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The global burden of type 2 diabetes (T2D) is growing, and the age of onset is widening, resulting in increasing numbers of young adults and elderly patients with T2D. Age-specific diabetes care needs have yet to be fully explored.
This study examined (1) differences in patient-reported and clinical characteristics by age group and (2) the effect of age on two proxy measures assessing psychological health and self-care adherence after adjusting for potential mediators.
A cross-sectional, correlational design was used. Adults with type 2 diabetes (T2D) were recruited from a university hospital in Korea between 2019 and 2020. Participants were divided into four groups based on years of age (40s and younger group [n = 27]; 50s group [n = 47]; 60s group [n = 54]; and 70s and older group [n = 48]) to compare patient-reported and clinical characteristics. Chi-square tests, ANOVA, Kruskal-Wallis tests, and logistic regression analysis were performed to assess group differences and effect of age on psychological health and self-care adherence.
Of 178 participants, two-thirds were men (n = 114; 64.41%). The mean ages in the 40s and younger, 50s, 60s, and 70s and older groups were 39.4, 54.7, 63.9, and 76.0 years, respectively. There were significant differences in patient-reported and clinical characteristics by age group. The youngest group reported the poorest psychological health and self-care behaviors. Although the oldest group showed the poorest physical functioning, this group also showed the highest self-care adherence and the best psychological health. Regarding clinical characteristics, traditional diabetes-related blood test results showed no significant group differences.
Age-specific diabetes care needs were identified in adults with T2D. Interventions to improve psychological health and priming effects of behavioral adherence need to be developed. Furthermore, meticulous investigation to detect potential complications early is essential in adults with T2D.
This study aimed to identify the main themes from exit interviews of adult patients with type 2 diabetes after completion of a diabetes education program. Eighteen participants with type 2 diabetes completed an exit interview regarding their program experience and satisfaction. Semistructured interview questions were used, and the interviews were auto-recorded. The interview transcripts were preprocessed and analyzed using four natural language processing–based text-mining techniques. The top 30 words from the term frequency and term frequency–inverse document frequency each were derived. In the N-gram analysis, the connection strength of “diabetes” and “education” was the highest, and the simultaneous connectivity of word chains ranged from a maximum of seven words to a minimum of two words. Based on the CONvergence of iteration CORrelation (CONCOR) analysis, three clusters were generated, and each cluster was named as follows: participation in a diabetes education program to control blood glucose, exercise, and use of digital devices. This study using text mining proposes a new and useful approach to visualize data to develop patient-centered diabetes education. As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths. To investigate spillover effects of organizational support for patient and workplace safety on safety outcomes and to examine the mediating role of safety compliance in these relationships.
A cross-sectional, correlational survey design.
This study analysed data from 1255 nurses in 34 Korean hospitals. A structured questionnaire was used including items from the Hospital Survey on Patient Safety Culture and Safety Compliance scales. Data were collected between February and June 2022. We employed structural equation modelling (SEM) for analysis with a significance level set at 0.05.
Organizational support for patient and workplace safety showed direct impacts on patient and workplace safety outcomes. Findings supported our hypotheses regarding spillover effects, as organizational support for patient safety was related to enhanced workplace safety and organizational support for workplace safety was associated with improved patient safety. SEM analysis showed safety compliance's mediating role. When the distribution of serial indirect effects was examined, three out of eight indirect pathways were statistically significant.
Improving organizational support for patient safety can lead to better workplace safety outcome, and enhancing support for workplace safety can result in better patient safety outcome. Given this mutually beneficial relationship, healthcare organizations should simultaneously promote safety in both areas rather than focusing on just one.
Study results highlight the need to recognize the interconnected nature of patient and workplace safety in order to achieve better overall safety outcomes.
This study shows that organizational safety efforts for patients and workers are interconnected and mutually beneficial. The study's results have both theoretical and practical implications in demonstrating that organizational support for both patient and workplace safety plays a strong role in promoting nurses' safety compliance and improving overall safety outcomes.
STROBE checklist.
No patient or public contribution.
by Se Min Kim, Namu Park, Hye Bin Park, JuKyung Lee, Changho Chun, Kyung Hoon Kim, Jong Seob Choi, Hyung Jin Kim, Sekyu Choi, Jung Hyun Lee
Triple-negative breast cancer (TNBC) demands urgent attention for the development of effective treatment strategies due to its aggressiveness and limited therapeutic options [1]. This research is primarily focused on identifying new biomarkers vital for immunotherapy, with the aim of developing tailored treatments specifically for TNBC, such as those targeting the PD-1/PD-L1 pathway. To achieve this, the study places a strong emphasis on investigating Ig genes, a characteristic of immune checkpoint inhibitors, particularly genes expressing Ig-like domains with altered expression levels induced by "cancer deformation," a condition associated with cancer malignancy. Human cells can express approximately 800 Ig family genes, yet only a few Ig genes, including PD-1 and PD-L1, have been developed into immunotherapy drugs thus far. Therefore, we investigated the Ig genes that were either upregulated or downregulated by the artificial metastatic environment in TNBC cell line. As a result, we confirmed the upregulation of approximately 13 Ig genes and validated them using qPCR. In summary, our study proposes an approach for identifying new biomarkers applicable to future immunotherapies aimed at addressing challenging cases of TNBC where conventional treatments fall short.
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