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In this paper, we explored how political knowledge related to generalised prejudice, defined as the common variance of three highly correlated specific prejudice concerning ethnicity, gender and sexual orientation. We aligned our hypotheses with the Cognitive Ability and Style to Evaluation (CASE) model, which postulates the mechanism underlying the relationship between individual-level cognitive variables and intergroup outcomes. As knowledge in its many forms correlates with and serves as a proxy of cognitive abilities, we hypothesised that political knowledge, when considered a precursor of prejudice, can be expected to act similarly to cognitive variables within the CASE model. We performed an empirical test of the hypothesised relationships on a nationally representative sample of Croatian students in their final year of secondary education (aged 17-19). As expected, there was a significant negative association between political knowledge and generalised prejudice, both direct and indirect via right-wing authoritarianism. Youth with higher political knowledge had significantly lower levels of generalised prejudice. In addition, while there were differences in the overall levels of political knowledge, right-wing authoritarianism and generalised prejudice between students attending different secondary education programmes, the pattern of relationships between these concepts was found to be stable across educational settings.

Abstract This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated relatively robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.

Objective: Psoriasis, a chronic inflammatory skin disease, significantly impacts patients’ quality of life. Over the last decade, therapeutic goals have aimed to complete skin clearance and restore normal patient activities, minimizing the disease’s impact on social, family, and work activities. Biologics have emerged as a promising solution to achieve better disease control without organ-specific side effects, helping meet these therapeutic goals. However, it was soon noticed that approximately 30% of patients do not sufficiently react to the therapy in the long term, and the need for switching biologics emerges. Findings: We present our experience with biologic switching over a specific period. Seventeen patients required a switch in biologic agents, with three undergoing a second switch. The cohort predominantly consisted of males (14 out of 17), with an average BMI of 29.81. The primary reasons for switching were secondary failure (loss of initial treatment efficacy), followed by primary failure. Adverse reactions were the least common cause, highlighting the satisfactory safety profile of biologics. One patient underwent dose escalation of secukinumab due to efficacy failure but ultimately ended up switching the biologic. Conclusion: Biologic agents approved for the treatment of psoriasis showed a favorable safety profile without compromising efficacy. The increasing demand for higher efficacy in psoriasis treatment aims to alleviate the disease’s multifaceted impact on patients. It is anticipated that biologic switching, primarily due to inadequate therapeutic response and less frequently due to adverse reactions, will become more prevalent in clinical practice. Literature and our clinical experience suggest that constitutional factors influence treatment success. As new agents and targets emerge, the established standards for biologic switching may require ongoing revision.

Digital healthcare has rapidly evolved during and in the post-COVID pandemic era, expanding the roles and responsibilities of community pharmacists. Services like telepharmacy, e-prescriptions, remote medication therapy management, and digital monitoring of chronic conditions, have evolved into everyday routine pharmacy practices. Pharmacists are at the forefront and the most accessible healthcare professionals for patients and are increasingly pivotal in providing comprehensive patient care, including digital patient care services. To ensure that future generations of pharmacists are digitally competent, it is crucial that digital health education is provided to pharmacy students. Furthermore, fostering high-quality multidisciplinary research, particularly in collaboration with medicine and other health disciplines, is essential for advancing the digital health skills of the future pharmacy workforce. Despite the growing use of digital health technologies, there are significant between-country differences in digital health education, the clinical settings in which digital health technologies are used, and their implementation in day-to-day practice. This commentary summarizes key insights from the International Digital Health Workshop held at the University of Sydney in November 2023. To help ensure pharmacists are included as participants in future digital health research, recent advances in digital health education and interprofessional research projects across three universities from far-off world regions were presented. Participants discussed a possible collaborative, interprofessional, and international research project on chronic disease prevention using digital health technologies. The need for interdisciplinary digital health curricula was highlighted in the workshop discussions, specifically tailored to address the knowledge requirements of pharmacists and other healthcare professionals.

Abstract Background Cellular stress associated with static-cold storage (SCS) and warm reperfusion of donor lungs can contribute to ischemia–reperfusion (IR) injury during transplantation. Adding cytoprotective agents to the preservation solution may be conducive to reducing graft deterioration and improving post-transplant outcomes. Methods SCS and warm reperfusion were simulated in human lung epithelial cells (BEAS-2B) by exposing cells to low potassium dextran glucose solution at 4 °C for different periods and then switching back to serum-containing culture medium at 37 °C. Transcriptomic analysis was used to explore potential cytoprotective agents. Based on its results, cell viability, caspase activity, cell morphology, mitochondrial function, and inflammatory gene expression were examined under simulated IR conditions with or without thyroid hormones (THs). Results After 18 h SCS followed by 2 h warm reperfusion, genes related to inflammation and cell death were upregulated, and genes related to protein synthesis and metabolism were downregulated in BEAS-2B cells, which closely mirrored gene profiles found in thyroid glands of mice with congenital hypothyroidism. The addition of THs (T3 or T4) to the preservation solution increases cell viability, inhibits activation of caspase 3, 8 and 9, preserves cell morphology, enhances mitochondrial membrane potential, reduces mitochondrial superoxide production, and suppresses inflammatory gene expression. Conclusion Adding THs to lung preservation solutions may protect lung cells during SCS by promoting mitochondrial function, reducing apoptosis, and inhibiting pro-inflammatory pathways. Further in vivo testing is warranted to determine the potential clinical application of adding THs as therapeutics in lung preservation solutions.

Non-human primate (NHP) spinal cord injury experiments exhibit high intersubject variability in biomechanical parameters even when a consistent impact protocol is applied to each subject. Optimizing impact parameters to reduce this variability through experiments is logistically challenging in NHP studies. Finite element models provide a complimentary tool to inform experimental design without the cost and complexity of live animal studies. A morphologically variable virtual population (N = 10) of NHPs quantified the interaction of morphological variability and different impact conditions in a unilateral cervical contusion, including impactor size (4 and 5 mm) and mediolateral alignment over the cord midline (0.5 and 1 mm). We explored the effect of these conditions on the magnitude and intersubject variability of impact force and cord lateral slippage. The study demonstrated that a 1-mm mediolateral alignment maximized peak forces and minimized lateral slippage. A 5-mm impactor was beneficial in increasing peak forces, whereas a 4-mm impactor reduced lateral slippage. Comparatively, intersubject variability in peak forces and lateral slippage were minimized with a 0.5-mm mediolateral alignment. The study highlights that impact parameters selected based on peak forces may not be beneficial in reducing variability. The study also showed that morphology was an important contributor to variability. Integrating morphology variability through a virtual population in an injury simulation to investigate mechanistic research questions will more effectively capture the heterogeneity of experiments and provide better insights for effective experimental design.

BackgroundTo investigate the impacts of the COVID-19 pandemic on the health workforce, we aimed to develop a framework that synergizes natural language processing (NLP) techniques and human-generated analysis to reduce, organize, classify, and analyze a vast volume of publicly available news articles to complement scientific literature and support strategic policy dialogue, advocacy, and decision-making. ObjectiveThis study aimed to explore the possibility of systematically scanning intelligence from media that are usually not captured or best gathered through structured academic channels and inform on the impacts of the COVID-19 pandemic on the health workforce, contributing factors to the pervasiveness of the impacts, and policy responses, as depicted in publicly available news articles. Our focus was to investigate the impacts of the COVID-19 pandemic and, concurrently, assess the feasibility of gathering health workforce insights from open sources rapidly. MethodsWe conducted an NLP-assisted media content analysis of open-source news coverage on the COVID-19 pandemic published between January 2020 and June 2022. A data set of 3,299,158 English news articles on the COVID-19 pandemic was extracted from the World Health Organization Epidemic Intelligence through Open Sources (EIOS) system. The data preparation phase included developing rules-based classification, fine-tuning an NLP summarization model, and further data processing. Following relevancy evaluation, a deductive-inductive approach was used for the analysis of the summarizations. This included data extraction, inductive coding, and theme grouping. ResultsAfter processing and classifying the initial data set comprising 3,299,158 news articles and reports, a data set of 5131 articles with 3,007,693 words was devised. The NLP summarization model allowed for a reduction in the length of each article resulting in 496,209 words that facilitated agile analysis performed by humans. Media content analysis yielded results in 3 sections: areas of COVID-19 impacts and their pervasiveness, contributing factors to COVID-19–related impacts, and responses to the impacts. The results suggest that insufficient remuneration and compensation packages have been key disruptors for the health workforce during the COVID-19 pandemic, leading to industrial actions and mental health burdens. Shortages of personal protective equipment and occupational risks have increased infection and death risks, particularly at the pandemic’s onset. Workload and staff shortages became a growing disruption as the pandemic progressed. ConclusionsThis study demonstrates the capacity of artificial intelligence–assisted media content analysis applied to open-source news articles and reports concerning the health workforce. Adequate remuneration packages and personal protective equipment supplies should be prioritized as preventive measures to reduce the initial impact of future pandemics on the health workforce. Interventions aimed at lessening the emotional toll and workload need to be formulated as a part of reactive measures, enhancing the efficiency and maintainability of health delivery during a pandemic.

Generative artificial intelligence (AI) models offer potential assistance in pain research data acquisition, yet concerns persist regarding data accuracy and reliability. In a comparative study, we evaluated open generative AI models’ capacity to acquire data on acute pain in rock climbers comparable to field research.

Abstract The role of social media in information dissemination and agenda-setting has significantly expanded in recent years. By offering real-time interactions, online platforms have become invaluable tools for studying societal responses to significant events as they unfold. However, online reactions to external developments are influenced by various factors, including the nature of the event and the online environment. This study examines the dynamics of public discourse on digital platforms to shed light on this issue. We analyzed over 12 million posts and news articles related to two significant events: the release of ChatGPT in 2022 and the global discussions about COVID-19 vaccines in 2021. Data was collected from multiple platforms, including Twitter, Facebook, Instagram, Reddit, YouTube, and GDELT. We employed topic modeling techniques to uncover the distinct thematic emphases on each platform, which reflect their specific features and target audiences. Additionally, sentiment analysis revealed various public perceptions regarding the topics studied. Lastly, we compared the evolution of engagement across platforms, unveiling unique patterns for the same topic. Notably, discussions about COVID-19 vaccines spread more rapidly due to the immediacy of the subject, while discussions about ChatGPT, despite its technological importance, propagated more gradually.

Cholesterol granuloma is a rare entity, which can develop in many regions of the body, accounting at most 1% of all mediastinal tumors. Etiology of this granuloma is still not clearly understood. The gold standard choice of treatment for cholesterol granuloma is total surgical resection. Symptomatic mediastinum granuloma can be easily diagnosed, but if mass effect is not evident then diagnosis of this tumor is really challenging. We present a rare case of huge cholesterol granuloma in the anterior mediastinum of the patient who underwent on elective coronary artery graft bypass surgery.

Objective Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students’ sleep quality. Methods In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention. Results Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = −0.51, 95% CI: [−1.55–0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9–59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7–61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep. Conclusions The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.

We present the case of a 49-year-old female of Caucasian European descent with chest tightness, fatigue, and palpitations, ultimately diagnosed with primary intracardiac angiosarcoma. Initial echocardiography revealed a significant mass within the right atrium, infiltrating the free wall. Surgical intervention included tumor excision and partial resection of the superior vena cava. Histopathological examination confirmed a high-grade angiosarcoma. Postoperative imaging identified a recurrent mass in the right atrium, suggestive of thrombus, alongside Takotsubo cardiomyopathy. Considering the elevated surgical risks and the presence of cardiomyopathy, management included anticoagulation therapy with Warfarin and adjuvant chemotherapy with Paclitaxel. Follow-up cardiac magnetic resonance imaging demonstrated a recurrent angiosarcoma with superimposed thrombus. This case presents the complex diagnostic and therapeutic landscape of angiosarcoma, highlighting the critical importance of early surgical intervention, advanced imaging techniques, and vigilant postoperative monitoring.