BACKGROUND: Snakebite envenomation (SBE) and scorpion sting envenomation (SSE) are significant neglected tropical diseases that primarily affect impoverished communities in rural areas of developing nations. A lack of understanding about snake and scorpion species and their distribution exacerbates the disabilities and fatalities caused by SBE and SSE. In Sudan, particularly in regions affected by ongoing conflicts where healthcare resources are scarce, social media platforms offer a cost-effective approach to addressing public health challenges. Our aim in this study is to highlight the benefits of using social media for data collection and health promotion in such environments.
METHODS: We present a cost-effective communication and data collection strategy implemented at the Toxic Organisms Research Centre (TORC) of the University of Khartoum, focusing on a Facebook group, \"Scorpions and Snakes of Sudan\", as our primary social media platform. Additionally, we discuss the lessons learned and the initial impact of this strategy on enhancing population health literacy.
RESULTS: The group community is composed of ~ 5000 members from 14 countries. During the period from January 2023 to January 2024, we received 417 enquiries about snakes and scorpions belonging to 11 families and composed of 55 species. In addition, 53 other enquiries covered a range of organisms and their tracks (e.g., spiders, skinks, chameleons, foxes, sun spiders, centipedes, lizards, moth larvae, and insect tracks). The first photographic evidence of Malpolon monspessulanus in Sudan was via the group activities. The rare species Telescopus gezirae, the Blue Nile cat snake, is also documented via the group member\'s queries. Recognizing the evolving nature of social media use in public health, we also address the current limitations and evidence gaps that need to be addressed to effectively translate best practices into policy.
CONCLUSIONS: In conclusion, utilizing Facebook as an institutional platform to share scientific information in simple Arabic language underscores the proactive roles that citizens, scientists, and public health stakeholders can play in leveraging social media for eHealth, eAwareness, and public health initiatives. This approach highlights the potential for collaborative efforts, particularly during crises, to maximize the benefits of social media in advancing public health.

Health inequalities amplified by the COVID-19 pandemic have disproportionately affected racialized and equity-deserving communities across Canada. In the Municipality of Peel, existing data, while limited, illustrates that individuals from racialized and equity-deserving communities continue to suffer, receive delayed care, and die prematurely. In response to these troubling statistics, grassroots community advocacy has called on health systems leaders in Peel to work with community and non-profit organizations to address the critical data and infrastructure gaps that hinder addressing the social determinants of health in the region. To support these advocacy efforts, we used a community-based participatory research approach to understand how we might build a data collection ecosystem across sectors, alongside community residents and service providers, to accurately capture the data about the social determinants of health. This approach involved developing a community engagement council, defining the problem with the community, mapping what data is actively collected and what is excluded, and understanding experiences of sociodemographic data collection from community members and service providers. Guided by community voices, our study focused on sociodemographic data collection in the primary care context and identified which service providers use and collect these data, how data are used in their work, the facilitators and barriers to data use and collection. Additionally, we gained insight into how sociodemographic data collection could be respectful, safe, and properly governed from the perspectives of community members. From this study, we identify a set of eight recommendations for sociodemographic data collection and highlight limitations. This foundational community-based work will inform future research in establishing data governance in partnership with diverse and equity-deserving communities.

Understanding the health effects of disasters is critical for effective preparedness, response, recovery, and mitigation. However, research is negatively impacted by both the limited availability of disaster data and the difficulty of identifying and utilizing disaster-specific and health data sources relevant to disaster research and management. In response to numerous requests from disaster researchers, emergency managers, and operational response organizations, 73 distinct data sources at the intersection of disasters and health were compiled and categorized. These data sources generally cover the entire United States, address both disasters and health, and are available to researchers at little or no cost. Data sources are described and characterized to support improved research and guide evidence-based decision making. Current gaps and potential solutions are presented to improve disaster data collection, utilization, and dissemination.

OBJECTIVE: Global navigation satellite system device-derived metrics are commonly represented by discrete zones with intensity often measured by standardizing volume to per-minute of activity duration. This approach is sensitive to imprecision in duration measurement and can lead to highly variable outcomes-transforming data from zones to a gradient may overcome this problem. The purpose of this study was to critically evaluate this approach for measuring team-sport activity demands.
METHODS: Data were collected from 129 first-team and 73 academy matches from a Scottish Premiership football club. Gradients were calculated for velocity, acceleration, and deceleration zones, along with per-minute values for several commonly used metrics. Means and 95% CIs were calculated for playing level, as well as first-team positional groups. Within-subject coefficients of variation were also calculated for match level, position, and individual groups.
RESULTS: The gradient approach showed consistency with per-minute metrics when measuring playing level and position groups. With coefficients of variation of 10.8% to 26.9%, the gradients demonstrated lower variability than most per-minute variables, which ranged from 10.7% to 84.5%.
CONCLUSIONS: Gradients are a potentially useful way of describing intensity in team sports and compare favorably to existing intensity variables in their ability to distinguish between match types and position groups, providing evidence that gradient variables can be used to monitor match and training intensity in team sports.

UNASSIGNED: The Society of Thoracic Surgeons and European System for Cardiac Operative Risk Evaluation (EuroSCORE) II risk scores are the most commonly used risk prediction models for in-hospital mortality after adult cardiac surgery. However, they are prone to miscalibration over time and poor generalization across data sets; thus, their use remains controversial. Despite increased interest, a gap in understanding the effect of data set drift on the performance of machine learning (ML) over time remains a barrier to its wider use in clinical practice. Data set drift occurs when an ML system underperforms because of a mismatch between the data it was developed from and the data on which it is deployed.
UNASSIGNED: In this study, we analyzed the extent of performance drift using models built on a large UK cardiac surgery database. The objectives were to (1) rank and assess the extent of performance drift in cardiac surgery risk ML models over time and (2) investigate any potential influence of data set drift and variable importance drift on performance drift.
UNASSIGNED: We conducted a retrospective analysis of prospectively, routinely gathered data on adult patients undergoing cardiac surgery in the United Kingdom between 2012 and 2019. We temporally split the data 70:30 into a training and validation set and a holdout set. Five novel ML mortality prediction models were developed and assessed, along with EuroSCORE II, for relationships between and within variable importance drift, performance drift, and actual data set drift. Performance was assessed using a consensus metric.
UNASSIGNED: A total of 227,087 adults underwent cardiac surgery during the study period, with a mortality rate of 2.76% (n=6258). There was strong evidence of a decrease in overall performance across all models (P<.0001). Extreme gradient boosting (clinical effectiveness metric [CEM] 0.728, 95% CI 0.728-0.729) and random forest (CEM 0.727, 95% CI 0.727-0.728) were the overall best-performing models, both temporally and nontemporally. EuroSCORE II performed the worst across all comparisons. Sharp changes in variable importance and data set drift from October to December 2017, from June to July 2018, and from December 2018 to February 2019 mirrored the effects of performance decrease across models.
UNASSIGNED: All models show a decrease in at least 3 of the 5 individual metrics. CEM and variable importance drift detection demonstrate the limitation of logistic regression methods used for cardiac surgery risk prediction and the effects of data set drift. Future work will be required to determine the interplay between ML models and whether ensemble models could improve on their respective performance advantages.

Digital and technological solutions (DTS) in cardiovascular nursing are profoundly transforming the landscape of patient care by integrating advanced data-driven approaches. DTS help to enhance patient outcomes and streamline clinical workflows, supporting the shift of the traditional roles of healthcare providers and patients towards more engaged and collaborative care processes. This article presents a perspective in this regard. The adoption of DTS, including mobile health applications and wearable devices, enables continuous monitoring and management of patient health, fostering significant improvements in cardiovascular health management. However, the rapid incorporation of such technologies presents various challenges, such as robust data standardization, the development of digital literacy among healthcare professionals, and addressing privacy and security concerns. Effective integration of DTS into nursing practice demands structured clinical curricula that equip nurses with essential technological skills and a deep understanding of ethical considerations. Theoretical frameworks should guide the systematic implementation and integration of digital tools, ensuring comprehensive consideration of the complexities involved in digital transformations in healthcare.

BACKGROUND: Chronic stress is highly prevalent in the German population. It has known adverse effects on mental health, such as burnout and depression. Known long-term effects of chronic stress are cardiovascular disease, diabetes, and cancer.
OBJECTIVE: This study aims to derive an interpretable multiclass machine learning model for predicting chronic stress levels and factors protecting against chronic stress based on representative nationwide data from the German Health Interview and Examination Survey for Adults, which is part of the national health monitoring program.
METHODS: A data set from the German Health Interview and Examination Survey for Adults study including demographic, clinical, and laboratory data from 5801 participants was analyzed. A multiclass eXtreme Gradient Boosting (XGBoost) model was constructed to classify participants into 3 categories including low, middle, and high chronic stress levels. The model\'s performance was evaluated using the area under the receiver operating characteristic curve, precision, recall, specificity, and the F1-score. Additionally, SHapley Additive exPlanations was used to interpret the prediction XGBoost model and to identify factors protecting against chronic stress.
RESULTS: The multiclass XGBoost model exhibited the macroaverage scores, with an area under the receiver operating characteristic curve of 81%, precision of 63%, recall of 52%, specificity of 78%, and F1-score of 54%. The most important features for low-level chronic stress were male gender, very good general health, high satisfaction with living space, and strong social support.
CONCLUSIONS: This study presents a multiclass interpretable prediction model for chronic stress in adults in Germany. The explainable artificial intelligence technique SHapley Additive exPlanations identified relevant protective factors for chronic stress, which need to be considered when developing interventions to reduce chronic stress.

BACKGROUND: In health care, diagnosis codes in claims data and electronic health records (EHRs) play an important role in data-driven decision making. Any analysis that uses a patient\'s diagnosis codes to predict future outcomes or describe morbidity requires a numerical representation of this diagnosis profile made up of string-based diagnosis codes. These numerical representations are especially important for machine learning models. Most commonly, binary-encoded representations have been used, usually for a subset of diagnoses. In real-world health care applications, several issues arise: patient profiles show high variability even when the underlying diseases are the same, they may have gaps and not contain all available information, and a large number of appropriate diagnoses must be considered.
OBJECTIVE: We herein present Pat2Vec, a self-supervised machine learning framework inspired by neural network-based natural language processing that embeds complete diagnosis profiles into a small real-valued numerical vector.
METHODS: Based on German outpatient claims data with diagnosis codes according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10), we discovered an optimal vectorization embedding model for patient diagnosis profiles with Bayesian optimization for the hyperparameters. The calibration process ensured a robust embedding model for health care-relevant tasks by aggregating the metrics of different regression and classification tasks using different machine learning algorithms (linear and logistic regression as well as gradient-boosted trees). The models were tested against a baseline model that binary encodes the most common diagnoses. The study used diagnosis profiles and supplementary data from more than 10 million patients from 2016 to 2019 and was based on the largest German ambulatory claims data set. To describe subpopulations in health care, we identified clusters (via density-based clustering) and visualized patient vectors in 2D (via dimensionality reduction with uniform manifold approximation). Furthermore, we applied our vectorization model to predict prospective drug prescription costs based on patients\' diagnoses.
RESULTS: Our final models outperform the baseline model (binary encoding) with equal dimensions. They are more robust to missing data and show large performance gains, particularly in lower dimensions, demonstrating the embedding model\'s compression of nonlinear information. In the future, other sources of health care data can be integrated into the current diagnosis-based framework. Other researchers can apply our publicly shared embedding model to their own diagnosis data.
CONCLUSIONS: We envision a wide range of applications for Pat2Vec that will improve health care quality, including personalized prevention and signal detection in patient surveillance as well as health care resource planning based on subcohorts identified by our data-driven machine learning framework.

Information, as the most elusive subject, is central to all forms of thought, governance, economic structure, science, and society. Regulation of information, especially within the healthcare field, is proving to be a difficult task globally, given the lack of a qualitative framework and understanding of the concept and properties of information (or data) itself. The presentation of the overall qualitative framework, comprising a qualitative analysis of information, data, and knowledge, will be valuable and of great assistance in delineating regulatory, ethical, and strategic trajectories. In addition, this framework provides insights (and answers) regarding (1) data privacy and protection; (2) delineations between information, data, and knowledge based on the important notion of trust; (3) a structured approach to establishing the necessary conditions for an open society and system, and the maintenance of said openness, based on the work of Karl Popper and Georg Wilhelm Friedrich Hegel; (4) an active agent approach that promotes autonomy and freedom and protects the open society; and (5) a data governance mechanism based on the work of Friedrich Hayek, which structures the current legal-ethical-financial and social society. This is insightful for questions relating to the extent of rights and duties, the extent of biological bodies and freedom, and the structure of relations in distributed networked systems. There is great value offered in this framework; furthermore, it provides critical insights and thoughts about (and uncovers the interplay between) academic culture, politics, science, society, and societal decay. Note that, in line with the ideas expressed in this manuscript, such as incorporation of personal experience (thereby mending the Kantian and Cartesian gap), a first-person perspective will be used, where relevant.

We aimed to assess salivary and seroprevalence of Toxoplasma immunoglobulins in risky populations and evaluate drug docking targeting TgERP. A cross-sectional study was conducted in Alexandria University hospitals\' outpatient clinics. 192 participants were enrolled from September 2022 to November 2023. Anti-Toxoplasma IgG and IgM were determined in serum and saliva by ELISA. An in-Silico study examined TgERP\'s protein-protein interactions (PPIs) with pro-inflammatory cytokine receptors, anti-inflammatory cytokine, cell cycle progression regulatory proteins, a proliferation marker, and nuclear envelope integrity-related protein Lamin B1. Our findings revealed that anti-T. gondii IgG were detected in serum (66.1%) and saliva (54.7%), with 2.1% of both samples were positive for IgM. Salivary IgG had 75.59% sensitivity, 86.15% specificity, 91.40% PPV, 64.40% NPP, 79.17% accuracy and fair agreement with serum IgG. On the other hand, the sensitivity, specificity, PPV, NPV, and accuracy in detecting salivary IgM were 75.0%, 99.47%, 75.0%, 99.47%, and 98.96%. AUC 0.859 indicates good discriminatory power. Examined synthetic drugs and natural products can target specific amino acids residues of TgERP that lie at the same binding interface with LB1 and Ki67, subsequently, hindering their interaction. Hence, salivary samples can be a promising diagnostic approach. The studied drugs can counteract the pro-inflammatory action of TgERP.