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Abstract Background Compassion fatigue is a significant issue in nursing, affecting both registered nurses and nursing students, potentially leading to burnout and reduced quality of care. During internships, compassion fatigue can shape nursing students’ career trajectories and intent to stay in the profession. Identifying those at high risk is crucial for timely interventions, yet existing tools often fail to account for within-group variability, limiting their ability to accurately predict compassion fatigue risk. Objectives This study aimed to develop and validate a predictive model for detecting the risk of compassion fatigue among nursing students during their placement. Design A cross-sectional study was used to capture the prevalence and associations of compassion fatigue among nursing interns, as it allows for timely assessment of key influencing factors without requiring long-term follow-up. Methods A convenience sampling strategy was used to recruit 2256 nursing students from all ten public junior colleges in Hunan province in China between December 2021 and June 2022. Participants completed questionnaires assessing compassion fatigue, professional identity, self-efficacy, social support, psychological resilience, coping styles, and demographic characteristics. Predictors were selected based on prior literature and theoretical frameworks related to compassion fatigue in nursing. Latent profile analysis was used to classify compassion fatigue levels, and potential predictors were identified through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Eight machine learning algorithms were applied to predict compassion fatigue, with performance assessed through cross-validation, calibration, and discrimination metrics. The best-performing model was further validated to ensure robustness. Results A three-profile model best fits the data, identifying low (55.73%), moderate (32.17%), and severe (12.10%) profiles for compassion fatigue. Generally, an area under the curve (AUC) above 0.700 is acceptable, and above 0.800 indicates good predictive performance. The AUC values for the eight machine learning models ranged from 0.644 to 0.826 for the training set and 0.651 to 0.757 for the test set, indicating moderate to good discriminatory ability. The eXtreme Gradient Boosting (XGBoost) performed best, with AUC values of 0.840, 0.768, and 0.731 in the training, validation, and test sets, respectively. Shapley Additive Explanation (SHAP) analysis interpreted the model by quantifying the contribution of each variable to the prediction, revealing that psychological resilience, professional identity, and social support were the key contributors to the risk of compassion fatigue. A user-friendly, web-based prediction tool for calculating the risk of compassion fatigue was developed. Conclusions The XGBoosting classifier demonstrates excellent performance, and implementing the online tool can help nursing administrators manage compassion fatigue effectively. It holds practical value for nursing education and practice by supporting early detection and intervention. Future research should validate its use across settings, and longitudinal studies could assess its long-term impact.

Abstract Background CD19 CAR T-cell therapy is a novel anti-cancer treatment that has produced remarkable responses in relapsed or refractory B-cell hematological malignancies. Cytokine Release Syndrome (CRS) is a dysregulated immune response that frequently occurs after CAR T-cell infusion. It can cause cardiac dysfunction and circulatory collapse negatively impacting outcomes and survival. To endure the insults of CRS, patients are typically screened for adequate cardiac reserve before treatment. The relationship between baseline cardiac function by echocardiography and the development of moderate to severe presentations of CRS is unclear. Methods This study aimed to identify baseline echocardiographic variables that can predict the development of hemodynamically significant CRS (CRS ≥ 2), evaluate their behavior at follow-up, and investigate the incidence of cancer therapy-related cardiac dysfunction (CTRCD). An observational retrospective cohort study of patients treated with CD19 CAR T-cell therapy with a baseline echocardiogram was performed. Demographic, clinical and echocardiographic variables were abstracted from the electronic health record. Patients were grouped and compared by the occurrence of CRS

Abstract Background Compassion fatigue impacts nurses’ well-being and work efficiency. Extensive research has explored its prevalence, but evidence regarding related factors is broadly categorized and lacks descriptive data. There’s also a lack of systematic reviews on compassion fatigue among nursing students during internships. Objectives To synthesize evidence on the levels and factors of compassion fatigue among nurses and nursing students during internships. Methods This systematic review is registered with PROSPERO (ID CRD42023444173). Literature searches were conducted in five databases (Cochrane Library, PubMed, EMBASE, CNKI, and WanFang) up to November 30, 2022, with updates planned until January 17, 2024, if necessary. Inclusion criteria covered studies reporting data on the prevalence and related factors of compassion fatigue or its dimensions, burnout and secondary traumatic stress, among registered nurses or nursing students during internships. Independent study selection, quality assessment, and data extraction were performed. The Agency for Healthcare Research and Quality tool was used for critical appraisal of study quality. Random-effects model analyses were conducted using Stata 17.0 to pool data on prevalence rates and mean scores of compassion fatigue. When comprehensive data on compassion fatigue were unavailable, its dimensions were analyzed for both prevalence and mean scores. Results Our review included 196 studies (73,034 nurses and 4,551 nursing students). For nurses, pooled mean scores for burnout and secondary traumatic stress were 26.81 (95% CI 26.28 to 27.35) and 25.88 (95% CI 25.39 to 26.37), respectively. For nursing students during internships, pooled mean scores were 29.16 (95% CI 26.95 to 31.37) and 25.64 (95% CI 20.95 to 30.34), respectively. Subgroup analyses revealed that post-COVID-19 pandemic, nurses exhibited higher compassion fatigue, especially in ICU or emergency departments. Evidence from 93 studies suggested that nurses’ burnout and secondary traumatic stress are both influenced by work environment, social support, job satisfaction, workload, and psychological capital (moderate to low-certainty evidence). For nursing students, psychological capital plays a significant role (moderate to low-certainty evidence). Conclusions Both registered nurses and nursing students experience moderate compassion fatigue. Work environment, social support, job satisfaction, workload, and psychological capital are key factors associated with burnout and secondary traumatic stress in registered nurses. For nursing students, psychological capital plays a similarly significant role. Given that this is the first systematic review to explore these factors among nursing students, further research is essential to deepen understanding and develop effective interventions.

In today’s fast-paced, digitalized world, dental educators and professionals alike need to try and stay up to date on current learning methods to provide the best possible education to the new generations of students. VR-haptic training allows educators to guide students in performing dental exercises within a digital environment using input methods with tactile force feedback features. This can reduce the mental burden of students due to fear of failure, as material losses are no longer a factor. Additionally, students can practice in their own time and pace, outside of teaching hours. Despite being digital, this mode of teaching has been shown to enable skill transfer to the real world, affording educators more flexibility with how to teach their students both before and during traditional methods of practical dental education. To help support the embracement of this new technology, a global VR-Haptic Thinkers Consortium was established in early 2024 and is now formed of 36 partner universities, institutions, and industrial companies. The second VR-Haptic Thinkers Meetup, held in mid-2024, was given the theme of “VR-Haptic Dentistry, Pedagogy, and Curriculum Evolution”. The theme of the meetup was timely as in recent years dental educators have all faced many challenges in education and research and have had to deal with rapid changes in supportive VR-haptic education. A robust program was featured to engage with the dental community members who are curious about the new technological advancements in dental education.

Abstract Background Marine sponges and their microbiomes are ecosystem engineers distributed across the globe. However, most research has focused on tropical and temperate sponges, while polar regions like Antarctica have been largely neglected. Despite its harsh conditions and geographical isolation, Antarctica is densely populated by sponges. In this study, we explored the extent of habitat specificity in the diversity, community composition, and microbial co-occurrence within Antarctic sponge microbiomes, in comparison to those from other marine environments. We used massive sequencing of 16S rRNA genes and integrated multiple databases to incorporate Antarctic sponges as a habitat in global microbiome analyses. Results Our study revealed significant differences in microbial diversity and community composition between Antarctic and non-Antarctic sponges. We found that most microorganisms present in Antarctic sponges are unique to the South Shetland Islands. Nitrosomonas oligotropha, Candidatus Nitrosopumilus, Polaribacter, SAR116 clade, and Low Salinity Nitrite-Oxidizing Bacteria (LS-NOB) are microbial members characterizing the Antarctic sponge microbiomes. Based on their exclusivity and presence across different sponges worldwide, we identified habitat-specific and habitat-generalist bacteria associated with each habitat. They are particularly abundant and connected within all the Antarctic sponges, suggesting that they may play a crucial role as keystone species within these sponge ecosystems. Conclusions This study provides significant insights into the microbial diversity and community composition of sponges in Antarctica and non-Antarctic ecoregions. Our findings provide evidence for habitat-specific patterns that differentiate the microbiomes of Antarctic sponges from elsewhere, indicating the strong influence of environmental selection and dispersal limitation wrapped into the Antarctic ecoregions to shape more similar microbial communities in distantly related sponges. This study contributes to understanding signatures of microbial community assembly in the Antarctic sponges and has important implications for the ecology and evolution of these unique marine environments.