Since 2024, the National Disease Control and Prevention Administration has implemented sentinel surveillance for acute respiratory infectious (ARI) diseases to monitor the epidemiology and etiology of multiple pathogens such as influenza virus and SARS-CoV-2. To ensure scientific rigor, standardized procedures, accurate and reliable data, and the efficient operation of network laboratories, a multidisciplinary expert panel-including representatives from the disease control and prevention system, sentinel hospitals, schools of public health, and research institutes-jointly developed the Expert Consensus on Quality Management for Sentinel Surveillance of ARI Diseases. The consensus was formulated through multiple rounds of discussion, investigation, and public consultation, incorporating national surveillance protocols, technical guidelines, scientific evidence, and practical experience. Focusing on establishing a comprehensive quality control system for the entire multi-pathogen surveillance process for ARI diseases, the consensus outlines key components covering all stages: target population definition; specimen collection, transport, aliquoting, and storage; laboratory infrastructure and equipment management; multiplex pathogen detection and gene sequencing; quality assurance of test results; biosafety; personnel training and assessment; and data management and analysis. This document aims to provide end-to-end technical guidance for quality control in ARI sentinel surveillance in China.

Advancements in monitoring adverse events following immunization in Iraq: Insights from the CIVIE project 2022-2023.

Purpose This research investigates the interplay between technology adaptation and macropolicies (MP) on the sustainability of supply chains in the steel industry in Iran. The sector holds significant importance for economic growth and environmental sustainability, making it a crucial area of investigation. The aim of the study is to provide supply chain executives and policymakers helpful knowledge to improve sustainability initiatives. Design/Methodology/Approach A quantitative research approach was utilized, employing a structured survey distributed to 160 industry experts. Regression analysis, path modeling and bootstrapping techniques were utilized to examine the mediating role of industry-specific challenges (ISC) in sustainable supply chain management (SSCM). Findings The results suggest that technological adaptation and MP significantly impact SSCM. However, the relationship is mediated by ISC, demonstrating that achieving sustainability objectives involves tackling supply-demand fluctuations, ecological impact and data inefficiencies. Practical implications This study offers actionable recommendations for policymakers and steel industry leaders to enhance sustainability through targeted regulations, improved data management strategies and the adaptation of emerging technologies. Originality/Value This study contributes to the literature by providing a sector-specific analysis of sustainability in industrial supply chains, particularly in a developing economy context. Particularly, it offers novel perspectives on how emerging economies might improve supply chain sustainability by highlighting the interplay of political, technological and industry-specific issues.

BackgroundMobile health (mHealth), leveraging mobile devices for health measurement and promotion, is rapidly growing. Smartphone cameras can perform photoplethysmography (PPG) to estimate pulse rate (PR) and other features of the cardiac cycle. However, establishing the validity of PR-PPG is essential before it can be adopted for health care applications. There is a pervasive belief that PR-PPG is analogous to heart rate (HR) derived from electrocardiograms (ECGs), and we will conduct a systematic review and meta-analysis to support or challenge this supposition.ObjectiveThis study aims to synthesize quantitative evidence on the validity of PPG derived from mobile devices (ie, smartphones) for the assessment of HR compared with the gold standard ECG assessment.MethodsA comprehensive literature search will be performed on CINAHL Ultimate, MEDLINE, ScienceDirect, and Scopus using a predefined search strategy. All retrieved citations will be imported into Rayyan for screening and data management. A minimum of 2 independent reviewers will conduct the title and abstract screening, followed by 2 independent reviewers who will perform full-text screening and data extraction. All stages will be guided by predefined inclusion and exclusion criteria, which will be pilot-tested to ensure consistency and reliability. Any discrepancies will be resolved through discussion with a third reviewer or during a research team meeting. Intrarater reliability will be quantified at the title and abstract stage and the full-text review stage using Cohen κ. To ensure clarity and consistency in the presentation of study characteristics and findings, both narrative synthesis and tabular formats will be used. This review will include studies that report the association and agreement between resting HR and PR from PPG using contact-based smartphone devices versus ECG as the gold standard. PPG signals will be obtained using a contact-based approach, defined as finger-on-camera measurements with the smartphone’s built-in camera and flash. Studies will be excluded if they (1) do not use PPG using contact-based smartphone devices, (2) compare PPG to another collection method other than ECG, or (3) are review articles or case studies.ResultsTo inform clinical procedures and future studies, the results will contain data on PR-PPG and HR-ECG association (correlations) and agreement (Bland-Altman analysis), sampling devices, and operating systems. This project is unfunded, and the initial screening is expected to start in the first quarter of 2026, with results anticipated to be published in the first quarter of 2027. The projected timeline for the study includes title and abstract screening from the first quarter of 2026, followed by full-text screening in the second quarter of 2026. Results are anticipated by the third quarter of 2026, with publication expected in the first quarter of 2027. Throughout this period, database searches will be regularly updated to capture any newly published studies meeting the inclusion criteria.ConclusionsThis review will provide a comprehensive understanding of the association and agreement between PR-PPG and HR-ECG. The findings may inform future adoption of PR-PPG and HR-ECG with insights into device or setting characteristics for best agreement or association.

BackgroundThe Zanzibar islands, Tanzania, have eliminated schistosomiasis as a public health problem since 2017 and need to rethink their intervention strategies to ensure that the progress made is sustained and advanced. We evaluated the performance of a novel surveillance-response approach for interrupting Schistosoma transmission on Pemba Island from 2020-2024.MethodologyIn low-prevalence implementation units (IUs), surveillance-response interventions were implemented, which consisted of active and reactive case finding, treatment of S. haematobium-positive individuals, and reactive snail control. The performance of the surveillance-response interventions was measured by sensitivity, timeliness and impact on prevalence. Annual cross-sectional surveys were conducted in schools and households to estimate the total number of individuals infected with S. haematobium in the area and the proportion identified by the surveillance-response approach. Urogenital schistosomiasis was diagnosed by egg microscopy.Principal FindingsAmong the 20 IUs in the study area, 15, 16, and 17 were considered low-prevalence IUs in the intervention periods in 2021, 2022, and 2023, respectively. Across the intervention periods, 4.6% (707/15509) among the schoolchildren included in active surveillance were tested S. haematobium-positive and treated. During reactive surveillance, at water bodies 8.2% (10/122) and in households 9.9% (45/454) of individuals were found infected and treated. Moreover, 47 among the 262 waterbodies were treated with molluscicide. The overall sensitivity of the surveillance-response approach across 2 periods, where complete surveillance data were available, was 23.0%. The timeliness of reactive interventions was 2 weeks. In the low-prevalence IUs, the prevalence in schoolchildren changed from 0.5% (7/1552) in 2021 to 0.4% (6/1653) in 2022, from 0.6% (12/2123) in 2022 to 0.7% (15/2240) in 2023, and from 0.4% (8/2287) in 2023 to 1.0% (27/2755) in 2024 after surveillance-response implementation. The respective prevalence in community members was 0.5% (14/2969) in 2021 and 0.7% (19/2928) in 2022, 0.6% (18/3175) in 2022 and 0.3% (10/2979) in 2023, and 0.4% (12/3257) in 2023 and 0.7% (22/3106) in 2024.ConclusionSurveillance-response interventions maintained the low S. haematobium prevalence, but interruption of transmission was not achieved. The overall sensitivity of the approach was low. Timeliness was very good but required strong communication and collaboration between the surveillance-response teams. To work on a larger scale, with good coverage and improved sensitivity, elimination programs will need a large number of well-trained staff and adequate tools for surveillance and response activities, data management, and communication.Trial registrationISRCTN, ISCRCTN91431493. Registered 11 February 2020, ISRCTN - ISRCTN91431493: Novel intervention strategies for schistosomiasis elimination in Zanzibar.

Objectives Artificial intelligence (AI) is increasingly being utilized across various fields of medicine, presenting significant potential for the future of healthcare. This review is to systematically outline the current applications of AI in the field of oral health management and to provide an in-depth analysis of the associated challenges and future opportunities. Methods The review was based on a systematic electronic literature search conducted across databases (PubMed, Web of Science, and Scopus) with the keywords including “artificial intelligence,” “AI in dentistry,” “tele-dentistry,” “oral health education,” and “oral health management.” English-language studies relevant to the application of AI across various aspects of oral health management were included based on independent assessments by two reviewers. Results We concluded that in the realm of oral health management, AI technology has diverse applications, including oral health education and counseling, monitoring, screening, diagnosis, treatment, follow-up care of oral diseases, and the collection and management of oral health data. By enhancing public awareness of oral health and improving self-management capabilities, AI can increase diagnostic accuracy, facilitate personalized treatments, support tele-dentistry, optimize the allocation of dental resources, and provide early warnings for oral diseases. These advancements collectively contribute to the efficiency and quality of oral health management. While AI demonstrates considerable promise in this field, several challenges remain, including inconsistencies in oral health data, limited availability and accessibility of data, the reliability of AI-driven results, and issues of bias and fairness in AI algorithms. Addressing these challenges is essential to fully harness the transformative potential of AI in oral health management. Conclusion Oral health management encompasses the comprehensive handling of oral health risk factors in individuals, populations, and communities through a series of measures and activities aimed at maintaining and promoting oral health. The ultimate goal is to achieve the greatest societal benefit in oral health at the lowest possible cost. By addressing challenges such as data consistency, availability, and reliability, as well as issues of bias and fairness in AI algorithms, AI may play a significant role in oral health management. Clinical relevance This paper reviews the role of artificial intelligence in the prevention, diagnosis and treatment of oral diseases, providing an important reference for the later application of artificial intelligence in oral health management.

With the advancement of flexible electronics and low-power circuit design, wearable sensing systems have emerged as a interdisciplinary research area within electronic and computer engineering. These kind of systems can support continual identification with non-disruptive, precise sensing of the physical signs of person with pliable, various type sensor arrays. Viewed from the perspective of the systems engineering approach, this paper classifies wearable device architectures into three mutually supportive electrical paths: the analog signal chain, the digital signal chain, and the energy self-sufficiency chain. At the signal chain level, it focuses on analog front-end design for flexible electrochemical, strain sensor, high-input-impedance Transimpedance Amplification design, differential anti-interference design, analog-to-digital conversion design to ensure that the signal-to-noise ratio and low-drift performance of the microampere-level signal are very high. In the digital chain the study is on the signal processing and information transmitting using an embedded MCU unit. Adaptive filtering, dynamic gain adjustment, and event-driven communication have achieved real-time, low-power data management: The energy chain combines biofuel cell (BFC) and power management unit (PMU), it's proposing the hybrid power chain, which would be combining NFC and the energy scheduling algorithm for autonomous power. Looking at it from the system level, it is hard to say that wearable electronics' core competitiveness comes from better analog, digital, but more likely the synergy between them: This provides a solution for field-effect transistors (FETs) and self-powered smart health trackers. It establishes a scalable implementation approach suitable for both low-power signal processing and energy-autonomous circuits.

A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.

The Zanzibar islands, Tanzania, have eliminated schistosomiasis as a public health problem since 2017 and need to rethink their intervention strategies to ensure that the progress made is sustained and advanced. We evaluated the performance of a novel surveillance-response approach for interrupting Schistosoma transmission on Pemba Island from 2020-2024. In low-prevalence implementation units (IUs), surveillance-response interventions were implemented, which consisted of active and reactive case finding, treatment of S. haematobium-positive individuals, and reactive snail control. The performance of the surveillance-response interventions was measured by sensitivity, timeliness and impact on prevalence. Annual cross-sectional surveys were conducted in schools and households to estimate the total number of individuals infected with S. haematobium in the area and the proportion identified by the surveillance-response approach. Urogenital schistosomiasis was diagnosed by egg microscopy. Among the 20 IUs in the study area, 15, 16, and 17 were considered low-prevalence IUs in the intervention periods in 2021, 2022, and 2023, respectively. Across the intervention periods, 4.6% (707/15509) among the schoolchildren included in active surveillance were tested S. haematobium-positive and treated. During reactive surveillance, at water bodies 8.2% (10/122) and in households 9.9% (45/454) of individuals were found infected and treated. Moreover, 47 among the 262 waterbodies were treated with molluscicide. The overall sensitivity of the surveillance-response approach across 2 periods, where complete surveillance data were available, was 23.0%. The timeliness of reactive interventions was 2 weeks. In the low-prevalence IUs, the prevalence in schoolchildren changed from 0.5% (7/1552) in 2021 to 0.4% (6/1653) in 2022, from 0.6% (12/2123) in 2022 to 0.7% (15/2240) in 2023, and from 0.4% (8/2287) in 2023 to 1.0% (27/2755) in 2024 after surveillance-response implementation. The respective prevalence in community members was 0.5% (14/2969) in 2021 and 0.7% (19/2928) in 2022, 0.6% (18/3175) in 2022 and 0.3% (10/2979) in 2023, and 0.4% (12/3257) in 2023 and 0.7% (22/3106) in 2024. Surveillance-response interventions maintained the low S. haematobium prevalence, but interruption of transmission was not achieved. The overall sensitivity of the approach was low. Timeliness was very good but required strong communication and collaboration between the surveillance-response teams. To work on a larger scale, with good coverage and improved sensitivity, elimination programs will need a large number of well-trained staff and adequate tools for surveillance and response activities, data management, and communication. ISRCTN, ISCRCTN91431493. Registered 11 February 2020, ISRCTN - ISRCTN91431493: Novel intervention strategies for schistosomiasis elimination in Zanzibar.

Model-informed drug development is increasingly integrated across the drug development continuum, enabling more efficient, cost-effective, and targeted trials while reducing reliance on animal studies. Achieving pharmacoequity requires not only equitable access to medicines but also to the data and knowledge that inform drug development and regulatory decisions. To address challenges in pharmacokinetic data sharing, PKRxiv (https://pkrxiv.org/) was developed as a discipline-specific repository designed around Findable, Accessible, Interoperable, Reusable (FAIR) principles. This tutorial introduces PKRxiv's rationale, design, data submission and access workflows, and practical use cases. Available datasets at the end of September 2025 include over 5,500 individual drug concentration-time data points from over 900 unique participants across 3 continents. The platform supports structured submission of pharmacokinetic, pharmacogenetic, and safety/efficacy data, with persistent digital object identifiers for discoverability and citation. Contributors can apply one of three data sharing models-unrestricted, noncommercial, or contributor-controlled-with optional embargo periods. Users can explore datasets using the Data Explorer or Data Cards, or submit requests after providing a statement of intended use case. It enables pooling of datasets across multiple studies. Recommendations to help advance the field are proposed as data sharing becomes more widely expected: obtaining consent for unspecified future research use of data, sharing data underlying peer-reviewed publications as standard practice, including discipline-specific repositories in data management plans, and incentivizing post-approval data sharing by industry. Supporting data from all therapeutic areas and population groups, PKRxiv is a critical step toward a more transparent, equitable, and collaborative future in clinical pharmacology research.

Deep ensemble model with blockchain technology for lung cancer detection with secured data sharing.

A prior knowledge-enhanced Transformer model for data anomaly identification and processing in industrial park wastewater treatment plants.

Blockchain for e-healthcare: a review on secure data management frameworks and future challenges

BMSES: Blockchain and mobile edge computing-based secure and energy-efficient system for healthcare data management

Digital government governance, financial technology, and regional digital economy development: A quasi-natural experiment based on big data management agency reform

This study aims to analyze and implement a Management Information System (MIS) to enhance the efficiency of student data management at SMK Bakti Karya Vocational High School. The primary challenge identified is the inadequate efficiency in student data management, which has been conducted manually, resulting in time-consuming processes and potential errors in data recording and processing. The research methodology employed is a system development approach utilizing the System Development Life Cycle (SDLC) framework, encompassing requirements analysis, system design, implementation, and testing phases. The research findings demonstrate that MIS implementation significantly improves student data management efficiency. The developed system successfully automates new student registration processes, academic data management, financial administration, and student data reporting. System testing reveals that data processing time decreased by 70% compared to the previous manual system. Furthermore, data accuracy increased substantially while reducing recording errors. The MIS implementation facilitates real-time access to student information for school personnel and streamlines the generation of required reports. The study concludes that MIS implementation proves effective in enhancing student data management efficiency and can be recommended for adoption in other educational institutions. The system's capacity to automate administrative processes, improve data accuracy, and provide real-time information access demonstrates its significant contribution to educational administration modernization.

Petroleum exploration is an industry that generates a large amount of data, but the datasets used are highly correlated and complex to process. To achieve intelligent management of petroleum data, we propose a multi-model framework based on deep learning networks. This framework combines the advantages of Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) to identify hot data that are more likely to be accessed by voting. In addition, we compare the performance of three commonly used time series prediction models for spatial prediction of petroleum exploration work areas. Experiments show that the multi-model framework outperforms traditional solutions by 25.3% and exhibits a 7.0% performance improvement compared to the best-performing LSTM model in a single model. LSTM is more suitable than Least Squares Regression (LSR) and Support Vector Regression (SVR) for spatial prediction of petroleum data, and a simple offset processing of the prediction results can cover more than 90% of real scenarios.

Privacy-preserving and Secure Health Data Management for Epidemics: A Cryptographic Approach

Background: The development of information technology in the health sector encourages the implementation of Electronic Medical Records (EMR) to improve the efficiency and quality of hospital services. UKI General Hospital has implemented EMR, but still faces technical and operational challenges that hinder system optimization. Objective: This study aims to analyze the implementation of EMR at UKI General Hospital and evaluate its impact on the quality of hospital services. Method: The study used a mixed-method approach with data collection techniques through questionnaires, interviews, and quantitative and qualitative data analysis. The sample consisted of health workers and administrative staff involved in the use of EMR. Results: The results of the study indicate that the implementation of EMR has increased efficiency in patient data management, but there are still obstacles such as limited infrastructure, lack of HR training, and difficulties in adapting to technology. Conclusion: The implementation of EMR contributes to improving the quality of hospital services, but improvements are still needed in technical aspects and HR readiness. Suggestion: Improvements in infrastructure, medical personnel training, and strategic policies are needed to support more effective implementation of EMR.

The rapid growth of marine imaging has outpaced our ability to efficiently analyse the imagery, creating challenges in data management, collaboration, and standardisation. This paper presents Squidle+, a web-based, collaborative platform for the end-to-end management, delivery, discovery, and annotation of marine imagery. Squidle+ provides a centralised portal and annotation repository while linking to imagery hosted on pre-existing cloud storage, eliminating data transfer and duplication. The system features a user-friendly interface with map-based exploration tools, advanced annotation workflows, and integrated analytics through a comprehensive API back-end. Collaboration is managed through user groups with granular permissions, while integrated QA/QC tools enable cross-validation between human annotators and Machine Learning (ML) algorithms. A key innovation is a framework to translate between multiple standardised or user-defined annotation vocabularies. This gives users the flexibility to construct data sets that target specific scientific questions and facilitates data reuse, cross-project syntheses, large-scale ML training, and broad summaries that can be fed into national-level reporting. Squidle+ has been developed in close collaboration with an active user community and currently contains datasets from several platforms and operators around the world. It is currently the largest known repository of openly accessible georeferenced marine images with associated annotations. Squidle+ streamlines complex workflows and significantly enhances the Findability, Accessibility, Interoperability, and Reusability (FAIR) of marine image data.