Analysis Of Global Data Research Articles

Exploration and Development of Quantum Computing Algorithms for Optimization, Cryptography, and Machine Learning Applications

Quantum computing, with its potential to revolutionize computation, relies fundamentally on the development of efficient algorithms to leverage its unparalleled processing capabilities. This research delves into the creation, exploration, and refinement of quantum computing algorithms, focusing on their applications in optimization, cryptography, and machine learning. Quantum algorithms such as Shor's and Grover's have demonstrated remarkable advantages in factorization and search problems, while more recent innovations are tackling complex challenges in global optimization and data analysis. By integrating principles of quantum mechanics—such as superposition, entanglement, and interference—these algorithms promise exponential speed-ups over classical counterparts in specific domains. This study critically analyzes existing quantum algorithms, proposes advancements in hybrid quantum-classical frameworks, and explores their implications for practical problem-solving. Through simulations and theoretical evaluations, it aims to bridge the gap between quantum theory and real-world applications, contributing to the evolution of quantum computing as a transformative technology.

Dynamic signatures: a mathematical approach to analysis

Abstract This study evaluates mathematical tools (principal component analysis, dynamic time warping, and the Kolmogorov–Smirnov hypothesis test) to analyse global and local data from dynamic signatures to reduce subjectivity and increase the reproducibility of handwriting examination using a two-step approach. A data set composed of 1800 genuine signature samples, 870 simulated signatures, and 60 disguises (30 formally similar or ‘autosimulated’ and 30 random but different from usual) provided by 30 volunteers was collected. The first step involved global data analysis using principal component analysis and a hypothesis test performed for 62 global characteristics, and associations of these characteristics were analysed through calculations of multivariate distance followed by a hypothesis test. The second step involved the analysis of local characteristics including vertical and horizontal positions, speed, pressure gradient, acceleration, and jerk point-to-point, by using dynamic time warping followed by a hypothesis test. Optimization of sensitivity and specificity metrics of the hypothesis test was explored by varying its stringency and observing accuracy rates for the simulated and genuine groups. A P-value threshold of 1 × 10−10 was found to be optimal, making the test more restrictive and yielding accuracy rates of 96.7% for genuine global data and 88.9% for simulated data. The same cutoff value for local characteristics provided an average accuracy rate of 95.4% for genuine samples and 94.7% for simulated samples, demonstrating high accuracy for both simulated and genuine samples. However, the method did not offer reasonable accuracy rates for disguises, consistent with observations in traditional handwriting examination. Our approach provided satisfactory results for forensic examination use. The visualization of graphs and signatures and analysis of all identifying elements of handwriting by the examining expert are still essential. In future studies, we plan to perform blind tests to validate our approach and propose a rigorous methodology.

Combined oxygen and glucose oscillations distinctly change the transcriptional and physiological state of Escherichia coli.

Escherichia coli, a common microbial host for industrial bioproduction, experiences a highly dynamic environment in industrial-scale bioreactors due to significant glucose and oxygen gradients. In this study, we mimic the combined gradients of glucose and oxygen in high-throughput bioreactors to study the transcriptional response of E. coli to industrial-scale conditions. Under oscillating oxygen conditions, E. coli formed less biomass and accumulated the anaerobic by-product acetate. With respect to oxygen-responsive genes, we found that genes of the TCA cycle and of different electron transport chain complexes were differentially expressed. A global analysis of the expression data revealed that oxygen oscillations had caused a transition towards a catabolite-repressed state and upregulation of several stress-related regulatory programs. Interestingly, the transcriptional changes persisted after oxygen limitation stopped. In contrast, the changes we observed due to glucose starvation, such as induction of the stringent response, were primarily transient. Most importantly, we found that effects of combined oxygen and glucose oscillations were distinct from the ones of oxygen and substrate oscillations alone, suggesting an important interplay between the different metabolic regimes in industrial-scale bioreactors.

Double edged tech: navigating the public health and legal challenges of digital twin technology

Abstract This research addresses the legal challenges posed by the implementation of digital twin technology in public health. Digital twins are advanced computational models which aim to replicate human organ functions and biological processes. Their goal is to enable the simulation of the entire human body and to personalize the outputs with individual patient’s health data. Digital twin technology promises to bridge gap between personalized medicine and precision public health. This research is based on a comprehensive review of the current European and Swiss legal frameworks for medical devices and data protection that apply to digital twin technology. Within these frameworks, digital twins are in a grey area, as they are only considered as medical device software when applied to individuals. They also incorporate artificial intelligence, machine learning, and data analytics, which are already a source of legal concern in a healthcare setting, but their effective use in public health raises additional issues. Preliminary findings suggest significant shortcomings in the legal frameworks regarding data privacy, and the potential for increased surveillance by health insurance systems, which could lead to human rights violations. The public health application of digital twins would require global data analysis of sensitive personal information, raising further questions about the secure storage, transmission, and processing of this data. The study concludes that current legal frameworks must be future proofed to manage the complexities introduced by digital twins in public health. In particular, it suggests that regulatory oversight of medical devices needs to be significantly improved in order to safely harness the potential of digital twin technologies for public health. Finally, the new European Union health data space and the forthcoming EU AI act will also play important roles in the implementation of digital twin technologies. Key messages • Applying digital twin technology in public health faces considerable challenges, particularly the issues around data privacy, potential discrimination, and increased surveillance. • We need to adapt and strengthen existing legal frameworks to effectively manage the complexities introduced by digital twins, ensuring that these technologies can be safely used for public health.

Cloud classification through machine learning and global horizontal irradiance data analysis

AbstractCloud observations and characterization are crucial owing to their influence on energy balance, climate, and weather. Their particular effects on radiation vary depending on different cloud parameters, such as cloud base or top height, water content, and cloud optical thickness, all of them closely related to the specific cloud type. Cloud classification therefore becomes a crucial task in meteorology, although it remains challenging for weather services worldwide owing to the intensive associated labor and cost. In this study we introduce a new low‐cost method for automating cloud classification based on a combination of ground‐based global horizontal irradiance (GHI) measurements, a clear‐sky model, and machine learning. Based on the hypothesis that different cloud types have their own GHI signatures, we trained different supervised learning algorithms using GHI data manually labeled by meteorological observers from time‐synchronized all‐sky images. Multiple time windows were extracted from each GHI series, with eight features defined in each case to characterize the sequence. The best outcome was achieved using an XGBoost model on features extracted on time windows of 33 min, obtaining an accuracy of 0.88 and a Cohen's kappa of 0.84 in a held‐out test set. The development presented in this study has the ability to provide low‐cost cloud classification from ground‐based observations, which is a challenge for weather services worldwide owing to intensive labor and cost.

Glucagon-like peptide-1 receptor agonists improve outcomes in individuals with type 2 diabetes with and without heart failure

BackgroundThe effectiveness of glucagon-like peptide-1 receptor agonists (GLP1Ras) for prevention of heart failure (HF) in patients with type 2 diabetes (T2DM) without HF and for risk of death in patients with T2DM with HF has not been fully elucidated in routine clinical practice. MethodsUsing the real-world global electronic medical record TriNetX database, individuals with T2DM and with or without HF who initiated either GLP1Ras or sitagliptin from 2017 to 2020 were retrospectively analyzed. In individuals with T2DM without HF, the primary outcome was a composite of all-cause mortality and a new diagnosis of HF within three years. In individuals with T2DM with HF, the primary outcome was all-cause mortality within three years. Propensity-score (PS) matching was used to adjust for over 100 baseline characteristics. ResultsA total of 65,598 individuals with T2DM without HF starting a GLP1Ras were PS matched with 65,598 starting sitagliptin. GLP1Ras were associated with a lower incidence of the composite endpoint (10.5 % versus 11.8 %, hazard ratio [HR] 0.82, [0.80–0.85], p < 0.001), mortality (HR 0.66 [0.63–0.69]) and new diagnosis of HF (HR 0.92 [0.88–0.96]). There were 6002 individuals in each group matched for T2DM and HF. Mortality was lower in the GLP1Ras group (17.6 % versus 22.8 %, HR 0.70 [0.65–0.76], p < 0.001). Results were consistent across subgroups. ConclusionsIn this global real-world data analysis, GLP1Ra use was associated with a lower risk of death and HF in individuals with T2DM without HF, and lower risk of death in those with HF.

Enhancing ocean color retrieval in sunglint regions: the efficacy of IPNOA over conventional atmospheric correction methods

Sunglint significantly impacts the extraction of ocean color information, particularly for sensors lacking tilt capabilities. Traditional atmospheric correction algorithms often fail to retrieve effective data in high-sunglint regions. The polynomial-based POLYMER method, applied to MERIS data, effectively addresses sunglint, although its accuracy decreases by about 15% in such conditions. To enhance data reliability in sunglint regions, we propose the Improved polynomial nonlinear optimization approach (IPNOA), a revision of the POLYMER atmospheric correction. IPNOA employs the QAA-RGR (quasi-analytical algorithm-red-green-bands-ratio) to refine the bio-optical ocean reflectance model. Additionally, due to the nonlinear optimization algorithm’s sensitivity to initial values, this study uses global 8-day average oceanic optical properties at 4 km resolution as the initial setting. The performance of IPNOA was initially evaluated using a synthetic dataset, with retrieved remote sensing reflectance (Rrs) closely matching the simulated Rrs across all wavelengths. The mean absolute percentage error (MAPE) remained below 1% for non-sunglint, moderate sunglint, and high sunglint conditions. Further analysis of in situ data revealed that IPNOA performs better, exceptionally at 412 nm, with a MAPE of 5.27% in sunglint regions. When processed by POLYMER, the dataset exhibited a MAPE of 68.47%. Finally, an analysis of global data from MODIS, VIIRS, and HY1C/D on July 15, 2022, showed good agreement among the three on a global scale. Above all, these results indicate that the IPNOA algorithm has strong potential for retrieving valid products in moderate, even high sunglint regions, offering practical benefits for expanding the spatial coverage of ocean color satellite data.

Global Health Indicators and Child Mortality Trends: Insights from a Global Panel Data Analysis of 200 Countries.

The aim of this study was to explore how medical resources and vaccine coverage relate to infant mortality rate (IMR) and under-five mortality rate (U-5MR), which are both key national health indicators. This longitudinal study was based on panel data from the national level of 200 countries. Data from 1990 to 2021 were grouped into seven regions based on geographic and epidemiological similarities. Regarding correlation, the high-income region showed a different trend from that shown by other regions. Health expenditure was positively associated with IMR and U-5MR globally. Number of medical doctors per 1,000 people was negatively associated with IMR and U-5MR globally. Hepatitis type B (HBV) and measles, first dose (MCV) were negatively associated with IMR and HBV, MCV, and Bacillus Calmette-Guérin were negatively associated with U-5MR globally. In quadratic regression, the correlation between the number of doctors and mortality stabilizes or plateaus at approximately four individuals. Overall vaccine coverage was positively correlated with mortality up to a certain threshold, beyond which it became negatively correlated. A higher number of doctors was consistently associated with decreased mortality, regardless of location, while other factors varied by region. Our study findings highlight the importance of implementing global strategies that are specific to each region's characteristics to reduce IMR and U-5MR.

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.

Mapping the SMEFT at high-energy colliders: from LEP and the (HL-)LHC to the FCC-ee

We present SMEFiT3.0, an updated global SMEFT analysis of Higgs, top quark, and diboson production data from the LHC complemented by electroweak precision observables (EWPOs) from LEP and SLD. We consider recent inclusive and differential measurements from the LHC Run II, alongside with a novel implementation of the EWPOs based on independent calculations of the relevant EFT contributions. We estimate the impact of HL-LHC measurements on the SMEFT parameter space when added on top of SMEFiT3.0, through dedicated projections extrapolating from Run II data. We quantify the significant constraints that measurements from two proposed high-energy circular e+e− colliders, the FCC-ee and the CEPC, would impose on both the SMEFT parameter space and on representative UV-complete models. Our analysis considers projections for the FCC-ee and the CEPC based on the latest running scenarios and includes Z-pole EWPOs, fermion-pair, Higgs, diboson, and top quark production, using optimal observables for both the W+W− and the tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ t\\overline{t} $$\\end{document} channels. The framework presented in this work may be extended to other future colliders and running scenarios, providing timely input to ongoing studies towards future high-energy particle physics facilities.

Strategies for Differential Diagnosis and Management of Ichthyosis in Newborns

Congenital ichthyosis represents agroup of heterogeneous inherited skin diseases characterized by generalized keratinization disorder in the form of hyperkeratosis, caused by abnormalities in epidermal differentiation. The neonatal period is crucial for patients with ichthyosis due to the risk of developing complications, comorbidities, and fatal outcomes. Most complications arise from impaired skin barrier function. This article discusses the manifestations of ichthyosis in newborns, describes risks and complications, and suggests strategies for monitoring and treating patients. Purpose of the study: to present contemporary data on differential diagnosis and treatment strategies for newborn patients with congenital ichthyosis. Materials and Methods: review of Russian and international sources dedicated to the etiology, diagnosis, and treatment of ichthyosis was conducted. Results: Differential diagnosis of ichthyosis in the neonatal period is acrucial task, hence the most life-threatening subtypes are considered. The most severe progression with ahigh risk of fatal outcomes is characteristic of Harlequin ichthyosis. Collodion baby presents with less pronounced symptoms, is not afinal diagnosis, and may later develop clinical manifestations typical of ichthyosiform erythroderma, lamellar ichthyosis, or self-resolving congenital ichthyosis. Netherton syndrome is characterized by acombination of congenital ichthyosis with structural abnormalities of the hair shaft and apropensity for atopy. Epidermolytic ichthyosis, accompanied by the formation of blisters and erosions on the skin, requires differential diagnosis not only with other forms of ichthyosis but also with bullous epidermolysis, staphylococcal scalded skin syndrome, and toxic epidermal necrolysis. Every newborn with described manifestations requires support from amultidisciplinary team of specialists and timely therapeutic and diagnostic measures aimed at preventing the development of complications and deterioration of the patient’s condition. Conclusion: An analysis of global literature data and the practical experience of the authors indicate the complexities of differential diagnosis of ichthyosis forms in the neonatal period. To improve skin condition, prevent infectious complications, and correct electrolyte and respiratory disturbances, symptomatic treatment should begin before adefinitive diagnosis is established.

RETRACTED: Relationships among fall cone flow index and consistency identifiers of fine-grained soils with emphasis on toughness limit

It is well known that plasticity characteristics are one of the most important factors controlling the engineering behavior of fine-grained soils. In this regard, Atterberg limits are used for evaluation of plasticity and strength behavior of soils. Of all the plasticity identifiers, fall cone flow index, which is the slope of the flow curve is a descriptor of plasticity and undrained shear strength characteristics of cohesive soils. On the other hand, toughness limit is also efficient in prediction of many index properties of soils including plasticity characteristics, compression index as well as residual friction angle. This study is an attempt to establish relationships among fall cone flow index and consistency identifiers of fine-grained soils. Emphasis is given on toughness limit, which is believed to be a practical and efficient approach in assessment of fall cone flow index. Regression-based equations establishing relationships between fall cone flow index, plasticity index, plasticity ratio, toughness limit and liquid limit were presented. Besides, the efficiency of artificial neural networks in prediction of toughness limit and fall cone flow index was investigated. It should be noted that established relationships using regression equations are based on regional data, which come up with coefficient of determination (R2) values ranging between 0.706 and 0.978. On the other hand, ANN models were used to model global data, R2 values were obtained to be between 0.586-0.972 and 0.522-0.889 for training and testing phases, respectively. Relationships concerning local and global data were presented, along with analysis of effectiveness of application of relationships established for analysis of global data.

No Clinically Significant Differences in Outcomes After Anterior Cruciate Ligament Reconstruction When Comparing Quadriceps, Bone–Patellar Tendon–Bone, and Hamstring Autografts of 9 mm or Greater

To determine whether there are patient-reported outcome differences in bone-patellar tendon-bone (BPTB), quadriceps tendon (QT), and hamstring (HS) grafts of comparable size. We performed a retrospective analysis of global registry data to include all patients who had an anterior cruciate ligament reconstruction with BPTB, QT, or HS autograft of at least 9 mm in diameter between 2010 and 2021 with complete 1- and 2-year outcome scores. We compared 1- and 2-year postoperative outcome scores (visual analog scale [VAS], Knee Injury and Osteoarthritis Outcome Score [KOOS] pain, Marx Activity Rating Scale [MARS], Veterans RAND 12-Item Health Survey [VR-12]) between BPTB, QT, and HS of the same size graft. In total, 2,318 subjects were included in the analysis, and all graft types showed improved patient-reported outcome measures at 1 and 2 years postoperatively when compared with baseline. The KOOS pain score for the BPTB group was significantly better than the HS (between-group difference= 2.71, P < .01) and QT (between-group difference= 2.51, P < .01) groups at 1 year, and the BPTB group was better than HS (between-group difference= 1.88, P < .01) at 2 years. However, the differences were small and not clinically meaningful. When we compared graft type, there were no differences in the percentage of patients who reached Minimal clinically important difference, or clinical scores VAS or MARS at 2-year follow-up. There is no clinically meaningful difference in KOOS-pain, VR-12, VAS, and MARS at 1 and 2 years postoperatively in patients having anterior cruciate ligament reconstruction with BPTB, HS, or QT if graft size is at least 9 mm in diameter. Level III, retrospective comparative study.

Roles and Responsibilities of the Global Specialist Digital Health Workforce: Analysis of Global Census Data.

The Global Specialist Digital Health Workforce Census is the largest workforce survey of the specialist roles that support the development, use, management, and governance of health data, health information, health knowledge, and health technology. This paper aims to present an analysis of the roles and functions reported by respondents in the 2023 census. The 2023 census was deployed using Qualtrics and was open from July 1 to August 13, 2023. A broad definition was provided to guide respondents about who is in the specialist digital health workforce. Anyone who self-identifies as being part of this workforce could undertake the survey. The data was analyzed using descriptive statistical analysis and thematic analysis of the functions respondents reported in their roles. A total of 1103 respondents completed the census, with data reported about their demographic information and their roles. The majority of respondents lived in Australia (n=870, 78.9%) or New Zealand (n=130, 11.8%), with most (n=620, 56.3%) aged 35-54 years and identifying as female (n=720, 65.3%). The top four occupational specialties were health informatics (n=179, 20.2%), health information management (n=175, 19.8%), health information technology (n=128, 14.4%), and health librarianship (n=104, 11.7%). Nearly all (n=797, 90%) participants identified as a manager or professional. Less than half (430/1019, 42.2%) had a formal qualification in a specialist digital health area, and only one-quarter (244/938, 26%) held a credential in a digital health area. While two-thirds (502/763, 65.7%) reported undertaking professional development in the last year, most were self-directed activities, such as seeking information or consuming online content. Work undertaken by specialist digital health workers could be classified as either leadership, functional, occupational, or technological. Future specialist digital health workforce capability frameworks should include the aspects of leadership, function, occupation, and technology. This largely unqualified workforce is undertaking little formal professional development to upskill them to continue to support the safe delivery and management of health and care through the use of digital data and technology.

Solar neutrinos and leptonic spin forces

We quantify the effects of light spin-zero particles with pseudoscalar couplings to leptons and scalar couplings to nucleons on the evolution of solar neutrinos. In this scenario the matter potential sourced by the nucleons in the Sun’s matter gives rise to spin precession of the relativistic neutrino ensemble. As such the effects in the solar observables are different if neutrinos are Dirac or Majorana particles. For Dirac neutrinos the spin-flavour precession results into left-handed neutrino to right-handed neutrino (i.e., active-sterile) oscillations, while for Majorana neutrinos it results into left-handed neutrino to right-handed antineutrino (i.e., active-active) oscillations. In both cases this leads to distortions in the solar neutrino spectrum which we use to derive constraints on the allowed values of the mediator mass and couplings via a global analysis of the solar neutrino data. In addition for Majorana neutrinos spin-flavour precession results into a potentially observable flux of solar electron antineutrinos at the Earth which we quantify and constrain with the existing bounds from Borexino and KamLAND.

Global analysis of LISA data with Galactic binaries and massive black hole binaries

Global analysis of LISA data with Galactic binaries and massive black hole binaries

Differential gene expression in irradiated potato tubers contributed to sprout inhibition and quality retention during a commercial scale storage

Current study is the first ever storage cum market trial of radiation processed (28 tons) of potato conducted in India at a commercial scale. The objective was to affirm the efficacy of very low dose of gamma radiation processing of potato for extended storage with retained quality and to understand the plausible mechanism at the gene modulation level for suppression of potato sprouting. Genes pertaining to abscisic acid (ABA) biosynthesis were upregulated whereas its catabolism was downregulated in irradiated potatoes. Additionally, genes related to auxin buildup were downregulated in irradiated potatoes. The change in the endogenous phytohormone contents in irradiated potato with respect to the control were found to be correlated well with the differential expression level of certain related genes. Irradiated potatoes showed retention of processing attributes including cooking and chip-making qualities, which could be attributed to the elevated expression of invertase inhibitor in these tubers. Further, quality retention in radiation treated potatoes may also be related to inhibition in the physiological changes due to sprout inhibition. Ecological and economical analysis of national and global data showed that successful adoption of radiation processing may gradually replace sprout suppressants like isopropyl N-(3-chlorophenyl) carbamate (CIPC), known to leave residue in the commodity, stabilize the wholesale annual market price, and provide a boost to the industries involved in product manufacturing.

Photophysics and thermally activated delayed fluorescence (TADF) of fullerene C70 and C70 methano and pseudodihydro monoadducts: A comparative study

A comparative study of the photophysical properties of pristine C70 and two C70 derivatives, including their thermally activated delayed fluorescence (TADF) is reported. It is observed that the addition of a substituent group to the C70 cage-like structure changes both the radiative and the nonradiative rate constants, the change being more pronounced for the nonradiative processes (S1→S0 internal conversion and S1→T1 and T1→S0 intersystem crossings). A new method of TADF analysis (global analysis of time-resolved and steady-state data) is presented and applied. Both derivatives are found to display a less efficient TADF when compared with C70.

Global quantitative analysis and visualization of big data and medical devices based on bibliometrics

BackgroundIn the big data era, the healthcare sector grapples with increased data volumes and the push for more intelligent medical devices. This challenge is marked by data silos, higher data processing needs, and the quest for personalized medicine, emphasizing the importance of integrating and analyzing diverse big data from medical devices. This study employs bibliometric analysis to thoroughly analyze the research trends in big data and medical device research, aiming to identify key developments, patterns, and potential future directions in the field. MethodsThis study employed the Web of Science Core Collection, BIOSIS Citation Index, and Derwent Innovations Index for conducting keyword searches on ’big data’ and ’medical devices’, focusing on English-language articles, reviews, and patents, while excluding duplicates. Quantitative analyses and visualizations were facilitated using tools such as R 4.3.1, VOSviewer, CiteSpace, and Tableau. The research assessed the impact of journals and academic contributions through metrics like the impact factor and G-Index. ResultsOur analysis covered 592 articles and 795 patents. Notably, the annual growth rate of articles reached 62.19%, with the primary contributions originating from China, the United States, India, and England. Among the identified publications, “IEEE Access” emerged as the most prominent journal. The research identified key trends in the application of big data within medical devices, including the extensive use of artificial intelligence (AI), deep learning, Internet of Things (IoT) technologies, genomic data analysis, and bioinformatics. Case studies show how big data and medical device integration, exemplified by Electronic Health Records (EHRs), Clinical Decision Support Systems (CDSS), and smart wearables, enhance diagnostic efficiency and care quality. The study further identified intensive care units, radiology departments, and oncology as key application departments leveraging this integration. ConclusionBig data and medical device integration have led to smart healthcare and personalized treatment, improving medical decision accuracy and inspiring new research directions. Future research efforts should explore the potential for incorporating even more expansive datasets and multilingual resources to further maximize the potential of this integration and advance medical technology in disease prevention, diagnosis, treatment, and management.

Global estimates on the reports of vaccine-associated myocarditis and pericarditis from 1969 to 2023: Findings with critical reanalysis from the WHO pharmacovigilance database.

Due to the limitation of previous studies examining adverse reports of myocarditis and pericarditis associated with vaccines other than the COVID-19 vaccine, there are challenges in establishing a comprehensive understanding of vaccine safety on a global scale. Hence, the objective of this study was to examine the worldwide burden of vaccine-associated pericarditis and myocarditis and the vaccines associated with these indications. This study utilized the World Health Organization international pharmacovigilance database, from which records of vaccine-associated pericarditis and myocarditis between 1969 and 2023 were extracted (over 130 million reports). We calculated global reporting counts, reported odds ratios (RORs), and information components (ICs) to discern the association between 19 vaccines and the occurrence of pericarditis and myocarditis across 156 countries and territories. We identified 49 096 reports (male, n = 30 013) of vaccine-associated pericarditis and myocarditis among 73 590 reports of all-cause pericarditis and myocarditis. There has been a significant increase in reports of vaccine-related cardiac adverse events over time, with a noteworthy surge observed after 2020, attributed to cases of pericarditis associated with COVID-19 mRNA vaccines. Smallpox vaccines were associated with most pericarditis and myocarditis reports (ROR: 73.68 [95% CI, 67.79-80.10]; IC [IC0.25]: 6.05 [5.91]), followed by COVID-19 mRNA vaccine (37.77 [37.00-38.56]; 3.07 [3.05]), anthrax vaccine (25.54 [22.37-29.16]; 4.58 [4.35]), typhoid vaccine (6.17 [5.16-7.38]; 2.59 [2.29]), encephalitis vaccine (2.00 [1.48-2.71]; 0.99 [0.47]), influenza vaccine (1.87 [1.71-2.04]; 0.90 [0.75]), and Ad5-vectored COVID-19 vaccine (1.40 [1.34-1.46]; 0.46 [0.39]). Concerning age and sex-specific risks, reports of vaccine-associated pericarditis and myocarditis were more prevalent among males and in older age groups. The age group between 12 and 17 years exhibited significant sex disproportion. Most of these adverse events had a short time to onset (median time: 1 day) and fatality rate was 0.44%. Our analysis of global data revealed an increase in pericarditis and myocarditis reports associated with vaccines, particularly live vaccines like smallpox and anthrax, notably in young males. While these adverse events are generally rare and mild, caution is warranted, especially for healthcare workers, due to potential myocardial injury-related in-hospital mortality. Further study with validated reporting is crucial to enhance accuracy in evaluating the correlation between vaccines and cardiac conditions for preventive measures.