Central Repository Research Articles

Private and Secure Distributed Deep Learning: A Survey

Traditionally, deep learning practitioners would bring data into a central repository for model training and inference. Recent developments in distributed learning, such as federated learning and deep learning as a service (DLaaS) do not require centralized data and instead push computing to where the distributed datasets reside. These decentralized training schemes, however, introduce additional security and privacy challenges. This survey first structures the field of distributed learning into two main paradigms and then provides an overview of the recently published protective measures for each. This work highlights both secure training methods as well as private inference measures. Our analyses show that recent publications while being highly dependent on the problem definition, report progress in terms of security, privacy, and efficiency. Nevertheless, we also identify several current issues within the private and secure distributed deep learning (PSDDL) field that require more research. We discuss these issues and provide a general overview of how they might be resolved.

The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity.

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7000 publications for >15000 traits, from which more than 625000 lead associations have been curated. Additionally, 85 000 full genome-wide summary statistics datasets-containing association data for all variants in the analysis-are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists.

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation

The precise monitoring of insect pest populations is the foundation of Integrated Pest Management (IPM) for pests of plants, humans, and animals. Digital technologies can be employed to address one of the main challenges, such as reducing the IPM workload and enhancing decision-making accuracy. In this study, digital technologies are used to deploy an automated trap for capturing images of insects and generating centralized repositories on a server. Subsequently, advanced computational models can be applied to analyze the collected data. The study provides a detailed description of the prototype, designed with a particular focus on its remote reconfigurability to optimize repository quality; and the server, accessible via an API interface to enhance system interoperability and scalability. Quality metrics are presented through an experimental study conducted on the constructed demonstrator, emphasizing trap reliability, stability, performance, and energy consumption, along with an objective analysis of image quality using metrics such as RMS contrast, Image Entropy, Image sharpness metric, Natural Image Quality Evaluator (NIQE), and Modulation Transfer Function (MFT). This study contributes to the optimization of the current knowledge regarding automated insect pest monitoring techniques and offers advanced solutions for the current systems.

Medical school service regions in Canada: exploring graduate retention rates across the medical education training continuum and into professional practice

ABSTRACT Purpose To create medical school service regions and examine national in-region graduate retention patterns across the medical education continuum and into professional practice as one approach to advancing social accountability in medical education. Methods Medical school service regions were created in Canada using publicly available data and mapped using Geographic Information System (GIS) software. Population size and density for each service region were calculated using census data. Retrospective data of medical graduates who completed their medical degrees between 2001–2015 (n = 19,971) were obtained from a centralized data repository and used to analyze in-region retention rates by medical specialty across the training continuum and five years into professional practice. Results Spatial inequities were observed across medical school service regions. Graduate retention patterns also varied across service region groups and medical specialties. Quebec (86.5%) and Ontario (80.4%) had above-average retention rates across the medical education continuum. Family medicine had the highest retention rates from undergraduate to postgraduate training (81.9%), while psychiatry had the highest retention rate across the training continuum and into professional practice (71.2%). The Alberta and British Columbia service region group demonstrated high retention rates across the training continuum and into professional practice and medical specialties, except for retention from undergraduate to postgraduate medical education. Conclusion This study highlights the importance of considering both medical specialty and practice location of graduates when planning and retaining the physician workforce. The observed retention patterns among graduates are a critical aspect of addressing societal needs and represent an intermediate step towards achieving health equity. Furthermore, graduate retention patterns serve as an outcome measure for schools to demonstrate their commitment to social accountability. Tracking and monitoring graduate outcomes may lead schools to actively collaborate with government agencies responsible for healthcare policy, which may ultimately improve physician workforce planning and promote more equitable healthcare access.

A data-driven operations management model: Implementing MIS for strategic decision making in tech businesses

In today’s rapidly evolving technological landscape, data-driven decision-making is critical for business growth and competitiveness. This paper presents a comprehensive operations management model that integrates Management Information Systems (MIS) to facilitate strategic decision-making in technology firms. The proposed model emphasizes the transformation of raw data into actionable insights, enabling organizations to optimize their operations and respond effectively to market dynamics. By systematically incorporating MIS into operational frameworks, the model provides a structured approach to data collection, analysis, and dissemination. It identifies key performance indicators (KPIs) that are essential for monitoring business performance and aligning operational activities with strategic objectives. Through case studies of leading tech companies, the model illustrates how data-driven methodologies can lead to improved forecasting, resource allocation, and overall operational efficiency. The framework is designed to be adaptable, allowing technology firms to customize the MIS components based on their unique business needs and market conditions. Key features of the model include real-time data analytics, predictive modeling, and user-friendly dashboards that enhance visibility across departments. These components empower decision-makers with timely and relevant information, fostering a culture of informed decision-making at all organizational levels. Moreover, the model addresses common challenges faced by tech businesses, such as data silos and integration issues. By promoting a centralized data repository and standardized reporting mechanisms, it enhances collaboration among teams and ensures consistency in data interpretation. The implications of this model extend beyond operational efficiency, influencing strategic initiatives such as product development, market expansion, and customer engagement. In conclusion, this data-driven operations management model not only supports strategic decision-making in technology firms but also positions them for sustainable growth in a competitive marketplace. By leveraging MIS effectively, organizations can harness the power of data to drive innovation and enhance overall business performance.

Maximising the Quality of Stroke Care: Reporting of Data Collection Methods and Resourcing in National Stroke Registries: A Systematic Review.

Stroke registries are tools for improving care and advancing research. We aim to describe the methodology and resourcing of existing national stroke registries. We conducted a systematic search of the published, peer-reviewed literature and grey literature examining descriptions of data collection methods and resourcing of national stroke registries published from 2012 to 2023. The systematic review was registered in PROSPERO (CRD42023393841). 101 records relating to 21 registries in 19 countries were identified. They universally employed web-based platforms for data collection. The principal profession of data collectors was nursing. All included the acute phase of care, 28% (6) registered the pre-hospital (ambulance) phase and 14% (3) included rehabilitation. 80% (17) collected outcome data. The registries varied in their approach to outcome data collection; in 9 registries it was collected by hospitals, in 2 it was collected by the registry, and 7 had linkage to national administrative databases allowing follow-up of a limited number of end points. Coverage of the total number of strokes varies from 6 to 95%. Despite widespread use of Electronic Health Records (EHRs) the ability to automatically populate variables remained limited. Governance and management structures are diverse, making it challenging to compare their resourcing. Data collection for clinical registries requires time and necessary skills and imposes a significant administrative burden on the professionals entering data. We highlight the role of clinical registries as powerful instruments for quality improvement. Future work should involve creating a central repository of stroke registries to enable the development of new registries and facilitate international collaboration.

ECBD: European chemical biology database.

The European Chemical Biology Database (ECBD, https://ecbd.eu) serves as the central repository for data generated by the EU-OPENSCREEN research infrastructure consortium. It is developed according to FAIR principles, which emphasize findability, accessibility, interoperabilityand reusability of data. This data is made available to the scientific community following open access principles. The ECBD stores both positive and negative results from the entire chemical biology project pipeline, including data from primary or counter-screening assays. The assays utilize a defined and diverse library of over 107000 compounds, the annotations of which are continuously enriched by external user supported screening projects and by internal EU-OPENSCREEN bioprofiling efforts. These compounds were screened in 89 currently deposited datasets (assays), with 48 already being publicly accessible, while the remaining will be published after a publication embargo period of up to 3 years. Together these datasets encompass ∼4.3 million experimental data points. All public data within ECBD can be accessed through its user interface, API or by database dump under the CC-BY 4.0 license.

Continuous Integration and Continuous Deployment (CI/CD) Optimization

The advent of Continuous Integration and Continuous Deployment (CI/CD) has fundamentally altered the landscape of software development, enabling teams to deliver updates with unprecedented speed and reliability. By automating the integration of code changes from multiple developers into a central repository, CI/CD practices ensure that software is continuously tested and deployed. This ongoing cycle not only facilitates quicker release cycles but also enhances collaboration among team members and fosters a culture of shared responsibility for code quality. Despite these advancements, organizations face significant challenges in optimizing their CI/CD pipelines. As software systems grow in complexity, the demand for swift and dependable deployments intensifies. This paper explores various techniques and strategies for optimizing CI/CD processes to minimize deployment times while maintaining system reliability. Key optimization methods discussed include: Parallelization of Build Processes: This technique involves breaking down the build process into smaller, independent tasks that can be executed concurrently. By leveraging distributed computing resources, organizations can significantly reduce build times, allowing for faster iterations and deployments. Dependency Caching: Caching dependencies can drastically decrease build times by reusing previously downloaded components. This approach not only speeds up the build process but also minimizes network load and enhances the overall efficiency of the CI/CD pipeline. Incremental Builds: Unlike full builds that compile the entire codebase, incremental builds focus on compiling only the changes made since the last build. This strategy reduces the amount of work needed for each build, accelerating the overall development process. The paper also delves into advanced rollback mechanisms such as blue-green deployments and canary releases. Blue-green deployments allow teams to maintain two identical production environments, enabling smooth transitions and quick rollbacks in case of issues. Canary deployments, on the other hand, introduce new features to a small subset of users before a full rollout, allowing teams to monitor the impact and catch potential failures early. Additionally, automated rollback mechanisms play a vital role in maintaining system reliability, ensuring that any failed deployments can be reverted swiftly to avoid downtime and user disruption. The impact of automation tools on deployment speed and error reduction is another critical aspect examined in this research. Automation frameworks can streamline various stages of the CI/CD process, from code integration to testing and deployment, minimizing human error and ensuring consistent, repeatable processes. By implementing robust automation strategies, organizations can not only accelerate their deployment cycles but also improve overall software quality.

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass.

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a "one-pot" process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

Challenges and opportunities for enhancing groundwater data access and usability in low- and middle-income countries: insights and recommendations from WaSH researchers and practitioners

ABSTRACT Groundwater provides more than half of all water for drinking and irrigation worldwide. Monitoring and managing groundwater resources is essential to achieve the Sustainable Development Goals. However, groundwater resources need to be better monitored. We explored practical approaches to improve groundwater monitoring for informed decision-making, focusing on low- and middle-income country settings. Case studies from a 2023 UNC Water and Health Conference side event highlighted innovative approaches, including open-source data modeling, groundwater monitoring in humanitarian contexts, and using in situ sensors to monitor drought resilience. The case studies described field data integration, remote sensing, and technology (e.g., Modflow-USG, ModelMuse, Drought Resilience Impact Platform). Barriers to groundwater data use were identified by side event participants, including inadequate data access, insufficient collaboration and leadership, and challenges in integrating diverse data and technology. Side event recommendations included creating a central groundwater monitoring data repository, building greater hydrogeology capacity in the WaSH sector, and increasing funding for data collection and monitoring. We urge government leaders to develop regulations and build internal capacity for sustainable groundwater management. This comprehensive approach aims to address challenges and promote informed decision-making for the long-term sustainability of groundwater resources.

Effectiveness and Side Effect Incidence in a Real-World Digital Weight-Loss Service Using Compounded Semaglutide: A Retrospective Comparative Study

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) were originally developed in the late 1980s as a class of antidiabetic medication. However, research over the past decade has found them to be a safe and effective weight-loss agent, which has led to the approval of GLP-1 RAs such as Semaglutide as a supplement to lifestyle obesity interventions in multiple countries. When Semaglutide has become commercially unavailable, digital weight-loss services (DWLSs) have prescribed a compounded form of the medication—a practice in which health professionals formulate a replica of the commercial medication to serve ongoing patient needs. Although compounding has been relatively common over the past century, prominent medical bodies have argued that compounding a relatively novel medication such as Semaglutide represents a major safety risk. This study retrospectively compared the weight and side effect outcomes of patients from a large Australian DWLS whose lifestyle coaching was supplemented with either compounded or pure Semaglutide (both groups following the same titration schedule). All data were extracted from the service’s central data repository. To be included in the weight loss analysis, patients needed to have received a minimum of four monthly medication orders between June 2023 and May 2024 and have submitted weight data between 90 and 150 days after the arrival of their first order. All patients who received at least one medication order within the same period were included in the side effect analysis. The mean four-month weight loss percentage was statistically lower in the compounded Semaglutide group (N = 923, M = 9.11, SD = ±5.76) compared to those in the pure Semaglutide group (n = 1858, M = 9.87, SD = ±6.46), t (2032) = −3.15, p = 0.0017. A statistically lower proportion of patients in the compounded Semaglutide group (71.61%) reported at least one side effect than patients in the pure Semaglutide group (77.40%) during the study period, X2 (1, N = 7683) = 32.32, p < 0.001. When side effects were disaggregated into severity categories, a statistical difference was only observed in mild side effects, X2 (1, N = 7683) = 59.16, p < 0.001. A significantly higher rate of patients from the pure Semaglutide group achieved the ten (50.54% vs. 44.64%), X2 (1, N = 7683) = 10.34, p < 0.001, and fifteen (21.42% vs. 12.78%), X2 (1, N = 7683) = 30.43, p < 0.001, percent weight loss thresholds than patients from the compounded Semaglutide group. The findings indicate that compounded Semaglutide can be used as a component of tightly controlled DWLSs with slightly less effectiveness and but with slightly lower side effect incidence when compared to pure Semaglutide.

Building capacity and capability for quality improvement: insights from a nascent regional health system

ObjectivesQuality improvement (QI) is critical in facilitating advancements in patient outcomes, system efficiency and professional growth. This paper aimed to elucidate the underlying rationale and framework guiding JurongHealth Campus (JHC),...

A three-tier approach to Internet of Things software architecture design

The Internet of Things (IoT) rapidly evolves, presenting challenges and opportunities. This study emphasises the critical role of software in advancing IoT technologies, focusing on machine learning (ML), fog computing, and process optimisation for security and resiliency. ML is pivotal in IoT for predicting equipment failures, evaluating process efficiency, and enabling informed decision- making through real-time data analysis. By integrating ML models directly into IoT devices (edge computing), latency is reduced, and data transmission needs are minimised. Fog computing and cloud computing address latency issues by moving computational resources closer to IoT devices, enhancing scalability and optimising network resource usage. Security remains a paramount concern due to the increasing number of connected devices and their vulnerabilities. IoT software must balance efficiency, security, and performance, employing deep learning for anomaly detection, blockchain for data transparency, and optimised encryption protocols. The trend towards distributed architectures like Edge Computing enhances system resilience by reducing latency and increasing fault tolerance. The proposed IoT system architecture is a three-tier structure consisting of Edge, Fog, and Cloud levels. At the Edge level, initial data processing occurs directly on IoT devices, reducing latency and network load. The Fog level processes data within the local network, utilising more powerful computational resources for complex tasks and ensuring security through advanced machine learning and encryption. The Cloud level serves as a central repository for long-term storage and global data analysis, leveraging containerisation and orchestration technologies for scalability and reliability. This multi-layered architecture ensures efficient data processing, high security, and adaptability, making it suitable for real-time applications. The study highlights the importance of software in optimising data processing across these levels, ensuring the IoT system’s resilience, scalability, and long-term sustainability.

Role of document management system for business processes optimization

The automation of work processes and seamless integration with various business services highlight the essential role of digital document management systems (DMS) in enhancing organizational flexibility, productivity, and adaptability. DMS serve as centralized repositories that streamline, automate, and interconnect business processes, thereby facilitating effective document management. The integration of DMS with Enterprise Resource Planning (ERP) systems provides a comprehensive solution for optimizing business workflows, addressing the needs for both operational efficiency and strategic management. The research paper delineates a business model for the life cycle management of electronic documents and records, focusing on the integration of processes such as document creation, management, approval, and secure distribution. This integrated approach enhances organizational structure, optimizes processes, and bolsters security, leading to significant cost reductions and increased work efficiency. The model emphasizes the importance of streamlined and automated document workflows in reducing manual errors and operational bottlenecks. Key features of DMS include advanced mechanisms for digitizing physical documents, ensuring that all data is captured accurately and efficiently. Centralized storage with version control allows for easy retrieval and management of documents, ensuring that the latest versions are always accessible. Additionally, advanced search and accessibility features enable quick and efficient document retrieval, which is crucial for maintaining high productivity levels. The study underscores the pivotal role of DMS in modern enterprises, advocating their implementation as a strategic move to drive operational excellence and competitive advantage. By integrating DMS with ERP systems, businesses can achieve a cohesive and efficient operational environment that supports both day-to-day activities and long-term strategic goals. This comprehensive approach to document management not only optimizes efficiency and reduces errors but also enhances the overall agility and responsiveness of the organization in a rapidly changing economic environment.

The Prospects and Challenges of Internet of Things (IoT)-based Public Healthcare System in Bangladesh

Purpose: The purpose of this study is to discern the prospects and challenges of integrating Internet of Things (IoT) into the public healthcare sector of Bangladesh. This is timely because the present public healthcare status of the country needs dire improvement. Hence, this study explores the future scope of IoT-based public healthcare systems to be introduced to the Directorate General of Health Services (DGHS) of Bangladesh henceforth recommends how the country can integrate the implementation of this technology to deliver better public healthcare services to its population. Methodology: The authors reviewed extensively literature from several reputed sources to identify the current public healthcare conditions, as well as the benefits and impediments of IoT-based public healthcare system in Bangladesh. Based on the searches pertaining to the data base, this study then outlines appropriate future scopes of IoT in the public healthcare of Bangladesh. Findings: This research identified major impediments such as lack of infrastructure, low motivation, awareness and acceptance as well as poor healthcare startups prevalent in the public health services of Bangladesh. To surmount the inefficiencies of the current services, the authors deliberated how IoT based public healthcare system could foster better monitoring; management and rehabilitation thereby improve the overall Bangladesh’s public healthcare system. Practical Implications: IoT healthcare organizations can also use technology to improve patient outcomes and increase efficiencies, which will lower the cost of treatment, reduce death rates and make it possible to give public healthcare to people who live in rural areas. Originality/Value: The researchers of this study are confident that the findings of this study will benefit other researchers, policymakers, healthcare service providers/designers and professionals to realize the potentials of IoT-based public healthcare system that could overcome the challenges faced and finally could deliver sound public healthcare facilities and services for Bangladeshis in the near future. Limitations: In the future, we plan to develop the Central Repository of patient database system using blockchain technology.

Effects of Polyunsaturated Fatty Acid/Saturated Fatty Acid Ratio and Different Amounts of Monounsaturated Fatty Acids on Adipogenesis in 3T3-L1 Cells.

(1) Background: Adipose tissue serves as a central repository for energy storage and is an endocrine organ capable of secreting various adipokines, including leptin and adiponectin. These adipokines exert profound influences on diverse physiological processes such as insulin sensitivity, appetite regulation, lipid metabolism, energy homeostasis, and body weight. Given the integral role of adipose tissue in metabolic regulation, it is imperative to investigate the effects of varying proportions and types of dietary fats on adipocyte function. In addition, our previous study showed that P/S = 5 and MUFA = 60% appeared to be beneficial in preventing white adipose tissue accumulation by decreasing plasma insulin levels and increasing hepatic lipolytic enzyme activities involved in β-oxidation. Therefore, the objective of this study was to explore the effects of a polyunsaturated fatty acid (PUFA) to saturated fatty acid (SFA) ratio of 5 and varying levels of monounsaturated fatty acids (MUFA = 30% or 60%) on lipogenesis. (2) Methods: We cultured 3T3-L1 mouse embryo fibroblasts in Dulbecco's modified Eagle's medium (DMEM) containing 10% bovine calf serum until confluent. Varying ratios of palmitic acid (PA), oleic acid (OA), and linoleic acid (LA) were first bound with bovine serum albumin (BSA) before being applied to 3T3-L1 adipocytes in low doses and in high doses. (3) Results: Low doses of P/S ratio = 5, MUFA = 60% (M60) fatty acids decreased the accumulation of triglycerides in mature adipocytes by decreasing the mRNA expression of adipogenic factors, such as peroxisome proliferator-activated receptors (PPARs), lipoprotein lipase (LPL), and glucose transporter-4 (GLUT-4), while increasing lipolytic enzyme (hormone-sensitive lipase, HSL) expression when compared to high doses of P/S ratio = 5, MUFA = 60% (M60), low and high doses of P/S ratio = 5, MUFA = 30% (M30). Furthermore, the treatment of M60 in low doses also decreased the secretion of leptin and increased the secretion of adiponectin in adipocytes. (4) Conclusions: The composition of P/S = 5, MUFA = 60% fatty acid in low doses appeared to result in anti-adipogenic effects on 3T3-L1 adipocytes due to the down-regulation of adipogenic effects and the transcription factor.

IoT based Smart Weather Monitoring with Flood and Earthquake Detection

Weather monitoring is essential for understanding the changing climatic conditions. Traditional weather monitoring system are slow, labor intensive, and outdated, often prone to error and inaccurate predictions. To overcome these challenges and provide an up-to date weather information, the IoT (Internet of Things) has been implemented in weather monitoring. This research presents an IoT-based weather monitoring system integrated with sensors to ensure accurate weather monitoring, including flood and earth quake detection. The collected data, such as temperature, humidity, atmospheric pressure, rainfall, flood levels, and seismic activity, is transmitted through IoT protocols to a centralized repository (Cloud) and visualized in the Blynk App. Flood sensors are strategically positioned in flood-prone areas, continuously monitoring water levels to detect abnormal rises indicative of potential floods. Similarly, seismic sensors detect ground vibrations and seismic activity, providing early alerts for earthquakes. The proposed system offers a comprehensive approach to monitor the weather, contributing to enhanced preparedness and mitigation strategies for natural disasters.

The antibiotic resistance profiles of Pseudomonas aeruginosa in the Asia Cornea Society Infectious Keratitis Study

PurposeTo describe the prevalence and antibiotic resistance profiles of Pseudomonasaeruginosa isolated from the Asia Cornea Society Infectious Keratitis Study (ACSIKS).MethodsAll bacterial isolates from ACSIKS underwent repeat microbiological identification in a central repository in Singapore. Minimum inhibitory concentration (MIC) determination was conducted for isolates of P. aeruginosa against thirteen antibiotics from 6 different classes, and categorized based on Clinical Laboratory Standard Institutes’ reference ranges. The percentage rates of resistance (non-susceptibility) to each antibiotic included isolates of both intermediate and complete resistance. Multi-drug resistance (MDR) was defined as non-susceptibility to at least one agent in three or more antimicrobial classes.ResultsOf the 1493 unique bacterial specimens obtained from ACSIKS, 319 isolates were of P. aeruginosa. The majority of isolates were from centers in India (n = 118, 37%), Singapore (n = 90, 28.2%), Hong Kong (n = 31, 9.7%) and Thailand (n = 30, 9.4%). The cumulative antibiotic resistance rate was the greatest for polymyxin B (100%), ciprofloxacin (17.6%) and moxifloxacin (16.9%), and lowest for cefepime (11.6%) and amikacin (13.5%). Isolates from India demonstrated the highest antibiotic resistance rates of all the centers, and included moxifloxacin (47.5%) and ciprofloxacin (39.8%). Forty-eight of the 59 MDR isolates also originated from India. Antibiotic resistance rates were significantly lower in the other ACSIKS centers, and were typically less than 10%.ConclusionsThe antibiotic resistance profiles of P. aeruginosa varied between different countries. While it was low for most countries, substantial antibiotic resistance and a significant number of multi-drug resistant isolates were noted in the centers from India.

The Impact of GeneXpert Cerebrospinal Fluid Testing on Tuberculous Meningitis Diagnosis in Routine Care in Botswana

Abstract Background Tuberculous meningitis (TBM) disproportionately impacts high–HIV prevalence, resource-limited settings where diagnosis is challenging. The GeneXpert platform has utility in TBM diagnosis, but uptake remains limited. In Botswana, before the introduction of GeneXpert, tuberculosis (TB) testing was only available through mycobacterial culture at the National TB Reference Laboratory. Data describing routine use of Xpert MTB/RIF for cerebrospinal fluid (CSF) testing in resource-limited settings are scarce. Methods Electronic records for patients with CSF tested in government facilities in Botswana between 2016 and 2022 were obtained from a central online repository as part of ongoing national meningitis surveillance. Samples were excluded from 1 site where Xpert MTB/RIF is performed universally. The proportion receiving TB-specific investigation on CSF and the number positive for Mycobacterium tuberculosis following increased Xpert MTB/RIF capacity were determined. Results The proportion of CSF samples receiving TB-specific investigation increased from 4.5% (58/1288) in 2016 to 29.0% (201/693) in 2022, primarily due to increased analysis with Xpert MTB/RIF from 0.9% (11/1288) to 23.2% (161/693). There was an overall decline in the annual number of CSF samples analyzed, but the proportion with microbiologically confirmed TBM increased from 0.4% to 1.2%. The proportion of samples tested for TB that were collected from health care facilities >100 km from the National TB Reference Laboratory increased with Xpert MTB/RIF rollout from 65.9% (87/132) to 78.0% (494/633). Conclusions In Botswana, access to TB culture is challenging in remote populations; more accessible near-patient testing using Xpert MTB/RIF increased the number of patients receiving TB-specific testing on CSF and the number of confirmed TBM cases.