Standard Data Research Articles

Reporting outcomes of minimally invasive glaucoma surgery.

This review presents guidelines for designing studies and reporting efficacy and safety outcomes in minimally invasive glaucoma surgery (MIGS) research. Adherence to reporting guidelines in MIGS studies is crucial for providers and patients to appraise surgical options. Recent guidelines have outlined appropriate methodology, efficacy outcomes, and safety reporting, so that study results are presented in an interpretable and uniform manner. MIGS are changing the glaucoma treatment paradigm by offering safer, less invasive alternatives to traditional filtering surgery. However, inconsistent reporting of outcomes in MIGS trials hampers comparison and clinical decision-making. Recent guidelines have aimed to highlight appropriate methodology and encourage standardization in reporting outcomes to improve the quality of MIGS literature. Key considerations include defining baseline intraocular pressure, reporting standardized demographic data, using consistent endpoints, presenting standardized figures, evaluating medication use, and documenting adverse events. By adhering to these guidelines, MIGS trials can offer clearer insights into surgical outcomes, aiding both surgeons and patients in treatment decisions.

Geospatial tools and data for health service delivery: opportunities and challenges across the disaster management cycle.

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Left atrial function predicts long-term outcome in hypertension and diabetes across genders

Abstract Introduction In the last decade, peak atrial longitudinal strain (PALS) was ascertained to be superior to conventional echocardiographic indices for secondary cardiovascular (CV) prevention. Purpose Standard and advanced echocardiography including PALS was evaluated to find predictors of prognosis in patients with arterial hypertension and/or diabetes for the optimization of primary CV prevention. Methods Hypertensive and/or diabetic patients >40 years old and in sinus rhythm who underwent a complete cardiological evaluation at our centre in 2008-2015 were retrospectively included. Personal history, physical examination, standard and advanced (speckle tracking) echocardiographic data were collected. The exclusion criteria were previous cv events or cardiac surgery, active pacemaker, more than moderate valvular regurgitation and/or stenosis, missing informed consent. All patients were followed up for a mean time of 11.2±1.3 years for the development of first atrial fibrillation (AF) episode, congestive heart failure hospitalization, transient ischemic attack, stroke, myocardial infarction/coronary revascularization, and CV death. Univariate and multivariable stepwise Cox regressions were performed to estimate Hazard Ratio (HR). Kaplan Meier curves (KM) compared the survival between patients with different ranges of Global PALS. Log-rank test was performed to compare the KM curves. Results This retrospective study included 292 adults (mean age 63.0±9.0 years, 50% female), among which 210 hypertensives and 67 diabetics. Mean left ventricular (LV) ejection fraction (EF) was 58.2±4.9%, mean GLS was -17.0±6.6%, mean left atrial volume was 52.9±24.9ml and mean PALS 29.7±11.1%. During follow up, 110 patients developed at least one cv event: 52 all-cause deaths, 28 CV deaths, 30 heart failure admissions, 31 first AF episode, 18 TIA/strokes, 25 myocardial infarction/revascularization. Dividing the population according to events occurrence, patients with events had similar EF (p= 0.06), while being older (65.8±9.6 vs 61.3±9.0 years) and with a worse diastolic function (E/E’ ratio 10.6±5.0 vs 8.7±3.6), LV longitudinal strain (GLS, -17.7±2.9 vs -15.7±3.4%) and PALS (34.6±9.9 vs 21.6±7.7%, all p <0.001). From univariate statistical analysis, age, E/E’, PAPs, GLS and PALS were all predictors of events, but only age (HR 1.03, p=0.03) and PALS (PALS<22.5% HR 18.99 and PALS 22.5-30 HR 7.68, both p<0.001) remained independently associated with outcome at multivariate analysis. Patients with PALS<22.5 have an event-free survival of 82.4% at 1 year, 49.8% at 5 years and 25.3% at 10 years compared to 100% at 1 year, 98.5% at 5 years and 92.5% at 10 years if PALS PALS>30% (Fig.1). The same results were confirmed in female population (Fig.2). Conclusions If confirmed by larger studies, PALS could be a quick, feasible and reliable part of cardiologic evaluation for prognostic stratification in primary CV prevention in hypertensive and/or diabetic patients.Fig. 1Fig. 2

Assessing the need for coronary angiography in high-risk acute coronary syndrome patients using artificial intelligence and computed tomography

Abstract Background/Introduction The use of coronary computed tomography angiography (CCTA) in managing high-risk acute coronary syndrome (ACS) patients is increasingly common. Yet, its role remains unvalidated in this context, posing challenges to traditional clinical decision-making algorithms. Physicians, accustomed to a hierarchical and structured approach involving symptom assessment, electrocardiograms, and biomarkers for deciding on the necessity of invasive coronary angiography (ICA), now face dilemmas when CCTA results challenge established diagnostic pathways and contradict clinical decisions based on usual criteria. Meanwhile, the potential of artificial intelligence (AI) which processes information in a way fundamentally different from human clinical reasoning, has been shown to aid decision-making in cardiovascular medicine. Yet, it has not been tested in this acute setting. Purpose This study aimed to evaluate the efficacy of the Chat Generative Pre-trained Transformer (ChatGPT) in guiding the need for ICA in high-risk ACS patients based on both standard clinical data and CCTA findings. Methods This investigation is a sub-study of a larger research project where high-risk ACS patients (all having a theoretical guideline-based indication for ICA) underwent CCTA prior to ICA. ChatGPT analyzed clinical vignettes containing patient data, electrocardiograms, troponin levels, and CCTA results to determine the necessity of ICA. The AI's recommendations were then compared to actual ICA findings to assess its decision-making accuracy. Results Among 86 patients with a clear indication for ICA, ChatGPT recommended against it for 19 patients, 16 of whom indeed had no significant findings at ICA (Figure). For 67 patients, ChatGPT advised proceeding with ICA, and a significant lesion was confirmed in 58 of them. Consequently, ChatGPT's overall accuracy stood at 86%, with a sensitivity of 95% and a specificity of 64%. The model's negative predictive value was 84%, and its positive predictive value was 87%. Conclusion These preliminary data suggest that ChatGPT can effectively assist in guiding the need for ICA among high-risk ACS patients, potentially reducing unnecessary procedures but further clinical studies to refine AI's role in clinical decision-making are required.ChatGPT's decision as per ICA's results

Data ecosystem using smart home technology to support healthy aging

Abstract Background Smart home technology has been widely used to mitigate the challenges that population aging brings. However, the lack of a centralized data ecosystem has prevented these technologies from realizing their full potential. This project aimed to construct a data ecosystem compatible with smart home sensors to monitor healthcare for seniors, improving their quality of life. Methods The infrastructure was constructed using a user-centered design approach, allowing smart devices such as motion, temperature sensors to operate independently and communicate through a Wi-Fi mesh network. API technology provides a reliable channel for real-time data collection. To ensure data privacy and security, extracted data is hosted in a cloud environment that provides comprehensive data protection, and cloud computing is utilized for high-performance computation tasks. Analytical models such as Machine Learning can be implemented to recognize indoor activities, achieving the goal of remote monitoring. Results This data ecosystem advances in privacy and security, interoperability, accessibility, and data-sharing. Unified authentication mechanism ensures data is transmitted among heterogeneous devices with minimal vulnerability. Additionally, the common communication protocol provides the foundation of interoperability between devices. With unobtrusive devices, inhabitants are not burdened with operation, ensuring their accessibility and improving their user experience. Lastly, the data is exchanged in a standardized JSON data format, enabling efficient data-sharing board data integration. Conclusions This project developed a data ecosystem based on existing smart home environments. The ecosystem is compatible with smart devices that families already have, effectively reducing additional healthcare costs. Implications of this study include helping health policy and decision makers to adopt more smart home ecosystems to keep older people healthy at home. Key messages • Study constructed a data ecosystem compatible with smart home sensors, achieving remote monitoring of indoor activities and improving the quality of life for seniors. • The developed ecosystem offers promotion in privacy and security, interoperability, accessibility, and efficient data-sharing, reducing healthcare costs.

The association between mammographic breast density, epicardial adipose tissue volume and coronary artery disease

Abstract Background Dysfunctional adipose tissue is strongly linked to cardiometabolic disease. Epicardial adipose tissue (EAT) which is measurable on cardiac computed tomography (CT) is well correlated with cardiovascular disease (CVD), however it is not reported clinically. Breast mammography mandates reporting of breast density (BD), which is also a marker of breast adipose tissue quantity with less dense breasts indicating greater adipose tissue volume. Given the widespread use of screening mammography, this presents an opportunity to employ BD as a possible surrogate for CVD risk. Purpose To evaluate the correlation between BD, EAT and coronary artery disease (CAD). Methods Single-centre, retrospective, cross-sectional study, including women who had both clinically indicated coronary computed tomography angiogram (CCTA) and mammography. Epicardial adipose tissue volume (EATv) was quantified using semi-automated software (QFAT 2.0). BD was visually assessed by standard 4-level Breast Imaging-Reporting and Data System (BI-RADS) grade with grade A-B representing low density, and C-D representing high density. CAD was categorised as presence/absence of any coronary artery plaque, and CAD severity was quantified using the Coronary Artery Disease Reporting and Data Systems (CAD-RADS) score, with CAD-RADS 1-2 representing mild disease, CAD-RADS 3 moderate disease and CAD-RADS 4 severe disease. CAD analyses were adjusted for modifiable (hypertension, dyslipidaemia, diabetes, body mass index) and non-modifiable (age, past smoking, family history, breast cancer) risk factors. Logistic regression was performed with results presented as Odds Ratio (OR) and [95% Confidence Intervals]. Results Among 153 patients (mean age 62±10), 103 (67.3%) had low BD (high breast adiposity). Compared with high BD, low BD patients were older, had greater rates of hypertension, had significantly higher mean EAT (106.6 ± 43.0 vs 81.0 ± 31.6ml, p<0.001) (Figure 1), and body mass index (30.5 ± 6.3 vs 26.4 ± 5.4kg/m², p<0.001). Adjusted for age and hypertension, EATv remained independently predictive of low BD (OR:1.02 [1.01-1.03], p=0.006). Low BD made up a higher proportion of mild (76.5%), moderate (73.9%) and severe (80.0%) disease. Low BD was strongly associated with presence of CAD (prevalence 75% vs 48%, OR 3.21 [1.58-6.53], p=0.001) independent of EATv (p=0.25), and independent of modifiable (OR: 2.69 [1.24-5.92], p=0.012) and non-modifiable (OR: 2.42 [1.04-5.85], p=0.047) risk factors (Figure 2). Conclusions Low breast density is associated with higher EATv and independently associated with CAD presence beyond EATv and other risk factors. Consequently, mammographic breast density may act as an early risk identification tool for CAD in women.

Mortality based right ventricle functional echocardiographic cutoffs in patients with compared to without tricuspid regurgitation

Abstract Introduction Tricuspid-annular-plane-systolic-excursion (TAPSE) and peak-lateral-tricuspid-annular-systolic-velocity (S’) are frequently used echocardiographic indices of right-ventricle (RV) function. Tricuspid regurgitation (TR) is present in 70-90% of the general population, and while trivial or mild TR may not have prognostic implications, several studies have demonstrated that ≥moderate TR is an independent predictor of adverse outcomes, and that increasing TR severity incurs an increased risk of mortality. The abnormality thresholds for TAPSE and S' are based on published mean and standard deviation data obtained from healthy adults, but not on outcome data. The effect of significant TR on the abnormality threshold of TAPSE and S’ prime is currently unknown. Purpose We aimed to re-examine the abnormality thresholds for TAPSE and S' based on their association with mortality in consecutive patients, stratified to with, or without, significant tricuspid regurgitation (TR). Methods We performed a retrospective analysis of consecutive patients undergoing echocardiography between 2011-2021 in a large tertiary center. TR was assessed using a semi-quantitative method. Cut-off values associated with excess mortality were assessed using spline curves in univariate, and multivariate Cox logistic regression analyses. Results A total of 24717 subjects were included in the current analysis with a median (interquartile range) follow up of 1321 (581-1902) days. 1143 (4.6%) subjects had clinically significant (≥moderate) TR. In the entire cohort, TAPSE<20.9 mm and S’ <10.9 cm/s were associated with excess mortality. In sub-group analysis, among subjects with significant TR, TAPSE<18.0 mm and S'<10.0 cm/s was the cutoff associated with excess mortality, while subjects without TR had a higher cutoff of TAPSE<21.5 mm and S'<10.9 cm/s. In a multivariate model adjusted for the presence of TR and baseline characteristics, TAPSE<20.9 mm (HR 1.20, 95% CI 1.14-1.27; p<0.001) and S’<10.9 cm/s (HR 1.07, 95% CI 1.01-1.13; p=0.015) were independent predictors for mortality. A sensitivity analysis, which included 14,664 patients with additional echocardiographic indices, demonstrated that low TAPSE remained an independent predictor of mortality (aOR=1.15; 95% CI: 1.07-1.24; p<0.001) Conclusion TAPSE and S’ thresholds associated with excess mortality are higher than those previously reported in healthy adults. The TAPSE and S' cutoffs associated with excess mortality were lower in patients with significant TR compared to patients without, suggesting that a personalized approach for their interpretation is needed.Central illustration

Towards large-scale analyses of settlement patterns in urbanizing landscapes—findings of first studies for India, Egypt, and China

Improved ability to assess and categorize the spatial characteristics of settlement patterns is required for a deeper understanding of how urbanization is driving land use and land cover transformation and its effects. Two approaches to the globally available settlement maps of the World Settlement Footprint 3D support a detailed assessment of spatial characteristics of settlement patterns in rural to urban landscapes and across scales: graph-based spatial network analysis and elements of fractal theory. Based on first comprehensive tests for the Punjab (India), the Nile Delta (Egypt) and the North China Plain, the results of our study suggest that the presented methods allow a quantitative and qualitative characterization and comparison of settlement patterns between different regions of the world. The approache allows to generate standardized baseline data for arbitrary regions in the world to analyze structuring principles of settlement hierarchies (e.g., self-organized fractal geometries) and their dependence on - or interaction with - cultural, political, socioeconomic, or environmental conditions.

1.N. Scientific session: Child injuries monitoring and reporting: Challenges and technological developments

Abstract Globally, more than 1,600 children and adolescents below the age of 19-years die every day from preventable injuries. Road traffic injuries, drowning and self-harm represent leading causes of death and injury among children and adolescents (WHO 2021; WHO Regional Office for Europe, 2021; Unicef, 2021). Most unintentional injuries in early childhood occur at home and children from less advantaged backgrounds are exposed to more hazardous home environments due to overcrowding or unsafe structures over which parents often have limited control (WHO, 2020). According to the IDB data analysis approximately 15% of home injuries could have been prevented by improved product safety and parental education. The COVID-19 pandemic and the conflict in Ukraine have created a complex and challenging situation for children and adolescents in the region, increasing their vulnerability and compromising their well-being. To efficiently prevent child injuries, one must account for a complex interplay between human factors as well as physical and socio-cultural environments. Multiple determinants of child injury cannot be addressed by the health sector alone, this is why a whole-of-government and a whole-of-society approaches are required. Schools have a crucial role to play for promoting the well-being of children and serve as a platform to increase access to health services. However, EU countries are still missing intersectoral collaboration and strong information systems to enable efficient response to the problem. Data on child injuries are still inconsistently collected in Europe due to substantial differences in coding systems, variation between injury mechanisms, different general policies pertaining to the admission of patients, and legal policies on reporting requirements. EU-wide networks such as the Eurosafe study (IDB network) collecting information on child injuries, are scarce. Likewise, access to healthcare services and support systems are unevenly distributed, particularly in underserved communities. Building country capacity in collecting, analysing and increasing the use of child injury data for decision making at global, regional, and national levels, is critical. Adequate epidemiological data on the burden of child injuries are necessary to allow governments prioritize areas for intervention and maximize the impact of their resources. Utilizing evidence-based tools and procedures and promoting intersectoral collaboration is vital. The workshop aims to discuss trends and inequalities of injury burden and present efficient measures and latest developments in monitoring and reporting child injuries across EU countries. The workshop will further discuss the methodological challenges in collecting and analysing data on child injuries. Evidence-informed tools for sharing health information and improved measurements of injury burden that are feasible and usable in both research and clinical contexts will be presented and success stories will be presented. Key messages • There is an EU-wide need for epidemiological data on the burden of child injuries. • Validated tools, well-designed injury data management systems and standard data collection procedures are missing.

Tackling Pertussis in Schools: A Public Health Unit’s Approach

Abstract Issue Pertussis is a highly infectious bacterial respiratory disease, easily transmitted in closed settings, such as schools. In Portugal, 2019 was the last year with a significant number of cases (83). As pertussis usually follows epidemic cycles from 3 to 5 years, another wave is expected in 2024. Children aged 1-5 years are particularly vulnerable as they no longer benefit from maternal vaccination and have not yet received the 5-year boost recommended in the national schedule. Description of the problem In April 2024, the Cascais Public Health Unit (PHU) addressed two possible pertussis cases in preschools. A streamlined response protocol was drafted, including an online contact tracing form and task distribution among response team members, along with templates and a scripted guide for standardized communication. Results We were provided a list of close contacts for the children and staff and shared an online form. In the first case, 34 in 35 identified contacts responded to the form, while in the second case, 38 in 55. When comparing both scenarios, the average scripted call duration decreased from 10 to 6 minutes. The PHU response to the first index case took 6 hours, involving 4 physicians and 5 nurses, whereas to the second took 4 hours, involving 3 physicians and 3 nurses. However, implementation requires at least one team member with basic information technology skills to set up the system. Lessons Clear communication channels between schools and public health authorities are vital for effective outbreak management. Streamlining communication processes with online forms and scripted conversation guides improved efficiency. Centralised data collection reduced response time and resource requirements in each outbreak. Key messages • Established communication channel, standardized data collection, and streamlined protocol allow more effective outbreak management. • Centralized data collection via digital tools is particularly useful in limited human resources settings.

National early warning systems for emerging AMR in high-income countries: a systematic review

Abstract Background An urgent need of implementing national surveillance systems for timely detection and reporting of emerging antimicrobial resistance (AMR) was recently advocated by the World Health Organization (WHO). However, public information on existing national early warning systems (EWSs) is often incomplete. Furthermore, when findings are available, understanding these systems is challenging due to different approaches used for data collection, reporting and definitions, with a comprehensive overview on this topic currently lacking. The aim of this study was to map existing EWSs for emerging AMR, focusing on high-income countries, and describe their main characteristics. Methods A systematic review was performed on bibliographic databases, and a targeted search was conducted on national websites. Any article, report or webpage describing national EWSs in high-income countries was eligible for inclusion. EWSs were identified considering the emerging AMR reporting WHO framework. Results We identified seven national EWSs in 72 high-income countries: two (Australia, Japan) in the East Asia and Pacific Region, three (France, Sweden, United Kingdom) in Europe and Central Asia, and two (United States, Canada) in North America. The systems were established quite recently; in most cases they covered both community and hospital settings, but their main characteristics varied widely across countries in terms of organization and microorganisms under surveillance, with also different definitions of emerging AMR and alert functioning. A formal system assessment was available only in Australia. Conclusions A broader implementation and investment of national surveillance systems that allow early detection of emerging AMR is still needed to establish EWSs in countries and regions lacking such capabilities. A more standardized data collection and reporting is also advisable to improve cooperation on a global scale. Key messages • This study provides a synthesis of publicly available information on national EWSs for emerging AMR in high-income countries, highlighting the urgent need for a broader implementation of such systems. • Main characteristics of EWSs have been outlined, varying widely across countries. Findings could help stakeholders in strengthening current standard national AMR surveillance systems.

Cluster Analysis of Migraine-associated Symptoms (CAMS) in youth: A retrospective cross-sectional multicenter study.

To conduct a retrospective cross-sectional multicenter study to validate the relationships between migraine-associated symptoms. Symptoms associated with headache-photophobia and phonophobia, nausea, and/or vomiting-are required criteria for migraine diagnosis based on the International Classification of Headache Disorders-Third Edition (ICHD-3). However, individuals with migraine report high rates of other symptoms (e.g., lightheadedness, difficulty thinking). We recently completed a single-center study assessing the relationships between an expanded set of migraine-associated symptoms. A pre-registered cross-sectional multicenter retrospective analysis was conducted on standardized questionnaire data of youth ages 6-17 years from two headache registries at pediatric tertiary care centers. Cluster Analysis of Migraine-associated Symptoms (CAMS) was implemented to assess associations between 11 migraine-associated symptoms. We explored differences between the two centers, and how CAMS was associated with demographics, including sex and age, and headache burden. There were 10,721 participants who were 66.5% female and had a median (interquartile range) age of 13 (10-15) years. The first three CAMS dimensions accounted for 46.5% of the variance and were consistent across sites. The first dimension indicated those reporting any migraine-associated symptoms were likely to report multiple. The second dimension separated symptoms into those included in ICHD-3 migraine diagnostic criteria and non-ICHD symptoms (e.g., lightheadedness, difficulty thinking). The third dimension separated sensory hypersensitivity and vestibular symptoms. An abundance of migraine-associated symptoms correlated with greater headache severity (Spearman's ρ = 0.18, 95% confidence interval [CI] 0.17-0.20; small effect size) and disability (ρ = 0.26, 95% CI 0.25-0.28; small effect size). We also observed differences in associated symptoms across age and sex. Associations between an expanded set of migraine-associated symptoms are informative for headache burden and reveal intriguing changes across child development and sex. We were able to replicate findings across two centers, indicating that these symptom clusters are inherent to migraine.

Advances in Plant Disease Diagnostics and Surveillance- A review

Plant diseases pose a significant threat to global food security, leading to substantial yield losses and economic impacts. Early detection and effective monitoring are crucial for managing plant diseases, yet traditional diagnostic methods such as visual inspections, serological tests, and molecular assays face limitations in sensitivity, specificity, and scalability. In recent years, advancements in diagnostic and surveillance technologies have revolutionized plant health management. Next-Generation Sequencing (NGS) enables comprehensive pathogen profiling, while CRISPR-based diagnostics offer rapid and highly specific detection. Similarly, biosensors and portable devices provide on-site diagnostics, and machine learning and AI applications enhance the analysis of complex datasets, supporting automated disease identification and predictive modeling. Concurrently, advances in disease surveillance through remote sensing technologies, including satellites and Unmanned Aerial Vehicles (UAVs), enable large-scale, real-time monitoring of crop health, detecting disease outbreaks and facilitating targeted interventions. Integrating these diverse technologies into multi-platform systems offers a holistic approach to plant disease management, combining molecular diagnostics, environmental monitoring, and digital platforms to support data-driven decision-making. Several challenges remain, including high costs, technical complexities, and the need for standardized data integration. Addressing these barriers is essential to ensure that these technologies are accessible and effective across various agricultural systems, particularly in resource-limited settings. Future research should focus on enhancing the robustness, affordability, and scalability of these tools while promoting interdisciplinary collaborations.

Mepacrine Flow Cytometry Assay for the Diagnosis of Platelet δ-granule Defects: Literature Review on Methods-Towards a Shared Detailed Protocol.

Accurate assessment of platelet secretion is essential for the diagnosis of inherited or acquired platelet function disorders and more specifically in identifying δ-storage pool disease. Mepacrine, a fluorescent dye, specifically accumulates in platelet δ-granules. The mepacrine flow cytometry (mepacrine FCM) assay has been used for more than half a century in the clinical laboratory as a diagnostic tool for platelet δ-granule disorders. The assay requires a small volume of blood, can be performed in thrombocytopenic patients, provides rapid assessment of δ-granule content and secretion, and, thus, enables differentiation between storage and release defects. There is however a broad heterogeneity in methods, reagents, and equipment used. Lack of standardization and limited data on analytical and clinical performances have led the 2022 ISTH SSC (International Society on Thrombosis and Haemostasis Scientific and Standardization Committee) Subcommittee on Platelet Physiology expert consensus to rate this assay as simple but of uncertain value. Yet, the data used by experts to formulate the recommendations were not discussed and even not mentioned. Guidance for laboratory studies of platelet secretion assay would be very helpful for clinical laboratories and health authorities especially considering the implications of the new In Vitro Diagnostic Regulation in Europe. The purpose of the present work was to review the reported methodologies for the mepacrine FCM assay and to offer an example of detailed protocol. This would help standardization and pave the way for more rigorous comparative studies.

Use and impact of clinical pathways across various healthcare settings: A protocol for an umbrella review of global evidence.

The proposed umbrella review aims to assess the use and impact of clinical pathways on professional practice, patient outcomes, length of hospital stay, hospital costs, patient satisfaction, and hospital staff satisfaction through a synthesis of existing systematic reviews and meta-analyses. Following PRIOR guidelines, a systematic search will be conducted in MEDLINE, Epistemonikos, and the Cochrane Library to identify relevant systematic reviews and meta-analyses, from inception till March 2024. Two reviewers will independently screen titles and abstracts, with a third resolving any disagreements. Full-text articles considered potentially relevant will be assessed for eligibility by the same process. The data extraction form will cover information about the review methods, characteristics of the included primary studies, the types of interventions evaluated, and the reported outcomes. This standardized data extraction form will be piloted by the review team on five to ten articles to ensure all relevant information is recorded. The quality of included systematic reviews and meta-analyses will be evaluated using AMSTAR 2. PROSPERO registration number is CRD42024529371. The study will present a narrative synthesis of the findings, addressing the clinical and methodological heterogeneity and assessing the impact of clinical pathways on various healthcare outcomes. This umbrella review will provide evidence-based insights into the effectiveness, challenges, and best practices of clinical pathways, guiding healthcare decision-making and identifying areas for future research. Results will be disseminated widely to inform policy and improve healthcare service delivery. No patient or public contribution, as this paper is a protocol of an umbrella review.

Effect of castor oil on esophageal stricture in rats and expression of ST-2, neopterin proteins in corrosive burn model

Corrosive esophageal burn (CEB) is a disease with high mortality and morbidity rates. The aim of this study was to determine the efficacy of castor oil, in preventing stricture development at the experimental CEB model. In addition to studying standard histopathological damage data, neopterin, IL-33, and ST-2 proteins were also studied for the first time. Fifty Wistar-Albino rats were divided into randomized 5 groups. [Sham group (G1) (n:10), Control group (G2) (n:10), Early Stage Topical Application group (G3)(n:10), Late Stage Topical Application group (G4) (n:10), Oral Application group (G5) (n:10)]. Weight measurement, esophageal length, histopathological damage score (HDS) and total stenosis score (TSS), tissue caspase-3 and VEGF staining, tissue hydroxyproline (HYP), blood TNF-alpha, IL-6, IL-33, Neopterin, and ST-2 levels were measured. In the castor oil application groups, weight gain was observed, the acute phase reaction decreased, submucosal/tunica muscularis fibrosis (TMF) and muscularis mucosal damage (MMD) were reduced, and TSS and HDS decreased. While no significant difference was detected in the ST-2 protein, which was used for the first time in this study model, a significant increase in neopterin protein was observed in the application groups. Results indicate the nutritional contribution of castor oil, as well as its tissue healing and esophageal stricture-preventing efficacy at histopathological and immune-histochemical levels.

Analysis and Comparison of Social Media Applications Using Forensic Software on Mobile Devices

With the integration of mobile systems into daily life, social media applications used especially on Android and iOS platforms contain a significant amount of sensitive information. Social media applications on mobile systems have huge personal and sensitive content. Therefore, it is important to design effective techniques for forensic analysis of social media applications and to detect personal data. In this research, three different paid mobile forensic software and 4 different brands and models of smartphones with different operating systems were used and analyzed. The study shows that private messages, e-mails, time information, shared data, location and time information, and other personal data can be obtained by a forensic expert who performs an examination, and it is seen that one software can access the deleted data, but another software cannot access it. In proportion to the technology used in today’s world, mobile forensics incidents are increasing day by day, and a competitive environment is created among the software used to illuminate these incidents. With this competition, software companies dealing with forensic informatics are trying to obtain different data to illuminate forensic events that may occur due to the active use of social media accounts with the developing technology, and software that does not meet the needs in the face of this situation remains in the background. The criminal elements in the investigation areas of mobile forensics differ daily, and the scope of crimes in the virtual environment is expanding with the developing technology. Therefore, mobile forensic analysis applications should be successful in social media applications other than standard data.

Lessons learnt from a real-time attribution and contextualisation trial in a National Meteorological and Hydrological Service

Abstract When a record hot month occurs, timely and credible attribution and contextualisation information can enhance public understanding and future preparedness. This is particularly effective if provided in real time by a National Meteorological and Hydrological Service (NMHS). Many NMHSs are working to integrate research-based attribution methods into their operational services. In this study, researchers and climate service staff collaborated to assess the feasibility of delivering such information swiftly and aligned with standard NMHS data and procedures. The record warm July (winter) temperatures of Tasmania, Australia in 2023 were chosen to illustrate the trial. Rapid results were available three days after the event. Approximately half of the unusual warmth was attributed to climate change, with the likelihood of breaking the previous record at least 17 times higher in the current climate compared to a stationary pre-industrial climate (14% vs. 0.4%). The warming trend became evident in the 1980s, and by 2060, average July temperatures in Tasmania match the record temperature of July 2023 under a high emissions scenario. However, average July minimum temperatures were not well modelled, necessitating the addition of a higher-resolution forecast-based attribution method. In subsequent analysis, almost all the forecast temperature anomaly, and reduced storm activity, was attributable to climate change. Statistical analysis revealed that a weak El Niño partly offset the unusual warmth. To expedite these additional approaches, information drawn from real-time forecasts could be used. Lessons learnt from this trial include technical improvements to align better with NMHS protocols including using consistent datasets and baselines, and refining and automating the method suite. Logistical and communication enhancements included training staff to run the suite, improving communication materials, and developing delivery channels. These learnings provide key considerations for NMHSs as they move towards providing timely and credible climate attribution and contextualisation information as part of their operational services.

Evaluation of Measure Dx, a Resource to Accelerate Diagnostic Safety Learning and Improvement.

Several strategies have been developed to detect diagnostic errors for organizational learning and improvement. However, few health care organizations (HCOs) have integrated these strategies into routine operations. To address this gap, the Agency for Healthcare Research and Quality released "Measure Dx: A Resource To Identify, Analyze, and Learn From Diagnostic Safety Events" in 2022. We conducted an evaluation of Measure Dx to measure feasibility of implementation and effects on short-term and intermediate outcomes related to diagnostic safety. Prospective observational study. Teams from 11 HCOs, primarily academic medical centers. Participants were asked to use Measure Dx over approximately 6months and attend monthly virtual learning collaborative sessions to share and discuss approaches to measuring diagnostic safety. Descriptive outcomes were gathered at the HCO level and included uptake of different case-finding strategies and the number of cases reviewed and confirmed to have diagnostic safety improvement opportunities. We collected information on organizational practices related to diagnostic safety at each HCO at baseline and at the conclusion of the project. The 11 HCOs completed all requirements for the evaluation. Each of the four diagnostic safety case finding strategies outlined in Measure Dx were used by at least three HCOs. Across the cohort, participants reviewed 703 cases using a standardized data collection instrument. Of those cases, 224 (31.8%) were identified as diagnostic safety events with improvement opportunities. Unexpectedly, self-ratings on the checklist assessment declined for several organizations. Use of Measure Dx can help accelerate implementation of systematic approaches to diagnostic error measurement and learning across a variety of HCOs, while potentially enabling HCOs to identify opportunities to improve diagnostic safety practices.

Profiling Standards to Improve Practical Interoperability

Abstract. Standard data models are key to enable a set of data integration functionalities, often characterised using the Findability, Accessibility, Interoperabilty and Reusability (FAIR) principles. However, standardisation is a process of trying to meet many requirements, and standard data models are inherently either very abstract or very comprehensive in the details. This results in several ambiguity pitfalls, inconsistent implementation of standard data models, which in turn hinders trust in the interoperability potential of standardised data, and complicates any integration processes. In practice profiling such standards is useful to overcome such issues to create more useful forms of standardised data for specific applications. However defining custom profiles typically requires a great deal of technical expertise in the underlying expression language of the standard. Maintaining access to this level of expertise is a challenge as profiles become outdated through the time and lose connection with the maintenance of the parent standard from which they originate. Therefore, in this paper, a scalable methodology is proposed, built on the OGC Building Blocks Model approach, that uses semantic modelling to support an easier composition of geospatial data models profiles which directly derive from available standards without losing the relevant dependencies that inform stakeholders which components are interoperable with other standards. The approach is tested within a digital building permit project (CHEK), in which data requirements derive from the semantics of city regulations and common geospatial standards (i.e., CityGML and INSPIRE) are used as reference.