Purpose This study aims to critically examine the ethical challenges posed by artificial intelligence (AI)-driven data mining in health care, particularly focusing on the limitations of traditional consent and regulatory frameworks. It proposes a reimagined model of care ethics and informed consent that is relational, participatory and justice-oriented. By introducing the “Ethical Data Mining Compass,” the paper seeks to guide health-care stakeholders in implementing ethical, transparent and inclusive AI systems that prioritize patient autonomy, accountability and data justice in clinical decision-making and digital health governance. Design/methodology/approach This conceptual paper uses a normative ethical analysis to explore the intersection of algorithmic decision-making and health-care data practices. Drawing from interdisciplinary literature in bioethics, data justice and care theory, it critiques existing regulatory frameworks and consent models. The study develops the “Ethical Data Mining Compass” as a theoretical framework by synthesizing principles of dynamic consent, participatory governance and relational ethics. This approach offers a structured lens for evaluating and guiding the ethical deployment of AI technologies in health-care systems, with particular attention to equity, transparency and patient-centered accountability. Findings The study finds that conventional models of informed consent and existing regulatory frameworks like General Data Protection Regulation and Health Insurance Portability and Accountability Act are insufficient to address the ethical complexities of AI-driven health care. It highlights that algorithmic opacity and asymmetrical power dynamics undermine patient autonomy and trust. The proposed “Ethical Data Mining Compass” offers a novel framework that integrates dynamic consent, data justice and participatory governance. This model enables more inclusive, transparent and accountable data practices, reframing consent as an ongoing, relational process. It positions ethical care as central to responsible AI implementation, ensuring that technological innovation aligns with patient rights and social equity. Originality/value This paper offers a novel contribution by reconceptualizing informed consent in the context of AI-driven health care through the lens of care ethics and data justice. Unlike traditional models that treat consent as a one-time transactional act, it introduces the “Ethical Data Mining Compass” as a dynamic and participatory framework. The study bridges gaps between ethical theory, health informatics and governance, providing a structured approach to guide responsible AI use in clinical settings. Its value lies in promoting a patient-centered, equitable and context-sensitive model for data ethics that addresses current regulatory and practical shortcomings in digital health systems.

The rapid evolution of telesurgery and advanced telemedicine technologies has expanded access to specialized surgical care while introducing complex legal, ethical, and regulatory challenges. This narrative review examines liability considerations associated with telesurgical practice, with particular attention to cross-border care, professional negligence, product liability, and the interaction between human and technological actors. Beyond legal accountability, the review highlights the central role of bioethical considerations, including patient autonomy, transparency, and equity, and explores how these principles are strained by physical distance, technological mediation, and jurisdictional fragmentation. Issues related to informed consent are examined in depth, particularly the adequacy of disclosure regarding technological risks, system failures, data transmission vulnerabilities, and the distribution of responsibility among clinicians, institutions, and technology providers. By synthesizing international legal frameworks, policy approaches, and ethical analyses, this review underscores recurring structural challenges in existing liability models and identifies areas where current consent practices may be insufficient for telesurgical contexts. The findings aim to inform clinicians, policymakers, and regulators seeking to support ethically sound, legally robust, and patient-centered implementation of telesurgery.

INTRODUCTION: Social work discourse regarding artificial intelligence (AI) in practice, research, and education has proliferated over the last 5 years, reflecting both excitement over its potential and ambivalence about its ethical challenges. However, the extent to which social work is fully engaging with the structure of AI and its enormous impacts on the environment, labour, and distribution of power remains unclear. METHODS: An integrative review of social work literature from 2020–2024 was conducted to address two research questions: 1) What is the nature of the social work discourse related to AI? 2) To what extent is the discourse addressing the structural aspects of AI? Integrative review is a methodology used to summarise empirical/theoretical/gray literature to provide a comprehensive understanding of a phenomenon and assess current themes, tensions, and gaps. FINDINGS: The literature overwhelmingly touted AI’s potential benefits for social work practice, research, and education. Discussions of ethical challenges, which often lacked depth and detail, narrowly focused on the downstream impacts of AI. The literature was almost entirely devoid of structural perspectives on AI. CONCLUSION: Findings suggest that social workers tend to view AI as an area of computation rather than a vast political, financial, and social system that impacts the experiences, opportunities, and lived environments of social workers and clients. Social workers will be better positioned to use AI responsibly and influence its development if they engage with AI from a structural perspective, committed to sustainability and justice.

The digital revolution has introduced important ethical challenges for younger generations, including the spread of hoaxes, plagiarism, and cyberbullying. This research examines the integration of generative Artificial Intelligence (AI) as a pedagogical partner in civic education to strengthen digital ethics grounded in Pancasila as Indonesia’s local wisdom. Employing a mixed-methods approach, data were collected through questionnaires administered to 332 senior and vocational high school students, Focus Group Discussions (FGDs) with 10 civic education teachers in Central Java Province, and in-depth interviews with experts in Civic and Character Education. The study findings reveal that, despite students' active use of AI, their critical thinking skills and digital ethics require further development. Teachers expressed an urgent need for adaptive, context-specific learning resources. Pancasila values provide a relevant and robust philosophical foundation for developing a digital ethics framework. Based on these findings, the study proposes an innovative framework that positions AI chatbots not merely as tools but as simulative and reflective learning partners. The framework seeks to transform learning from simple knowledge transmission to value-oriented education, enabling teachers to act as facilitators who guide students in actualising Pancasila values within the digital space.

BackgroundNarrative nursing is recognized as a vital relational ethical practice for upholding patient dignity and counteracting the dehumanizing effects of the biomedical model. However, its implementation in China's high-pressure, task-oriented hospital environments creates profound ethical tensions. The clash between the ideal of humanistic care and the reality of efficiency logic often precipitates moral distress among nurses, yet their lived ethical experiences within these systemic constraints remain under-explored.AimTo explore the ethical challenges, moral distress, and resilience strategies encountered by Chinese clinical nurses when implementing narrative nursing in resource-constrained settings.Research designA qualitative descriptive phenomenological design was adopted to capture the essence of nurses' lived experiences.Participants26 registered nurses were recruited via purposive sampling from six clinical departments (including Oncology, Obstetrics, and Hepatobiliary Surgery) within a tertiary Grade-A hospital in China. Data were collected through six focus group discussions.Ethical considerationsThe study was approved by the Institutional Review Board of The Eighth Affiliated Hospital of Southern Medical University. Written informed consent was obtained from all participants, and anonymity was strictly maintained throughout the data analysis process.FindingsThe analysis revealed three core themes: (1) Spatial Constraints and Privacy Dilemmas: The lack of auditory privacy in crowded wards created a "panopticon" environment, transforming sensitive narratives into public performances and compelling patients to engage in self-censorship, thereby compromising dignity; (2) Systemic Barriers and Moral Distress: The conflict between "fast time" (task completion) and "slow time" (narrative engagement) rendered narrative care as invisible work that is unrecognized by performance appraisals. Nurses faced the risk of becoming containers of trauma due to a lack of institutional emotional support; (3) Strategies to Maintain Moral Agency: Nurses reclaimed agency by utilizing fragmented time to construct ethical moments. They derived reciprocal professional nourishment and cognitive reframing from patient interactions, which served as a sustainable source of moral resilience.ConclusionImplementing narrative nursing in this context is a profound struggle against spatial injustice and structural devaluation. Relying solely on nurses' individual volunteerism to sustain this practice is inherently unsustainable. To foster a genuine ethical climate, healthcare institutions must move beyond rhetoric to legitimate narrative engagement as a core competency, re-engineer clinical spaces for privacy, and establish systemic safety valves for emotional labor.

Purpose Law enforcement agencies in the United States are relying on state fusion centers for intelligence to develop actionable, data-driven reports that increase efficiency and improve investigations in crime prevention and homeland security. This study assesses the extent to which artificial intelligence and machine learning (AI/ML) are increasingly shaping intelligence operations in law enforcement and the functions of state fusion centers in supporting intelligence-led policing (ILP). The reliance and integration of AI/ML is improving analytic accuracy, situational awareness and information and data sharing and collaboration among law enforcement and homeland security agencies. This study examines the state of contemporary academic literature, assesses AI/ML applications used in law enforcement and builds a conceptual and theoretical framework centered on ILP policing. It also relies on empirical data, case study applications, and DHS assessments to explore the degree to which AI-driven processes and analytics enhance criminal intelligence, investigative efficiencies, situational awareness and predictive policing. The analysis, while focusing on the opportunities and challenges of using AI/ML tools in law enforcement, also highlights the need for ethical governance, transparency and accountability when relying on advanced technologies for crime prevention and policing. Design/methodology/approach This study utilizes qualitative methods, including a thematic content analysis of government and think tank/practitioner reports as well as academic literature on the benefits, costs and ethical factors regarding variations in the implementation of AI/ML tools for law enforcement intelligence products and resource allocation. For cross-validation of operational outcomes, it examines publicly available information in the DHS Fusion Center Annual Assessment, Bureau of Justice Statistics, LEMAS and RAND Corporation assessments of intelligence-led policing. Findings Qualitative Results Federal and state sources report fusion centers and law enforcement agencies integrating advanced analytic and ML-enabled tools into each step in the criminal intelligence lifecycle process. However, ethical and structural challenges limit and constrain technology-driven narratives in fusion centers. Given these challenges, there is a consistent qualitative and thematic pattern: state fusion centers now function as criminal intelligence analytic hubs or resources that leverage the most contemporary analytic and data-driven tools for criminal intelligence and law enforcement investigations. Interrelated themes describe AI/ML technologies in terms of shaping, constraining and complicating the intelligence lifecycle in fusion centers and law enforcement operations. Seven specific themes emerged from latent coding are illustrated in the chart. Research limitations/implications There are limitations on the collection of quantitative data since DHS, leading think tanks and NGOs do not disclose specific figures on the proportion of AI/ML tools. The DHS Fusion Center Annual Assessment process monitors technology adoption and the growth of analytic capabilities throughout the national network of fusion centers; however, the specific quantitative statistics are not disclosed in public summaries (DHS, 2024). Second, publicly available data and information constitute the bulk of empirical sources. Consequently, this study relies primarily on qualitative narrative reporting, not quantitative performance metrics. Third, publication bias is likely present in industry and government sources as these reports may provide overly optimistic observations and conclusions while overlooking ethical dilemmas, failures and challenges. Moreover, qualitative thematic analysis could reflect broader structural narratives as opposed to empirical outcomes. Finally, since AI/ML adoption varies across fusion centers and according to technology levels, qualitative themes identified in this study must be read as representative patterns and not as universally generalizable. Originality/value Fusion center utilization of AI/ML technologies is as much an operational tool as it is a policy, governance and ethical challenge. Successful and professional use in support of law enforcement is about placing technological innovations firmly within institutional accountability and constitutional guardrails. On the one hand, AI/ML tools are enhancing analytical intelligence production by accelerating analytic workflows, predictive modeling and expanding data/information integration capabilities. AI/ML are extending ILP concepts by offering improvements in situational awareness and threat identification and operational efficiencies. On the other, substantial constraints hinder responsible use of these technologies. In the absence of standardized oversight frameworks, data-quality issues, algorithmic bias and the lack of professional development, workforce capacity and critical skills on the part of fusion center analysts will cancel the benefits of these tools.

As artificial intelligence (AI) becomes increasingly embedded in clinical workflows, clinicians encounter ethical challenges that traditional, principle-based medical ethics education may not adequately address. Empirical evidence on clinicians' experiences with AI-related ethics is limited, constraining curricular improvement. This qualitative study explores how early-career doctors in Singapore perceive and negotiate ethical dilemmas arising from clinical AI use and translates findings into an operationalised competence framework for medical education. Between April and June 2025, we conducted semi-structured interviews with 30 early-career doctors (1-5 years post-graduation) from nine public healthcare institutions in Singapore. Purposive sampling ensured diversity across specialties, institutions, gender and ethnicity. Interviews explored participants' AI-related ethical challenges in day-to-day practice and their perceptions of ethics training in medical school. Data were analysed using Braun and Clarke's (2022) reflexive thematic analysis, with codes developed iteratively and informed by the four classical bioethical principles as sensitising concepts-autonomy, beneficence, non-maleficence and justice. Interdisciplinary reflexive discussions guided the construction and interpretation of themes. Participants reported limited formal AI education. Seven recurring practical ethical challenges were identified: (1) system opacity, (2) dataset bias and generalisability, (3) data privacy and consent in networked environments, (4) insufficient patient-specific contextualisation of outputs, (5) risks of hallucinations, (6) ambiguous accountability and (7) cognitive offloading. These themes reframed classical bioethical principles through epistemic, relational and institutional lenses. Ethical competence for AI-mediated care requires integrated epistemic and relational capacities beyond technical literacy or traditional medical ethics. We propose the Digital-Age Clinical AI Ethics Competence (DCEC) framework, comprising four domains of epistemic awareness, relational integrity, reflexive accountability and adaptive professionalism, anchored by ethical digital literacy (EDL). Each domain is operationalised with specific learning activities and assessment strategies such as Objective Structured Clinical Examination(OSCE) stations, reflective portfolios and ethics viva. We discuss implications for curriculum design, faculty development and competency-based assessment.

The rapid adoption of generative artificial intelligence (GenAI) in nursing education presents urgent ethical challenges, particularly as students employ these tools in high-stakes clinical prioritization tasks. Although general AI literacy initiatives exist, nursing-specific resources for addressing risks of bias, inequity, and misinformation in GenAI-mediated decision making remain limited. This study adapts an interdisciplinary, open-access AI ethics learning toolkit to develop a conceptual, nursing-focused mini-toolkit. The adaptation integrates three complementary ethical frameworks: Rest's Four-Component Model at the learner level (moral sensitivity, judgment, motivation, and character), cyberethics principles at the professional level (autonomy, nonmaleficence, beneficence, justice, and explicability), and Chan's ecological model at the institutional level (pedagogy, governance, and operations). Collectively, these frameworks scaffold ethical reasoning across individual, professional, and systemic domains. The resulting mini-toolkit includes case vignettes that simulate GenAI-supported prioritization scenarios, reflection prompts to cultivate bias recognition and accountability, and rubric criteria to guide faculty in assessing ethical reasoning and oversight. Rather than prescribing fixed answers, the toolkit creates structured opportunities for dialogue, inquiry, and professional judgment. By embedding ethical reasoning into prioritization pedagogy, the toolkit positions faculty and students as critical evaluators rather than passive users of GenAI, reinforcing nursing's commitment to equity, justice, and patient-centered care. Situated within a broader global movement toward responsible and human-centered AI integration, this study contributes to nursing ethics by translating abstract principles into pedagogically actionable tools and modeling a methodology for adapting interdisciplinary frameworks into nursing-specific applications. Future work should pilot and evaluate the toolkit in authentic educational contexts, examining its impact on ethical reasoning, bias recognition, and collaborative decision making.

BackgroundDue to their constant exposure to complex clinical situations, nurses face numerous ethical challenges that can significantly impact their clinical decision-making. Moral disengagement is one such challenge, whereby nurses become detached from their professional values and ethical principles. Understanding this phenomenon is crucial for improving ethical practice and supporting nurses in maintaining moral integrity in demanding healthcare environments.AimThis phenomenological study aimed to explore Iranian nurses' experiences of moral disengagement in clinical practice.Research designA qualitative study with an interpretative phenomenological approach was conducted. A total of 18 nurses working in university hospitals in Iran were selected via purposive sampling. Data were collected through in-depth, semi-structured, face-to-face interviews and analyzed using Diekelmann's interpretative phenomenological method.Ethical considerationsThis study was approved by the local Ethics Review Committee. Each participant provided written informed consent. Confidentiality was ensured through anonymization of transcripts, secure data storage, and the use of participant codes.FindingsData analysis resulted in four main themes and ten subthemes. The main themes include "Detachment from the ethical value system," "Moral instability in the shadow of professional challenges," "Echoes of a presence void of commitment," and "The necessity of reviving forgotten ethical ideals."ConclusionMoral disengagement is a complex, multifaceted phenomenon that stems not only from personal attributes but also from structural, cultural, and organizational challenges within the healthcare system. To prevent and address this issue in the Iranian context, it is essential to foster moral identity, rebuild supportive organizational climates, and restore a meaningful sense of caregiving within the nursing profession through targeted education, ethical leadership, and systemic reforms.

The digital transformation of e-commerce has dramatically reshaped Indonesia’s business landscape, with transaction values projected to reach USD 80 billion by 2025. Nevertheless, this rapid expansion has simultaneously produced critical ethical challenges, including price manipulation, exploitation of personal data, and structural injustice that disadvantages MSME actors. This study aims to analyze the relevance of Islamic business ethics norms from Al-Ghazali’s perspective within the context of the Shopee platform and to formulate applicable strengthening strategies. Utilizing a qualitative method through a literature-based approach examining Al-Ghazali’s works and 45 contemporary studies (2015–2025), this research investigates four fundamental principles of Islamic business ethics: mashlahah (public benefit), niyyah (sincere intention), sidq (honesty), and ‘adl (justice). The findings reveal that the implementation of these principles through features such as rating systems, product reviews, and dispute-resolution centers can enhance consumer protection by up to 65%. However, significant obstacles remain, including uneven product transparency (40%), misuse of reviews (15–20%), and the dominance of profit-driven motives (70%). The study proposes five key strategic recommendations: digital ethics education, AI-Blockchain-based verification systems, integration of mashlahah into recommendation algorithms, multi-stakeholder collaboration, and the development of an Islamic Business Ethics Index (IBEI). Consistent implementation of Al-Ghazali’s ethical principles is expected to foster an e-commerce ecosystem that is equitable, transparent, and oriented toward collective welfare.

Contemporary warfare is no longer confined to physical battlefields, as cyber operations have become an integral component of military strategy and international diplomacy in the twenty-first century. These operations increasingly influence how conflicts are conducted, how sovereignty is exercised, and how international law is interpreted and applied. Although cyber activities have attracted growing attention, there remains limited scholarly examination of how they blur the distinction between peace and armed conflict while placing strain on established principles of sovereignty and international humanitarian law. The India-Pakistan confrontation provides a relevant illustration of how cyber capabilities are combined with conventional military technologies, giving rise to strategic, operational, legal, and ethical challenges that remain insufficiently explored. This article draws on news reports, open-source materials, official documents, and academic literature to examine contemporary cyber operations. Through close analysis, it identifies emerging patterns that are often overlooked, particularly the increasing emphasis on information control rather than territorial domination. Such developments complicate deterrence, generate legal and normative uncertainty, and raise difficult questions concerning attribution and accountability in cyberspace. The analysis highlights the need for states to adopt coherent cyber policies, develop clearer legal frameworks, and improve interagency coordination in responding to cyber-enabled threats. Overall, the article demonstrates how cyber operations are reshaping conflict behavior, weakening the traditional influence of international law, and redefining security policy in an increasingly digital and information-driven environment.

Introduction: Toxic shock syndrome (TSS) is a rare but potentially fatal condition caused by exotoxins produced by Streptococcus pyogenes or Staphylococcus aureus. Streptococcal toxic shock syndrome (STSS) is characterized by rapid clinical deterioration and requires prompt recognition, aggressive supportive care, and multidisciplinary management. Case Presentation: We report the case of a 63-year-old previously healthy Angolan man visiting Portugal who presented to the Emergency Department with a seven-day history of painful swelling of the left thigh and inguinal region, unresponsive to non-steroidal anti-inflammatory drugs (NSAIDs). He rapidly developed septic shock with multiorgan failure, requiring invasive mechanical ventilation, vasopressor support, and renal replacement therapy, and was admitted to the Intensive Care Unit. Blood cultures were negative, but Streptococcus pyogenes was isolated from a skin biopsy culture, supporting the diagnosis of STSS. The patient improved with targeted antibiotic therapy and was extubated after five days. Subsequently, he developed severe upper gastrointestinal bleeding from a bleeding antral ulcer, complicated by refusal of blood transfusion, and was managed conservatively. Conclusion: STSS is a fulminant and life-threatening condition. Early diagnosis, aggressive organ support, and multidisciplinary care are essential. NSAIDs use may exacerbate disease severity and contribute to gastrointestinal complications. Refusal of blood transfusion poses major clinical and ethical challenges in the management of critically ill patients.

Abstract Ethics has become a central concern in applied linguistics, with researchers from both qualitative and quantitative paradigms increasingly engaging with ethical considerations. While methodological guidelines have been proposed to support ethical research practices (De Costa, 2024), it remains unclear to what extent these are implemented and reported. Narrative inquiry, in particular, poses complex ethical challenges due to its relational and often deeply personal nature. Although qualitative traditions have long led ethical reflections in applied linguistics, ethical enactment and transparency in narrative inquiry remain inconsistent. To explore this issue, we conducted a methodological synthesis of 332 narrative inquiry studies published between 2012 and 2023, examining ethical practices across study design, recruitment, data collection, and analysis. Findings reveal that while issues like anonymity were commonly addressed, other areas – such as IRB approval, participant incentives, considerations for vulnerable populations, and data sharing – showed marked variation. Drawing on current literature, we propose empirically grounded recommendations to strengthen ethical reporting in narrative research. Rather than associating macro-ethics and micro-ethics with specific paradigms, we integrate both to explore how ethical principles are enacted in context. Given the relational and situated nature of narrative inquiry, this review responds to a timely need for more transparent and reflexive ethical practice in the field.

How Do We Do This? Exploring Supervisor and Supervisee Perspectives in Navigating Ethical Challenges in Transnational Supervision

Abstract Background Electronic health record (EHR) data, a key form of routinely collected patient data, offer great potential for medical research and the development of artificial intelligence (AI) tools. However, because these data are primarily gathered for health care rather than research, it often lacks the quality needed for AI training, raising both methodological and ethical concerns. While previous studies have reviewed the ethical implications of both routinely collected patient data and AI separately, their intersection, where AI is applied to such data, remains largely unexplored. Objective This study aimed to examine the ethical challenges that arise at the intersection of EHR data and AI development and to derive practice-oriented recommendations using the Dutch LEAPfROG (Leveraging Real-World Data to Optimize Pharmacotherapy Outcomes in Multimorbid Patients Using Machine Learning and Knowledge Representation Methods) project as a guiding case. Methods We used a mixed methods design combining a scoping literature review with a systematic search and 2 stakeholder workshops structured by the guidance ethics approach, reflecting a staged and iterative process aligned with the LEAPfROG project’s development phases. The review identified 25 relevant publications from 2014 to 2024. The workshops, conducted with 17 and 13 participants respectively, included patients, clinicians, ethicists, data officers, and AI developers. Both workshops used dialogue to identify ethical values, impacts, and action points, focusing on a case study of drug-induced acute kidney injury. Results The analysis highlighted four major themes: (1) data privacy, transparency, and consent, including challenges of meaningful consent and risks of reidentification; (2) public trust and regulatory challenges, such as fragmented oversight and inconsistent governance; (3) fair representation and model generalizability, where incomplete or biased data may perpetuate health inequities; and (4) responsible AI integration in clinical practice, including concerns about clinical tropism, administrative burden, and the risk of overreliance on AI outputs. Both literature and stakeholder perspectives underscore the risk of decontextualization when EHR data are reused and emphasize the importance of clearly defining the purpose of data reuse to ensure real-world applicability and foster trust. Conclusions Responsible AI development requires explicit attention to how EHR data are produced, interpreted, and governed in practice, recognizing that data quality and meaning are shaped by the clinical, institutional, and social contexts in which they originate. Technical solutions or top-down regulation alone are insufficient. Instead, stakeholder-led and context-sensitive approaches are needed to define the purposes, risks, and benefits of medical AI. Grounded in the realities of health care practice and in the perspectives of patients, clinicians, and data custodians, these approaches can strengthen transparency, fairness, and clinical relevance, ensuring that EHR data are used ethically and effectively to support equitable and trustworthy AI innovation.

CRISPR/Cas9 and reproductive failure: applications, ethical challenges, and future perspectives in human germline genome editing.

Ethical challenges around mandatory vaccination among nurses: A systematic review of qualitative and quantitative evidence.

Ethical, clinical, and legal challenges of mental health care in prisons: between constraints and clinical integrity.

Traditional laboratory practicals exploring cardiovascular physiology and pharmacology rely on mammalian models, presenting ethical, financial, and logistical challenges. Danio rerio (zebrafish) larvae offer a compelling alternative that aligns with the partial replacement principle of replacement, reduction, and refinement (the 3Rs), while providing an opportunity for students to develop desirable in vivo skills to improve their employability. Here, we introduce an engaging set of in vivo laboratory practicals suitable for large undergraduate cohorts that utilizes larval zebrafish to investigate cardiac ion channels and receptors. The practical involves two 3-hour sessions where students measure heart rate in 72- and 96-hour postfertilization larvae in response to various treatments. The first session introduces students to handling larval zebrafish before exploring the effects of a reduced ambient temperature and application of the commonly used zebrafish anesthetic tricaine (MS-222) on both heart rate and the zebrafish startle reflex. Finally, students apply the well-known adrenergic agonist adrenaline. The second session empowers students to develop their own testable hypothesis regarding which ion channels or receptors are likely to influence zebrafish heart rate, providing them with the autonomy to select two pharmacologically active drugs from a carefully curated list [e.g. isoproterenol (β-adrenergic receptor agonist), propranolol (β-adrenergic receptor antagonist), and nifedipine (L-type calcium channel blocker)] that will enable them to address their hypothesis. Students' subsequent data for analysis allows them to develop an understanding of the conserved and divergent aspects of cardiac physiology between zebrafish and mammalian systems, and an appreciation of the importance of appropriate model selection in physiological and pharmacological research.NEW & NOTEWORTHY The document outlines how large-scale undergraduate practical classes involving Danio rerio (zebrafish) can be used to teach cardiovascular physiology. It emphasizes the educational value of using live zebrafish to explore heart rate, drug effects, and homeostasis. The process supports active, inquiry-based learning, fostering engagement, critical thinking, and collaborative skills. It also addresses ethical and logistical considerations. Overall, the approach effectively combines hands-on experimental experience with core physiological concepts in an impactful educational format.

Beyond the genome: Ethical, social and legal implications of epilepsy genetics.