increases its antibacterial and antioxidant capabilities due to fermentation process. The purpose of the resent work was to assess the antioxidant and probiotic properties of oat and soy milk that had been fermented with three different strains of Lactiplantibacillus plantarum (L. plantarum) including L. plantarum 12–3, L. plantarum K25, and L. plantarum YW11 isolated from Tibetan Kefir. Different validated assays were used to evaluate the probiotic properties, adhesion and survival in the digestive system (stomach, acid and bile salts resistance), antioxidant and antimicrobial activities and safety (ABTS and DPPH scavenging assays) of these strains. Results of the study showed that soya milk and oat milk fermented with L. plantarum strains possess promising probiotic, antibacterial and antioxidant properties. These results can be helpful to produce dairy-free probiotic replacements, which are beneficial for those who are unable to consume dairy products due to dietary or allergic restrictions

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Impaired IL-23-dependent induction of IFN-gamma underlies mycobacterial disease in patients with inherited TYK2 deficiency

Human cells homozygous for rare loss-of-expression (LOE) TYK2 alleles have impaired, but not abolished, cellular responses to IFN-alpha/beta (underlying viral diseases in the patients) and to IL-12 and IL-23 (underlying mycobacterial diseases). Cells homozygous for the common P1104A TYK2 allele have selectively impaired responses to IL-23 (underlying isolated mycobacterial disease). We report three new forms of TYK2 deficiency in six patients from five families homozygous for rare TYK2 alleles (R864C, G996R, G634E, or G1010D) or compound heterozygous for P1104A and a rare allele (A928V). All these missense alleles encode detectable proteins. The R864C and G1010D alleles are hypomorphic and loss-of-function (LOF), respectively, across signaling pathways. By contrast, hypomorphic G996R, G634E, and A928V mutations selectively impair responses to IL-23, like P1104A. Impairment of the IL-23-dependent induction of IFN-gamma is the only mechanism of mycobacterial disease common to patients with complete TYK2 deficiency with or without TYK2 expression, partial TYK2 deficiency across signaling pathways, or rare or common partial TYK2 deficiency specific for IL-23 signaling.ANRS Nord-Sud ; CIBSS ; CODI ; Comité para el Desarrollo de la Investigación ; Fulbright Future Scholarshi