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INTRODUCTION: The secondary use of data in clinical environments offers significant opportunities to enhance medical research and practices. However, extracting data from generic data structures, particularly the Entity-Attribute-Value (EAV) model, remains challenging. This study addresses these challenges by developing a methodological approach to convert EAV-based data into a format more suitable for analysis. BACKGROUND: The EAV model is widely used in clinical information systems due to its adaptability, but it often complicates data retrieval for research purposes due to its vertical data structure and dynamic schema. OBJECTIVE: The objective of this study is to develop a methodological approach to address the handling of these generic data structures, Methods: We introduce a five-step methodological approach: 1) understanding the specific clinical processes to determine data collection points and involved roles; 2) analysing the data source to understand the data structure and metadata; 3) reversing a use-case-specific data structure to map the front-end data input to its storage format; 4) analysing the content to identify medical information and establish connections; and 5) managing schema changes to maintain data integrity. RESULTS: Applying this method to the hospital information system has shown that EAV-based data can be converted into a structured format, suitable for research. This conversion reduced data sparsity and improved the manageability of schema changes without affecting other classes of data. CONCLUSION: The developed approach provides a systematic method for handling complex data relationships and maintaining data integrity in clinical systems using EAV models. This approach facilitates the secondary use of clinical data, enhancing its utility for medical research and practice.

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Background: Across the world, health data generation is growing exponentially. The continuous rise of new and diversified technology to obtain and handle health data places health information management and governance under pressure. Lack of data linkage and interoperability between systems undermines best efforts to optimise integrated health information technology solutions. Objective: This research aimed to provide a bibliometric overview of the role of interoperability and linkage in health data management and governance. Method: Data were acquired by entering selected search queries into Google Scholar, PubMed, and Web of Science databases and bibliometric data obtained were then imported to Endnote and checked for duplicates. The refined data were exported to Excel, where several levels of filtration were applied to obtain the final sample. These sample data were analysed using Microsoft Excel (Microsoft Corporation, Washington, USA), WORDSTAT (Provalis Research, Montreal, Canada) and VOSviewer software (Leiden University, Leiden, Netherlands). Results: The literature sample was retrieved from 3799 unique results and consisted of 63 articles, present in 45 different publications, both evaluated by two specific in-house global impact rankings. Through VOSviewer, three main clusters were identified: (i) e-health information stakeholder needs; (ii) e-health information quality assessment; and (iii) e-health information technological governance trends. A residual correlation between interoperability and linkage studies in the sample was also found. Conclusion: Assessing stakeholders' needs is crucial for establishing an efficient and effective health information system. Further and diversified research is needed to assess the integrated placement of interoperability and linkage in health information management and governance. Implications: This research has provided valuable managerial and theoretical contributions to optimise system interoperability and data linkage within health information research and information technology solutions.

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BACKGROUND: Identification of pulmonary embolism (PE) across a cohort currently requires burdensome manual review. Previous approaches to automate capture of PE diagnosis have either been too complex for widespread use or have lacked external validation. We sought to develop and validate the Regular Expression Aided Determination of PE (READ-PE) algorithm, which uses a portable text-matching approach to identify PE in reports from computed tomography with angiography (CTA). METHODS: We identified derivation and validation cohorts of final radiology reports for CTAs obtained on adults (≥ 18 years) at two independent, quaternary academic emergency departments (EDs) in the United States. All reports were in the English language. We manually reviewed CTA reports for PE as a reference standard. In the derivation cohort, we developed the READ-PE algorithm by iteratively combining regular expressions to identify PE. We validated the READ-PE algorithm in an independent cohort, and compared performance against three prior algorithms with sensitivity, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), and the F1 score. RESULTS: Among 2948 CTAs in the derivation cohort 10.8 % had PE and the READ-PE algorithm reached 93 % sensitivity, 99 % specificity, 94 % PPV, 99 % NPV, and 0.93 F1 score, compared to F1 scores ranging from 0.50 to 0.85 for three prior algorithms. Among 1206 CTAs in the validation cohort 9.2 % had PE and the algorithm had 98 % sensitivity, 98 % specificity, 85 % PPV, 100 % NPV, and 0.91 F1 score. CONCLUSIONS: The externally validated READ-PE algorithm identifies PE in English-language reports from CTAs obtained in the ED with high accuracy. This algorithm may be used in the electronic health record to accurately identify PE for research or surveillance. If implemented at other EDs, it should first undergo local validation and may require maintenance over time.

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In the age of information explosion, the exponential growth of digital data far exceeds the capacity of current mainstream storage media. DNA is emerging as a promising alternative due to its higher storage density, longer retention time, and lower power consumption. To date, commercially mature DNA synthesis and sequencing technologies allow for writing and reading of information on DNA with customization and convenience at the research level. However, under the disconnected and nonspecialized mode, DNA data storage encounters practical challenges, including susceptibility to errors, long storage latency, resource-intensive requirements, and elevated information security risks. Herein, we introduce a platform named DNA-DISK that seamlessly streamlined DNA synthesis, storage, and sequencing on digital microfluidics coupled with a tabletop device for automated end-to-end information storage. The single-nucleotide enzymatic DNA synthesis with biocapping strategy is utilized, offering an ecofriendly and cost-effective approach for data writing. A DNA encapsulation using thermo-responsive agarose is developed for on-chip solidification, not only eliminating data clutter but also preventing DNA degradation. Pyrosequencing is employed for in situ and accurate data reading. As a proof of concept, DNA-DISK successfully stored and retrieved a musical sheet file (228 bits) with lower write-to-read latency (4.4 min of latency per bit) as well as superior automation compared to other platforms, demonstrating its potential to evolve into a DNA Hard Disk Drive in the future.

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Near-infrared (NIR) light, valuable for its biological penetration and invisibility to the human eye, is a crucial tool in biomedicine, environmental monitoring, anticounterfeiting, and information encryption, yet traditional NIR luminescent materials are often unstable in humid conditions. Here, a highly stable MgGeO3:Mn2+ glass ceramic (GC) with NIR luminescence was successfully synthesized. As-obtained GC700 boasts exceptional luminescent capabilities and possesses abundant trap structures, enabling data inscription with a 405 nm laser and retrieval via laser/thermal excitation. Moreover, the emission peak of Mn2+ can be manipulated from 630 to 691 nm by increasing the annealing treatment temperature. With the harnessing of the effective NIR emission, stable carrier characteristics, and numerous trap structures, there is potential for application in information encryption. Accordingly, we explored the application of MgGeO3:Mn2+ GC (GC700 and GC800) samples in precious three-dimensional (3D) information storage and NIR mechanoluminescence (ML) for biological tissue imaging. These applications demonstrate the potential and versatility of electron-capturing NIR luminescent materials in a range of cutting-edge fields.

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Digital information encoded in polymers has been exclusively decoded by mass spectrometry. However, the size limit of analytes in mass spectrometry restricts the storage capacity per chain. In addition, sequential decoding hinders random access to the bits of interest without full-chain sequencing. Here we report the shotgun sequencing of a 512-mer sequence-defined polymer whose molecular weight (57.3 kDa) far exceeds the analytical limit of mass spectrometry. A 4-bit fragmentation code was implemented at aperiodic positions during the synthetic encoding of 512-bit information without affecting storage capacity per chain. Upon activating the fragmentation code, the polymer chain splits into 18 oligomers, which could be individually decoded by tandem-mass sequencing. These sequences were computationally reconstructed into a full sequence using an error-detection method. The proposed sequencing method eliminates the storage limit of a single polymer chain and allows random access to the bits of interest without full-chain sequencing.

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Zero-dimensional (0D) hybrid metal halides have been emerged as room-temperature phosphorescence (RTP) materials, but synchronous optimization of multiple phosphorescence performance in one structural platform remains less resolved, and stable RTP activity in aqueous medium is also unrealized due to serious instability toward water and oxygen. Herein, we demonstrated a photophysical tuning strategy in a new 0D hybrid zinc halide family of (BTPP)2ZnX4 (BTPP = benzyltriphenylphosphonium, X = Cl and Br). Infrequently, the delicate combination of organic and inorganic species enables this family to display multiple ultralong green afterglow and efficient self-trapped exciton (STE) associated cyan phosphorescence. Compared with inert luminescence of [BTPP]+ cation, incorporation of anionic [ZnX4]2- effectively enhance the spin-orbit coupling effect, which significantly boosts the photoluminescence quantum yield (PLQY) up to 30.66% and 54.62% for afterglow and phosphorescence, respectively. Synchronously, the corresponding luminescence lifetime extend to 143.94 ms and 0.308 µs surpassing the indiscernible phosphorescence of [BTPP]X salt. More importantly, this halide family presents robust RTP emission with nearly unattenuated PLQY in water and harsh condition (acid and basic aqueous solution) over half a year. The highly efficient integrated afterglow and STE phosphorescence as well as ultrahigh aqueous state RTP realize multiple anti-counterfeiting applications in wide chemical environments.

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Light-induced ferroelectric polarization in 2D layered ferroelectric materials holds promise in photodetectors with multilevel current and reconfigurable capabilities. However, translating this potential into practical applications for high-density optoelectronic information storage remains challenging. In this work, an α-In2Se3/Te heterojunction design that demonstrates spatially resolved, multilevel, nonvolatile photoresponsivity is presented. Using photocurrent mapping, the spatially localized light-induced poling state (LIPS) is visualized in the junction region. This localized ferroelectric polarization induced by illumination enables the heterojunction to exhibit enhanced photoresponsivity. Unlike previous reports that observe multilevel polarization enhancement in electrical resistance, the device shows nonvolatile photoresponsivity enhancement under illumination. After polarization saturation, the photocurrent increases up to 1000 times, from 10-12 to 10-9 A under the irradiation of a 520 nm laser with a power of 1.69 nW, compared to the initial state in a self-driven mode. The photodetector exhibits high detectivity of 4.6×1010 Jones, with a rise time of 27 µs and a fall time of 28 µs. Furthermore, the device's localized poling characteristics and multilevel photoresponse enable spatially multiplexed optical information storage. These results advance the understanding of LIPS in 2D ferroelectric materials, paving the way for optoelectronic information storage technologies.

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Precise control over the organic composition is crucial for tailoring the distinctive structures and properties of hybrid metal halides. However, this approach is seldom utilized to develop materials that exhibit stimuli-responsive circularly polarized luminescence (CPL). Herein, we present the synthesis and characterization of enantiomeric hybrid zinc bromides: biprotonated ((R/S)-C12H16N2)ZnBr4 ((R/S-LH2)ZnBr4) and monoprotonated ((R/S)-C12H15N2)2ZnBr4 ((R/S-LH1)2ZnBr4), derived from the chiral organic amine (R/S)-2,3,4,9-Tetrahydro-1H-carbazol-3-amine ((R/S)-C12H14N2). These compounds showcase luminescent properties; the zero-dimensional biprotonated form emits green light at 505 nm, while the monoprotonated form, with a pseudo-layered structure, displays red luminescence at 599 and 649 nm. Remarkably, the reversible local protonation-deprotonation behavior of the organic cations allows for exposure to polar solvents and heating to induce reversible structural and luminescent transformations between the two forms. Theoretical calculations reveal that the lower energy barrier associated with the deprotonation process within the pyrrole ring is responsible for the local protonation-deprotonation behavior observed. These enantiomorphic hybrid zinc bromides also exhibit switchable circular dichroism (CD) and CPL properties. Furthermore, their chloride counterparts were successfully obtained by adjusting the halogen ions. Importantly, the unique stimuli-responsive CPL characteristics position these hybrid zinc halides as promising candidates for applications information storage, anti-counterfeiting, and information encryption.

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Metal-organic frameworks (MOFs) have attained broad research attention in the areas of sensors, resistive memories, and optoelectronic synapses on the merits of their intriguing physical and chemical properties. In this review, recent progress on the synthesis of MOFs and their electronic applications is introduced and discussed. Initially, the crystal structures and properties of MOFs encompassing optical, electrical, and chemical properties are discussed in brief. Subsequently, advanced synthesis methods for MOFs are introduced, categorized into hydrothermal approach, microwave synthesis, mechanochemical synthesis, and electrochemical deposition. After that, the various roles of MOFs in widespread applications, including sensing, information storage, optoelectronic synapses, machine learning, and artificial intelligence, are discussed, highlighting their versatility and the innovative solutions they provide to long-standing challenges. Finally, an outlook on remaining challenges and a future perspective for MOFs are proposed.

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INTRODUCTION AND PURPOSE: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security. METHODS AND RESULTS: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing. DISCUSSION AND CONCLUSIONS: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology.

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BACKGROUND: Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand. OBJECTIVE: In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses. METHODS: Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers. RESULTS: D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain. CONCLUSIONS: The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.

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Monitoring national capacity of pediatric intensive care (PICU) beds is important for resource allocation. We describe the development and application of an online tool for bed occupancy monitoring during the respiratory epidemic of 2023 in Uruguay. A 5-minute survey was sent out daily to each PICU designated staff utilizing an instant messaging application, throughout the 12-week of 2023 winter season. The survey gathered information on staffed bed capacity, occupancy rates, invasive mechanical ventilation (IMV) demand and number of children admitted for lower respiratory tract infections (LRTIs). Data was manually entered using the REDcap Software platform. A comparison between PICUs from the capital city Montevideo (MVD) and the inland region (INN) was performed. Participating PICUs had access to a daily dashboard with nationwide data. We collected data from 100% (n=20) of Uruguayan PICUs, 68% in MVD (2.72 PICU beds per-10,000 <18 years) and 32% in the INN (0.86 PICU beds per-10,000 <18 years). National average bed occupancy rate was 55.5%, without differences between MVD and INN. However, LRTI admissions and IMV incidence were notably higher among INN PICUs compared with MVD PICUs, 83.0% vs. 71% (P<0.01) and 19.9% vs. 14.7% (P<0.01) respectively. During one week occupancy exceeded 80% without differences between MVD and INN. The implementation of a nationwide PICU bed monitoring tool was possible. Although occupancy rates did not reach critical levels at a national grade, regional differences were found that merit further study to improve vital PICUs resource allocation.

Monitorear la capacidad nacional de camas de cuidados intensivos pediátricos (UCIP) es importante para la asignación de recursos. Describimos el desarrollo y aplicación de una herramienta online para el seguimiento de la ocupación de camas durante la epidemia respiratoria de 2023 en Uruguay. Se envió diariamente una encuesta de 5 minutos a cada personal designado de la UCIP mediante una aplicación de mensajería instantánea, durante las 12 semanas de la temporada de invierno de 2023. La encuesta recopiló información sobre la capacidad de camas con personal, las tasas de ocupación, la demanda de ventilación mecánica invasiva (IMV) y el número de niños admitidos por infecciones del tracto respiratorio inferior (LRTI). Los datos se ingresaron manualmente utilizando la plataforma del software REDcap. Se realizó una comparación entre UCIP de la capital Montevideo (MVD) y del interior (INN). Las UCIP participantes tuvieron acceso a un panel diario con datos a nivel nacional. Se recolectaron datos del 100% (n=20) de las UCIP uruguayas, el 68% en MVD (2,72 camas UCIP por 10.000 <18 años) y el 32% en el INN (0,86 camas UCIP por 10.000 <18 años). La tasa de ocupación de camas promedio nacional fue del 55,5%, sin diferencias entre MVD e INN. Sin embargo, los ingresos por LRTI y la incidencia de IMV fueron notablemente mayores entre las UCIP INN en comparación con las UCIP MVD, 83,0% frente a 71% (P<0,01) y 19,9% frente a 14,7% (P<0,01), respectivamente. Durante una semana la ocupación superó el 80% sin diferencias entre MVD y INN. Fue posible implementar una herramienta de monitoreo de camas UCIP a nivel nacional. Aunque las tasas de ocupación no alcanzaron niveles críticos a nivel nacional, se encontraron diferencias regionales que merecen más estudios para mejorar la asignación de recursos vitales de las UCIP.

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The increasing availability of time series data depicting the evolution of physical system properties has prompted the development of methods focused on extracting insights into the system behavior over time, discerning whether it stems from deterministic or stochastic dynamical systems. Surrogate data testing plays a crucial role in this process by facilitating robust statistical assessments. This ensures that the observed results are not mere occurrences by chance, but genuinely reflect the inherent characteristics of the underlying system. The initial process involves formulating a null hypothesis, which is tested using surrogate data in cases where assumptions about the underlying distributions are absent. A discriminating statistic is then computed for both the original data and each surrogate data set. Significantly deviating values between the original data and the surrogate data ensemble lead to the rejection of the null hypothesis. In this work, we present various surrogate methods designed to assess specific statistical properties in random processes. Specifically, we introduce methods for evaluating the presence of autodependencies and nonlinear dynamics within individual processes, using Information Storage as a discriminating statistic. Additionally, methods are introduced for detecting coupling and nonlinearities in bivariate processes, employing the Mutual Information Rate for this purpose. The surrogate methods introduced are first tested through simulations involving univariate and bivariate processes exhibiting both linear and nonlinear dynamics. Then, they are applied to physiological time series of Heart Period (RR intervals) and respiratory flow (RESP) variability measured during spontaneous and paced breathing. Simulations demonstrated that the proposed methods effectively identify essential dynamical features of stochastic systems. The real data application showed that paced breathing, at low breathing rate, increases the predictability of the individual dynamics of RR and RESP and dampens nonlinearity in their coupled dynamics.

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Photonic elastomers, capable of converting imperceptible deformations into visible colors, show significant potential in smart materials. However, instantaneous deformation is arduous to record accurately due to the disappearance of optical information after deformation recovery. Herein, inspired by the folding structures of iridocytes in cephalopods, a stress- and moisture-triggered wrinkling and erasure effect is proposed to be introduced in the construction of a photonic elastomer. Implemented in a dual-network polymer framework with modulatable locking, it allows for reversible deformation storage. The photonic elastomer comprises a surface one-dimensional photonic crystal (1DPC) and a poly(dimethylsiloxane) (PDMS) substrate. The deformed 1DPC lattice transforms into a wrinkled state due to a substrate deformation mismatch, preserving strain-induced structural color information through interchain hydrogen bonding and crystalline shape-locking in dual-network polymers. Reading the color provides multidimensional information about the instantaneous deformation degree and distribution. Moreover, the moisture-induced shape-memory feature of the 1DPC can be triggered with a minute amount of water, like fingertip perspiration or humidity change (35% to 80%), to restore the original color. This stress/moisture-responsive photonic elastomer, with its dynamically reconfigurable wrinkle-lattice, holds great promise for applications in mechanical sensing, inkless writing, and anticounterfeiting, significantly enhancing the versatility of photonic materials.

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INTRODUCTION: An organisation's ability to learn and adapt is key to its long-term performance and success. Although calls to improve learning within and across health organisations and systems have increased in recent years, global health is lagging behind other sectors in attention to learning, and applications of conceptual models for organisational learning to this field are needed. LEVERAGING THE 4I FRAMEWORK: This article proposes modifications to the 4I framework for organisational learning (which outlines the processes of intuition, interpretation, integration and institutionalisation) to guide the creation, retention and exchange of knowledge within and across global health organisations. PROPOSED EXPANSIONS: Two expansions are added to the framework to account for interorganisational learning in the highly interconnected field: (1) learning pathways across organisations via formal or informal partnerships and communities of practice and (2) learning pathways to and from macro-level 'coordinating bodies' (eg, WHO). Two additional processes are proposed by which interorganisational learning occurs: interaction across partnerships and communities of practice, and incorporation linking global health organisations to coordinating bodies. Organisational politics across partnerships, communities of practice and coordinating bodies play an important role in determining why some insights are institutionalised while others are not; as such, the roles of the episodic influence and systemic domination forms of power are considered in the proposed additional organisational learning processes. DISCUSSION: When lessons are not shared across partnerships, communities of practice or the research community more broadly, funding may continue to support global health studies and programmes that have already been proven ineffective, squandering research and healthcare resources that could have been invested elsewhere. The '6I' framework provides a basis for assessing and implementing organisational learning approaches in global health programming, and in health systems more broadly.

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BACKGROUND: Asia consists of diverse nations with extremely variable health care systems. Integrated real-world data (RWD) research warehouses provide vast interconnected data sets that uphold statistical rigor. Yet, their intricate details remain underexplored, restricting their broader applications. OBJECTIVE: Building on our previous research that analyzed integrated RWD warehouses in India, Thailand, and Taiwan, this study extends the research to 7 distinct health care systems: Hong Kong, Indonesia, Malaysia, Pakistan, the Philippines, Singapore, and Vietnam. We aimed to map the evolving landscape of RWD, preferences for methodologies, and database use and archetype the health systems based on existing intrinsic capability for RWD generation. METHODS: A systematic scoping review methodology was used, centering on contemporary English literature on PubMed (search date: May 9, 2023). Rigorous screening as defined by eligibility criteria identified RWD studies from multiple health care facilities in at least 1 of the 7 target Asian nations. Point estimates and their associated errors were determined for the data collected from eligible studies. RESULTS: Of the 1483 real-world evidence citations identified on May 9, 2023, a total of 369 (24.9%) fulfilled the requirements for data extraction and subsequent analysis. Singapore, Hong Kong, and Malaysia contributed to ≥100 publications, with each country marked by a higher proportion of single-country studies at 51% (80/157), 66.2% (86/130), and 50% (50/100), respectively, and were classified as solo scholars. Indonesia, Pakistan, Vietnam, and the Philippines had fewer publications and a higher proportion of cross-country collaboration studies (CCCSs) at 79% (26/33), 58% (18/31), 74% (20/27), and 86% (19/22), respectively, and were classified as global collaborators. Collaboration with countries outside the 7 target nations appeared in 84.2% to 97.7% of the CCCSs of each nation. Among target nations, Singapore and Malaysia emerged as preferred research partners for other nations. From 2018 to 2023, most nations showed an increasing trend in study numbers, with Vietnam (24.5%) and Pakistan (21.2%) leading the growth; the only exception was the Philippines, which declined by -14.5%. Clinical registry databases were predominant across all CCCSs from every target nation. For single-country studies, Indonesia, Malaysia, and the Philippines favored clinical registries; Singapore had a balanced use of clinical registries and electronic medical or health records, whereas Hong Kong, Pakistan, and Vietnam leaned toward electronic medical or health records. Overall, 89.9% (310/345) of the studies took >2 years from completion to publication. CONCLUSIONS: The observed variations in contemporary RWD publications across the 7 nations in Asia exemplify distinct research landscapes across nations that are partially explained by their diverse economic, clinical, and research settings. Nevertheless, recognizing these variations is pivotal for fostering tailored, synergistic strategies that amplify RWD's potential in guiding future health care research and policy decisions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/43741.

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A good intelligent learning model is the key to complete recognition of scene information and accurate recognition of specific targets in intelligent unmanned system. This study proposes a new associative memory model based on the semi-tensor product (STP) of matrices, to address the problems of information storage capacity and association. First, some preliminaries are introduced to facilitate modeling, and the problem of information storage capacity in the application of discrete Hopfield neural network (DHNN) to associative memory is pointed out. Second, learning modes are equivalently converted into their algebraic forms by using STP. A memory matrix is constructed to accurately remember these learning modes. Furthermore, an algorithm for updating the memory matrix is developed to improve the association ability of the model. And another algorithm is provided to show how our model learns and associates. Finally, some examples are given to demonstrate the effectiveness and advantages of our results. Compared with mainstream DHNNs, our model can remember learning modes more accurately with fewer nodes.