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This study aims to describe the characteristics of hospitalised COVID-19 patients in a tertiary care hospital close to an international airport in Japan and to compare these characteristics among different waves during the pandemic.
Retrospective observational study.
Tertiary care centre in Japan.
All patients diagnosed with COVID-19 who were hospitalised between January 2020 and April 2022 were included.
Clinical characteristics, characteristics of admission, treatments and outcomes were investigated and compared among six pandemic waves.
A total of 827 patients were included. The median age was 58.0 years. More than half of the patients (58.3%) had at least one comorbidity. The majority of patients (89.0%) were domestically infected patients admitted under the Infectious Diseases Law, while the remaining patients (11.0%) were those diagnosed during airport quarantine and admitted under the Quarantine Act. Hospital-acquired COVID-19 infection occurred in 7.0% of cases, and mainly during the sixth wave. Overall, some form of oxygen therapy, high-flow oxygen devices, invasive mechanical ventilation (IMV) and extracorporeal membrane oxygenation was provided in 46.3%, 10.4%, 4.5% and 1.5% of cases, respectively. Only 1.8% of patients were treated in the intensive care unit (ICU), and 59.5% of patients on IMV were managed in the non-ICU ward. The in-hospital mortality rate was 5.8%. Median age, percentages of some comorbidities, vaccination coverage, medications for COVID-19, types of supportive care and ICU admissions differed significantly among waves.
This study suggests that patient characteristics, vaccination coverage, standard of treatment and severity of illness changed across waves during the COVID-19 pandemic. Intensive care delivery in non-ICU wards was unavoidable due to limited ICU capacity, which may be a key consideration when preparing for future pandemics.
To examine how cultural health brokers, as trusted intermediaries between formal systems and diverse ethnocultural communities, help navigate decisional conflict and misinformation regarding COVID-19 vaccination and to identify how their work contributes to system resilience in crisis contexts.
A community-based participatory action sensemaking research project to capture the real-time work of cultural health brokers in helping people navigate decisional conflict for vaccination.
Multicultural Health Broker Cooperative in Edmonton, Alberta where brokers speak 54 languages and serve more than 10 000 people from diverse ethnolinguistic communities. 28 cultural health brokers (9 male; experience 4–25 years) contributed to data collection and analysis between 16 September 2021 and 16 December 2021.
The brokers captured real-time reflections and self-interpretations in the SenseMaker platform through a theoretically informed, codesigned, mixed-method data collection tool. The team engaged in 13 weekly, 90 minute, audio-recorded and transcribed sessions: seven focused on understanding and action planning and five reflecting on the SenseMaker data, the focus of the thematic analysis. Data were managed in NVivo (QSR International, Version 12, 2018).
Brokers collected 277 narratives and conducted 13 sensemaking sessions. Understanding and purpose were identified in 68% of narratives as key to achieving coherence; 81% of narratives highlighted trust as crucial to what was needed for action; 62% of narratives reflected on a potential risk, with loss of trust a concern in 70% of them. A rich understanding of the sources of decisional conflict and misinformation was achieved and managed through outreach. There were four entwined components to navigation of the evolving complexity of COVID-19 vaccination: (1) building and sustaining trust; (2) strengthening relationships; (3) creating safe spaces for collective sensemaking and solution finding; and (4) leveraging cultural and social capital to address barriers. Through these mechanisms, brokers reduced decisional conflict and misinformation, supporting informed, values-congruent decisions.
Cultural health brokers, embedded within communities and linked to formal systems, play a critical role in crisis response by fostering trust, mobilising resources and enabling collective sensemaking. This study demonstrates how these intermediaries’ contextually and culturally attuned work provides a model for building system resilience for future crisis response.
by Temitope Ruth Folorunso, Gabriel Silva, Marilis E. Girón, Tess Lindow, Micah Persyn, Lori Eckhardt, Janna R. Willoughby
Effective culturing and DNA extraction protocols are essential for advancing research on fungal pathogens of brown spot needle blight (BSNB) that infect loblolly pine (Pinus taeda) and other Pinus species. We evaluated the performance of four widely used fungal media, including Sabouraud dextrose, malt extract, potato dextrose, and yeast extract peptone dextrose, in both solid (agar) and liquid (broth) formats, quantifying fungal growth through colony diameter and biomass accumulation over a three-week period. Sabouraud dextrose agar and broth consistently supported the most rapid and extensive growth in both formats, while potato dextrose underperformed across these metrics. To identify an optimal protocol for downstream molecular applications, we also compared four DNA extraction methods, three of which were modified variants of the CTAB (cetyl-trimethyl-ammonium bromide) chemistry as well as the Qiagen DNeasy kit following the yeast DNA extraction protocol. DNA yield, quantified by fluorometry, was highest for the high-salt CTAB polyvinylpyrrolidone (PVP) protocol and DNA purity (assessed by 260/280 absorbance ratio) was optimal for both PVP and Qiagen extractions. From these comparisons, we suggest that Sabouraud dextrose culturing combined with CTAB PVP extraction for use as a robust and accessible pipeline for generating high-quality fungal DNA.
This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing diagnosis of chronic pain and explore the potential of artificial intelligence (AI) in enhancing clinical decision-making in Italian healthcare settings. Three machine learning algorithms—XGBoost, gradient boosting, and BERT—were optimized through a grid search approach to identify the most suitable hyperparameters for each model. Therefore, the performance of the algorithms was evaluated and compared using Cohen's κ coefficient. This statistical measure assesses the level of agreement between the predicted classifications and the actual data labels. Results demonstrated XGBoost's superior performance, whereas BERT showed potential in handling complex Italian language structures despite data volume and domain specificity limitations. The study highlights the importance of algorithm selection in clinical applications and the potential of machine learning in healthcare, specifically addressing the challenges of Italian medical language processing. This work contributes to the growing field of artificial intelligence in nursing, offering insights into the challenges and opportunities of implementing machine learning in Italian clinical practice. Future research could explore integrating multimodal data, combining text analysis with physiological signals and imaging data, to create more comprehensive and accurate chronic pain classification models tailored to the Italian healthcare system. 
FreshRSS 