Suchergebnisse

INTRODUCTION: Invasive meningococcal disease is uncommon but associated with a high-case fatality rate. Carriage prevalence of the causative bacteria, Neisseria meningitidis, is high in adolescents. A large (n=34 500) cluster randomised controlled trial (RCT) to assess the impact of a meningococcal B (MenB) vaccine on meningococcal carriage was implemented in the state of South Australia (SA) for year 10, 11 and 12 senior school students in 2017–2018. This study will assess the impact of MenB vaccine (4CMenB) on carriage prevalence in school leavers in SA, 1 and 2 years after implementation of the cluster RCT in adolescents. Measuring the impact of population programmes on carriage can assist in informing future meningococcal immunisation programmes such as targeted age groups and use of catch-up campaigns. METHODS AND ANALYSIS: This repeat cross-sectional study will assess carriage prevalence in 2018 and 2019. All school leavers who attended year 12 in any school in SA in 2018 or 2019 will be invited to participate in this study. An oropharyngeal swab will be taken from each participating student and a risk factor questionnaire completed by the student following informed consent. Students will attend clinics at SA universities, technical colleges, and metropolitan, rural and remote government council clinics. Confirmed vaccination history will allow a comparison in carriage prevalence between vaccinated and unvaccinated school leavers. A sample size of 4096 students per year will provide 80% power to detect a 20% difference in carriage prevalence of disease-causing meningococci (defined as genogroup A, B, C, W, X or Y) between years. ETHICS AND DISSEMINATION: The study was approved by the Women's and Children's Health Network Human Research Ethics Committee. Results will be published in international peer review journals and presented at national and international conferences. TRIAL REGISTRATION NUMBER: NCT03419533; Pre-results

Introduction: Invasive meningococcal disease is uncommon but associated with a high-case fatality rate. Carriage prevalence of the causative bacteria, Neisseria meningitidis, is high in adolescents. A large (n=34 500) cluster randomised controlled trial (RCT) to assess the impact of a meningococcal B (MenB) vaccine on meningococcal carriage was implemented in the state of South Australia (SA) for year 10, 11 and 12 senior school students in 2017–2018. This study will assess the impact of MenB vaccine (4CMenB) on carriage prevalence in school leavers in SA, 1 and 2 years after implementation of the cluster RCT in adolescents. Measuring the impact of population programmes on carriage can assist in informing future meningococcal immunisation programmes such as targeted age groups and use of catch-up campaigns.Methods and analysis: This repeat cross-sectional study will assess carriage prevalence in 2018 and 2019. All school leavers who attended year 12 in any school in SA in 2018 or 2019 will be invited to participate in this study. An oropharyngeal swab will be taken from each participating student and a risk factor questionnaire completed by the student following informed consent. Students will attend clinics at SA universities, technical colleges, and metropolitan, rural and remote government council clinics. Confirmed vaccination history will allow a comparison in carriage prevalence between vaccinated and unvaccinated school leavers. A sample size of 4096 students per year will provide 80% power to detect a 20% difference in carriage prevalence of disease-causing meningococci (defined as genogroup A, B, C, W, X or Y) between years.Ethics and dissemination: The study was approved by the Women's and Children's Health Network Human Research Ethics Committee. Results will be published in international peer review journals and presented at national and international conferences.

Bluetooth has constantly evolved from its cradle in 1997 to the last 5.2 version in 2020. With each update and amendment, it has gained in speed, range, and versatility. One of the latest introductions was the Bluetooth Mesh Profile (BMP) making it a technology suitable for a wide variety of applications. Nevertheless, BMP was designed to maintain the compatibility with Bluetooth version 4 devices already deployed in the market. This imposes some restrictions that place Bluetooth Mesh under other competing technologies like Zigbee or Thread in terms of throughput performance. In this paper we propose two mechanisms to overcome these limitations and take advantage of the new extended advertising capabilities introduced with Bluetooth 5. These mechanisms are presented as modifications to the current protocol stack to allow the transmission of larger data structures. Thus, it is possible to boost the throughput of Bluetooth Mesh making it suitable to more demanding applications like, for example, image transmission. The first proposal is designed as an adaptation layer to avoid modifying the standard in its current form. The second makes minimal changes to the frame structure at the different layers enabling the user to accommodate possible encapsulations (i.e., tunneling) without incurring IPv6-layer fragmentation. We have analyzed both solutions and compared them with the current BMP in terms of throughput, delay, and energy consumption for different channel conditions and network size. The results show that except for very small messages or poor channel conditions the proposals improve the throughput and delay of the current BMP. ; This work was supported in part by the Spanish Ministry of Science through the European Regional Development Funds (ERDF) under Project RTI2018-099880-B-C32, Project RTI2018-095684-B-I00, and Project RTI2018-099063-B-I00; and in part by the Government of Aragon (Reference Group) under Grant T31_20R. ; Peer Reviewed ; Postprint (published version)

This paper describes a wireless mesh network testbed for research in rapid deployment and auto-configuration of mesh nodes. Motivated by the needs of first responders and military personnel arriving to an incident area, we developed and tested an automated deployment algorithm that indicates when a mesh node needs to be deployed as the coverage area grows. Conventional radios can experience severe coverage limitations inside structures such as hi-rise buildings, subterranean buildings, caves, and underground mines. The approach examined here is to deploy wireless relays that extend coverage through multihop communication using a deployment algorithm that employs physical layer measurements. A flexible platform based on IEEE 802.11 radios has been implemented and tested in a subterranean laboratory complex where conventional public safety radios have no coverage. Applications tested include two-way voice, data, and location information. This paper describes the testbed, presents experimental results, and recommends areas for further study and development in rapidly-deployable multihop networks.

Objectives To determine the antibiotic susceptibility of N. gonorrhoeae in the Lao People's Democratic Republic (Laos). Methods We obtained 158 gonococcal isolates from 12,281 genital samples routinely submitted to a diagnostic laboratory in Vientiane, Laos between 2011 and 2015 and determined their susceptibility to five antibiotics by a standard disk diffusion method. Results The rates of resistance to penicillin (by beta-lactamase production), tetracycline and ciprofloxacin were 89.9%, 99.3% and 84.8% respectively. All isolates were sensitive to ceftriaxone and spectinomycin. Conclusions This situation is similar to that in neighboring countries, but fortunately means that the latest Lao national guidelines for treating gonorrhoea should still be effective.

Neisseria gonorrhoeae, designated by the CDC as a critical priority pathogen, is responsible for over 1 million new infections annually (CDC, 2021). The rapid proliferation of antimicrobial resistance (AMR) within N. gonorrhoeae, driven by horizontal gene transfer and point mutations in key loci such as penA, 23S rRNA, and mtrR, has necessitated continuous revisions to antibiotic regimens. This study investigates genomic mutations within bacterial DNA contigs to identify molecular biomarkers associated with resistance phenotypes. A Support Vector Machine (SVM) model was trained on 9967 patient-derived genomic contigs, focusing on resistance mechanisms against azithromycin, ciprofloxacin, and cefixime—the most commonly prescribed antibiotics targeting N. gonorrhoeae. The SVM model achieved a classification accuracy of 90.3%, underscoring the efficacy of machine learning in the characterization of resistance mechanisms at the molecular level. These findings support the integration of genomic data and machine learning approaches for biomarker discovery in the context of precision antimicrobial therapy.