BACKGROUND: The high-flow nasal cannula (HFNC) device is commonly used to treat pediatric severe acute asthma. However, there is little evidence regarding its effectiveness in real-world practice.
OBJECTIVE: We sought to compare the physiologic effects and clinical outcomes for children treated for severe acute asthma with HFNC versus matched controls.
METHODS: This was a single-center retrospective matched cohort study at a quaternary care children\'s hospital. Children ages 2-18 hospitalized for severe acute asthma from 2015 to 2022 were included. Encounters receiving treatment with HFNC within the first 24 h of hospitalization were included as cases. Controls were primarily treated with oxygen facemask. Logistic regression 1:1 propensity score matching was done using demographics, initial vital signs, and medications. The primary outcome was an improvement in clinical asthma symptoms in the first 24 h of hospitalization measured as percent change from initial.
RESULTS: Of 693 eligible cases, 443 were matched to eligible controls. Propensity scores were closely aligned between the cohorts, with the only significant difference in clinical characteristics being a higher percentage of patients of Black race in the control group (54.3% vs. 46.6%; p = 0.02). Compared to the matched controls, the HFNC cohort had smaller improvements in heart rate (-11.5% [-20.9; -0.9] vs. -14.7% [-22.6;-5.7]; p < 0.01), respiratory rate (-14.3% [-27.9;5.4] vs. -16.7% [-31.5;0.0]; p = 0.03), and pediatric asthma severity score (-14.3% [-28.6;0.0] vs. -20.0% [-33.3;0.0]; p < 0.01) after 24 h of hospitalization. The HFNC cohort also had longer pediatric intensive care unit (PICU) length of stay (LOS) (1.5 days [1.1;2.1] vs. 1.2 days [0.9;1.8]; p < 0.01) and hospital LOS (2.8 days [2.1;3.8] vs. 2.5 days [1.9;3.4]; p < 0.01). When subgrouping to younger patients (2-3 years old), or those with the highest severity scores (PASS > 9), those treated with HFNC had no difference in clinical symptom improvements but maintained a longer PICU LOS.
CONCLUSIONS: Encounters using HFNC for severe acute pediatric asthma had decreased clinical improvement in 24 h of hospitalization compared to matched controls and increased LOS. Specific subgroups of younger patients and those with the highest severity scores showed no differences in clinical symptom improvement suggesting differential effects in specific patient populations.

UNASSIGNED: To address the challenges of sharing clinical research data through the implementation of cloud-based virtual desktops, enhancing collaboration among researchers while maintaining data security.
UNASSIGNED: This case study details the deployment of virtual desktops at UMass Chan Medical School (UMass Chan). The process involved forming a Research Informatics Steering Executive workgroup, identifying key requirements, implementing Amazon WorkSpaces, and establishing configurable data management for research support.
UNASSIGNED: Key lessons include the significance of collaboration, balancing user-friendliness and functionality, flexibility in data management, maximizing virtual desktop efficiency within budget constraints, and continuous user feedback. The implementation of virtual desktops supports secure collaborative research, advancing medical knowledge and improving healthcare outcomes.
UNASSIGNED: The structured approach to implementing virtual desktops addresses data security, regulatory compliance, and real-time collaboration challenges. Continuous feedback and iterative improvements have enhanced the system\'s effectiveness.
UNASSIGNED: The successful implementation of virtual desktops at UMass Chan demonstrates the potential for such systems to support secure, collaborative research, offering insights for similar initiatives in other academic health centers.

The promise of artificial intelligence (AI) has generated enthusiasm among patients, healthcare professionals, and technology developers who seek to leverage its potential to enhance the diagnosis and management of an increasing number of chronic and acute conditions. Point-of-care testing (POCT) increases access to care because it enables care outside of traditional medical settings. Collaboration among developers, clinicians, and end users is an effective best practice for solving clinical problems. A common set of clearly defined terms that are easily understood by research teams is a valuable tool that fosters these collaborations. We present brief, accurate, and clear descriptions of terms and techniques used to develop new device and decision support technologies in association with their most common applications to POCT. This lexicon of terms used to describe AI and machine learning techniques is quick reference for healthcare professionals, researchers, developers, and patients. Commonly used methods and techniques are tabulated and presented with text providing the context of their common usage and required data characteristics. Finally, we summarize model effectiveness measurement and the assessment of component features contributions. Artificial intelligence (AI) refers to non-human techniques that infer meaning from sets of data. It can produce generalizations, classifications, predictions, and can identify associations using automated learning methods. This guide provides an overview of these methods and their application to point-of-care testing.

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Bone and cartilage tissues are physiologically dynamic organs that are systematically regulated by mechanical inputs. At cellular level, mechanical stimulation engages an intricate network where mechano-sensors and transmitters cooperate to manipulate downstream signaling. Despite accumulating evidence, there is a notable underutilization of available information, due to limited integration and analysis. In this context, we conceived an interactive web tool named MechanoBone to introduce a new avenue of literature-based discovery. Initially, we compiled a literature database by sourcing content from Pubmed and processing it through the Natural Language Toolkit project, Pubtator, and a custom library. We identified direct co-occurrence among entities based on existing evidence, archiving in a relational database via SQLite. Latent connections were then quantified by leveraging the Link Prediction algorithm. Secondly, mechanobiological pathway maps were generated, and an entity-pathway correlation scoring system was established through weighted algorithm based on our database, String, and KEGG, predicting potential functions of specific entities. Additionally, we established a mechanical circumstance-based exploration to sort genes by their relevance based on big data, revealing the potential mechanically sensitive factors in bone research and future clinical applications. In conclusion, MechanoBone enables: 1) interpreting mechanobiological processes; 2) identifying correlations and crosstalk among molecules and pathways under specific mechanical conditions; 3) connecting clinical applications with mechanobiological processes in bone research. It offers a literature mining tool with visualization and interactivity, facilitating targeted molecule navigation and prediction within the mechanobiological framework of bone-related cells, thereby enhancing knowledge sharing and big data analysis in the biomedical realm.

BACKGROUND: Clinical practice settings have increasingly become dependent on the use of digital or eHealth technologies such as electronic health records. It is vitally important to support nurses in adapting to digitalized health care systems; however, little is known about nursing graduates\' experiences as they transition to the workplace.
OBJECTIVE: This study aims to (1) describe newly qualified nurses\' experiences with digital health in the workplace, and (2) identify strategies that could help support new graduates\' transition and practice with digital health.
METHODS: An exploratory descriptive qualitative design was used. A total of 14 nurses from Eastern and Western Canada participated in semistructured interviews and data were analyzed using inductive content analysis.
RESULTS: Three themes were identified: (1) experiences before becoming a registered nurse, (2) experiences upon joining the workplace, and (3) suggestions for bridging the gap in transition to digital health practice. Findings revealed more similarities than differences between participants with respect to gaps in digital health education, technology-related challenges, and their influence on nursing practice.
CONCLUSIONS: Digital health is the foundation of contemporary health care; therefore, comprehensive education during nursing school and throughout professional nursing practice, as well as organizational support and policy, are critical pillars. Health systems investing in digital health technologies must create supportive work environments for nurses to thrive in technologically rich environments and increase their capacity to deliver the digital health future.

UNASSIGNED: Connecting individuals to existing community resources is critical to addressing social needs and improving population health. While there is much ongoing informatics work embedding social needs screening and referrals into health care systems and their electronic health records, there has been less focus on the digital ecosystem and needs of community-based organizations (CBOs) providing or connecting individuals to these resources.
UNASSIGNED: We used human-centered design to develop a digital platform for CBOs, focused on identification of health and social resources and communication with their clients.
UNASSIGNED: Centered in the Develop phase of the design process, we conducted in-depth interviews in 2 phases with community-based organizational leadership and staff to create and iterate on the platform. We elicited and mapped participant feedback to theory-informed domains from the Technology Acceptance Model, such as Usefulness and Ease of Use, to build the final product and summarized all major design decisions as the platform development proceeded.
UNASSIGNED: Overall, we completed 22 interviews with 18 community-based organizational leadership and staff in 2 consecutive Develop phases. After coding of the interview transcripts, there were 4 major themes related to usability, relevance, and external factors impacting use. Specifically, CBOs expressed an interest in a customer relationship management software to manage their client interactions and communications, and they needed specific additional features to address the scope of their everyday work, namely (1) digital and SMS text messaging communication with clients and (2) easy ways to identify relevant community resources based on diverse client needs and various program eligibility criteria. Finally, clear implementation needs emerged, such as digital training and support for staff using new platforms. The final platform, titled \"Mapping to Enhance the Vitality of Engaged Neighborhoods (MAVEN),\" was completed in the Salesforce environment in 2022, and it included features and functions directly mapped to the design process.
UNASSIGNED: Engaging community organizations in user-centered design of a health and social resource platform was essential to tapping into their deep expertise in serving local communities and neighborhoods. Design methods informed by behavioral theory can be similarly employed in other informatics research. Moving forward, much more work will be necessary to support the implementation of platforms specific to CBOs\' needs, especially given the resources, training, and customization needed in these settings.

OBJECTIVE: The purpose of this study was to define current practices related to beta-lactam/beta-lactamase inhibitor (BL/BLI) dose descriptions in hospitals that provide care for pediatric patients and to identify perceived implications of standardizing BL/BLI dose communication and ordering to a total drug-based strategy.
METHODS: A 27-item electronic survey was distributed via 4 pediatric pharmacy and infectious diseases listservs. Survey questions pertained to hospital demographics, dosing communication practices, BL/BLI ordering and labeling practices, obstacles to safe BL/BLI use, and the effects of potential standardization to a total drug communication strategy. SPSS was used for quantitative analysis and MAXQDA was used for qualitative analysis.
RESULTS: A total of 140 unique survey responses were analyzed after exclusion of incomplete responses and reconciliation of multiple responses from the same institution. Overall, 56.2% of institutions order BL/BLIs by BL component for pediatric patients, and 22% of institutions order by BL component for adult patients. Approximately half (51.8%) of respondents felt that standardizing to total drug would have a negative effect at their institution; perception of potential effect varied based on the institution\'s ordering strategy.
CONCLUSIONS: Communication and ordering of BL/BLIs is inconsistent across institutions and between pediatric and adult patients. In the short term, the perception is that standardization would compound institutional challenges.

The production of renewable biofuel through microalgae and green technology can be a promising solution to meet future energy demands whilst reducing greenhouse gases (GHG) emissions and recovering energy for a carbon-neutral bio-economy and environmental sustainability. Recently, the integration of Energy Informatics (EI) technology as an emerging approach has ensured the feasibility and enhancement of microalgal biotechnology and bioenergy applications. Integrating EI technology such as artificial intelligence (AI), predictive modelling systems and life cycle analysis (LCA) in microalgae field applications can improve cost, efficiency, productivity and sustainability. With the approach of EI technology, data-driven insights and decision-making, resource optimization and a better understanding of the environmental impact of microalgae cultivation could be achieved, making it a crucial step in advancing this field and its applications. This review presents the conventional technologies in the microalgae-based system for wastewater treatment and bioenergy production. Furthermore, the recent integration of EI in microalgal technology from the AI application to the modelling and optimization using predictive control systems has been discussed. The LCA and techno-economic assessment (TEA) in the environmental sustainability and economic point of view are also presented. Future challenges and perspectives in the microalgae-based wastewater treatment to bioenergy production integrated with the EI approach, are also discussed in relation to the development of microalgae as the future energy source.

The Alzheimer\'s Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer\'s research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNI established a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNI set a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approach not only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer\'s disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. HIGHLIGHTS: Accelerating biomarker discovery and drug development for Alzheimer\'s disease. Alzheimer\'s Disease Neuroimaging Initiative\'s (ADNI\'s) open data sharing drives scientific progress. Data exploration and coupled analytics to data archives.