Personal Response Research Articles

Courageous Black and White voices from South Africa: Discussion of Letsatsi’s “The complexity of my blackness” and Barber’s “Context, race, power and culture: The third in a therapeutic dyad”

ABSTRACT This discussion focuses on Letsatsi’s “The Complexity of My Blackness” (2025, this issue) and Barber’s “Context, Race, Power, and Culture: The Third in a Therapeutic Dyad” (2025, this issue). Both uniquely South African papers focus on race and context, raising profound and appropriate uncertainty about whether or not I should, as a privileged White woman, take on the role of a discussant. However, thoughts about what would be perpetuated by my silence, and Letsatsi’s comment (2025, this issue) that if we do not “show up”, there is little chance of achieving the mutual recognition we value in our professional work and lives, led to my decision to show up. But with limited aims—to share only those thoughts that were evoked in me, and my personal responses to both papers.

Advice over empathy: a national cross-sectional examination of communication in united states dietetic education

BackgroundEmpathy is an important element of patient communication and is well operationalized in established approaches to one-on-one communication, such as motivational interviewing (MI). The accurate demonstration of empathy is especially important when seeking to facilitate a change in behavior, such as in lifestyle or diet, given these issues often involve psychological barriers versus knowledge deficits. Registered dietitians are a key licensed healthcare provider tasked with facilitating changes in diet, but the extent to which current educational standards in the United States (US) contribute to cultivation of empathy as a professional skill has not been systematically evaluated. The objective of this study was to characterize therapeutic empathy and communication among US dietetics students.MethodsA cross-sectional survey-based study was conducted among US-based dietetics students, recruited via forwarded e-mail messages from directors of accredited programs. Personal characteristics and responses to the Helpful Responses Questionnaire (HRQ)– a validated measure of therapeutic empathy– were collected. All HRQ responses (6/participant) were reviewed and scored from 1 to 5 based on the use of reflections and communication roadblocks (1 = least empathetic, 5 = most empathetic) per established methods. One mean total HRQ score was computed per participant and precise roadblocks used were tabulated. Given the distribution of the final data set, a binary variable was created to capture whether participants had a total HRQ score of 1 or > 1. Logistic regression and chi-square tests were used to identify participant attributes associated with achieving scores > 1.ResultsParticipants (n = 506) were mostly white females (95.06% female, 79.05% white). The mean total HRQ score was 1.21 (SD: 0.47), with n = 175 participants (34.6%) achieving a mean total HRQ score > 1. Age, being married, having a previous non-nutrition-related career, or enrollment in a standalone internship were associated with HRQ score > 1 (P ≤ 0.001). The most common roadblock used was advising followed by using logic.ConclusionUS-based dietetics students may not be consistently using empathetic communication, instead prioritizing problem-solving-oriented strategies (e.g., giving advice). Exploring optimal strategies to training that facilitate improved empathy, and the relative importance of empathy versus problem-solving strategies in facilitating behavior change, are important next steps. Further, the HRQ was successfully implemented, and its use should be replicated in other regions and populations.

Assessing Grizzly Bear Safety Signage Designs: Applying a Personal Responses Tour through an Outdoor Gallery Installation

Visitor-wildlife conflict is a persistent Leave No Trace management challenge for parks and protected areas in the American West. Throughout the Greater Yellowstone Ecoregion (GYE), conflict with grizzly bears is occurring with increasing frequency—especially near roadways. The chance to experience close encounters with bears is highly sought after by visitors despite the serious risk to both humans and animals. This “bear blindness” results in patterns of inappropriate viewing behaviors, such as approaching bears from the roadway on foot, and remains a top managerial concern. Efforts to mitigate human-bear conflict through indirect strategies (e.g., education) are longstanding, but traditional messaging strategies inadequately influence visitor actions. This study evaluates the design elements and messaging strategies of roadside bear safety signage, centering on the capacity to capture attention, convey the authority behind the message, and persuade visitors to keep a safe distance from bears. Guided by the Personal Responses Tour (PRT) methodology, researchers evaluated viewer reflections using a gallery of prototype signs with varied approaches to assess their perceptions of design effectiveness for bear safety messaging in roadside settings. Through qualitative thematic analysis, key design principles were identified—including the importance of legibility in roadside settings, telegraphic messaging, and association with agency visual branding—that enhance the efficacy of safety communications. Findings from this study offer actionable insights for mitigating human-grizzly conflicts through improved safety messaging on the roadside.

Integrating generative artificial intelligence into design and art course: Effects on student achievement, motivation, and self-efficacy

ABSTRACT As digital technologies evolve, traditional teaching methods in design and art education are increasingly insufficient to meet students’ learning needs. While Generative Artificial Intelligence (GenAI) has introduced transformative applications in education, its impact on design and art curricula remains underexplored. To address this gap, this study employed a quasi-experimental design with 64 first-year undergraduate students to examine the effects of GenAI in a design and art course. Participants were divided into an experimental group, using a learning system integrated with a ChatGPT-driven pedagogical agent, and a control group, receiving traditional multimedia instruction. Learning achievement, motivation, and self-efficacy were assessed through validated scales and questionnaires. Results showed that the ChatGPT-driven system provided personalised responses and immediate feedback, significantly enhancing student achievement, motivation, and self-efficacy. These findings underscore the feasibility of integrating GenAI into design and art education and offer valuable insights for educators and curriculum developers.

Archiving Uncertainty: Leveraging Crowdsourcing Methodology in Documenting Personal Experiences of COVID-19

The COVID-19 pandemic, declared a global crisis by the World Health Organization on 11 March 2020, dramatically altered society’s daily routines, social interactions, and digital media consumption. This paper aims to evaluate how leveraging crowdsourcing techniques in combination with digital archiving strategies can effectively capture real-time autobiographical reflections during a period of crisis. The study focuses on the formation of the Latvian Pandemic Diary Collection, initiated through an open-call crowdsourcing initiative that was launched on 17 March 2020 to collect people’s recordings of their personal thoughts and emotional responses in this period of profound change and uncertainty. Methodologically, the project employed a versatile rapid-response crowdsourcing framework and dynamic digital archiving strategy, coupled with an intense outreach and social media campaign to encourage diary submissions. The diary project has resulted in an open-access digital collection of 2,333 daily entries by 238 participants, offering unique insights into the societal shift towards digital modes of professional, social, and educational interaction in response to the pandemic’s constraints. Although the methodology applied proved effective in eliciting relatively widespread public engagement, difficulties became apparent in relation to the scalability of the approach and getting different segments of society to participate in the project through the crowdsourced-driven digital collection of autobiographical narratives in a rapidly changing social context.

INTELLIGENT PERSONAL MEMORY ASSISTANT

ABSTRACT: The growing need for personalized digital assistants has led to the development of systems that can effectively store, process, and retrieve user-specific data through natural language interactions. This paper introduces a Personal Memory Assistant (PMA), designed to handle both voice and text inputs, enabling users to store queries and retrieve relevant information when required. The system incorporates a range of Natural Language Processing (NLP) methods, such as tokenization, stopword removal, and Term Frequency-Inverse Document Frequency (TF-IDF), to efficiently analyze and interpret user inputs. For voice- based inputs, a speech-to-text mechanism is employed, offering users the flexibility to switch between voice and text seamlessly. User queries and data are stored in a cloud environment using Firebase, which ensures real-time synchronization and scalability of the stored information. Upon receiving a query, the system applies TF- IDF to match the input with previously stored data, facilitating accurate and contextually relevant retrieval. This approach allows the system to manage structured and unstructured data efficiently. By combining advanced NLP techniques with cloud based storage and real-time data processing, the PMA delivers personalized responses, enhancing user engagement and interaction. The paper demonstrates the potential of integrating cloud technologies and NLP methods to improve the functionality of digital assistants in providing context-aware, tailored responses. Keywords: Personal Memory Assistant (PMA), Natural Language Processing (NLP), Tokenization, Stopword Removal-TF- IDF, Speech-to-Text, Firebase, Cloud.

Understanding miners' risk decisions: A multimodal analysis of personality, job burnout, and behavioral responses

Understanding miners' risk decisions: A multimodal analysis of personality, job burnout, and behavioral responses

Are you feeling what I'm feeling? Momentary interactions between personal and perceived peer subjective response predict craving and continued drinking in young adults.

Are you feeling what I'm feeling? Momentary interactions between personal and perceived peer subjective response predict craving and continued drinking in young adults.

Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging.

Artificial intelligence (AI) is revolutionizing cancer imaging, enhancing screening, diagnosis, and treatment options for clinicians. AI-driven applications, particularly deep learning and machine learning, excel in risk assessment, tumor detection, classification, and predictive treatment prognosis. Machine learning algorithms, especially deep learning frameworks, improve lesion characterization and automated segmentation, leading to enhanced radiomic feature extraction and delineation. Radiomics, which quantifies imaging features, offers personalized treatment response predictions across various imaging modalities. AI models also facilitate technological improvements in non-diagnostic tasks, such as image optimization and automated medical reporting. Despite advancements, challenges persist in integrating AI into healthcare, tracking accurate data, and ensuring patient privacy. Validation through clinician input and multi-institutional studies is essential for patient safety and model generalizability. This requires support from radiologists worldwide and consideration of complex regulatory processes. Future directions include elaborating on existing optimizations, integrating advanced AI techniques, improving patient-centric medicine, and expanding healthcare accessibility. AI can enhance cancer imaging, optimizing precision medicine and improving patient outcomes. Ongoing multidisciplinary collaboration between radiologists, oncologists, software developers, and regulatory bodies is crucial for AI's growing role in clinical oncology. This review aims to provide an overview of the applications of AI in oncologic imaging while also discussing their limitations.

AI Chatbot for Enhancing Mental Health

This project introduces a healthcare chatbot designed to improve mental well-being through mood-responsive interactions. The system integrates a Large Language Model (LLM)-powered contextual understanding, enabling adaptive and personalized responses to user input. By analyzing users' language patterns, the chatbot adapts its responses to provide context-aware, empathetic support that aligns with the user's current emotional state. Built on the Rasa framework with Natural Language Understanding (NLU) capabilities, this chatbot offers personalized, therapeutic conversations aimed at alleviating symptoms of mental health issues like stress and loneliness. This healthcare tool shows potential for enhancing user engagement and supporting mental health recovery by offering a more personalized and language-sensitive approach to care.

CRIMINOLOGICAL STUDY OF THE MAIN FORMS OF NON-CRIMINAL VIOLENCE IN MARITAL RELATIONS: BASED ON THE ANALYSIS OF PUBLIC OPINION

The article touches upon the problem of spousal violence. For the analysis of spousal violence was conducted a survey of students of extramural education. The subject of the questionnaire is not criminal forms of violence. The study is based on the theory of symmetric violence. The answers to the questions are analyzed: "Has the spouse used violence against the spouse over the past year?" and "Has he been a victim of violence in the past year?". The attitude of respondents to police intervention in the resolution of family conflicts was studied. The article investigates the opinion about the admissibility of spousal violence. A comparison is made between the responses of persons previously married, currently married, and those who have no experience of married life.

Abstract 6255: Patient response to immune checkpoint inhibitors (ICI) informs neoantigen prioritization: Applications to personalized cancer vaccines (PCVs) and ICI response prediction

Abstract Accurate identification of neoantigens that can stimulate a cytotoxic immune response is essential in designing PCVs. Here, we describe the design and performance of a neoantigen prioritization model for PCV design and ICI response prediction.The machine learning (ML)-based neoantigen prioritization model was implemented using tumor and matched normal whole exome sequencing data and clinical information to identify candidate neoantigens. Neoantigens were grouped across combinations of three characteristics: MHC-I binding IC50, T-cell immunogenicity predictions, and inferred gene expression. For each neoantigen combination, ICI response, as measured by progression-free survival (PFS), was compared using Cox proportional hazards tests. Statistically significant hazard ratios were used as a prioritization metric for each neoantigen combination. Model performance for neoantigen prioritization was evaluated using three publicly available datasets (TESLA N=8, NCI N=97, HiTIDE N=10) with known immunogenic, true-positive (TP) neoantigens. The percent of TP neoantigens prioritized within the top 100, 50, and 20 per patient was computed. Furthermore, a real-world clinical cohort (N=543; training 80%, test 20%) and an additional 66 patients with solid tumors from the Bespoke IO clinical study were utilized to validate our model for ICI response prediction as compared to TMB.In this cohort, hazard ratios of candidate neoantigens correlated with higher inferred gene expression and inversely correlated with IC50 and T-cell activation rank. In a separate analysis, we demonstrated that TCGA-based inferred gene expression, in the absence of patient-matched RNAseq, was sufficient to identify immunogenic neoantigens. Across all three test sets, the known TPneoantigens were consistently prioritized in the top 100 (NCI 89.7%, TESLA 91.4%, HiTIDE 96.4%) and top 50 (NCI 77.2%, TESLA 80.0%, HiTIDE 78.6%). Approximately half of TP neoantigens were prioritized in the top 20 (NCI 54.5%, TESLA 54.3%, HiTIDE 46.4%). These results outperformed other methods that have used TESLA data. The classification of patients into non-responders and responders was significantly better by our model for ICI response prediction [melanoma (HR: 13.69, p=0.0005), NSCLC (HR: 5.79, p=0.036) and MSI-H CRC (HR: 23.1, p=0.0001)] compared to TMB [melanoma (HR: 2.56, p=0.07), NSCLC (HR: 1.04, p=0.96) and MSI-H CRC (HR: 3.31, p=0.09)]. Similar trends were observed in the blinded validation cohort [(model: HR=3.16, p=0.006) vs. TMB (HR=1.47, p=0.33)].Our ML-based neoantigen prioritization model is the first to our knowledge to be informed by in vivo ICI response that effectively identifies and prioritizes immunogenic neoantigens for PCV design and ICI response prediction. Citation Format: Robert Burns, Sara L. Bristow, J. Bryce Ortiz, Meenakshi Malhotra, Jasreet Hundal, Aaron M. Rosenfeld, Helio A. Costa, Alexey Aleshin, Matthew Rabinowitz. Patient response to immune checkpoint inhibitors (ICI) informs neoantigen prioritization: Applications to personalized cancer vaccines (PCVs) and ICI response prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6255.

Abstract 4724: Evaluating immunotherapy and combination therapy efficacy in kidney cancer using a multicellular 3D immune-microtumor model

Advanced renal cell carcinoma (RCC) presents a significant treatment challenge, as response rates to immunotherapies, receptor-tyrosine kinase inhibitors (TKIs) and combination therapies remain unpredictable. A functional precision medicine approach that models tumor-immune interactions can help tailor patient-specific therapy selection, reducing ineffective treatment cycles. Pear Bio’s 3D immune-microtumor platform recreates the RCC tumor immune microenvironment (TiME), retaining relevant cell subpopulations to evaluate drug efficacy. Using fresh RCC tumor tissues and matched blood, the Pear Bio platform generates immune-microtumor cultures within a custom hydrogel optimized to sustain tumor, immune, and stromal cells (N=20). Tumor cells are co-cultured with activated peripheral blood mononuclear cells (PBMCs), with CD8+ T cells and CD8- PBMCs tracked via fluorescent dyes. FDA-approved RCC therapies, including immunotherapies (e.g. ipilimumab, nivolumab, pembrolizumab) and TKIs (e.g. axitinib, lenvatinib), are administered as monotherapies or in combination. Over 4 days, 3D confocal imaging assesses functional metrics including immune cell infiltration and tumor cell death, quantified through a proprietary computer vision pipeline. Immunofluorescence (IF) of 3D immune-microtumors (N=20) demonstrated retention of diverse TiME cell types—including tumor, endothelial, epithelial, immune, and cancer-associated fibroblasts—at day 0 and day 4 of culture. RNAseq confirmed transcriptional similarity of cells pre- and post-culture, with cell deconvolution further supporting immune compartment retention. Therapy dosing was optimized through molecular assays (IF, Luminex, flow cytometry, in-cell western) and functional screens on immune-microtumors (N=45). Post optimization, 3D immune-microtumors were used to test clinical treatment regimens and assess combo therapy responses in RCC in our trial (PEAR-TREE N=20 ISRCTN10001405). Pembrolizumab monotherapy increased PBMC and CD8+ cell infiltration by 26% and 14%, respectively, and boosted tumor cell death by 15% compared to controls (N=5). When combined with lenvatinib or axitinib, pembrolizumab further reduced tumor cell viability by 10% and 19%, respectively. This functional analysis reveals intra-patient response variability, underscoring the immune-microtumor model’s potential to capture personalized RCC responses to combination therapies. This 3D immune-microtumor model allows for precise ex vivo testing of combination therapies in RCC, capturing patient-specific interactions within a functional TiME. Current clinical validation in the PEAR-TREE2 RCC trial (NCT06264479) aims to further correlate ex vivo findings with in vivo outcomes, holding promise to guide optimized, personalized RCC treatment. Citation Format: Eleonora Peerani, Keqian Nan, Jonathan Ient, Polina Maximchik, Nourdine K. Bah, Jay Kearney, Jessica A. Hudson, Roseanna K. Hare, Farah Sangkolah, George R. de Fraine, Kerrie L. Loughrey, Elli Tham, Francesco Iori, Matthew H. Williams, Maxine G. Tran, Duleek N. Ranatunga. Evaluating immunotherapy and combination therapy efficacy in kidney cancer using a multicellular 3D immune-microtumor model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4724.

Retail Food Environment in an Urban Resettlement Colony of Delhi: A Community-Based Cross-Sectional Study

Abstract Background: The changing retail food environment contributes to increasing burden of obesity and noncommunicable diseases. The study assessed the density of retail stores along with availability, price, and placement of food options in a low-income urban community of Delhi, India. Materials and Methods: An audit of retail stores was conducted using an adapted Nutrition Environment Measure Survey tool covering nine locally relevant food items. For each item, healthier and less healthy options were identified based on global recommendations. Data were collected by personal observation and response of the shopkeeper. The study area was estimated using geospatial methods and population by electoral list. Results are expressed as proportions for availability, price ratio of less healthy to healthier food items, and density of stores per 100,000 population and per sq. km. Results: Among 70 stores, 88.2%, 7.4%, and 4.4% were small and large corner stores and small convenience stores, respectively. The density of food retail stores was 304 stores per sq. km and 99 stores per 100,000 population. A similar proportion of healthier and less healthy options were available for items like wheat flour, pulses, salt, milk, and bread. The price ratio was <1 for all items except for wheat flour and milk. The mean (95% confidence interval) number of less healthy items displayed at the cash counter, 7.38 (6.48–8.28), was significantly higher (P < 0.001) than that of healthier items, 2.74 (2.28–3.20). Conclusions: The food environment was characterized by being predominantly small corner stores based on high density, equal availability of less healthy and healthy options, and adverse pricing and placement for healthier options.

Performance of artificial intelligence chatbots in responding to the frequently asked questions of patients regarding dental prostheses

BackgroundArtificial intelligence (AI) chatbots are increasingly used in healthcare to address patient questions by providing personalized responses. Evaluating their performance is essential to ensure their reliability. This study aimed to assess the performance of three AI chatbots in responding to the frequently asked questions (FAQs) of patients regarding dental prostheses.MethodsThirty-one frequently asked questions (FAQs) were collected from accredited organizations’ websites and the “People Also Ask” feature of Google, focusing on removable and fixed prosthodontics. Two board-certified prosthodontists evaluated response quality using the modified Global Quality Score (GQS) on a 5-point Likert scale. Inter-examiner agreement was assessed using weighted kappa. Readability was measured using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) indices. Statistical analyses were performed using repeated measures ANOVA and Friedman test, with Bonferroni correction for pairwise comparisons (α = 0.05).ResultsThe inter-examiner agreement was good. Among the chatbots, Google Gemini had the highest quality score (4.58 ± 0.50), significantly outperforming Microsoft Copilot (3.87 ± 0.89) (P =.004). Readability analysis showed ChatGPT (10.45 ± 1.26) produced significantly more complex responses compared to Gemini (7.82 ± 1.19) and Copilot (8.38 ± 1.59) (P <.001). FRE scores indicated that ChatGPT’s responses were categorized as fairly difficult (53.05 ± 7.16), while Gemini’s responses were in plain English (64.94 ± 7.29), with a significant difference between them (P <.001).ConclusionsAI chatbots show great potential in answering patient inquiries about dental prostheses. However, improvements are needed to enhance their effectiveness as patient education tools.

Leveraging AI Chatbots for Enhanced Web-Based Knowledge Management Portals

The rapid digital transformation has reshaped knowledge management, driving the need for advanced solutions to manage information's increasing volume and complexity. This paper investigates the role of AI chatbots in enhancing web-based knowledge management portals, focusing on their ability to streamline information retrieval, deliver personalized responses, and support continuous learning. By examining the design, benefits, and limitations of chatbots, the study emphasizes their capacity to provide instant, conversational interactions that mimic human interviews, fostering user engagement and improved learning outcomes. However, challenges persist, particularly in chatbots' understanding of conversational nuances and contextual accuracy. The research employs qualitative and quantitative methods, analysing chatbot integration in knowledge portals, and finds that AI chatbots significantly improve system accessibility, responsiveness, and scalability. This contributes to enhanced organizational productivity and more informed decision-making, positioning chatbots as key enablers in modern knowledge management systems.

How Personality and Affective Responses Are Associated with Skepticism Towards Virtual Reality in Medical Training-A Pre-Post Intervention Study.

Virtual reality (VR) offers a safe, immersive environment for medical training, but some users remain skeptical about a broader implementation. Our study aims to explore how personality traits, affective responses, and task-related perceptions correlate with attitudes towards VR-based medical emergency training. Forty-seven medical students participated in a 30-minute VR emergency training. Personality traits were assessed using the short version of the Big Five Inventory beforehand, while affective responses (using the Positive and Negative Affect Schedule, PANAS), stress, and motivation were measured before and after the training. Participants also rated the sessions' difficulty, cognitive challenge, and technical maturity of the VR program and their acceptance of VR for training and examination purposes. Cluster analysis identified three groups: Cluster 1, characterized by low technical affinity, limited prior VR experience, and high extraversion, demonstrated the greatest increase in negative affective responses and the lowest VR acceptance. In contrast, cluster 3, with high technical affinity and neuroticism, experienced more positive affective responses and increased motivation, expressing high acceptance of VR for training purposes but some reservation regarding its use in examinations. Cluster 2 displayed balanced affective responses and strong support for VR use in both settings. Thematic analysis identified perceived lack of control due to insufficient medical knowledge, technical issues, and simulation sickness as sources of negative affective responses. In conclusion, personality and affective responses may play a significant role in shaping the attitude towards VR training applications. Uncovering emotional barriers to VR adoption among skeptical users and understanding their underlying reasons may inform future strategies for overcoming them. Given the relatively small sample size, results of this preliminary study should be expanded through further examination of diverse populations and a broader range of VR applications.

Emotional responses during communicational comfort: the effect of personality through the prism of process communication model

ABSTRACT The present study investigated the link between personality and emotional response modulation during an interview. Ninety participants were filmed responding to different processes of communication: they were asked to respond to questions that required them to answer with either facts or opinions. Emotionally-tinged and complicit exchanges were proposed and directive ways of communicating were offered so as to get them into action or to appeal to their imagination. Their skin conductance responses were recorded at the same time. Personality traits were assessed through process communication model (PCM) questionnaire. The results suggested that everybody could receive each process 1 , nevertheless, emotional responses varied according to PCM Base Type. Although only Persister Base participants showed significant differences from all other Base Types, we observed that participants’ emotional responses were modulated according to the different processes sent: offering a connection through opinions generated a high emotional response, as did create intimacy, while participants had a low emotional response when asked to visualise a situation by projecting themselves. These results reinforce the idea that adapting one's communication to one's interlocutor personality enables easier exchanges in dual communication situations. What's more, respecting inter-individual differences fosters greater tolerance, while increasing everyone's relational agility.

AN ESSAY ON HANNAH ARENDT'S UNDERSTANDING OF THOUGHT: THE HEIDEGGERIAN CONTEXT

The understanding of thinking is analyzed, which is ontological, whereas computational-instrumental thinking is not. But the version developed by Arendt has a different vector - if the ontological character of thinking according to Heidegger is revealed as a person's response to the questioning of being, as a way of self-realization of being, while due to Arendt, it is explained the ontological nature of thinking as a human responsibility, therefore ontology turns into ethics.

Safety, Tolerability, and Immunogenicity of aH5N1 Vaccine in Adults with and Without Underlying Immunosuppressive Conditions.

Background: Pandemic influenza may cause substantial morbidity and mortality, especially in older adults and those with immunosuppressive conditions. Methods: In this phase 3, stratified, randomized, controlled, observer-blind, multicenter trial, we evaluated the safety, tolerability, and immunogenicity of an adjuvanted H5N1 vaccine (aH5N1) vs. active control (MF59-adjuvanted trivalent seasonal inactivated influenza vaccine [aTIV]) in 539 adults aged 18-60 and ≥61 years. Participants were further stratified into subgroups that were healthy (18-60 years, n = 91; ≥61 years, n = 89) or had prespecified immunosuppressive conditions (18-60 years, n = 180; ≥61 years, n = 179). Antibody responses were measured with microneutralization and single radial hemolysis (SRH) assays. Results: aH5N1 increased antibody responses in healthy persons and those with immunosuppressive conditions in both age groups, with SRH geometric mean ratios (GMRs) > 2.5 and >2.0 in participants aged 18-60 and ≥61 years, respectively, meeting former Committee for Medicinal Products for Human Use (CHMP) criteria. Responses measured with the microneutralization and SRH assays were consistent with previous studies of aH5N1. Conclusions: The aH5N1 vaccine had a clinically acceptable safety and tolerability profile with an AE profile comparable to that observed in previous aH5N1 studies. These findings support the viability of aH5N1 as a pre-pandemic influenza vaccine for the immunization of at-risk individuals when an antigenically matched pandemic influenza vaccine is not yet available.